Retailers Need To Hit The Reset Button

Timothy Hood

The retail landscape is fundamentally changing as today’s consumers carry your store (as well as your major competitors) in their pocket. Customers are shopping on multiple channels and using many devices, with or without going into a bricks-and-mortar location.

Retailers are looking for competitive advantage as they transform the customer experience and the entire purchase process. Part of this process is looking for strategies to gain insights into the changing buying behavior of their customers. This can all be attained using Big Data analytics to offer a personalized customer experience.

Many retailers are taking advantage of Big Data analytics solutions. According to a recent Penton study, the majority of retailers understand the importance of using data to their advantage.

  • 66% believe Big Data is necessary to the overall growth of their company
  • 63% believe Big Data is critical to the success of their business

While many retailers are already reaping the benefits of cutting-edge analytics, some others are still questioning how to go about it.

According to the study sponsored by HDS (Hitachi Data Systems), SAP, and Intel, more than half of retail respondents don’t have appropriate systems in place to deal with Big Data, and close to one-third of retailers don’t even know where to begin.

What matters to retailers

The best place to start is to determine what matters to your retail business. According to the study, the following issues are top-of-mind for retailers when it comes to Big Data insight:

  • 60%: The ability to track customer purchasing
  • 49%: Customer profiling
  • 48%: Staff effectiveness

Retailers can use Big Data analytics to address all of these issues.

How can Big Data analytics help?

One of the biggest drivers for retailers to use analytics is customer insight. Using analytics allows retailers to track and predict customer behavior, and enables businesses to personalize customer interactions through multiple channels. It can harness both transactional and non-transactional data so retailers can create timely and valuable messaging to consumers exactly when it’s needed to enhance the customer experience.

This helps a retailer have deeper insight into customer behavior, and enables more informed business decisions and actionable merchandising decisions.

Don’t hesitate to take advantage of Big Data

No matter where you stand on analytics, the longer you wait to get started, the more market share you can potentially lose to your competitors who are already engaging with customers on a more personal level with the help of analytics.

Big Data analytics may still be in its early stages for some retailers, but you should consider using your customer data strategically in order to get a jump on the competition instead of being left behind.

For an in-depth look at data analytics and the customer experience, download the SAP eBook, Digital Disruption: How Digital Technology is Transforming Our World.

To learn more about business innovation in the digital era, download the SAP eBook, The Digital Economy: Reinventing the Business World.

Comments

Timothy Hood

About Timothy Hood

Timothy Hood is the Global Vice President, Strategy and Technology, Retail Industry Business Unit, at SAP. He is responsible for defining, communicating and executing the SAP Retail Strategy in addition to go to market responsibility for the SAP platform & technology solutions for the retail industry and managing ISV partners.

Real-Time Data Analytics: On Demand vs. Continuous

Jen Cohen Crompton

Real-Time Data Analytics touches almost every part of a business – from the data managers and enterprise architects, to marketing and sales managers – they all need to collect, use, and analyze data to shape their roles and strategies. Now, more than ever, businesses are focused on the immediate implications of data and monitoring it often to stay abreast of what is happening. We are in the Digital Age, which is fast-moving and requires a sense of immediacy.

In general, real-time analytics can be defined as the use of, or the capacity to use, available enterprise data and resources when needed. It consists of dynamic analysis and reporting, based on data entered into a system less than 60 seconds ago. In other words, this is viewing and using data as wanted, needed, and requested.

There are two specific and useful types of real-time data analysis – On-Demand and Continuous, which are differentiated by the reactive and proactive approaches – or pull and push.

On-Demand Real-Time Analytics is reactive because it waits for users to request a query and then delivers the analytics. This is used when someone within a company needs to take a pulse on what is happening right this minute. This data might be pulled during a marketing campaign to find out what is happening from a sales perspective, or from a web analyst who wants to monitor site traffic to avoid a potential crash.

Continuous Real-Time Analytics is more proactive and alerts users with continuous updates in real-time. Think of this as analytics running in the background and being pushed through on a predetermined basis. This type of data can provide a changing visualization of action on a website – maybe a line graph of site activity so analysts can monitor changing patterns. Continuous Real-Time Analytics can be considered business intelligence in action.

Both On-Demand and Continue Real-Time Analytics have their time and place within a business strategy and each can be used to help build effective strategies.

So here’s just a little more on why and type of Real-Time Analytics matter…
Real-Time Analytics can be fed through dashboards to update team members in real-time on what is going on within or outside of the business – factors that can have an instant effect. An example is how we view the stock market. We watch the stock market in real-time to see what is happening and we often view it through a continuously updated visual representation on a wesbite, TV or mobile device. We use the data to drive our actions – trades, investments, etc. All of those actions are deeply dependent upon time and understanding what is happening right this very minute.

Another important result is monitoring customer behaviors. When focusing on CRM, real-time analytics provide data about customer behavior at this very moment, which can foster quicker decision making and immediate changes or potentially “fixes” when necessary. This, in turn, can strengthen customer relationships and increase the value of engagements.

Comments

Real-Time Data Analytics: On Demand vs. Continuous

Jen Cohen Crompton

Real-Time Data Analytics touches almost every part of a business – from the data managers and enterprise architects, to marketing and sales managers – they all need to collect, use, and analyze data to shape their roles and strategies. Now, more than ever, businesses are focused on the immediate implications of data and monitoring it often to stay abreast of what is happening. We are in the Digital Age, which is fast-moving and requires a sense of immediacy.

In general, real-time analytics can be defined as the use of, or the capacity to use, available enterprise data and resources when needed. It consists of dynamic analysis and reporting, based on data entered into a system less than 60 seconds ago. In other words, this is viewing and using data as wanted, needed, and requested.

There are two specific and useful types of real-time data analysis – On-Demand and Continuous, which are differentiated by the reactive and proactive approaches – or pull and push.

On-Demand Real-Time Analytics is reactive because it waits for users to request a query and then delivers the analytics. This is used when someone within a company needs to take a pulse on what is happening right this minute. This data might be pulled during a marketing campaign to find out what is happening from a sales perspective, or from a web analyst who wants to monitor site traffic to avoid a potential crash.

Continuous Real-Time Analytics is more proactive and alerts users with continuous updates in real-time. Think of this as analytics running in the background and being pushed through on a predetermined basis. This type of data can provide a changing visualization of action on a website – maybe a line graph of site activity so analysts can monitor changing patterns. Continuous Real-Time Analytics can be considered business intelligence in action.

Both On-Demand and Continue Real-Time Analytics have their time and place within a business strategy and each can be used to help build effective strategies.

So here’s just a little more on why and type of Real-Time Analytics matter…
Real-Time Analytics can be fed through dashboards to update team members in real-time on what is going on within or outside of the business – factors that can have an instant effect. An example is how we view the stock market. We watch the stock market in real-time to see what is happening and we often view it through a continuously updated visual representation on a wesbite, TV or mobile device. We use the data to drive our actions – trades, investments, etc. All of those actions are deeply dependent upon time and understanding what is happening right this very minute.

Another important result is monitoring customer behaviors. When focusing on CRM, real-time analytics provide data about customer behavior at this very moment, which can foster quicker decision making and immediate changes or potentially “fixes” when necessary. This, in turn, can strengthen customer relationships and increase the value of engagements.

Comments

Konstanze Werle

About Konstanze Werle

Konstanze Werle is a Director of Industries Marketing at SAP. She is a content marketing specialist with a particular focus on the travel and transportation, engineering and construction and real estate industries worldwide. Her goal is to help companies in these industries to simplify their business by sharing latest trends and innovation in their industry.

Real-Time Data Analytics: On Demand vs. Continuous

Jen Cohen Crompton

Real-Time Data Analytics touches almost every part of a business – from the data managers and enterprise architects, to marketing and sales managers – they all need to collect, use, and analyze data to shape their roles and strategies. Now, more than ever, businesses are focused on the immediate implications of data and monitoring it often to stay abreast of what is happening. We are in the Digital Age, which is fast-moving and requires a sense of immediacy.

In general, real-time analytics can be defined as the use of, or the capacity to use, available enterprise data and resources when needed. It consists of dynamic analysis and reporting, based on data entered into a system less than 60 seconds ago. In other words, this is viewing and using data as wanted, needed, and requested.

There are two specific and useful types of real-time data analysis – On-Demand and Continuous, which are differentiated by the reactive and proactive approaches – or pull and push.

On-Demand Real-Time Analytics is reactive because it waits for users to request a query and then delivers the analytics. This is used when someone within a company needs to take a pulse on what is happening right this minute. This data might be pulled during a marketing campaign to find out what is happening from a sales perspective, or from a web analyst who wants to monitor site traffic to avoid a potential crash.

Continuous Real-Time Analytics is more proactive and alerts users with continuous updates in real-time. Think of this as analytics running in the background and being pushed through on a predetermined basis. This type of data can provide a changing visualization of action on a website – maybe a line graph of site activity so analysts can monitor changing patterns. Continuous Real-Time Analytics can be considered business intelligence in action.

Both On-Demand and Continue Real-Time Analytics have their time and place within a business strategy and each can be used to help build effective strategies.

So here’s just a little more on why and type of Real-Time Analytics matter…
Real-Time Analytics can be fed through dashboards to update team members in real-time on what is going on within or outside of the business – factors that can have an instant effect. An example is how we view the stock market. We watch the stock market in real-time to see what is happening and we often view it through a continuously updated visual representation on a wesbite, TV or mobile device. We use the data to drive our actions – trades, investments, etc. All of those actions are deeply dependent upon time and understanding what is happening right this very minute.

Another important result is monitoring customer behaviors. When focusing on CRM, real-time analytics provide data about customer behavior at this very moment, which can foster quicker decision making and immediate changes or potentially “fixes” when necessary. This, in turn, can strengthen customer relationships and increase the value of engagements.

Comments

Angelica Valentine

About Angelica Valentine

Angelica is the Marketing Manager at Wiser. Wiser collects and analyzes online and in-store data with unmatched speed, scale and accuracy. She is experienced in strategy and creation for cross-channel content. Angelica is passionate about growing engagement and conversion rates through excellent content. Her work has also appeared on VentureBeat, Bigcommerce, Retail Touchpoints, and more. She holds a Bachelor’s degree in Sociology from Barnard College of Columbia University in New York City.

Real-Time Data Analytics: On Demand vs. Continuous

Jen Cohen Crompton

Real-Time Data Analytics touches almost every part of a business – from the data managers and enterprise architects, to marketing and sales managers – they all need to collect, use, and analyze data to shape their roles and strategies. Now, more than ever, businesses are focused on the immediate implications of data and monitoring it often to stay abreast of what is happening. We are in the Digital Age, which is fast-moving and requires a sense of immediacy.

In general, real-time analytics can be defined as the use of, or the capacity to use, available enterprise data and resources when needed. It consists of dynamic analysis and reporting, based on data entered into a system less than 60 seconds ago. In other words, this is viewing and using data as wanted, needed, and requested.

There are two specific and useful types of real-time data analysis – On-Demand and Continuous, which are differentiated by the reactive and proactive approaches – or pull and push.

On-Demand Real-Time Analytics is reactive because it waits for users to request a query and then delivers the analytics. This is used when someone within a company needs to take a pulse on what is happening right this minute. This data might be pulled during a marketing campaign to find out what is happening from a sales perspective, or from a web analyst who wants to monitor site traffic to avoid a potential crash.

Continuous Real-Time Analytics is more proactive and alerts users with continuous updates in real-time. Think of this as analytics running in the background and being pushed through on a predetermined basis. This type of data can provide a changing visualization of action on a website – maybe a line graph of site activity so analysts can monitor changing patterns. Continuous Real-Time Analytics can be considered business intelligence in action.

Both On-Demand and Continue Real-Time Analytics have their time and place within a business strategy and each can be used to help build effective strategies.

So here’s just a little more on why and type of Real-Time Analytics matter…
Real-Time Analytics can be fed through dashboards to update team members in real-time on what is going on within or outside of the business – factors that can have an instant effect. An example is how we view the stock market. We watch the stock market in real-time to see what is happening and we often view it through a continuously updated visual representation on a wesbite, TV or mobile device. We use the data to drive our actions – trades, investments, etc. All of those actions are deeply dependent upon time and understanding what is happening right this very minute.

Another important result is monitoring customer behaviors. When focusing on CRM, real-time analytics provide data about customer behavior at this very moment, which can foster quicker decision making and immediate changes or potentially “fixes” when necessary. This, in turn, can strengthen customer relationships and increase the value of engagements.

Comments

Real-Time Data Analytics: On Demand vs. Continuous

Jen Cohen Crompton

Real-Time Data Analytics touches almost every part of a business – from the data managers and enterprise architects, to marketing and sales managers – they all need to collect, use, and analyze data to shape their roles and strategies. Now, more than ever, businesses are focused on the immediate implications of data and monitoring it often to stay abreast of what is happening. We are in the Digital Age, which is fast-moving and requires a sense of immediacy.

In general, real-time analytics can be defined as the use of, or the capacity to use, available enterprise data and resources when needed. It consists of dynamic analysis and reporting, based on data entered into a system less than 60 seconds ago. In other words, this is viewing and using data as wanted, needed, and requested.

There are two specific and useful types of real-time data analysis – On-Demand and Continuous, which are differentiated by the reactive and proactive approaches – or pull and push.

On-Demand Real-Time Analytics is reactive because it waits for users to request a query and then delivers the analytics. This is used when someone within a company needs to take a pulse on what is happening right this minute. This data might be pulled during a marketing campaign to find out what is happening from a sales perspective, or from a web analyst who wants to monitor site traffic to avoid a potential crash.

Continuous Real-Time Analytics is more proactive and alerts users with continuous updates in real-time. Think of this as analytics running in the background and being pushed through on a predetermined basis. This type of data can provide a changing visualization of action on a website – maybe a line graph of site activity so analysts can monitor changing patterns. Continuous Real-Time Analytics can be considered business intelligence in action.

Both On-Demand and Continue Real-Time Analytics have their time and place within a business strategy and each can be used to help build effective strategies.

So here’s just a little more on why and type of Real-Time Analytics matter…
Real-Time Analytics can be fed through dashboards to update team members in real-time on what is going on within or outside of the business – factors that can have an instant effect. An example is how we view the stock market. We watch the stock market in real-time to see what is happening and we often view it through a continuously updated visual representation on a wesbite, TV or mobile device. We use the data to drive our actions – trades, investments, etc. All of those actions are deeply dependent upon time and understanding what is happening right this very minute.

Another important result is monitoring customer behaviors. When focusing on CRM, real-time analytics provide data about customer behavior at this very moment, which can foster quicker decision making and immediate changes or potentially “fixes” when necessary. This, in turn, can strengthen customer relationships and increase the value of engagements.

Comments

Uli Muench

About Uli Muench

Uli Muench is Global Vice President of the Automotive Industry Business Unit at SAP.

Real-Time Data Analytics: On Demand vs. Continuous

Jen Cohen Crompton

Real-Time Data Analytics touches almost every part of a business – from the data managers and enterprise architects, to marketing and sales managers – they all need to collect, use, and analyze data to shape their roles and strategies. Now, more than ever, businesses are focused on the immediate implications of data and monitoring it often to stay abreast of what is happening. We are in the Digital Age, which is fast-moving and requires a sense of immediacy.

In general, real-time analytics can be defined as the use of, or the capacity to use, available enterprise data and resources when needed. It consists of dynamic analysis and reporting, based on data entered into a system less than 60 seconds ago. In other words, this is viewing and using data as wanted, needed, and requested.

There are two specific and useful types of real-time data analysis – On-Demand and Continuous, which are differentiated by the reactive and proactive approaches – or pull and push.

On-Demand Real-Time Analytics is reactive because it waits for users to request a query and then delivers the analytics. This is used when someone within a company needs to take a pulse on what is happening right this minute. This data might be pulled during a marketing campaign to find out what is happening from a sales perspective, or from a web analyst who wants to monitor site traffic to avoid a potential crash.

Continuous Real-Time Analytics is more proactive and alerts users with continuous updates in real-time. Think of this as analytics running in the background and being pushed through on a predetermined basis. This type of data can provide a changing visualization of action on a website – maybe a line graph of site activity so analysts can monitor changing patterns. Continuous Real-Time Analytics can be considered business intelligence in action.

Both On-Demand and Continue Real-Time Analytics have their time and place within a business strategy and each can be used to help build effective strategies.

So here’s just a little more on why and type of Real-Time Analytics matter…
Real-Time Analytics can be fed through dashboards to update team members in real-time on what is going on within or outside of the business – factors that can have an instant effect. An example is how we view the stock market. We watch the stock market in real-time to see what is happening and we often view it through a continuously updated visual representation on a wesbite, TV or mobile device. We use the data to drive our actions – trades, investments, etc. All of those actions are deeply dependent upon time and understanding what is happening right this very minute.

Another important result is monitoring customer behaviors. When focusing on CRM, real-time analytics provide data about customer behavior at this very moment, which can foster quicker decision making and immediate changes or potentially “fixes” when necessary. This, in turn, can strengthen customer relationships and increase the value of engagements.

Comments

Real-Time Data Analytics: On Demand vs. Continuous

Jen Cohen Crompton

Real-Time Data Analytics touches almost every part of a business – from the data managers and enterprise architects, to marketing and sales managers – they all need to collect, use, and analyze data to shape their roles and strategies. Now, more than ever, businesses are focused on the immediate implications of data and monitoring it often to stay abreast of what is happening. We are in the Digital Age, which is fast-moving and requires a sense of immediacy.

In general, real-time analytics can be defined as the use of, or the capacity to use, available enterprise data and resources when needed. It consists of dynamic analysis and reporting, based on data entered into a system less than 60 seconds ago. In other words, this is viewing and using data as wanted, needed, and requested.

There are two specific and useful types of real-time data analysis – On-Demand and Continuous, which are differentiated by the reactive and proactive approaches – or pull and push.

On-Demand Real-Time Analytics is reactive because it waits for users to request a query and then delivers the analytics. This is used when someone within a company needs to take a pulse on what is happening right this minute. This data might be pulled during a marketing campaign to find out what is happening from a sales perspective, or from a web analyst who wants to monitor site traffic to avoid a potential crash.

Continuous Real-Time Analytics is more proactive and alerts users with continuous updates in real-time. Think of this as analytics running in the background and being pushed through on a predetermined basis. This type of data can provide a changing visualization of action on a website – maybe a line graph of site activity so analysts can monitor changing patterns. Continuous Real-Time Analytics can be considered business intelligence in action.

Both On-Demand and Continue Real-Time Analytics have their time and place within a business strategy and each can be used to help build effective strategies.

So here’s just a little more on why and type of Real-Time Analytics matter…
Real-Time Analytics can be fed through dashboards to update team members in real-time on what is going on within or outside of the business – factors that can have an instant effect. An example is how we view the stock market. We watch the stock market in real-time to see what is happening and we often view it through a continuously updated visual representation on a wesbite, TV or mobile device. We use the data to drive our actions – trades, investments, etc. All of those actions are deeply dependent upon time and understanding what is happening right this very minute.

Another important result is monitoring customer behaviors. When focusing on CRM, real-time analytics provide data about customer behavior at this very moment, which can foster quicker decision making and immediate changes or potentially “fixes” when necessary. This, in turn, can strengthen customer relationships and increase the value of engagements.

Comments

Shawn Slack

About Shawn Slack

Shawn Slack is the Director of Information Technology and Chief Information Officer for the City of Mississauga.

Real-Time Data Analytics: On Demand vs. Continuous

Jen Cohen Crompton

Real-Time Data Analytics touches almost every part of a business – from the data managers and enterprise architects, to marketing and sales managers – they all need to collect, use, and analyze data to shape their roles and strategies. Now, more than ever, businesses are focused on the immediate implications of data and monitoring it often to stay abreast of what is happening. We are in the Digital Age, which is fast-moving and requires a sense of immediacy.

In general, real-time analytics can be defined as the use of, or the capacity to use, available enterprise data and resources when needed. It consists of dynamic analysis and reporting, based on data entered into a system less than 60 seconds ago. In other words, this is viewing and using data as wanted, needed, and requested.

There are two specific and useful types of real-time data analysis – On-Demand and Continuous, which are differentiated by the reactive and proactive approaches – or pull and push.

On-Demand Real-Time Analytics is reactive because it waits for users to request a query and then delivers the analytics. This is used when someone within a company needs to take a pulse on what is happening right this minute. This data might be pulled during a marketing campaign to find out what is happening from a sales perspective, or from a web analyst who wants to monitor site traffic to avoid a potential crash.

Continuous Real-Time Analytics is more proactive and alerts users with continuous updates in real-time. Think of this as analytics running in the background and being pushed through on a predetermined basis. This type of data can provide a changing visualization of action on a website – maybe a line graph of site activity so analysts can monitor changing patterns. Continuous Real-Time Analytics can be considered business intelligence in action.

Both On-Demand and Continue Real-Time Analytics have their time and place within a business strategy and each can be used to help build effective strategies.

So here’s just a little more on why and type of Real-Time Analytics matter…
Real-Time Analytics can be fed through dashboards to update team members in real-time on what is going on within or outside of the business – factors that can have an instant effect. An example is how we view the stock market. We watch the stock market in real-time to see what is happening and we often view it through a continuously updated visual representation on a wesbite, TV or mobile device. We use the data to drive our actions – trades, investments, etc. All of those actions are deeply dependent upon time and understanding what is happening right this very minute.

Another important result is monitoring customer behaviors. When focusing on CRM, real-time analytics provide data about customer behavior at this very moment, which can foster quicker decision making and immediate changes or potentially “fixes” when necessary. This, in turn, can strengthen customer relationships and increase the value of engagements.

Comments

Daniel Schmid

About Daniel Schmid

Daniel Schmid was appointed Chief Sustainability Officer at SAP in 2014. Since 2008 he has been engaged in transforming SAP into a role model of a sustainable organization, establishing mid and long term sustainability targets. Linking non-financial and financial performance are key achievements of Daniel and his team.

Real-Time Data Analytics: On Demand vs. Continuous

Jen Cohen Crompton

Real-Time Data Analytics touches almost every part of a business – from the data managers and enterprise architects, to marketing and sales managers – they all need to collect, use, and analyze data to shape their roles and strategies. Now, more than ever, businesses are focused on the immediate implications of data and monitoring it often to stay abreast of what is happening. We are in the Digital Age, which is fast-moving and requires a sense of immediacy.

In general, real-time analytics can be defined as the use of, or the capacity to use, available enterprise data and resources when needed. It consists of dynamic analysis and reporting, based on data entered into a system less than 60 seconds ago. In other words, this is viewing and using data as wanted, needed, and requested.

There are two specific and useful types of real-time data analysis – On-Demand and Continuous, which are differentiated by the reactive and proactive approaches – or pull and push.

On-Demand Real-Time Analytics is reactive because it waits for users to request a query and then delivers the analytics. This is used when someone within a company needs to take a pulse on what is happening right this minute. This data might be pulled during a marketing campaign to find out what is happening from a sales perspective, or from a web analyst who wants to monitor site traffic to avoid a potential crash.

Continuous Real-Time Analytics is more proactive and alerts users with continuous updates in real-time. Think of this as analytics running in the background and being pushed through on a predetermined basis. This type of data can provide a changing visualization of action on a website – maybe a line graph of site activity so analysts can monitor changing patterns. Continuous Real-Time Analytics can be considered business intelligence in action.

Both On-Demand and Continue Real-Time Analytics have their time and place within a business strategy and each can be used to help build effective strategies.

So here’s just a little more on why and type of Real-Time Analytics matter…
Real-Time Analytics can be fed through dashboards to update team members in real-time on what is going on within or outside of the business – factors that can have an instant effect. An example is how we view the stock market. We watch the stock market in real-time to see what is happening and we often view it through a continuously updated visual representation on a wesbite, TV or mobile device. We use the data to drive our actions – trades, investments, etc. All of those actions are deeply dependent upon time and understanding what is happening right this very minute.

Another important result is monitoring customer behaviors. When focusing on CRM, real-time analytics provide data about customer behavior at this very moment, which can foster quicker decision making and immediate changes or potentially “fixes” when necessary. This, in turn, can strengthen customer relationships and increase the value of engagements.

Comments

Michael Laprocido

About Michael Laprocido

Mike Laprocido serves as a Strategic Industry Advisor for SAP. He is responsible for developing thought leadership and driving SAP solution adoption in the chemical and oil and gas industries. With over three decades in various executive roles at BP Oil, BP Chemicals, Kuraray America, Panda Energy and IBM prior to joining SAP, Mike has gained a broad and deep industry knowledge base that he leverages to help his clients to innovate and transform their business through the application of digital technology.

Real-Time Data Analytics: On Demand vs. Continuous

Jen Cohen Crompton

Real-Time Data Analytics touches almost every part of a business – from the data managers and enterprise architects, to marketing and sales managers – they all need to collect, use, and analyze data to shape their roles and strategies. Now, more than ever, businesses are focused on the immediate implications of data and monitoring it often to stay abreast of what is happening. We are in the Digital Age, which is fast-moving and requires a sense of immediacy.

In general, real-time analytics can be defined as the use of, or the capacity to use, available enterprise data and resources when needed. It consists of dynamic analysis and reporting, based on data entered into a system less than 60 seconds ago. In other words, this is viewing and using data as wanted, needed, and requested.

There are two specific and useful types of real-time data analysis – On-Demand and Continuous, which are differentiated by the reactive and proactive approaches – or pull and push.

On-Demand Real-Time Analytics is reactive because it waits for users to request a query and then delivers the analytics. This is used when someone within a company needs to take a pulse on what is happening right this minute. This data might be pulled during a marketing campaign to find out what is happening from a sales perspective, or from a web analyst who wants to monitor site traffic to avoid a potential crash.

Continuous Real-Time Analytics is more proactive and alerts users with continuous updates in real-time. Think of this as analytics running in the background and being pushed through on a predetermined basis. This type of data can provide a changing visualization of action on a website – maybe a line graph of site activity so analysts can monitor changing patterns. Continuous Real-Time Analytics can be considered business intelligence in action.

Both On-Demand and Continue Real-Time Analytics have their time and place within a business strategy and each can be used to help build effective strategies.

So here’s just a little more on why and type of Real-Time Analytics matter…
Real-Time Analytics can be fed through dashboards to update team members in real-time on what is going on within or outside of the business – factors that can have an instant effect. An example is how we view the stock market. We watch the stock market in real-time to see what is happening and we often view it through a continuously updated visual representation on a wesbite, TV or mobile device. We use the data to drive our actions – trades, investments, etc. All of those actions are deeply dependent upon time and understanding what is happening right this very minute.

Another important result is monitoring customer behaviors. When focusing on CRM, real-time analytics provide data about customer behavior at this very moment, which can foster quicker decision making and immediate changes or potentially “fixes” when necessary. This, in turn, can strengthen customer relationships and increase the value of engagements.

Comments

Data Management and Retention Requirements

Irfan Khan

In his annual state of the union speech last month President Barack Obama made a passing reference to the need for the U.S. to train more people in data management to supply the needs of companies. A little later in the speech he talked about how some new, targeted government regulations would benefit honest businesses while rooting out the bad apples. Maybe he was thinking that those newly trained data managers would be able to help companies with the advanced data management techniques his undefined regulations would require.

Don’t get me wrong. I’m not against all regulations. And I’m certainly not opposed to giving tuition credits to students wanting to study the art of data management. But, as the politicians like to say, “let’s be perfectly clear”: modern government regulations require IT professionals to implement new data management policies to prove they are in compliance with changes in the law.

For example, in 2006 the European Union issued a directive to communications carriersforcing them to hold on to subscriber usage data for six to 24 months. That’s so the companies can quickly respond to legal authorities who need to access data for criminal investigations. While some operators may already keep the information, it’s often stored offline. In the case of the EU directive, the information must be able to be accessed without delay by authorities armed with a warrant.

The way the EU directive was written means that wire line, wireless, and ISP operators must retain 15 categories of data. And because the time periods vary, the amount of data to be stored is unpredictable. As you can imagine, the EU also imposed some hefty data security demands as well as unique access requirements. For example, some legal authorities may send their warrants by FAX, e-mail, or even letters via the postal service.

Needless to say, the regulations don’t spell out exactly how carriers should implement the data retention policy. They simply need to do so.

It’s not just the EU creating rules affecting corporate data management. Japan is now considering revising its strict data protection policyfor consumers. The U.S. is in a political battle between those that want tighter Internet controls for copyright holders. And many other nations are designing new laws that affect how companies manage their data.

As I’ve argued here before, having a chief data officer would give enterprises a huge competitive advantage by being able to anticipate the impact new regulations would have on an organization’s data management strategy. In fact, it is increasingly paramount for large multinational companies to have a C-level data officer. Without one, the enterprise lacks a critical resource to compete in today’s global markets.

I agree with President Obama. Data management is, indeed, an excellent career choice for young people. After all, companies need smart people who understand its strategic importance and know how to react quickly when the politicians change the rules on data management for business. Again. And again.

Comments

Tags:

Awareness

Data Management and Retention Requirements

Irfan Khan

In his annual state of the union speech last month President Barack Obama made a passing reference to the need for the U.S. to train more people in data management to supply the needs of companies. A little later in the speech he talked about how some new, targeted government regulations would benefit honest businesses while rooting out the bad apples. Maybe he was thinking that those newly trained data managers would be able to help companies with the advanced data management techniques his undefined regulations would require.

Don’t get me wrong. I’m not against all regulations. And I’m certainly not opposed to giving tuition credits to students wanting to study the art of data management. But, as the politicians like to say, “let’s be perfectly clear”: modern government regulations require IT professionals to implement new data management policies to prove they are in compliance with changes in the law.

For example, in 2006 the European Union issued a directive to communications carriersforcing them to hold on to subscriber usage data for six to 24 months. That’s so the companies can quickly respond to legal authorities who need to access data for criminal investigations. While some operators may already keep the information, it’s often stored offline. In the case of the EU directive, the information must be able to be accessed without delay by authorities armed with a warrant.

The way the EU directive was written means that wire line, wireless, and ISP operators must retain 15 categories of data. And because the time periods vary, the amount of data to be stored is unpredictable. As you can imagine, the EU also imposed some hefty data security demands as well as unique access requirements. For example, some legal authorities may send their warrants by FAX, e-mail, or even letters via the postal service.

Needless to say, the regulations don’t spell out exactly how carriers should implement the data retention policy. They simply need to do so.

It’s not just the EU creating rules affecting corporate data management. Japan is now considering revising its strict data protection policyfor consumers. The U.S. is in a political battle between those that want tighter Internet controls for copyright holders. And many other nations are designing new laws that affect how companies manage their data.

As I’ve argued here before, having a chief data officer would give enterprises a huge competitive advantage by being able to anticipate the impact new regulations would have on an organization’s data management strategy. In fact, it is increasingly paramount for large multinational companies to have a C-level data officer. Without one, the enterprise lacks a critical resource to compete in today’s global markets.

I agree with President Obama. Data management is, indeed, an excellent career choice for young people. After all, companies need smart people who understand its strategic importance and know how to react quickly when the politicians change the rules on data management for business. Again. And again.

Comments

Konstanze Werle

About Konstanze Werle

Konstanze Werle is a Director of Industries Marketing at SAP. She is a content marketing specialist with a particular focus on the travel and transportation, engineering and construction and real estate industries worldwide. Her goal is to help companies in these industries to simplify their business by sharing latest trends and innovation in their industry.

Tags:

Awareness

Data Management and Retention Requirements

Irfan Khan

In his annual state of the union speech last month President Barack Obama made a passing reference to the need for the U.S. to train more people in data management to supply the needs of companies. A little later in the speech he talked about how some new, targeted government regulations would benefit honest businesses while rooting out the bad apples. Maybe he was thinking that those newly trained data managers would be able to help companies with the advanced data management techniques his undefined regulations would require.

Don’t get me wrong. I’m not against all regulations. And I’m certainly not opposed to giving tuition credits to students wanting to study the art of data management. But, as the politicians like to say, “let’s be perfectly clear”: modern government regulations require IT professionals to implement new data management policies to prove they are in compliance with changes in the law.

For example, in 2006 the European Union issued a directive to communications carriersforcing them to hold on to subscriber usage data for six to 24 months. That’s so the companies can quickly respond to legal authorities who need to access data for criminal investigations. While some operators may already keep the information, it’s often stored offline. In the case of the EU directive, the information must be able to be accessed without delay by authorities armed with a warrant.

The way the EU directive was written means that wire line, wireless, and ISP operators must retain 15 categories of data. And because the time periods vary, the amount of data to be stored is unpredictable. As you can imagine, the EU also imposed some hefty data security demands as well as unique access requirements. For example, some legal authorities may send their warrants by FAX, e-mail, or even letters via the postal service.

Needless to say, the regulations don’t spell out exactly how carriers should implement the data retention policy. They simply need to do so.

It’s not just the EU creating rules affecting corporate data management. Japan is now considering revising its strict data protection policyfor consumers. The U.S. is in a political battle between those that want tighter Internet controls for copyright holders. And many other nations are designing new laws that affect how companies manage their data.

As I’ve argued here before, having a chief data officer would give enterprises a huge competitive advantage by being able to anticipate the impact new regulations would have on an organization’s data management strategy. In fact, it is increasingly paramount for large multinational companies to have a C-level data officer. Without one, the enterprise lacks a critical resource to compete in today’s global markets.

I agree with President Obama. Data management is, indeed, an excellent career choice for young people. After all, companies need smart people who understand its strategic importance and know how to react quickly when the politicians change the rules on data management for business. Again. And again.

Comments

Angelica Valentine

About Angelica Valentine

Angelica is the Marketing Manager at Wiser. Wiser collects and analyzes online and in-store data with unmatched speed, scale and accuracy. She is experienced in strategy and creation for cross-channel content. Angelica is passionate about growing engagement and conversion rates through excellent content. Her work has also appeared on VentureBeat, Bigcommerce, Retail Touchpoints, and more. She holds a Bachelor’s degree in Sociology from Barnard College of Columbia University in New York City.

Tags:

Awareness

Data Management and Retention Requirements

Irfan Khan

In his annual state of the union speech last month President Barack Obama made a passing reference to the need for the U.S. to train more people in data management to supply the needs of companies. A little later in the speech he talked about how some new, targeted government regulations would benefit honest businesses while rooting out the bad apples. Maybe he was thinking that those newly trained data managers would be able to help companies with the advanced data management techniques his undefined regulations would require.

Don’t get me wrong. I’m not against all regulations. And I’m certainly not opposed to giving tuition credits to students wanting to study the art of data management. But, as the politicians like to say, “let’s be perfectly clear”: modern government regulations require IT professionals to implement new data management policies to prove they are in compliance with changes in the law.

For example, in 2006 the European Union issued a directive to communications carriersforcing them to hold on to subscriber usage data for six to 24 months. That’s so the companies can quickly respond to legal authorities who need to access data for criminal investigations. While some operators may already keep the information, it’s often stored offline. In the case of the EU directive, the information must be able to be accessed without delay by authorities armed with a warrant.

The way the EU directive was written means that wire line, wireless, and ISP operators must retain 15 categories of data. And because the time periods vary, the amount of data to be stored is unpredictable. As you can imagine, the EU also imposed some hefty data security demands as well as unique access requirements. For example, some legal authorities may send their warrants by FAX, e-mail, or even letters via the postal service.

Needless to say, the regulations don’t spell out exactly how carriers should implement the data retention policy. They simply need to do so.

It’s not just the EU creating rules affecting corporate data management. Japan is now considering revising its strict data protection policyfor consumers. The U.S. is in a political battle between those that want tighter Internet controls for copyright holders. And many other nations are designing new laws that affect how companies manage their data.

As I’ve argued here before, having a chief data officer would give enterprises a huge competitive advantage by being able to anticipate the impact new regulations would have on an organization’s data management strategy. In fact, it is increasingly paramount for large multinational companies to have a C-level data officer. Without one, the enterprise lacks a critical resource to compete in today’s global markets.

I agree with President Obama. Data management is, indeed, an excellent career choice for young people. After all, companies need smart people who understand its strategic importance and know how to react quickly when the politicians change the rules on data management for business. Again. And again.

Comments

Tags:

Awareness

Data Management and Retention Requirements

Irfan Khan

In his annual state of the union speech last month President Barack Obama made a passing reference to the need for the U.S. to train more people in data management to supply the needs of companies. A little later in the speech he talked about how some new, targeted government regulations would benefit honest businesses while rooting out the bad apples. Maybe he was thinking that those newly trained data managers would be able to help companies with the advanced data management techniques his undefined regulations would require.

Don’t get me wrong. I’m not against all regulations. And I’m certainly not opposed to giving tuition credits to students wanting to study the art of data management. But, as the politicians like to say, “let’s be perfectly clear”: modern government regulations require IT professionals to implement new data management policies to prove they are in compliance with changes in the law.

For example, in 2006 the European Union issued a directive to communications carriersforcing them to hold on to subscriber usage data for six to 24 months. That’s so the companies can quickly respond to legal authorities who need to access data for criminal investigations. While some operators may already keep the information, it’s often stored offline. In the case of the EU directive, the information must be able to be accessed without delay by authorities armed with a warrant.

The way the EU directive was written means that wire line, wireless, and ISP operators must retain 15 categories of data. And because the time periods vary, the amount of data to be stored is unpredictable. As you can imagine, the EU also imposed some hefty data security demands as well as unique access requirements. For example, some legal authorities may send their warrants by FAX, e-mail, or even letters via the postal service.

Needless to say, the regulations don’t spell out exactly how carriers should implement the data retention policy. They simply need to do so.

It’s not just the EU creating rules affecting corporate data management. Japan is now considering revising its strict data protection policyfor consumers. The U.S. is in a political battle between those that want tighter Internet controls for copyright holders. And many other nations are designing new laws that affect how companies manage their data.

As I’ve argued here before, having a chief data officer would give enterprises a huge competitive advantage by being able to anticipate the impact new regulations would have on an organization’s data management strategy. In fact, it is increasingly paramount for large multinational companies to have a C-level data officer. Without one, the enterprise lacks a critical resource to compete in today’s global markets.

I agree with President Obama. Data management is, indeed, an excellent career choice for young people. After all, companies need smart people who understand its strategic importance and know how to react quickly when the politicians change the rules on data management for business. Again. And again.

Comments

Uli Muench

About Uli Muench

Uli Muench is Global Vice President of the Automotive Industry Business Unit at SAP.

Tags:

Awareness

Data Management and Retention Requirements

Irfan Khan

In his annual state of the union speech last month President Barack Obama made a passing reference to the need for the U.S. to train more people in data management to supply the needs of companies. A little later in the speech he talked about how some new, targeted government regulations would benefit honest businesses while rooting out the bad apples. Maybe he was thinking that those newly trained data managers would be able to help companies with the advanced data management techniques his undefined regulations would require.

Don’t get me wrong. I’m not against all regulations. And I’m certainly not opposed to giving tuition credits to students wanting to study the art of data management. But, as the politicians like to say, “let’s be perfectly clear”: modern government regulations require IT professionals to implement new data management policies to prove they are in compliance with changes in the law.

For example, in 2006 the European Union issued a directive to communications carriersforcing them to hold on to subscriber usage data for six to 24 months. That’s so the companies can quickly respond to legal authorities who need to access data for criminal investigations. While some operators may already keep the information, it’s often stored offline. In the case of the EU directive, the information must be able to be accessed without delay by authorities armed with a warrant.

The way the EU directive was written means that wire line, wireless, and ISP operators must retain 15 categories of data. And because the time periods vary, the amount of data to be stored is unpredictable. As you can imagine, the EU also imposed some hefty data security demands as well as unique access requirements. For example, some legal authorities may send their warrants by FAX, e-mail, or even letters via the postal service.

Needless to say, the regulations don’t spell out exactly how carriers should implement the data retention policy. They simply need to do so.

It’s not just the EU creating rules affecting corporate data management. Japan is now considering revising its strict data protection policyfor consumers. The U.S. is in a political battle between those that want tighter Internet controls for copyright holders. And many other nations are designing new laws that affect how companies manage their data.

As I’ve argued here before, having a chief data officer would give enterprises a huge competitive advantage by being able to anticipate the impact new regulations would have on an organization’s data management strategy. In fact, it is increasingly paramount for large multinational companies to have a C-level data officer. Without one, the enterprise lacks a critical resource to compete in today’s global markets.

I agree with President Obama. Data management is, indeed, an excellent career choice for young people. After all, companies need smart people who understand its strategic importance and know how to react quickly when the politicians change the rules on data management for business. Again. And again.

Comments

Tags:

Awareness

Data Management and Retention Requirements

Irfan Khan

In his annual state of the union speech last month President Barack Obama made a passing reference to the need for the U.S. to train more people in data management to supply the needs of companies. A little later in the speech he talked about how some new, targeted government regulations would benefit honest businesses while rooting out the bad apples. Maybe he was thinking that those newly trained data managers would be able to help companies with the advanced data management techniques his undefined regulations would require.

Don’t get me wrong. I’m not against all regulations. And I’m certainly not opposed to giving tuition credits to students wanting to study the art of data management. But, as the politicians like to say, “let’s be perfectly clear”: modern government regulations require IT professionals to implement new data management policies to prove they are in compliance with changes in the law.

For example, in 2006 the European Union issued a directive to communications carriersforcing them to hold on to subscriber usage data for six to 24 months. That’s so the companies can quickly respond to legal authorities who need to access data for criminal investigations. While some operators may already keep the information, it’s often stored offline. In the case of the EU directive, the information must be able to be accessed without delay by authorities armed with a warrant.

The way the EU directive was written means that wire line, wireless, and ISP operators must retain 15 categories of data. And because the time periods vary, the amount of data to be stored is unpredictable. As you can imagine, the EU also imposed some hefty data security demands as well as unique access requirements. For example, some legal authorities may send their warrants by FAX, e-mail, or even letters via the postal service.

Needless to say, the regulations don’t spell out exactly how carriers should implement the data retention policy. They simply need to do so.

It’s not just the EU creating rules affecting corporate data management. Japan is now considering revising its strict data protection policyfor consumers. The U.S. is in a political battle between those that want tighter Internet controls for copyright holders. And many other nations are designing new laws that affect how companies manage their data.

As I’ve argued here before, having a chief data officer would give enterprises a huge competitive advantage by being able to anticipate the impact new regulations would have on an organization’s data management strategy. In fact, it is increasingly paramount for large multinational companies to have a C-level data officer. Without one, the enterprise lacks a critical resource to compete in today’s global markets.

I agree with President Obama. Data management is, indeed, an excellent career choice for young people. After all, companies need smart people who understand its strategic importance and know how to react quickly when the politicians change the rules on data management for business. Again. And again.

Comments

Shawn Slack

About Shawn Slack

Shawn Slack is the Director of Information Technology and Chief Information Officer for the City of Mississauga.

Tags:

Awareness

Data Management and Retention Requirements

Irfan Khan

In his annual state of the union speech last month President Barack Obama made a passing reference to the need for the U.S. to train more people in data management to supply the needs of companies. A little later in the speech he talked about how some new, targeted government regulations would benefit honest businesses while rooting out the bad apples. Maybe he was thinking that those newly trained data managers would be able to help companies with the advanced data management techniques his undefined regulations would require.

Don’t get me wrong. I’m not against all regulations. And I’m certainly not opposed to giving tuition credits to students wanting to study the art of data management. But, as the politicians like to say, “let’s be perfectly clear”: modern government regulations require IT professionals to implement new data management policies to prove they are in compliance with changes in the law.

For example, in 2006 the European Union issued a directive to communications carriersforcing them to hold on to subscriber usage data for six to 24 months. That’s so the companies can quickly respond to legal authorities who need to access data for criminal investigations. While some operators may already keep the information, it’s often stored offline. In the case of the EU directive, the information must be able to be accessed without delay by authorities armed with a warrant.

The way the EU directive was written means that wire line, wireless, and ISP operators must retain 15 categories of data. And because the time periods vary, the amount of data to be stored is unpredictable. As you can imagine, the EU also imposed some hefty data security demands as well as unique access requirements. For example, some legal authorities may send their warrants by FAX, e-mail, or even letters via the postal service.

Needless to say, the regulations don’t spell out exactly how carriers should implement the data retention policy. They simply need to do so.

It’s not just the EU creating rules affecting corporate data management. Japan is now considering revising its strict data protection policyfor consumers. The U.S. is in a political battle between those that want tighter Internet controls for copyright holders. And many other nations are designing new laws that affect how companies manage their data.

As I’ve argued here before, having a chief data officer would give enterprises a huge competitive advantage by being able to anticipate the impact new regulations would have on an organization’s data management strategy. In fact, it is increasingly paramount for large multinational companies to have a C-level data officer. Without one, the enterprise lacks a critical resource to compete in today’s global markets.

I agree with President Obama. Data management is, indeed, an excellent career choice for young people. After all, companies need smart people who understand its strategic importance and know how to react quickly when the politicians change the rules on data management for business. Again. And again.

Comments

Daniel Schmid

About Daniel Schmid

Daniel Schmid was appointed Chief Sustainability Officer at SAP in 2014. Since 2008 he has been engaged in transforming SAP into a role model of a sustainable organization, establishing mid and long term sustainability targets. Linking non-financial and financial performance are key achievements of Daniel and his team.

Tags:

Awareness

Data Management and Retention Requirements

Irfan Khan

In his annual state of the union speech last month President Barack Obama made a passing reference to the need for the U.S. to train more people in data management to supply the needs of companies. A little later in the speech he talked about how some new, targeted government regulations would benefit honest businesses while rooting out the bad apples. Maybe he was thinking that those newly trained data managers would be able to help companies with the advanced data management techniques his undefined regulations would require.

Don’t get me wrong. I’m not against all regulations. And I’m certainly not opposed to giving tuition credits to students wanting to study the art of data management. But, as the politicians like to say, “let’s be perfectly clear”: modern government regulations require IT professionals to implement new data management policies to prove they are in compliance with changes in the law.

For example, in 2006 the European Union issued a directive to communications carriersforcing them to hold on to subscriber usage data for six to 24 months. That’s so the companies can quickly respond to legal authorities who need to access data for criminal investigations. While some operators may already keep the information, it’s often stored offline. In the case of the EU directive, the information must be able to be accessed without delay by authorities armed with a warrant.

The way the EU directive was written means that wire line, wireless, and ISP operators must retain 15 categories of data. And because the time periods vary, the amount of data to be stored is unpredictable. As you can imagine, the EU also imposed some hefty data security demands as well as unique access requirements. For example, some legal authorities may send their warrants by FAX, e-mail, or even letters via the postal service.

Needless to say, the regulations don’t spell out exactly how carriers should implement the data retention policy. They simply need to do so.

It’s not just the EU creating rules affecting corporate data management. Japan is now considering revising its strict data protection policyfor consumers. The U.S. is in a political battle between those that want tighter Internet controls for copyright holders. And many other nations are designing new laws that affect how companies manage their data.

As I’ve argued here before, having a chief data officer would give enterprises a huge competitive advantage by being able to anticipate the impact new regulations would have on an organization’s data management strategy. In fact, it is increasingly paramount for large multinational companies to have a C-level data officer. Without one, the enterprise lacks a critical resource to compete in today’s global markets.

I agree with President Obama. Data management is, indeed, an excellent career choice for young people. After all, companies need smart people who understand its strategic importance and know how to react quickly when the politicians change the rules on data management for business. Again. And again.

Comments

Michael Laprocido

About Michael Laprocido

Mike Laprocido serves as a Strategic Industry Advisor for SAP. He is responsible for developing thought leadership and driving SAP solution adoption in the chemical and oil and gas industries. With over three decades in various executive roles at BP Oil, BP Chemicals, Kuraray America, Panda Energy and IBM prior to joining SAP, Mike has gained a broad and deep industry knowledge base that he leverages to help his clients to innovate and transform their business through the application of digital technology.

Tags:

Awareness

Data Management and Retention Requirements

Irfan Khan

In his annual state of the union speech last month President Barack Obama made a passing reference to the need for the U.S. to train more people in data management to supply the needs of companies. A little later in the speech he talked about how some new, targeted government regulations would benefit honest businesses while rooting out the bad apples. Maybe he was thinking that those newly trained data managers would be able to help companies with the advanced data management techniques his undefined regulations would require.

Don’t get me wrong. I’m not against all regulations. And I’m certainly not opposed to giving tuition credits to students wanting to study the art of data management. But, as the politicians like to say, “let’s be perfectly clear”: modern government regulations require IT professionals to implement new data management policies to prove they are in compliance with changes in the law.

For example, in 2006 the European Union issued a directive to communications carriersforcing them to hold on to subscriber usage data for six to 24 months. That’s so the companies can quickly respond to legal authorities who need to access data for criminal investigations. While some operators may already keep the information, it’s often stored offline. In the case of the EU directive, the information must be able to be accessed without delay by authorities armed with a warrant.

The way the EU directive was written means that wire line, wireless, and ISP operators must retain 15 categories of data. And because the time periods vary, the amount of data to be stored is unpredictable. As you can imagine, the EU also imposed some hefty data security demands as well as unique access requirements. For example, some legal authorities may send their warrants by FAX, e-mail, or even letters via the postal service.

Needless to say, the regulations don’t spell out exactly how carriers should implement the data retention policy. They simply need to do so.

It’s not just the EU creating rules affecting corporate data management. Japan is now considering revising its strict data protection policyfor consumers. The U.S. is in a political battle between those that want tighter Internet controls for copyright holders. And many other nations are designing new laws that affect how companies manage their data.

As I’ve argued here before, having a chief data officer would give enterprises a huge competitive advantage by being able to anticipate the impact new regulations would have on an organization’s data management strategy. In fact, it is increasingly paramount for large multinational companies to have a C-level data officer. Without one, the enterprise lacks a critical resource to compete in today’s global markets.

I agree with President Obama. Data management is, indeed, an excellent career choice for young people. After all, companies need smart people who understand its strategic importance and know how to react quickly when the politicians change the rules on data management for business. Again. And again.

Comments

Tags:

Awareness

Hack the CIO

By Thomas Saueressig, Timo Elliott, Sam Yen, and Bennett Voyles

For nerds, the weeks right before finals are a Cinderella moment. Suddenly they’re stars. Pocket protectors are fashionable; people find their jokes a whole lot funnier; Dungeons & Dragons sounds cool.

Many CIOs are enjoying this kind of moment now, as companies everywhere face the business equivalent of a final exam for a vital class they have managed to mostly avoid so far: digital transformation.

But as always, there is a limit to nerdy magic. No matter how helpful CIOs try to be, their classmates still won’t pass if they don’t learn the material. With IT increasingly central to every business—from the customer experience to the offering to the business model itself—we all need to start thinking like CIOs.

Pass the digital transformation exam, and you probably have a bright future ahead. A recent SAP-Oxford Economics study of 3,100 organizations in a variety of industries across 17 countries found that the companies that have taken the lead in digital transformation earn higher profits and revenues and have more competitive differentiation than their peers. They also expect 23% more revenue growth from their digital initiatives over the next two years—an estimate 2.5 to 4 times larger than the average company’s.

But the market is grading on a steep curve: this same SAP-Oxford study found that only 3% have completed some degree of digital transformation across their organization. Other surveys also suggest that most companies won’t be graduating anytime soon: in one recent survey of 450 heads of digital transformation for enterprises in the United States, United Kingdom, France, and Germany by technology company Couchbase, 90% agreed that most digital projects fail to meet expectations and deliver only incremental improvements. Worse: over half (54%) believe that organizations that don’t succeed with their transformation project will fail or be absorbed by a savvier competitor within four years.

Companies that are making the grade understand that unlike earlier technical advances, digital transformation doesn’t just support the business, it’s the future of the business. That’s why 60% of digital leading companies have entrusted the leadership of their transformation to their CIO, and that’s why experts say businesspeople must do more than have a vague understanding of the technology. They must also master a way of thinking and looking at business challenges that is unfamiliar to most people outside the IT department.

In other words, if you don’t think like a CIO yet, now is a very good time to learn.

However, given that you probably don’t have a spare 15 years to learn what your CIO knows, we asked the experts what makes CIO thinking distinctive. Here are the top eight mind hacks.

1. Think in Systems

A lot of businesspeople are used to seeing their organization as a series of loosely joined silos. But in the world of digital business, everything is part of a larger system.

CIOs have known for a long time that smart processes win. Whether they were installing enterprise resource planning systems or working with the business to imagine the customer’s journey, they always had to think in holistic ways that crossed traditional departmental, functional, and operational boundaries.

Unlike other business leaders, CIOs spend their careers looking across systems. Why did our supply chain go down? How can we support this new business initiative beyond a single department or function? Now supported by end-to-end process methodologies such as design thinking, good CIOs have developed a way of looking at the company that can lead to radical simplifications that can reduce cost and improve performance at the same time.

They are also used to thinking beyond temporal boundaries. “This idea that the power of technology doubles every two years means that as you’re planning ahead you can’t think in terms of a linear process, you have to think in terms of huge jumps,” says Jay Ferro, CIO of TransPerfect, a New York–based global translation firm.

No wonder the SAP-Oxford transformation study found that one of the values transformational leaders shared was a tendency to look beyond silos and view the digital transformation as a company-wide initiative.

This will come in handy because in digital transformation, not only do business processes evolve but the company’s entire value proposition changes, says Jeanne Ross, principal research scientist at the Center for Information Systems Research at the Massachusetts Institute of Technology (MIT). “It either already has or it’s going to, because digital technologies make things possible that weren’t possible before,” she explains.

2. Work in Diverse Teams

When it comes to large projects, CIOs have always needed input from a diverse collection of businesspeople to be successful. The best have developed ways to convince and cajole reluctant participants to come to the table. They seek out technology enthusiasts in the business and those who are respected by their peers to help build passion and commitment among the halfhearted.

Digital transformation amps up the urgency for building diverse teams even further. “A small, focused group simply won’t have the same breadth of perspective as a team that includes a salesperson and a service person and a development person, as well as an IT person,” says Ross.

At Lenovo, the global technology giant, many of these cross-functional teams become so used to working together that it’s hard to tell where each member originally belonged: “You can’t tell who is business or IT; you can’t tell who is product, IT, or design,” says the company’s CIO, Arthur Hu.

One interesting corollary of this trend toward broader teamwork is that talent is a priority among digital leaders: they spend more on training their employees and partners than ordinary companies, as well as on hiring the people they need, according to the SAP-Oxford Economics survey. They’re also already being rewarded for their faith in their teams: 71% of leaders say that their successful digital transformation has made it easier for them to attract and retain talent, and 64% say that their employees are now more engaged than they were before the transformation.

3. Become a Consultant

Good CIOs have long needed to be internal consultants to the business. Ever since technology moved out of the glasshouse and onto employees’ desks, CIOs have not only needed a deep understanding of the goals of a given project but also to make sure that the project didn’t stray from those goals, even after the businesspeople who had ordered the project went back to their day jobs. “Businesspeople didn’t really need to get into the details of what IT was really doing,” recalls Ferro. “They just had a set of demands and said, ‘Hey, IT, go do that.’”

Now software has become so integral to the business that nobody can afford to walk away. Businesspeople must join the ranks of the IT consultants.

But that was then. Now software has become so integral to the business that nobody can afford to walk away. Businesspeople must join the ranks of the IT consultants. “If you’re building a house, you don’t just disappear for six months and come back and go, ‘Oh, it looks pretty good,’” says Ferro. “You’re on that work site constantly and all of a sudden you’re looking at something, going, ‘Well, that looked really good on the blueprint, not sure it makes sense in reality. Let’s move that over six feet.’ Or, ‘I don’t know if I like that anymore.’ It’s really not much different in application development or for IT or technical projects, where on paper it looked really good and three weeks in, in that second sprint, you’re going, ‘Oh, now that I look at it, that’s really stupid.’”

4. Learn Horizontal Leadership

CIOs have always needed the ability to educate and influence other leaders that they don’t directly control. For major IT projects to be successful, they need other leaders to contribute budget, time, and resources from multiple areas of the business.

It’s a kind of horizontal leadership that will become critical for businesspeople to acquire in digital transformation. “The leadership role becomes one much more of coaching others across the organization—encouraging people to be creative, making sure everybody knows how to use data well,” Ross says.

In this team-based environment, having all the answers becomes less important. “It used to be that the best business executives and leaders had the best answers. Today that is no longer the case,” observes Gary Cokins, a technology consultant who focuses on analytics-based performance management. “Increasingly, it’s the executives and leaders who ask the best questions. There is too much volatility and uncertainty for them to rely on their intuition or past experiences.”

Many experts expect this trend to continue as the confluence of automation and data keeps chipping away at the organizational pyramid. “Hierarchical, command-and-control leadership will become obsolete,” says Edward Hess, professor of business administration and Batten executive-in-residence at the Darden School of Business at the University of Virginia. “Flatter, distributive leadership via teams will become the dominant structure.”

5. Understand Process Design

When business processes were simpler, IT could analyze the process and improve it without input from the business. But today many processes are triggered on the fly by the customer, making a seamless customer experience more difficult to build without the benefit of a larger, multifunctional team. In a highly digitalized organization like Amazon, which releases thousands of new software programs each year, IT can no longer do it all.

While businesspeople aren’t expected to start coding, their involvement in process design is crucial. One of the techniques that many organizations have adopted to help IT and businesspeople visualize business processes together is design thinking (for more on design thinking techniques, see “A Cult of Creation“).

Customers aren’t the only ones who benefit from better processes. Among the 100 companies the SAP-Oxford Economics researchers have identified as digital leaders, two-thirds say that they are making their employees’ lives easier by eliminating process roadblocks that interfere with their ability to do their jobs. Ninety percent of leaders surveyed expect to see value from these projects in the next two years alone.

6. Learn to Keep Learning

The ability to learn and keep learning has been a part of IT from the start. Since the first mainframes in the 1950s, technologists have understood that they need to keep reinventing themselves and their skills to adapt to the changes around them.

Now that’s starting to become part of other job descriptions too. Many companies are investing in teaching their employees new digital skills. One South American auto products company, for example, has created a custom-education institute that trained 20,000 employees and partner-employees in 2016. In addition to training current staff, many leading digital companies are also hiring new employees and creating new roles, such as a chief robotics officer, to support their digital transformation efforts.

Nicolas van Zeebroeck, professor of information systems and digital business innovation at the Solvay Brussels School of Economics and Management at the Free University of Brussels, says that he expects the ability to learn quickly will remain crucial. “If I had to think of one critical skill,” he explains, “I would have to say it’s the ability to learn and keep learning—the ability to challenge the status quo and question what you take for granted.”

7. Fail Smarter

Traditionally, CIOs tended to be good at thinking through tests that would allow the company to experiment with new technology without risking the entire network.

This is another unfamiliar skill that smart managers are trying to pick up. “There’s a lot of trial and error in the best companies right now,” notes MIT’s Ross. But there’s a catch, she adds. “Most companies aren’t designed for trial and error—they’re trying to avoid an error,” she says.

To learn how to do it better, take your lead from IT, where many people have already learned to work in small, innovative teams that use agile development principles, advises Ross.

For example, business managers must learn how to think in terms of a minimum viable product: build a simple version of what you have in mind, test it, and if it works start building. You don’t build the whole thing at once anymore.… It’s really important to build things incrementally,” Ross says.

Flexibility and the ability to capitalize on accidental discoveries during experimentation are more important than having a concrete project plan, says Ross. At Spotify, the music service, and CarMax, the used-car retailer, change is driven not from the center but from small teams that have developed something new. “The thing you have to get comfortable with is not having the formalized plan that we would have traditionally relied on, because as soon as you insist on that, you limit your ability to keep learning,” Ross warns.

8. Understand the True Cost—and Speed—of Data

Gut instincts have never had much to do with being a CIO; now they should have less to do with being an ordinary manager as well, as data becomes more important.

As part of that calculation, businesspeople must have the ability to analyze the value of the data that they seek. “You’ll need to apply a pinch of knowledge salt to your data,” advises Solvay’s van Zeebroeck. “What really matters is the ability not just to tap into data but to see what is behind the data. Is it a fair representation? Is it impartial?”

Increasingly, businesspeople will need to do their analysis in real time, just as CIOs have always had to manage live systems and processes. Moving toward real-time reports and away from paper-based decisions increases accuracy and effectiveness—and leaves less time for long meetings and PowerPoint presentations (let us all rejoice).

Not Every CIO Is Ready

Of course, not all CIOs are ready for these changes. Just as high school has a lot of false positives—genius nerds who turn out to be merely nearsighted—so there are many CIOs who aren’t good role models for transformation.

Success as a CIO these days requires more than delivering near-perfect uptime, says Lenovo’s Hu. You need to be able to understand the business as well. Some CIOs simply don’t have all the business skills that are needed to succeed in the transformation. Others lack the internal clout: a 2016 KPMG study found that only 34% of CIOs report directly to the CEO.

This lack of a strategic perspective is holding back digital transformation at many organizations. They approach digital transformation as a cool, one-off project: we’re going to put this new mobile app in place and we’re done. But that’s not a systematic approach; it’s an island of innovation that doesn’t join up with the other islands of innovation. In the longer term, this kind of development creates more problems than it fixes.

Such organizations are not building in the capacity for change; they’re trying to get away with just doing it once rather than thinking about how they’re going to use digitalization as a means to constantly experiment and become a better company over the long term.

As a result, in some companies, the most interesting tech developments are happening despite IT, not because of it. “There’s an alarming digital divide within many companies. Marketers are developing nimble software to give customers an engaging, personalized experience, while IT departments remain focused on the legacy infrastructure. The front and back ends aren’t working together, resulting in appealing web sites and apps that don’t quite deliver,” writes George Colony, founder, chairman, and CEO of Forrester Research, in the MIT Sloan Management Review.

Thanks to cloud computing and easier development tools, many departments are developing on their own, without IT’s support. These days, anybody with a credit card can do it.

Traditionally, IT departments looked askance at these kinds of do-it-yourself shadow IT programs, but that’s changing. Ferro, for one, says that it’s better to look at those teams not as rogue groups but as people who are trying to help. “It’s less about ‘Hey, something’s escaped,’ and more about ‘No, we just actually grew our capacity and grew our ability to innovate,’” he explains.

“I don’t like the term ‘shadow IT,’” agrees Lenovo’s Hu. “I think it’s an artifact of a very traditional CIO team. If you think of it as shadow IT, you’re out of step with reality,” he says.

The reality today is that a company needs both a strong IT department and strong digital capacities outside its IT department. If the relationship is good, the CIO and IT become valuable allies in helping businesspeople add digital capabilities without disrupting or duplicating existing IT infrastructure.

If a company already has strong digital capacities, it should be able to move forward quickly, according to Ross. But many companies are still playing catch-up and aren’t even ready to begin transforming, as the SAP-Oxford Economics survey shows.

For enterprises where business and IT are unable to get their collective act together, Ross predicts that the next few years will be rough. “I think these companies ought to panic,” she says. D!


About the Authors

Thomas Saueressig is Chief Information Officer at SAP.

Timo Elliott is an Innovation Evangelist at SAP.

Sam Yen is Chief Design Officer at SAP and Managing Director of SAP Labs.

Bennett Voyles is a Berlin-based business writer.

Read more thought provoking articles in the latest issue of the Digitalist Magazine, Executive Quarterly.
Comments

Tags:

CEO Priorities And Challenges In The Digital World

Dr. Chakib Bouhdary

Digital transformation is here, and it is moving fast. Companies are starting to realize the enormous power of digital technologies like artificial intelligence (AI), Internet of things (IoT) and blockchain. These technologies will drive massive opportunities—and threats—for every company, and they will impact all aspects of business, including the business model. In fact, business velocity has never been this fast, yet it will never be this slow again.

To move quickly, companies need to be clear on what they want to achieve through digital transformation and understand the possible roadblocks. Based on my meetings with customer executives across regions and industries, I have learned that CEOs often have the same three priorities and face the same three challenges:

1. Customer experience – No longer defined by omnichannel and personalized marketing.

Not surprisingly, 92 percent of digital leaders focus on customer experience. However, this is no longer just about omnichannel and personalized marketing – it is about the total customer experience. Businesses are realizing that they need to reimagine their value proposition and orchestrate changes across the value chain – from the first point of interaction to manufacturing, to shipment, to service – and be able to deliver the total customer experience. In some cases, it will even be necessary to change the core product or service itself.

2. Step change in productivity – Transform productivity and cost structure through digital technologies.

Businesses have been using technology to achieve growth for decades, but by combining emerging technologies, they can now achieve a significant productivity boost and reduce costs. For this to happen, companies must first identify the scenarios that will drive significant change in productivity, prioritize them based on value, and then determine the right technologies and solutions. Both Mckinsey and Boston Consulting Group expect a 15 to 30 percent improvement in productivity through digital advancements – blowing the doors off business-as-usual and its incremental productivity growth of 1 to 2 percent.

3. Employee engagement – Fostering a culture of innovation should be at the core of any business.

Companies are looking to create an environment that encourages creativity and innovation. Leaders are attracting the needed talent and building the right skill sets. Additionally, they aim for ways to attract a diverse workforce, improve collaborations, and empower employees – because engaged employees are crucial in order to achieve the best results. This Gallup study reveals that approximately 85 percent of employees worldwide are performing below their potential due to engagement issues.

As CEOs work towards achieving these three desired outcomes, they face some critical challenges that they must address. I define the top three challenges as follows: run vs. innovate, corporate cholesterol, and digital transformation roadmap.

1. Run vs. innovate – To be successful you must prioritize the future.

The foremost challenge that CEOs are facing is how they can keep running current profitable businesses while investing in future innovations. Quite often these two conflict as most executives mistakenly prioritize the first and spend much less time on the latter. This must change. CEOs and their management teams need to spend more time thinking about what digital is for them, discuss new ideas, and reimagine the future. According to Gartner, approximately 50 percent of boards are pushing their CEOs to make progress on digital. Although this is a promising sign, digital must become a priority on every CEOs agenda.

2. Corporate cholesterol – Do not let company culture get in the way of change.

The older the company is, the more stuck it likely is with policies, procedures, layers of management, and risk averseness. When a company’s own processes get in the way of change, that is what I call “corporate cholesterol.” CEOs need to change the culture, encourage cross-team collaborations, and bring in more diverse thinking to reduce the cholesterol levels. In fact, both Mckinsey and Capgemini conclude that culture is the number-one obstacle to digital effectiveness.

3. Digital transformation roadmap – Digital transformation is a journey without a destination.

Many CEOs struggle with their digital roadmap. Questions like: Where do I start? Can a CDO or another executive run this innovation for me? What is my three- to five-year roadmap? often come up during the conversations. Most companies think that there is a set roadmap, or a silver bullet, for digital transformation, but that is not the case. Digital transformation is a journey without a destination, and each company must start small, acquire the necessary skills and knowledge, and continue to innovate.

It is time to face the digital reality and make it a priority. According to KPMG, 70 percent to 80 percent of CEOs believe that the next three years are more critical for their company than the last fifty. And there is good reason to worry, as 75 percent of S&P 500 companies from 2012 will be replaced by 2027 at the current disruption rate.

Download this short executive document. 

Comments

Dr. Chakib Bouhdary

About Dr. Chakib Bouhdary

Dr. Chakib Bouhdary is the Digital Transformation Officer at SAP. Chakib spearheads thought leadership for the SAP digital strategy and advises on the SAP business model, having led its transformation in 2010. He also engages with strategic customers and prospects on digital strategy and chairs Executive Digital Exchange (EDX), which is a global community of digital innovation leaders. Follow Chakib on LinkedIn and Twitter