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Data: The Foundation Of Real-Time Digital Business

R “Ray” Wang

From Big Data to small data, the digital world measures and values every interaction. Digital technology enables every touch point, click, conversation, picture, and byte of digital exhaust to be used to improve decision-making. In fact, data provides the foundation for success in a real-time digital business. This is why organizations must carefully design a data strategy as the first step in digital transformation.

To get started, successful organizations map out a data-to-decisions framework (see Figure 1). This framework uses all types of upstream and downstream data (for example, structured, unstructured, big, small, and contextual) to align with business processes, creating information flows. From order to cash, procure to pay, campaign to lead, hire to retire, and incident to resolution, context is applied to information flows.

In the next step, algorithms apply context attributes such as role, relationship, weather, product, geo-spatial location, time, sentiment, and even intent to the information flows. The bigger the data set, the more opportunities for algorithms to find patterns of insight. The goals are to ask questions of the data and expose patterns of insight, using performance, deduction, inference, and prediction.

Traditionally, most systems stop after discovering insight. In a digital business, though, insight powers the ability to guide decision-making. By using the ability to take actions based on data, organizations can consider how to identify the next best actions, make recommendations, suggest risk mitigation, and even suggest that no actions be taken. By designing a data-to-decisions framework, organizations gain the ability to build a digital business and enable real-time business.

Once a data-to-decisions foundation is established, organizations can think about how they can apply the framework to augment decision-making. Successful leaders start by putting together a list of questions they seek answers for. They prioritize that list and then begin addressing these questions within the data-to-decisions framework. The secret to success is not what answers can be provided, but what questions should be asked. Successful organizations learn how to ask questions that have never been asked before, sometimes by employing techniques such as design thinking.

Figure 1: Use the data-to-decisions framework to drive real-time business
Data-real time

With mastery of data to decisions, organizations eventually will move from real-time to right-time models. Real-time immediately provides data to decisions as requested, resulting in a data deluge. Unfortunately, real time on its own may not be fast enough. Organizations may need to anticipate when data should be delivered. Why? Real time describes the speed at which the transformation from data to decisions must occur. Right time is about the precision that relevant, contextual information can provide once cognitive capabilities are applied to the data-to-decisions framework. In other words, right-time systems ensure organizations see what they need to see before they even know they need it.

So where do you begin?

1. Start by identifying the questions your organization seeks to answer.
2. Ask what traits make up the most valuable products, employees, customers, and suppliers. These traits drive the questions around what context matters.
3. Determine the information flows and business processes that drive context.
4. Understand the people and devices touched to provide the next level of journey mapping.
5. Apply the data sources and channels of data to recommendation engines and decision frameworks.

After taking these 5 steps, you can then start creating big data business models powered by insight. Digital technologies, data, and algorithms should all be aggressively used to create business models that take advantage of insights. Visibility, relevance, and immediacy will come from these insights-based business models. The goals are to simplify the complexity of decision making and enable real-time digital business.

Learn more how real-time business is impacting companies like yours.

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R “Ray” Wang

About R “Ray” Wang

R “Ray” Wang is the Principal Analyst, Founder, and Chairman of Silicon Valley based Constellation Research, Inc. He’s also the author of the popular business strategy and technology blog “A Software Insider’s Point of View”. With viewership in the 10’s of millions of page views a year, his blog provides insight into how disruptive technologies and new business models such as digital transformation impact brands, enterprises, and organizations. Wang has held executive roles in product, marketing, strategy, and consulting at companies such as Forrester Research, Oracle, PeopleSoft, Deloitte, Ernst & Young, and Johns Hopkins Hospital. His new best selling book Disrupting Digital Business, published by Harvard Business Review Press and globally available in Spring of 2015, provides insights on why 52% of the Fortune 500 have been merged, acquired, gone bankrupt, or fallen off the list since 2000. In fact, this impact of digital disruption is real. However, it’s not the technologies that drive this change. It’s a shift in how new business models are created.

Data Analysts And Scientists More Important Than Ever For The Enterprise

Daniel Newman

The business world is now firmly in the age of data. Not that data wasn’t relevant before; it was just nowhere close to the speed and volume that’s available to us today. Businesses are buckling under the deluge of petabytes, exabytes, and zettabytes. Within these bytes lie valuable information on customer behavior, key business insights, and revenue generation. However, all that data is practically useless for businesses without the ability to identify the right data. Plus, if they don’t have the talent and resources to capture the right data, organize it, dissect it, draw actionable insights from it and, finally, deliver those insights in a meaningful way, their data initiatives will fail.

Rise of the CDO

Companies of all sizes can easily find themselves drowning in data generated from websites, landing pages, social streams, emails, text messages, and many other sources. Additionally, there is data in their own repositories. With so much data at their disposal, companies are under mounting pressure to utilize it to generate insights. These insights are critical because they can (and should) drive the overall business strategy and help companies make better business decisions. To leverage the power of data analytics, businesses need more “top-management muscle” specialized in the field of data science. This specialized field has lead to the creation of roles like Chief Data Officer (CDO).

In addition, with more companies undertaking digital transformations, there’s greater impetus for the C-suite to make data-driven decisions. The CDO helps make data-driven decisions and also develops a digital business strategy around those decisions. As data grows at an unstoppable rate, becoming an inseparable part of key business functions, we will see the CDO act as a bridge between other C-suite execs.

Data skills an emerging business necessity

So far, only large enterprises with bigger data mining and management needs maintain in-house solutions. These in-house teams and technologies handle the growing sets of diverse and dispersed data. Others work with third-party service providers to develop and execute their big data strategies.

As the amount of data grows, the need to mine it for insights becomes a key business requirement. For both large and small businesses, data-centric roles will experience endless upward mobility. These roles include data anlysts and scientists. There is going to be a huge opportunity for critical thinkers to turn their analytical skills into rapidly growing roles in the field of data science. In fact, data skills are now a prized qualification for titles like IT project managers and computer systems analysts.

Forbes cited the McKinsey Global Institute’s prediction that by 2018 there could be a massive shortage of data-skilled professionals. This indicates a disruption at the demand-supply level with the needs for data skills at an all-time high. With an increasing number of companies adopting big data strategies, salaries for data jobs are going through the roof. This is turning the position into a highly coveted one.

According to Harvard Professor Gary King, “There is a big data revolution. The big data revolution is that now we can do something with the data.” The big problem is that most enterprises don’t know what to do with data. Data professionals are helping businesses figure that out. So if you’re casting about for where to apply your skills and want to take advantage of one of the best career paths in the job market today, focus on data science.

I’m compensated by University of Phoenix for this blog. As always, all thoughts and opinions are my own.

For more insight on our increasingly connected future, see The $19 Trillion Question: Are You Undervaluing The Internet Of Things?

The post Data Analysts and Scientists More Important Than Ever For the Enterprise appeared first on Millennial CEO.

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Daniel Newman

About Daniel Newman

Daniel Newman serves as the Co-Founder and CEO of EC3, a quickly growing hosted IT and Communication service provider. Prior to this role Daniel has held several prominent leadership roles including serving as CEO of United Visual. Parent company to United Visual Systems, United Visual Productions, and United GlobalComm; a family of companies focused on Visual Communications and Audio Visual Technologies. Daniel is also widely published and active in the Social Media Community. He is the Author of Amazon Best Selling Business Book "The Millennial CEO." Daniel also Co-Founded the Global online Community 12 Most and was recognized by the Huffington Post as one of the 100 Business and Leadership Accounts to Follow on Twitter. Newman is an Adjunct Professor of Management at North Central College. He attained his undergraduate degree in Marketing at Northern Illinois University and an Executive MBA from North Central College in Naperville, IL. Newman currently resides in Aurora, Illinois with his wife (Lisa) and his two daughters (Hailey 9, Avery 5). A Chicago native all of his life, Newman is an avid golfer, a fitness fan, and a classically trained pianist

When Good Is Good Enough: Guiding Business Users On BI Practices

Ina Felsheim

Image_part2-300x200In Part One of this blog series, I talked about changing your IT culture to better support self-service BI and data discovery. Absolutely essential. However, your work is not done!

Self-service BI and data discovery will drive the number of users using the BI solutions to rapidly expand. Yet all of these more casual users will not be well versed in BI and visualization best practices.

When your user base rapidly expands to more casual users, you need to help educate them on what is important. For example, one IT manager told me that his casual BI users were making visualizations with very difficult-to-read charts and customizing color palettes to incredible degrees.

I had a similar experience when I was a technical writer. One of our lead writers was so concerned with readability of every sentence that he was going through the 300+ page manuals (yes, they were printed then) and manually adjusting all of the line breaks and page breaks. (!) Yes, readability was incrementally improved. But now any number of changes–technical capabilities, edits, inserting larger graphics—required re-adjusting all of those manual “optimizations.” The time it took just to do the additional optimization was incredible, much less the maintenance of these optimizations! Meanwhile, the technical writing team was falling behind on new deliverables.

The same scenario applies to your new casual BI users. This new group needs guidance to help them focus on the highest value practices:

  • Customization of color and appearance of visualizations: When is this customization necessary for a management deliverable, versus indulging an OCD tendency? I too have to stop myself from obsessing about the font, line spacing, and that a certain blue is just a bit different than another shade of blue. Yes, these options do matter. But help these casual users determine when that time is well spent.
  • Proper visualizations: When is a spinning 3D pie chart necessary to grab someone’s attention? BI professionals would firmly say “NEVER!” But these casual users do not have a lot of depth on BI best practices. Give them a few simple guidelines as to when “flash” needs to subsume understanding. Consider offering a monthly one-hour Lunch and Learn that shows them how to create impactful, polished visuals. Understanding if their visualizations are going to be viewed casually on the way to a meeting, or dissected at a laptop, also helps determine how much time to spend optimizing a visualization. No, you can’t just mandate that they all read Tufte.
  • Predictive: Provide advanced analytics capabilities like forecasting and regression directly in their casual BI tools. Using these capabilities will really help them wow their audience with substance instead of flash.
  • Feature requests: Make sure you understand the motivation and business value behind some of the casual users’ requests. These casual users are less likely to understand the implications of supporting specific requests across an enterprise, so make sure you are collaborating on use cases and priorities for substantive requests.

By working with your casual BI users on the above points, you will be able to collectively understand when the absolute exact request is critical (and supports good visualization practices), and when it is an “optimization” that may impact productivity. In many cases, “good” is good enough for the fast turnaround of data discovery.

Next week, I’ll wrap this series up with hints on getting your casual users to embrace the “we” not “me” mentality.

Read Part One of this series: Changing The IT Culture For Self-Service BI Success.

Follow me on Twitter: @InaSAP

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The Future of Cybersecurity: Trust as Competitive Advantage

Justin Somaini and Dan Wellers

 

The cost of data breaches will reach US$2.1 trillion globally by 2019—nearly four times the cost in 2015.

Cyberattacks could cost up to $90 trillion in net global economic benefits by 2030 if cybersecurity doesn’t keep pace with growing threat levels.

Cyber insurance premiums could increase tenfold to $20 billion annually by 2025.

Cyberattacks are one of the top 10 global risks of highest concern for the next decade.


Companies are collaborating with a wider network of partners, embracing distributed systems, and meeting new demands for 24/7 operations.

But the bad guys are sharing intelligence, harnessing emerging technologies, and working round the clock as well—and companies are giving them plenty of weaknesses to exploit.

  • 33% of companies today are prepared to prevent a worst-case attack.
  • 25% treat cyber risk as a significant corporate risk.
  • 80% fail to assess their customers and suppliers for cyber risk.

The ROI of Zero Trust

Perimeter security will not be enough. As interconnectivity increases so will the adoption of zero-trust networks, which place controls around data assets and increases visibility into how they are used across the digital ecosystem.


A Layered Approach

Companies that embrace trust as a competitive advantage will build robust security on three core tenets:

  • Prevention: Evolving defensive strategies from security policies and educational approaches to access controls
  • Detection: Deploying effective systems for the timely detection and notification of intrusions
  • Reaction: Implementing incident response plans similar to those for other disaster recovery scenarios

They’ll build security into their digital ecosystems at three levels:

  1. Secure products. Security in all applications to protect data and transactions
  2. Secure operations. Hardened systems, patch management, security monitoring, end-to-end incident handling, and a comprehensive cloud-operations security framework
  3. Secure companies. A security-aware workforce, end-to-end physical security, and a thorough business continuity framework

Against Digital Armageddon

Experts warn that the worst-case scenario is a state of perpetual cybercrime and cyber warfare, vulnerable critical infrastructure, and trillions of dollars in losses. A collaborative approach will be critical to combatting this persistent global threat with implications not just for corporate and personal data but also strategy, supply chains, products, and physical operations.


Download the executive brief The Future of Cybersecurity: Trust as Competitive Advantage.


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Unleash The Digital Transformation

Kadamb Goswami

The world has changed. We’ve seen massive disruption on multiple fronts – business model disruption, cybercrime, new devices, and an app-centric world. Powerful networks are crucial to success in a mobile-first, cloud-first world that’s putting an ever-increasing increasing amount of data at our fingertips. With the Internet of Things (IoT) we can connect instrumented devices worldwide and use new data to transform business models and products.

Disruption

Disruption comes in many forms. It’s not big or scary, it’s just another way of describing change and evolution. In the ’80s it manifested as call centers. Then, as the digital landscape began to take shape, it was the Internet, cloud computing … now it’s artificial intelligence (AI).

Digital transformation

Digital transformation means different things to different companies, but in the end I believe it will be a simple salvation that will carry us forward. If you Bing (note I worked for Microsoft for 15 years before experiencing digital transformation from the lens of the outside world), digital transformation, it says it’s “the profound and accelerating transformation of business activities, processes, competencies, and models to fully leverage the changes and opportunities of digital technologies and their impact across society in a strategic and prioritized way.” (I’ll simplify that; keep reading.)

A lot of today’s digital transformation ideas are ripped straight from the scripts of sci-fi entertainment, whether you’re talking about the robotic assistants of 2001: A Space Odyssey or artificial intelligence in the Star Trek series. We’re forecasting our future with our imagination. So, let’s move on to why digital transformation is needed in our current world.

Business challenges

The basic challenges facing businesses today are the same as they’ve always been: engaging customers, empowering employees, optimizing operations, and reinventing the value offered to customers. However, what has changed is the unique convergence of three things:

  1. Increasing volumes of data, particularly driven by the digitization of “things” and heightened individual mobility and collaboration
  1. Advancements in data analytics and intelligence to draw actionable insight from the data
  1. Ubiquity of cloud computing, which puts this disruptive power in the hands of organizations of all sizes, increasing the pace of innovation and competition

Digital transformation in plain English

Hernan Marino, senior vice president, marketing, & global chief operating officer at SAP, explains digital transformation by giving specific industry examples to make it simpler.

Automobile manufacturing used to be the work of assembly lines, people working side-by-side literally piecing together, painting, and churning out vehicles. It transitioned to automation, reducing costs and marginalizing human error. That was a business transformation. Now, we are seeing companies like Tesla and BMW incorporate technology into their vehicles that essentially make them computers on wheels. Cameras. Sensors. GPS. Self-driving vehicles. Syncing your smartphone with your car.

The point here is that companies need to make the upfront investments in infrastructure to take advantage of digital transformation, and that upfront investment will pay dividends in the long run as technological innovations abound. It is our job to collaboratively work with our customers to understand what infrastructure changes need to be made to achieve and take advantage of digital transformation.

Harman gives electric companies as another example. Remember a few years ago, when you used to go outside your house and see the little power meter spinning as it recorded the kilowatts you use? Every month, the meter reader would show up in your yard, record your usage, and report back to the electric company.

Most electric companies then made a business transformation and installed smart meters – eliminating the cost of the meter reader and integrating most homes into a smart grid that gave customers access to their real-time information. Now, as renewable energy evolves and integrates more fully into our lives, these same electric companies that switched over to smart meters are going to make additional investments to be able to analyze the data and make more informed decisions that will benefit both the company and its customers.

That is digital transformation. Obviously, banks, healthcare, entertainment, trucking, and e-commerce all have different needs than auto manufacturers and electric companies. It is up to us – marketers and account managers promoting digital transformation – to identify those needs and help our clients make the digital transformation as seamlessly as possible.

Digital transformation is more than just a fancy buzzword, it is our present and our future. It is re-envisioning existing business models and embracing a different way of bringing together people, data, and processes to create more for their customers through systems of intelligence.

Learn more about what it means to be a digital business.

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Goswami Kadamb

About Goswami Kadamb

Kadamb is a Senior Program Manager at SAP where he is responsible for developing and executing strategic sales program with Concur SaaS portfolio. Prior to that he led several initiatives with Microsoft's Cloud & Enterprise business to enable Solution Sales & IaaS offerings.