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Analytics Innovation, Disruption, And Transformation

Timo Elliott

I’ll be presenting a session on Analytics Innovation, Disruption, and Transformation at the BI2017 conference in Orlando next week. This post is an introduction to some of the themes I’ll be covering.

Analytics has been around for a long time, and it’s undergone some major changes. But one thing never seems to change: It’s still the hottest technology space in the industry.

For example, this is Gartner’s list of the top strategic trends for 2017 – and you can see that “intelligence” is at the top of the list, including machine learning, intelligent apps, and intelligent things.

 

BI & Analytics is yet again the top priority on Gartner’s long-running annual CIO survey, as it has been for ten out of the last twelve years.

 

Note that this is actually a very unusual situation. Normally, a new technology becomes a priority, companies invest money in it, and then it fades back down the list now that the new “problem” has been solved.

Consider mobile, for example: When smartphones and tablets came out, it suddenly became a high priority to support them, but now that the technology is in place, it has faded back down the list.

So why has analytics stayed so high for so long? Why haven’t we “fixed it” yet?

One reason analytics remains a hot topic is that the amount and variety of data available has skyrocketed, constantly creating new analytic challenges. But even more importantly, analytics has become an essential part of digital transformation.

For the last few decades, we’ve typically thought of business intelligence as a byproduct of our operational processes. In other words, we manufacture products, ship them around the world, and sell them to customers. Each of these processes generates a lot of data, and we use that data to keep track of operations and create more optimized processes in the future.

 

This remains as true and important today as it’s ever been in the past. But now there’s another dimension coming into play. Organizations are increasingly realizing that digital transformation doesn’t just require new processes – it requires a new approach to implementing processes. They have to be more agile, more intelligent, and more responsive to change.

 

These new processes flip the traditional equation on its head. New digital processes are created on the fly by analytics.

The typical customer journey is a great example. In the old days, purchasing a product was a fairly linear process, and companies characterized as a “sales funnel.” But now it’s more like a “write your own adventure” book – where there are many different possible interaction paths. At each point in the process, the customer gets to choose the next chapter.

Analytics is used to help guide the customer towards the “right” choice at each point, indicating what other products they may be interested in, or offering discounts to encourage immediate purchase.

In other words, every “customer process” is unique, with analytics doing all the work, creating thousands or millions of personalized “processes” based on the needs of each individual.

Because these new processes are analytics-powered rather than hard-coded, they can be much more agile and responsive to change – indeed, new machine learning approaches mean that they can even update and optimize themselves.

Effectively creating and managing these kinds of flexible, on-the-fly processes is THE big new opportunity in digital business. But it also means that analytics has to have a more process-oriented approach, not just treated as a series of one-off decisions — and this is an area where traditional BI leaders have an advantage over the “islands of innovation” approach of tools that focus only on data discovery and visualization.

Gartner believes that information and analytics will be used to reinvent, digitalize, or eliminate 80% of today’s business processes.

Analytics is no longer just an afterthought to the “real business” – it’s the heart of the new business models of the future.

Analytics also enables “live businesses.” A live business is one that anticipates, simulates, and innovates new business opportunities, and that looks to create the future rather than just reporting on the past.

Here’s the analogy I like to use: Imagine this expensive crystal vase has been knocked over and is plummeting to the floor. That’s the equivalent of something going wrong in your business – an overdue production, a late delivery, or an unhappy customer.

Traditionally, businesses would have been stuck with analyzing the puddle and shards of glass on the floor after the vase had broken, in order to figure out what went wrong, who to blame, and how to avoid it next time.

But what if you could actually catch the vase before it hit the ground—in other words, if you knew about the business problem before you lost money or ruined customer satisfaction? That’s live business. To achieve this, you need a seamless, real-time link between operations and analytics and this plays to the strengths of new in-memory operational+analytics solutions such as the SAP HANA platform.

But while analytics is a hot topic, it doesn’t mean that it’s without problems. Various reports indicate that the reported success rate of BI deployments has stalled. For example, Howard Dresner found that BI initiatives described as successful dropped from 41% to 35% in 2015.

It’s worth noting that it’s not completely clear what BI “success” really means — it’s largely a subjective measure because few organizations actually define what success would look like before they start on a projects. But I believe user satisfaction is falling because business expectations are rising even faster than BI technology improvements.

This has real consequences. In particular, some organizations continue to implement only “old-style,” centralized business intelligence. This is increasingly is out of phase with the needs of today’s more analytics-savvy business users. Gartner calls these people “BI-nosaurs” and warns that the comet that might wipe them out is coming .

Here are some of the typical complaints of today’s analytics users.

First, users find analytics too slow, with almost a third having to wait days or weeks for a BI request. People would like to access information themselves without needing IT, but a third said that they find their enterprise BI too complex, too complicated, and too cumbersome to use. Finally, almost half the data that business people want to access is now from outside the organization, and is therefore unlikely to be in the corporate system in the first place.

For all these reasons, the penetration rate of analytics remains low in organizations, with many reporting that fewer than 10% of employees using BI – although again, it’s not always clear what “using” means – often the data is used indirectly; for example, cut-and-pasted into a spreadsheet or presentation. But there’s obviously a huge opportunity to get more data to more people, both inside and outside the organization.

From the point of view of business users, traditional analytics organizations look like the taxi companies that have been displaced by more flexible car-sharing applications like Uber and Lyft. Many people found taxis too expensive and annoyingly hard to find when they wanted one – but there was no alternative, so they put up with it. Now there are lots of lightweight analytics products available, and business departments increasingly have their own IT budget to spend.

The result it that the older ways of doing things are being disrupted, and just like the taxi companies, traditional analytics organizations have to adapt to the new tools and new ways of working in order to compete effectively.

So how should analytics organizations react to these trends? I’ll be following up with future posts on areas such as supporting “modern BI,” the new Big Data architectures, the adoption of predictive and machine learning, and changes to how companies are organizing for BI.

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Timo Elliott

About Timo Elliott

Timo Elliott is an Innovation Evangelist for SAP and a passionate advocate of analytics, business intelligence, and digital transformation. He was the eighth employee of BusinessObjects and for the last 25 years, he has worked closely with SAP customers around the world on new technology directions and their impact on real-world organizations. His articles appear regularly in publications such as Forbes, ZDNet, The Guardian, and Digitalist Magazine. He has worked in the UK, Hong Kong, New Zealand, and Silicon Valley, and currently lives in Paris, France. He has a degree in Econometrics and a patent in mobile analytics. 

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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How Digital Transformation Is Rewriting Business Models

Ginger Shimp

Everybody knows someone who has a stack of 3½-inch floppies in a desk drawer “just in case we may need them someday.” While that might be amusing, the truth is that relatively few people are confident that they’re making satisfactory progress on their digital journey. The boundaries between the digital and physical worlds continue to blur — with profound implications for the way we do business. Virtually every industry and every enterprise feels the effects of this ongoing digital transformation, whether from its own initiative or due to pressure from competitors.

What is digital transformation? It’s the wholesale reimagining and reinvention of how businesses operate, enabled by today’s advanced technology. Businesses have always changed with the times, but the confluence of technologies such as mobile, cloud, social, and Big Data analytics has accelerated the pace at which today’s businesses are evolving — and the degree to which they transform the way they innovate, operate, and serve customers.

The process of digital transformation began decades ago. Think back to how word processing fundamentally changed the way we write, or how email transformed the way we communicate. However, the scale of transformation currently underway is drastically more significant, with dramatically higher stakes. For some businesses, digital transformation is a disruptive force that leaves them playing catch-up. For others, it opens to door to unparalleled opportunities.

Upending traditional business models

To understand how the businesses that embrace digital transformation can ultimately benefit, it helps to look at the changes in business models currently in process.

Some of the more prominent examples include:

  • A focus on outcome-based models — Open the door to business value to customers as determined by the outcome or impact on the customer’s business.
  • Expansion into new industries and markets — Extend the business’ reach virtually anywhere — beyond strictly defined customer demographics, physical locations, and traditional market segments.
  • Pervasive digitization of products and services — Accelerate the way products and services are conceived, designed, and delivered with no barriers between customers and the businesses that serve them.
  • Ecosystem competition — Create a more compelling value proposition in new markets through connections with other companies to enhance the value available to the customer.
  • Access a shared economy — Realize more value from underutilized sources by extending access to other business entities and customers — with the ability to access the resources of others.
  • Realize value from digital platforms — Monetize the inherent, previously untapped value of customer relationships to improve customer experiences, collaborate more effectively with partners, and drive ongoing innovation in products and services,

In other words, the time-tested assumptions about how to identify customers, develop and market products and services, and manage organizations may no longer apply. Every aspect of business operations — from forecasting demand to sourcing materials to recruiting and training staff to balancing the books — is subject to this wave of reinvention.

The question is not if, but when

These new models aren’t predictions of what could happen. They’re already realities for innovative, fast-moving companies across the globe. In this environment, playing the role of late adopter can put a business at a serious disadvantage. Ready or not, digital transformation is coming — and it’s coming fast.

Is your company ready for this sea of change in business models? At SAP, we’ve helped thousands of organizations embrace digital transformation — and turn the threat of disruption into new opportunities for innovation and growth. We’d relish the opportunity to do the same for you. Our Digital Readiness Assessment can help you see where you are in the journey and map out the next steps you’ll need to take.

Up next I’ll discuss the impact of digital transformation on processes and work. Until then, you can read more on how digital transformation is impacting your industry.

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Ginger Shimp

About Ginger Shimp

With more than 20 years’ experience in marketing, Ginger Shimp has been with SAP since 2004. She has won numerous awards and honors at SAP, including being designated “Top Talent” for two consecutive years. Not only is she a Professional Certified Marketer with the American Marketing Association, but she's also earned her Connoisseur's Certificate in California Reds from the Chicago Wine School. She holds a bachelor's degree in journalism from the University of San Francisco, and an MBA in marketing and managerial economics from the Kellogg Graduate School of Management at Northwestern University. Personally, Ginger is the proud mother of a precocious son and happy wife of one of YouTube's 10 EDU Gurus, Ed Shimp.