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The Top 10 Trends In Analytics 2013

Timo Elliott

The Top 10 Trends In Analytics 2013

I’ve been passionate about analytics for over twenty years – but my head is still spinning with the amount of change currently going on in the analytics industry. Here’s my quick personal view of the top 10 trends in Analytics and Business Intelligence for 2013 — what did I miss?

1. Analytics And Business Intelligence Are Still #1

According to Gartner’s latest CIO survey, the top business priority is back to enterprise growth, and analytics and business intelligence remains the number one technology priority for 2013. And the next three technologies on the priority list (mobile, cloud, and collaboration) are all key areas for analytic innovation.

2. Increasing Analytic Maturity

Thanks to greater industry maturity and new technology opportunities, most organizations are making steps from Descriptive Analytics (“what happened?”) and Diagnostic Analytics (“why did it happen?”) towards Predictive Analytics (“what will happen”) – with Prescriptive Analytics (“how can we make it happen”) as the next frontier.

3. In-Memory is Ripping Up The Old Rules

In-memory computing is providing an opportunity to rethink information systems from scratch. According to Gartner, in-memory: “isn’t only about SAP HANA, isn’t new, isn’t unproven, isn’t only about big companies, and isn’t only about analytics”:

“In-memory computing will have a long-term disruptive impact by radically changing users’ expectations, application design principles, and vendor’s strategy”

4. Breaking Down Old Barriers

In-memory breaks down long-standing analytics barriers. For example, in-memory computing platform SAP HANA supports structured and unstructured data in a single system, and includes a sophisticated, embedded text analysis engine. Predictive or advanced analytics no longer requires a separate system – powerful analytic algorithms are available directly in-memory, without any unnecessary data movement, and thousands of times faster than disk-based predictive system.

5. Operations and Analytics Are No Longer Separate

For forty years, operational systems and analytic systems have been separate because of technology limitations. That’s now changing with in-memory platforms. For example, with SAP Business Suite on HANA, transactional data is written directly to memory, where it is instantly available without any of the analytic compromises that have plagued earlier “real-time” analytics.

6. Big Data is a Big Deal

In addition to traditional “transaction data”, it’s now feasible to analyze “interaction data” (events before, after, and around a transaction, such as the products that were considered but then not purchased) and “observation data” (such as data streamed from sensors). Algorithms such as MapReduce and projects such as Hadoop have introduced new opportunities for storing and analyzing data that was previously ignored because of technology limitations. Actuaries are finding new careers and glory as “data scientists”. These new technologies have more than proved their worth in niche or standalone systems, but need to better integrated with existing corporate environments.

7. Analytics Moves To The Core

Analytics is no longer an afterthought to your transaction systems — it’s the heart of your future information infrastructure. The data you are storing now you will still have in 15 or 20 years time, while your applications may be long gone. The next generation of information infrastructures will combine big data, transactional data, analytic data and “content” into a single, coherent set of services that Gartner calls an “information capabilities framework”:

“The information capabilities framework is the people-, process- and technology-agnostic set of capabilities needed to describe, organize, integrate, share and govern an organization’s information assets in an application-independent manner in support of its enterprise information management (EIM) goals.”

SAP is working on this vision with the “real time data platform”, combining SAP HANA with Hadoop, Sybase ASE, Sybase IQ, Sybase ESP – and (crucially) end-to-end information governance.

8. Optimizing the User Experience

Today’s information consumers demand the same ease-of-use and immediate access they get in the consumer world. Business people want to be able to grab and mix information on the fly, without having to wait for it to be loaded into a corporate data warehouse. Data discovery tools such as SAP Visual Intelligence cover this essential demand – without sacrificing the corporate needs for enterprise governance. And of course, people expect a smooth, mobile-ready BI experience with integrated social collaboration, and the option of using a cloud-based infrastructure.

9. Information as an Asset

Along with all the technology changes, there have been big changes to analytics culture. Information is no longer a byproduct of manufacturing processes – it is fast-becoming a key part of the products themselves. Today’s retailers and service providers want to offer “customer experiences” that are tailored to individuals, optimized for the moment, and coherent over time – and that requires powerful new data platforms. As information becomes part of revenue generation, interest in information and control over budgets are swiftly moving to the business units, rather than traditional IT. This is creating new opportunities, but also new IT pressures and organizational issues.

10. The Revenge of Information Governance

As the technology gets more and more powerful, it becomes even more important to fix one of the oldest and least tractable barriers to successful BI: the pain of integrating multiple sets of quality data. Better integration between “big data,” traditional analytic systems, and transaction systems must also involve investments in data governance and solutions such as SAP Information Steward.

What did I miss? Add a comment below…

The Next Round of the Analytics Revolution

If you’d like to find out more about any of these trends, don’t hesitate to contact me, and I’ll help point you to the best experts available. If you’re interested in SAP Analytics technology, should follow the Business Intelligence areas of the SAP Community Network, subscribe to the SAP Analytics Blog, follow @sapanalytics or @timoelliott on Twitter, and join us at the analytics campus of SAPPHIRE NOW and ASUG 2013 in Orlando, May 14-16 to explore industry changes in depth, hear about companies that are implementing analytics in new way, and talk face-to-face with the experts.

[Note that a version of this post originally appeared on the SAPPHIRE NOW area of the SAP Community Network]

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About Timo Elliott

Timo Elliott is an innovation evangelist and international conference speaker who has presented to business and IT audiences in over forty countries around the world. A 23-year veteran of SAP BusinessObjects, Elliott works closely with SAP development and innovation centers around the world on new technology directions. His popular Business Analytics blog at timoelliott.com tracks innovation in analytics and social media, including topics such as big data, collaborative decision-making, and social analytics. Prior to Business Objects, Elliott was a computer consultant in Hong Kong and led analytics projects for Shell in New Zealand. He holds a first-class honors degree in Economics with Statistics from Bristol University, England.

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Why 3D Printed Food Just Transformed Your Supply Chain

Hans Thalbauer

Numerous sectors are experimenting with 3D printing, which has the potential to disrupt many markets. One that’s already making progress is the food industry.

The U.S. Army hopes to use 3D printers to customize food for each soldier. NASA is exploring 3D printing of food in space. The technology could eventually even end hunger around the world.

What does that have to do with your supply chain? Quite a bit — because 3D printing does more than just revolutionize the production process. It also requires a complete realignment of the supply chain.

And the way 3D printing transforms the supply chain holds lessons for how organizations must reinvent themselves in the new era of the extended supply chain.

Supply chain spaghetti junction

The extended supply chain replaces the old linear chain with not just a network, but a network of networks. The need for this network of networks is being driven by four key factors: individualized products, the sharing economy, resource scarcity, and customer-centricity.

To understand these forces, imagine you operate a large restaurant chain, and you’re struggling to differentiate yourself against tough competition. You’ve decided you can stand out by delivering customized entrees. In fact, you’re going to leverage 3D printing to offer personalized pasta.

With 3D printing technology, you can make one-off pasta dishes on the fly. You can give customers a choice of ingredients (gluten-free!), flavors (salted caramel!), and shapes (Leaning Towers of Pisa!). You can offer the personalized pasta in your restaurants, in supermarkets, and on your ecommerce website.

You may think this initiative simply requires you to transform production. But that’s just the beginning. You also need to re-architect research and development, demand signals, asset management, logistics, partner management, and more.

First, you need to develop the matrix of ingredients, flavors, and shapes you’ll offer. As part of that effort, you’ll have to consider health and safety regulations.

Then, you need to shift some of your manufacturing directly into your kitchens. That will also affect packaging requirements. Logistics will change as well, because instead of full truckloads, you’ll be delivering more frequently, with more variety, and in smaller quantities.

Next, you need to perfect demand signals to anticipate which pasta variations in which quantities will come through which channels. You need to manage supply signals source more kinds of raw materials in closer to real time.

Last, the source of your signals will change. Some will continue to come from point of sale. But others, such as supplies replenishment and asset maintenance, can come direct from your 3D printers.

Four key ingredients of the extended supply chain

As with our pasta scenario, the drivers of the extended supply chain require transformation across business models and business processes. First, growing demand for individualized products calls for the same shifts in R&D, asset management, logistics, and more that 3D printed pasta requires.

Second, as with the personalized entrees, the sharing economy integrates a network of partners, from suppliers to equipment makers to outsourced manufacturing, all electronically and transparently interconnected, in real time and all the time.

Third, resource scarcity involves pressures not just on raw materials but also on full-time and contingent labor, with the necessary skills and flexibility to support new business models and processes.

And finally, for personalized pasta sellers and for your own business, it all comes down to customer-centricity. To compete in today’s business environment and to meet current and future customer expectations, all your operations must increasingly revolve around rapidly comprehending and responding to customer demand.

Want to learn more? Check out my recent video on digitalizing the extended supply chain.

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Hans Thalbauer

About Hans Thalbauer

Hans Thalbauer is the Senior Vice President, Extended Supply Chain, at SAP. He is responsible for the strategic direction and the Go-To-Market of solutions for Supply Chain, Logistics, Engineering/R&D, Manufacturing, Asset Management and Sustainability at SAP.

How to Design a Flexible, Connected Workspace 

John Hack, Sam Yen, and Elana Varon

SAP_Digital_Workplace_BRIEF_image2400x1600_2The process of designing a new product starts with a question: what problem is the product supposed to solve? To get the right answer, designers prototype more than one solution and refine their ideas based on feedback.

Similarly, the spaces where people work and the tools they use are shaped by the tasks they have to accomplish to execute the business strategy. But when the business strategy and employees’ jobs change, the traditional workspace, with fixed walls and furniture, isn’t so easy to adapt. Companies today, under pressure to innovate quickly and create digital business models, need to develop a more flexible work environment, one in which office employees have the ability to choose how they work.

SAP_Digital_Emotion_BRIEF_image175pxWithin an office building, flexibility may constitute a variety of public and private spaces, geared for collaboration or concentration, explains Amanda Schneider, a consultant and workplace trends blogger. Or, she adds, companies may opt for customizable spaces, with moveable furniture, walls, and lighting that can be adjusted to suit the person using an unassigned desk for the day.

Flexibility may also encompass the amount of physical space the company maintains. Business leaders want to be able to set up operations quickly in new markets or in places where they can attract top talent, without investing heavily in real estate, says Sande Golgart, senior vice president of corporate accounts with Regus.

Thinking about the workspace like a designer elevates decisions about the office environment to a strategic level, Golgart says. “Real estate is beginning to be an integral part of the strategy, whether that strategy is for collaborating and innovating, driving efficiencies, attracting talent, maintaining higher levels of productivity, or just giving people more amenities to create a better, cohesive workplace,” he says. “You will see companies start to distance themselves from their competition because they figured out the role that real estate needs to play within the business strategy.”

The SAP Center for Business Insight program supports the discovery and development of  new research-­based thinking to address the challenges of business and technology executives.

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Sam Yen

About Sam Yen

Sam Yen is the Chief Design Officer for SAP and the Managing Director of SAP Labs Silicon Valley. He is focused on driving a renewed commitment to design and user experience at SAP. Under his leadership, SAP further strengthens its mission of listening to customers´ needs leading to tangible results, including SAP Fiori, SAP Screen Personas and SAP´s UX design services.

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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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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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