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2012: The Year Analytics Means Business

Timo Elliott

2012-the-year-analytics-means-business

The real trend this year is not the technology. It’s about helping business people make better decisions, and actually change the way companies do business. Analytics has always been about transforming business, but the recent huge changes in analytic technology have created interesting new opportunities for business innovation.

Most organizations are now starting to understand the technical opportunities, but many struggle to apply those new opportunities to their business processes. This blog post attempts to explain what’s going on in the analytics market and give concrete examples of how other companies have implemented the new technologies in “game-changing” ways (sorry kittens).

Wrenching Change and A Foggy Outlook

The chart below illustrates the wrenching effects of recent financial problems on the world gross domestic product: companies today have to be ready to react to unprecedentedly fast changes to their economic environment.

fast-wrenching-change

And the economic environment is fraught with extreme uncertainty. This year, the people who run the world will change, and so will many of the policies of the countries they manage. Financial markets have still not completely stabilized, notably with the future of the Euro still not assured.

Companies have reacted to this uncertainty by slashing costs and accumulating cash, and now need to start investing that cash into future development. Since interest rates are low and the business outlook is still uncertain, many of them are using the money for new technology that can help them prepare for the future.

a-foggy-outlook

In particular, companies want better visibility about what’s going on in their market, and increased organizational agility in order to be able to deal with change fast. It’s like driving in the fog without a map – in order to survive, you should invest in better visibility, brakes, and steering to be able to spot and avoid fast-moving objects looming out of the fog.

Analytics provides these capabilities: business intelligence to peer into the road ahead, risk-management to provide fast alerts to new obstacles, and flexible financial planning systems to help swerve around them.

Analytics: Hotter Than Ever

Companies are investing heavily in analytics:

Fast-Moving Technology

Analytics technology has been changing fast. On the back end, new technologies have come together to provide what Gartner calls “extreme data performance”. These include in-memory, column data stores, in-database calculations, massively parallel architectures, complex event processing, Big Data / NoSQL / Hadoop, and cloud architectures.

The combination of these technologies provides a opportunity to access massive amounts of a greater variety of data, faster, and more flexibly. The key opportunity is that these new platforms “collapse the stack” so that organizations can implement and update analytic projects much faster than ever before.

technology-behind-new-analytic-platforms

And on the front end, various technologies are coming together to provide unprecedented levels of context-based “actionable insights”, including self-service data discovery, advanced visualization including maps, mobile analytics, predictive analytics, collaborative decision-support. They help provide more action-oriented interfaces optimized for the context of the users, both inside and outside the organization.

technology-behind-actionable-insights

These technology advances are clearly important, and we’re going to continue to see great improvements this year. The new opportunities have reached a tipping point similar to the rise of digital cameras vs. analog photography – and you don’t want to leave it too late to make the change, like Kodak, which recently filed for bankruptcy protection!

However, the real opportunity is using these new possibilities not only to improve analytics but fundamentally rethink key business processes.

High Resolution Management

high-resolution-management[3]University researchers have pointed out that today’s management techniques are based on the limitations of information scarcity:

“How many times has someone in your company uttered, “We don’t have that level of accuracy in the information, so we have to make aggregated estimates”? Under the current paradigm, it is sometimes impossible to drill down and understand what is happening at a highly detailed level.”

They coined the term “High Resolution Management” to describe what becomes possible with the new technology opportunities:

“We contend that these technologies will change drastically how management makes decisions. Why? Because with access to the finest granularities of information, management will be able to move freely from macro to micro levels and will be able to measure, plan and act accordingly. With increased resolution come more options to drill down, eliminate inefficiencies and cut costs.”

Lets take a look at three different types of High Resolution Management opportunity, letting companies:

  • Remove bottlenecks
  • Rethink business
  • Flip business models

Remove Bottlenecks

Better technology always means business opportunity, but the new analytic platforms are rapidly eliminating some of the key bottlenecks that have prevented organizations from getting value from their data:

Faster, more flexible data access. Companies like Red Bull have been able to speed up and simplify their data warehousing environments. Using the HANA in-memory database, the company can now load detailed data twenty-five times faster into their data warehouse, and they were able to eliminate several levels of data staging, increasing the flexibility of the solution.

Data volumes and complexity. Companies like Colgate-Palmolive, Provimi, and Danone have long had access to vast amounts of detailed data about their production facilities and sales channels – but the quantity of data meant that they were unable to run full analytics in a reasonable time frame. That has now changed. For example, according to Colgate-Palmolive CIO Tom Greene:

“We will be able to run analytics at a local level on specific brands and locations, and at the lowest level of detail in real time”

And Danone can now measure the carbon emissions of 35,000 different products, with new systems that:

“collect, measure, and analyze data across the entire product life-cycle, from sourcing through production, transport, retail, distribution, consumption, and end of cycle”

New forms of data: ‘unstructured’ data such as text has long been difficult to effectively analyze and incorporate into mainstream corporate analytics. The new systems make it much easier for companies like Medtronic to access and analyze the large amount of complaints and feedback data they receive about their products, combine it with other data sources, and provide it to business users with dynamic interfaces:

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New interfaces and users. Companies like Altron have been able to get the data to their users where they needed it. As Debra-Lynn Marais, Group Information Manager explains:

“The days of our users and execs being in the office have gone. They work from home or on the road. We had to develop a solution that gets information out to where our people are. Everything we do is mobile first. In addition, it’s less cumbersome and cheaper to buy and use a tablet than any other form.”

Rethink Business

Many companies are going beyond “just” improving their existing analytic capabilities, using analytics in new ways to change the way they do business. Instead of analytics being something that is used to monitor and eventually improve a business process, analytics is becoming a more fundamental part of the business process itself.

freshdirect_truck

Proactive Analytics. Instead of using analytics only to assess previous performance, companies are using the new capabilities to get data fast enough to make a real difference. For example, online grocer Fresh Direct, instead of just understanding what problems happened yesterday, can now understand what problems will happen in the next few hours, so they can actually fix them before a customer is impacted:

“FreshDirect has an operations center that manages its fleet of delivery trucks. In a large metropolitan area like New York, traffic doesn’t always flow predictably. A traditional approach to BI would be to print a report showing the level of on-time deliveries (OTDs) the day before and then ask the transportation department what went wrong for the orders that were delivered late. FreshDirect uses analytics in a more impactful way.”

“The company monitors the delivery rate of every truck and enters that data into the BI system on an ongoing basis. Every hour, it uses the previous hour’s data to predict how many deliveries will be on-time in the next hour. If the predicted OTD rate is below FreshDirect’s target, the company sends out an auxiliary truck or trucks to help make deliveries. The company holds 10 trucks in reserve for just this purpose.”

HMH-books

Integrated Risk Assessments. Among other products, publisher Houghton Mifflin Harcourt produces educational books. Schools pass orders in June or July after the end of the school year, and then expect delivery for the start of the next school year in September. Getting books printed during the summer is expensive, as many publishers compete for the limited supply of printers available.

To avoid these extra costs, Houghton Mifflin Harcourt uses using sophisticated, risk-based forecasting. The company prints books in January or February, when printing is much cheaper. In order to minimize of excess inventory, it has carefully analyzed all the causes of previous forecasts, and now takes account of all the different things that influence book obsolescence.

Before, the buying team just ordered based on the volume forecast from sales. Now they have much greater context for their decisions. For example, if there’s a vote coming up on schools funding that may result in the canceling of a math adoption program for the year, they can decide to hold back on those purchases until the outlook is clearer. The fast, more accurate forecasting mechanism has saved them tens of millions of dollars, and they have more of the products their customers want.

New Customer Services. International grocery chain Casino is rolling out a new mobile shopping application for its customers. It provides data from its enterprise systems directly to its customers, resulting in increased shopping convenience and increased customer loyalty.

casino-mobile-application

German healthcare provider AOK (“the good health organization”) is committed to helping its members avoid illnesses in the first place. It is planning to introduce a new, market-differentiating service: personalized healthcare advice for each customer, with tools that:

“Conduct real-time analyses of the tremendous amounts of medical data we receive, recognize potential health risks, assemble various preventive care programs and respond to those risks appropriately and ahead of time.”

As an added bonus, they also believe that this tailored prevention program will result in significant cost reductions by preventing expensive unneeded treatments.

bchydro

BC Hydro is saving $70 million dollars a year through the installation of new smart electricity meters, using SAP systems, and offering new services to commercial customers based on the new data possibilities. Companies like Centrica are planning to use SAP’s Smart Data Analytics, giving them deep understanding into consumer consumption.

Flip Business Models

The really interesting opportunity for businesses is where companies have managed to use analytics to fundamentally flip the way their businesses work: instead of analytics being part of a process, it “becomes the business model”.

Tim Ferriss, author of the 4-hour workweek, is an interesting example of this. He didn’t do what most authors do: write a book, and then figure out how to publicize it. He used an analytics-first approach: he bought Google Ads, with mockups of book covers, with a variety of titles of books that he might be interested in writing – and then wrote the book that got the most clickthroughs! This is one step beyond using analytics such as focus-groups, which are typically there to validate existing products. The next generation of products and services are being created “on the fly” based on an analysis-first approach.

The clothing brand Zara shook up fashion retailing with “analytics first” – instead of having a designer creating clothes and then trying to sell them six months later, they realized new manufacturing techniques meant they could create clothes “in the moment”. They could observe what people were wearing in the street, quickly make small batches of variations on that theme, and get them into the stores. If they sold well they made more, if they didn’t sell they discounted quickly. Instead of a season-oriented, “batch” business, they switched to a flow-oriented business, using new technology capabilities.

The new analytic platforms mean that this analytics-first approach is available to many more businesses than in the past. For example, T-Mobile is in the process of transforming the way they attract customers. Instead of laboriously creating a range of rate plans, promoting them, and analyzing the results, they now use analytics to automatically create hundreds of more complex, personalized rate plans. They then throw them out into the market, monitor in real time, and quickly cull any that aren’t successful. It’s a way of doing business that would have been inconceivable in the past, and a lot more common in the future.

Conclusion

2012 is the year to rethink your analytic technology to take account of new opportunities:

  • On the back end, for extreme data performance
  • On the front end, for actionable insights

And it’s time to rethink your business:

  • Remove today’s bottlenecks to successful analytics caused by data volumes, data variety, or data access
  • Rethink business processes by embedding real-time decisions
  • Create new products and services that could only exist because of today’s analytic power

Organizations are using this technology to change the way they do business. If you run an analytics project, you are in the forefront of these changes – it’s your job to help explain to the rest of the business how these technologies should be changing their existing processes. Good luck!

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If you’re interested in slides that go along with this article, please see this post about the recent Gartner BI Summit in London that includes a download of my presentation at the conference.

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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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