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The Decision Revolution: Marching Toward a New Reality in Analytics

by James Fisher | SAPinsider

January 1, 2013

We are in the midst of a “decision revolution.” Simply collecting and analyzing data is no longer enough. Instead, companies must spread intelligent data across the enterprise – and be able to act upon it. In this article, SAP’s James Fisher discusses how the analytics landscape has changed and what companies can do to leverage intelligent data.

We are at an inflection point in the world of analytics that will change how companies make and manage business decisions, according to James Fisher, SAP’s Vice President of Marketing for Analytics. Companies that pass this point will no longer simply collect and analyze historical data in a centralized warehouse, but will spread “intelligent data” across the enterprise to enhance the decision-making process at all levels. It’s a “decision revolution,” as Fisher calls it, and the weapon in this revolution is a new form of analytics spanning collaborative, social, mobile, and predictive — all designed to run for the new information consumer in the world of big data.

SAPinsider recently spoke with Fisher about the state of analytics. Like all revolution leaders, Fisher has a deep enthusiasm for driving change. He sees a clear path from the current state to a new reality in enterprise analytics, and he’s eager to deliver it to SAP customers.

Q: “Decision revolution” is how you describe recent changes to the analytics landscape. Can you explain?

A: Even with all of the analytics capabilities we have in place today, people still have to look at the data and make a judgment call based on it. The human process of making a decision is the same as it always has been. What has changed is the process around making that decision within an organization.

Today, an organization doesn’t need an information system, but rather a decision management system that enables users to make the best decisions for the company. To do that, users across the entire enterprise need intelligent data. There are more and more people involved in decision making at every level of the business — it’s not just happening in the corner office. And SAP is revolutionizing the decision management system to support this decision revolution.

Q: What are the challenges in getting intelligent data to users?

A: First and foremost is the proliferation of users. More enterprise users than ever before need access to analytics to help them with their decision-making processes. Gartner predicts that by 2014, 50% of enterprise employees will need access to analytics to help them do their jobs, and by 2020, 75% of them will.1 That shift requires a huge change in the way in which the organization deploys analytics solutions — both physically, in terms of the infrastructure, and also culturally, in the way the organization empowers people to use information to make decisions and manage associated risks.

The second challenge is the higher expectations of users. In the iPad era, what users expect from analytics applications is very different from a decade ago. Mobility, for instance, has changed the way users expect to be able to interact with analytics. They want analytics solutions to be smart, but simple to use — and always accessible.

The third challenge is the type and volume of data available. Data volume is growing exponentially, and the type of data being created is broadening as well. For example, when you booked a flight 20 years ago, you’d call the airline and give them your name and address. Today, when you book a flight online, you need to provide a credit card number, passenger preferences, dietary restrictions, frequent flier information, and more. All of that information is stored and used by the airline to manage your account and predict future business needs.

However, the point is not just that user expectations have changed or that there is a data explosion, but that we are now truly able to realize the benefits of using and leveraging this data across the enterprise to innovate our business processes and better serve our customers.

Q: How can a company leverage that intelligent data?

A: There are three steps I want to highlight here:

  • Inform your enterprise
  • Align your people
  • Adapt to a changing market

First, analytics users in the enterprise need to be informed at the moment they are making the decision, not weeks before or hours after that decision has been made. To some extent, this is what SAP has been doing for decades. However, now more than ever, employees need to have all this data at their fingertips and visibility into any possible risks that could be involved with whatever decisions they make. Information has to be accessible in real time, and we need to put that information in employees’ hands wherever they may be. Obviously, mobility and real-time analytics have had a dramatic impact on this first step.

We must also extend the reach of information to a broadening set of users and make certain that this information is accurate, timely, and trustworthy. Users also need to know business context to make the right decisions; you can’t just hand them a pile of data. That’s where data quality, master data management, and data validation all bring value. It’s also where robust risk management processes are most needed, and why we are all responsible for educating employees on the use of analytics in a decision-making process.

So mobility is an enabler to the goal of getting more users access to analytics, and it facilitates that expansion. But the driver here is really the recognition that people can make better decisions if they have the right information.

Q: You mentioned the need to align your people. Where are companies with that?

A: Companies do a good job of planning at a high level, but they struggle to align employees’ day-to-day activities with the high-level plan. That’s where companies should focus their energy and attention. People who are making decisions and executing on them need to have the right context to know that their work is supporting the company’s high-level strategy and goals. That’s how they succeed at their jobs. With the right analytics capabilities, decisions can be made by people at all levels of the organization — including those with the most knowledge in specific areas.

When thinking about analytics, you should think about collaboration capabilities as well. These need to be inherently intertwined because involving more people in a decision can bring significant benefits. People working out in the field can provide a perspective that someone in the head office cannot, and people in different geographies and roles can provide new insights. The ability to harness the input from a broader set of individuals improves decision making and the accuracy of business plans. This opportunity is even more powerful when you can then harness the knowledge of your customers by exploiting the growing wealth of social data that exists.

For example, we have a customer that delivers groceries to consumers. The company was receiving a lot of complaints that eggs were broken when they arrived at consumers’ doors. A high-level corporate team noticed this and suggested trying new packaging and delivery methods to avoid delivering broken eggs. No luck. So upper management asked the delivery drivers for their input and it became clear that the drivers with the fewest complaints were the ones who opened the boxes of eggs to check if any were broken before taking them. It wasn’t a packaging or delivery issue at all. But an executive could never have known that, and drivers might not have felt empowered to bring it up had they not been asked.

Q: Finally, how do analytics help companies adapt to a changing market?

A: By being well informed and well aligned, companies can review what has happened already and execute their plans. Understanding business risks and knowing how you will react to them is also key. But by taking the next step and using predictive analytics, users can see what’s likely to take place in the future and make decisions based on intelligent data. That’s how they use analytics to agilely adapt to a changing environment.

In the past, because predictive analytics were typically based on very complicated models, they were usually only accessible to the highest level of data scientists. But today, updated technology allows predictive solutions to be rolled out to a much wider audience, who will in turn make more informed decisions and be better aligned with corporate goals since they can see possible outcomes of their decisions before they make them.

So when we use enabling technologies, such as mobility and predictive analytics, to spread intelligent data to a broader set of users, the decisions being made across the enterprise at all levels improve. Users are informed, they are aligned to business goals, and they can adapt to changing environments. Ultimately, that’s how the entire business succeeds, since this is how it can better innovate processes and address the needs of individual customers more effectively.


James Fisher
Vice President of Marketing, Analytics



Gartner, “Market Trends: Analytics, Business Intelligence and Performance Management to be All-Pervasive by 2020” by Don Sommer and Rita L. Sallam (June 29, 2012). [back]

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