It has become eminently apparent that most professional workers are knowledge workers whose success or failure largely depends on access to information. Yet too many companies still wall off access to business insights behind a dedicated analytics department. But now, a better understanding of advances in embedded intelligence and predictive analytics are making it possible for analysts and decision-makers to be one and the same. After years of premature declarations, the ability to create a “predict and act” enterprise has quietly emerged. The question remains whether companies have a culture that empowers team members at various levels in the organization to manage information responsibly and to make decisions in a uniform manner.
Leading software providers such as SAP and Oracle are now embedding BI in releases of their enterprise resource planning solutions, instead of just bolting it on. When transactional and analytical capabilities are integrated into a single user interface, the power of data-driven insights will be in the hands of workers on the front line of the business. While many knowledge workers will breathe easier knowing they can go to one place for both analyzing data and executing decisions, this change alone is not enough to drastically improve decision-making processes. The next step is to combine historical data with forward-looking scenario-based analysis, or predictive analytics, thereby equipping knowledge workers with a decision-making platform.
It’s time we went back to the future in business information management. For years as consumers, we have experienced how helpful it is to use historical information to make recommendations. Every time someone buys a book on Amazon, a list of recommended future purchases is provided based on the individual’s transactional history and profiles of people who bought similar products. The same user experience-based offers exist on iTunes. If we can be predictive with book and song purchases, why can’t we do the same with suggestions for new customers to target, new products to add to our portfolio or new markets to enter?
The enterprise has been pursuing accessible, real-time BI for well over 15 years, and predictive analytics technology has been around for about a decade. However, there is renewed interest in embedded, predictive BI as a new breed of corporate leaders comes to view their organizations not as a sum of assets – people, processes and technologies – but as a series of decisions that need to be made on a daily basis. Senior executives recognize the difference they could see in the bottom line if decisions could be made based on better insights. In a 2008 Capgemini survey of international companies across multiple industries, senior executives said they could improve business performance by 27 percent if they were better able to mine the data they collected. These leaders perceive – rightly so – that access to better information could enable knowledge workers to respond proactively in rapidly changing environments, to use a single view of the truth to make sharper decisions, and to create sustainable and measurable value by achieving tactical and strategic business objectives.
But the desire to have integrated, real-time and predictive business insights and the ability to use them to greatest advantage are two different things.
From Fiction to Reality
We recommend a strategy of readiness so that as BI-muscled ERP applications become available, enterprises will be poised to exploit them as quickly and effectively as possible.
To understand the relevance of any new technology, it’s best to begin with an information strategy. What daily decisions have the greatest impact on the bottom line? Who makes those decisions today, and at what levels should those decisions be made in the future? In order to take advantage of small windows of opportunity, which decisions can we accelerate or automate entirely? Are data governance processes in place to ensure that all decisions are based upon high quality information?
When these questions are answered and embedded BI is available, enterprises will have that long-desired but elusive single view of the truth shared by knowledge workers across the enterprise. Not only would mission-critical processes share up-to-date and timely data, but that data would also be packaged in a forward-looking context for each user to apply in his or her daily decision-making.
An information strategy isn’t only concerned with the power of the technology itself; it’s also about defining an approach to deploying that technology, developing a deep culture of understanding around why information is important to all knowledge workers and equipping those knowledge workers with the tools to make faster, better decisions. Making that happen requires processes for governance and control: How will we manage decisions being made throughout the organization? How will the master data be created, distributed and managed? Where will the data come from? Who will own it? A structured, standardized process for data management fosters consistency, efficiency and accountability. Effective data governance should also allow some creativity. With so much focus over the last few years on master data management, which is quite important, the field of business information management has spent an inadequate amount of time looking at more creative ways to mash up data for insights that others might not have.
For example, by combining demographics data with historical sales numbers, a consumer products company may be able to determine in what geographies they should launch new or existing products. This is only possible if a company has the tools to bring together external and internal data in a meaningful manner and connect the insights to decision-making processes.
Questions about data governance and decision-making processes lead to a close look at enterprise culture. Training people on the technical functionality of new applications is important, but even more important is making sure knowledge workers understand the importance of routinely using business insight to achieve the company’s mission. As the day of one-size-fits-all ERP comes to an end, a new user-centric strategy takes hold – a strategy that Forrester Research calls “persona-driven provisioning,” in which IT tailors the workforce technology toolkit to the needs of specific employee groups based on a deep understanding of their needs.
Getting from bolted-on BI to back-to-the-future intelligence calls for a roadmap of steps and milestones that lead to a well-defined end state. An “adapt-adopt-transform” philosophy can guide an enterprise in devising a business-driven information strategy, determining workforce opportunities to deliver more value through the timely use of information, choosing the “best fit” technology solution, preparing the enterprise for implementation and making information the driving force behind daily decision-making and value creation.
Implementing this roadmap of technologies, processes, data governance and knowledge worker training will take time. The sooner enterprises can implement and execute this vision, the more efficient and effective they will become at realizing the value of BI.