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Big data is getting bigger. Information is coming from instrumented, interconnected supply chains transmitting real-time data about fluctuations in everything from market demand to the weather. Additionally, strategic information has started arriving through unstructured digital channels: social media, smart phone applications and an ever-increasing stream of emerging Internet-based gadgets. It’s no wonder six out of 10 respondents said the organization has more data than it knows how to use effectively.
All this data must be molded into an information foundation that is integrated, consistent and trustworthy, which were the leading data priorities cited by our respondents. Every phase of implementation needs to align the data foundation to an overall information agenda. The information agenda accelerates the organization’s ability to share and deliver trusted information across all applications and processes. It sets up information to serve as a strategic asset for the organization. (see Figure 10.)
The information agenda identifies foundational information practices and tools while aligning IT and business goals through enterprise information plans and financially justified deployment road maps. This agenda helps establish necessary links between those who drive the priorities of the organization by line of business and set the strategy, and those who manage data and information.
A comprehensive agenda also enables analytics to keep pace with changing business goals. An executive at one company, for example, told us it “had it down to a science” when it came to understanding the impact of price changes on single products and single channels. But the company was blindsided when it shifted to a customer-centric strategy, restructuring around bundled products and dynamic pricing across channels. Because its data marts had been developed de facto over time, the company found itself struggling to understand which tools and information were needed to go forward.
Lastly, building the analytic foundation under the guidance of a forward-looking information agenda enables organizations to keep pace with advances in mathematical sciences and technology. Without an enterprise-wide information agenda, units are likely to explore these new developments independently and adopt them inconsistently, a difficult path for gaining full business benefits from analytics.
Outline for an information agenda The information agenda provides a vision and high-level road map for information that aligns business needs to growth in analytics sophistication with the underlying technology and processes spanning:
- Information governance policies and tool kits — from little oversight to fully implemented policies and practices
- Data architecture — from ad hoc to optimal physical and logical views of structured and unstructured information and databases
- Data currency — from only historical data to a real-time view of all information
- Data management, integration and middleware — from subject area data and content in silos to enterprise information that is fully embedded into business processes with master content and master data management
- Analytical tool kits based upon user needs — from basic search, query and reporting to advanced analytics and visualization.
The information agenda is a key enabler of analytic initiatives by providing the right information and tools at the right times based upon business-driven priorities.
IBM CASE STUDY: Insurer Limits Risk by Establishing an Agenda for Today and Tomorrow
Under pressure from increasing competition, a financial firm recognized that growth — and survival — depended upon gaining a better understanding of its business quickly. For this, it needed an analytic foundation for strategic subject areas — first finance, then operations, then customers.
The firm completed a series of tightly scoped projects to increase analytic capabilities over time, with each wave realizing value to help fund the next. Business needs determined the order in which enterprise data would be reported to the analytic warehouse. To speed the efforts and time to value, business users assessed precisely which data elements were needed most. Common data definitions were negotiated to create a language across product lines and business units.
The organization took a phased approach to building its data environment. For finance and operations, this meant selecting data that supported an enterprise-wide set of KPIs. All other data was put on hold. To decide which customer data was most important, the organization determined which questions they most needed to answer, first by business unit and then across the enterprise — to find those with the greatest organizational overlap. Again, all other data would have to wait.
In this way, the organization was able to fast-track development of a robust data warehouse. As early projects produced a return on their investments and more resources became available, the data warehouse could grow.
This is part 7 of 10 from the 2010 New Intelligent Enterprise Global Executive Study and Research Project.
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