by Kevin Mackey

Today’s Environment

Originally prevalent in retail, marketing, and financial sectors, companies in all industries now find it imperative to gather data about customers in an effort to understand them better and increase market share. According to Gartner Group’s latest research, gathering these data and using them to drive decision making—in other words, creating a business intelligence strategy—ranks as the top priority for CIOs.

For companies who are actively developing or supporting an existing strategy, the good news is that the tools, technology, and people who support business intelligence and analytics are continually improving. But consider the bad news: approximately 70-80 percent of business intelligence initiatives miss the mark—from failure to ask the right questions to failure to consider the overall needs of the business. Five years ago, good analytics gave organizations a competitive advantage, but to stay ahead in an increasingly demanding economic landscape, it is imperative for organizations to become more data driven. If your organization has an existing business intelligence strategy or is about to develop one, how can you ensure success?

Point B’s Perspective

Much has been written about the technology and data that support business intelligence, but much less has been written about strategy and how to use data to gain competitive advantage. We believe that technology and data are only as good as the strategy they belong to. And an effective and holistic business intelligence strategy considers much more than data and technology. It requires a candid look at your business, thorough planning, the right people and resources, and heaps of good, old-fashioned know-how. Here, we propose six fundamental considerations for developing a sound business intelligence strategy to gain competitive advantage.

1. Secure and maintain executive buy-in and support.

Business intelligence initiatives often fail, and this is rarely because of the technology or lack of data. A recent study by MIT Sloan Management Review states that the most common barriers to adopting a BI strategy are managerial and cultural, and this reinforces what our experience tells us. The most fundamental component of any BI strategy is initial and ongoing executive sponsorship and effective change management. When leaders rally around the cause, anchor it in overall business objectives, and make it part of everyday conversations, the initiative has a greater chance of getting adopted. But if this executive sponsorship is hard to come by, how do you get it? One time-tested way we recommend is to deliver quick, high-impact wins and publicize them broadly across the organization. Sharing these victories makes it fairly straightforward to demonstrate the benefits of becoming an organization that makes decisions based on data instead of intuition or perception.

2. Establish clear business objectives.

Begin by identifying the business objectives that will be achieved by your business intelligence strategy. For example, what are your organization’s most important objectives? How can business intelligence support and enable these objectives? While the connection between business strategies and your data and analytics strategy may seem obvious to you, it’s very important to explicitly identify and communicate the connection. For most organizations, business objectives are usually heavily tied to customers—retaining them, earning their loyalty, and gaining new customers and revenue through referrals and new intelligence—in other words, making customers “sticky.” Such stickiness and customer engagement prevents “showrooming”, an undesirable practice in which shoppers test out a product in-store, but purchase online via a different etailer. Savvy retailers are combating this practice by using their business intelligence data to create a more personalized experience for shoppers – making it all but impossible for shoppers to want to purchase elsewhere. Retail isn’t the only industry working to create a more personalized experience for their consumers to make good on their business objectives. Healthcare providers also have some skin in the game. Gathering and mining important information about patients, such as medical history, healthcare services utilization, and medication adherence helps providers offer a more personalized experience and develop processes to improve patient care and reduce costs.

3. Understand the analytic capabilities you need to achieve your objectives.

Based on your business objectives and strategies, what analytic capabilities do you need to develop? What don’t you know that you need to know? If you could have any one insight, what would it be? Companies often ask questions that just scratch the surface of what’s possible on the analytics capability spectrum. “What happened last month?” and “Why?” are important questions to answer, but the value of these descriptive analytic questions and answers pale in comparison to predictive and prescriptive analytics, which looks to anticipate behaviors based on past actions. We recommend that our clients identify functions across the organization where analytic capabilities will drive the greatest value. For example, a current analytic “holy grail” for some of our retail clients is the capability to combine historical data with real-time geospatial information to make the most compelling offer at the right time and in the right place to each customer. This analytic capability is often called prescriptive analytics, or optimization and another perfect example of how retailers are fighting the “showrooming” phenomenon. We also suggest that clients should build for scale. Of course, it’s difficult to know exactly what capabilities you’ll need tomorrow, but the key is to define and develop what you currently need, and build a platform for incremental analytic capabilities as you learn. This type of capability drives tremendous value, but also drives requirements for new data (more, better, faster), technology, talent and processes.

4. Strike a balance of people, process, technology and data.

After you define the analytics capabilities to achieve your business objectives, the right combination of people, process, technology, and data are paramount. Appoint a strong, visible leader and carve out the proper organizational structure to ensure that your business intelligence program receives the attention and resources it needs. When it comes to business intelligence, most think of resources as being capital investments in the form of expensive data servers, but the more critical resources are people and the expertise they provide. Data architects, developers, testers, and report designers are all functional roles important to a solid analytics strategy. Also, depending upon the analytical skills your strategy requires, some organizations find it necessary to hire external expertise. Later, when analytics becomes a core competency, you can shift to in-house talent. Processes to guide people are also required. Although no two organizations are the same, most benefit from fundamental processes that build upon and drive ongoing value to your analytics investment. These fundamental components include:

 Data governance–establishing oversight capabilities clarifies data ownership, minimizes risk, and promotes collaboration.  

 User support and request management– establishing a framework helps effectively and efficiently respond to user requests.

 Program management and project governance– prioritizing programs and projects helps deliver new and improved capabilities with speed and agility.

 Data quality–proactively identifying and resolving data quality prevents issues that could jeopardize the return on your investment.

Further, analytic technology includes multiple interconnected components that come together to deliver timely, trusted data to support performance management and decision making. Given the multitude of business intelligence and analytic technologies available, organizations must establish a framework for evaluating technology solutions that most closely align to your analytic capability requirements. Organizations often find that they already have effective tools in-house and they are just not taking advantage of them. If additional tools are required, as with any software or technology purchase, we recommend performing a thorough vendor selection assessment based upon criteria defined by business and analytic capability needs. And in terms of data, organizations don’t often realize what data they have available. We recommend that organizations first understand what data are available in the organization today, and how that can be harnessed to produce meaningful analytics to drive better decision making. Similar to technology, there are multitudes of data sources that you can choose to leverage. The key is to start by selecting those data sources most closely tied to your most important business objectives. New technologies are making increasingly easy to ingest new sources of data and decide later how they will be used. But we recommend that as you get started, you remain focused on those sources most closely tied to business objectives and analytic capabilities.

5. Develop a roadmap.

Combining people, process, technology, and data into a winning combination is just the start. We also recommend that organizations develop comprehensive roadmaps to deliver early and ongoing value and measure your progress. If your analytics initiative is just getting started, we often recommend carefully selecting a single, obtainable, high-impact initiative to go after first – this enables you to demonstrate the benefits of sound analytics and garner momentum and ongoing support. We also recommend that roadmaps also include at least one business critical initiative. Grabbing onto the coattails of a major initiative can propel your strategy and program forward. In our experience, organizations that build the proper infrastructure and capabilities into their roadmap, but lack an important business initiative, can suffer from fatigue and often, failure. In addition, your roadmap should also define a repeatable development and delivery model for new capabilities. Although your organization may have an existing Software Development Life Cycle in place, you may not be able to leverage it for your analytics strategy. Common development methodologies, such as Waterfall or Agile, offer different benefits, and it’s important to choose a methodology that meshes with your environment and organization and all good roadmaps should shed light on obstacles and constraints—including organizational resistance to change—and provide solutions for removing them. But no matter how thoughtful and thorough the approach you outline in your roadmap, due to the significant investment, you will benefit from gaining business leader buy-in early to ensure continuous, long-term support.

6. Create an analytics methodology for operations and execution.

Organizations sometimes develop great new business intelligence capabilities, but fail to establish a framework and process for how to operate and execute with this new insight. Business leaders should create a methodology for ongoing analysis and insight-driven decision-making. Our clients who are most invested in competing on analytics develop what we call a closed-loop decision making framework in which they:

 Establish a regular cadence or “Rhythm of Business” for insight-driven decision-making

 Establish what they need to measure

 Execute, test hypotheses, measure, adjust, and repeat

 Don’t wait for the perfect capabilities and tools; they realize progress can be made toward their overall strategy today

By developing a sound and repeatable methodology, you can expect to gain increased collaboration and productivity across key teams, more effective decision making at the leadership level, and improved data management. The last mile matters most. Insights are critical, but true value is only realized when organizations take new or improved action based upon their insights.

The Bottom Line

The adoption barriers that organizations face most when implementing a business intelligence strategy are managerial and cultural rather than related to data and technology. Implementing a framework centered on the six considerations discussed above will not only help business leaders understand the importance of data analytics within their organization, but will also help deploy capabilities that ultimately strengthen their organization. Analytics and insight are not the end, but a means for business leaders to make better decisions and drive improved business outcomes. Insights enable decisions, but effective organizations leverage data for action