Launching an Analytics Practice: Ten Steps to Success
Everyone wants to move beyond reporting to deliver value-added insights through analytics. The problem is that few organizations know where to begin. Here is a ten-step guide for launching a vibrant analytics practice.
Launching the Practice
Step 1: Find an Analyst. You can’t do analytics without an analyst! Most companies have one or more analysts burrowed inside a department. Look for someone who is bright, curious, and understands key business processes inside and out. The analyst should like to work with numbers, have strong Excel, SQL, OLAP, and database skills, and ideally understand some statistics and data mining tools.
Step 2: Find a Business Person. The quickest way to kill an analytics practice is to talk about predictive models, optimization, or statistics with a business person. Instead, find one or more executives who are receptive to testing key assumptions about how the business works. For example, a retail executive might want to know, “Why do some customers stop buying our product?” A social service agency might want to know, “Which spouses are most likely not to pay alimony?” Ask them to dream up as many hypotheses to their questions as possible and then use those as inputs for your analysis.
Step 3: Gain Sponsorship. If step two piqued an executive’s interest, then you have a sponsor. Tell the sponsor what resources you need, if any, to conduct the test. Perhaps you need permission to free up an analyst for a week or two or hire a consultant to conduct the analysis. Ideally, you should be able to make do with people and tools you have inhouse. A good analyst can work miracles with Excel and SQL and there are many open source data mining packages on the market today as well as low cost statistical add-ins for Excel and BI tools.
Step 4: Don’t Get Uppity. “You never want to come across smarter than the executive you are supporting,” says Matthew Schwartz, a former director of business analytics at Corporate Express. Don’t ever portray the model results as “the truth”; executives don’t trust models unless they make intuitive sense or prove their value in dollars and cents. For example, Schwartz was able to get his director of marketing to buy in to the results of a market basket analysis for Web site recommendations because the director recognized the model’s cross-selling logic: “Ah! It knows that people are buying office kits for new employees.”
Step 5: Make It Actionable. A model is worthless if people can’t act on it. This often means embedding the model in an operational application, such as a Web site or customer-facing application, or distributing the results in reports to salespeople or customer service representatives. In either case, you need to strip out the mathematics and decompose the model so it’s understandable and usable by people in the field. For example, a sales report might say, “These five customers are likely to stop purchasing office products from us because they haven’t bought toner in four weeks.”
Step 6: Make It Proactive. The kiss of death for an analytical model is to tell people something they already know. Rather than tell salespeople about customers who are purchasing fewer products than the prior period are likely to churn (like the example I gave in step five above), tell them about customers who will buy fewer products in the future because they have fallen below a critical statistical threshold and are vulnerable to competitive offers. Or, rather than forecast the number loans that will go into default, identify the characteristics of good loans and bake that criteria into the loan origination process. If you deliver results that enable people to work proactively, you’ll become an overnight hero.
Sustaining the Analytics Practice
Let’s assume your initial modeling efforts worked their magic and garnered you strong executive sponsorship. How do you build and sustain an analytics practice? What organizational and technical strategies do you employ to ensure that your analysts are as productive as possible? The following four steps will solidify your analytics practice.
Step 7: Centralize and Standardize the Data. The thing that slows down analysts the most is having to collect data spread across multiple systems and then clean, harmonize, and integrate it. Only then can they start to analyze the data. Obviously, this is what a data warehouse is designed to do, not an analyst. But a data warehouse only helps if it contains all or most of the data analysts need in a format they can readily use so they don’t have to hunt and reconcile data on their own. Typically, analytical modelers need wide, flat tables with hundreds of attributes to create models.
Step 8: Provide Open Access to Data. Data warehouse administrators need to give analysts access to the data warehouse without having to file a request and wait weeks for an answer. Rather than broker access to the data warehouse, administrators should create analytical sandboxes using partitions and workload management that let analysts upload their own data and comingle it with data in the warehouse. This creates an analytical playground for analysts and keeps them from creating renegade data marts under their desks.
Step 9: Centralize Analysts. Contrary to current practice, it’s best to centralize
analysts in an Analytical Center of Excellence under the supervision of a Director of Analytics. This creates a greater sense of community and camaraderie among analysts and gives them more opportunities for advancement within the organization. It also minimizes the chance that they’ll be lured away by recruiters. Although they may be part of a shared services group, analysts should be physically embedded within the departments they support and have dotted line responsibility to those department heads.
Step 10: Offload Reporting. The quickest to undermine the productivity of your top analysts is force them to field requests for ad hoc reports from business users. To eliminate the reporting backlog, the BI team and analysts need to work together to create a self-service BI architecture that empowers business users to generate their own reports and views. When designed properly, these interactive reports and dashboards will meet 60% to 80% of users’ information needs, freeing up business analysts and BI report developers to focus on more value-added activities.
So there you have it, ten steps to analytical nirvana. Easy to write, hard to do! Keep me informed about your analytics journey and the lessons you learn along the way! I’d love to hear your stories. You can reach me at firstname.lastname@example.org.
Posted by Wayne Eckerson on February 26, 2010