TDWI Experts in: Business Intelligence


  • 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, likes to work with numbers and has strong Excel and SQL skills, and is comfortable working with various types of databases. Ideally, the analyst should understand statistics and have a working knowledge of OLAP 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 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 2 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 already have in-house. 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 because director recognized the model's logic for cross-selling office products: "Ah! 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 dissect the model so it's understandable and usable. 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 and likely to churn (as in the example in step 5), tell them about customers who may stop purchasing because they have fallen below a critical statistical threshold and are vulnerable to competitive offers. You'll become an overnight hero. Here's another example: Rather than forecast the number loans that will go into default, identify the characteristics of good loans and bake that information into the loan origination process.

    Sustaining the Analytics Practice

    Let's assume your initial modeling efforts worked their magic and garnered 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 analysts start to study 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 to 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 percent of users' information needs, freeing up business analysts and BI report developers to focus on more value-added activities.

    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.

Vendor Q&A

  • MicrosoStrategy on Visualizations

    Q: What are the biggest mistakes BI pros make when creating visualizations for their data?

    A: The biggest mistakes made, even by the best BI professionals, are in understanding which visualizations to use and the workflow in using them effectively. These two factors are what determine the usability of the information for the business user. These users need to be able to find what they're looking for quickly and make sense of it easily.

    Here are three common mistakes BI pros make:

    1. Visualization Selection: It is essential for information comprehension to understand when it is appropriate (and not appropriate) to use a specific visualization. Are you comparing a group of items or are you just trying to highlight an individual element value? Depending on the type of analysis you're performing, ensure that the visualization helps get the user to their desired objective quickly and easily.

    2. Graph Overkill: A dashboard will often feature a large number of standard graphs in a single display. Although individually effective in conveying the information, these may be hard for the user to understand together, making analysis difficult. Instead, think about how you could leverage visualizations (like a heat map) to improve data discovery.

    3. Visibility: Screen real estate is critical for data comprehension. By nature, most users will tend to read from left to right and from top to bottom. Developers should build-in a workflow that moves down and to the right with each interactive choice or action. Another important visibility factor is color. Colors should be used to accentuate critical data -- such as key thresholds being met or missed. Involving your graphic designer for color selection and usage is always a good idea!

    Answers provided by Calli Wright, Senior Product Manager, MicroStrategy

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