I just attended a project kickoff meeting where one of the project sponsors used this video as part of his presentation to stress the goal of increased profitability through using business intelligence to find trends for use in forecasting demand. As you’ll see, the commercial (or promo, as I should call it, as I don’t know if this was a television ad or some other type of paid advertising) uses a bakery as a mini case study to show that mining the sales data can reveal hidden patterns in the customer purchases. It turns out that Europeans eat more cake on rainy days!
Here’s an interesting white paper on “self-service” business intelligence. Here’s the link: “Agile BI: Power to the People”
About 175 IT leaders were surveyed about their business intelligence agility and implementations. It defined the 20 percent of users as best-in-class, 50 percent as average, and the remaining as “laggards.” The authors, White and Castellina, suggested that avenues toward self-service functionality show the clearest path toward agile BI. On that front, BI endeavors returned the most rewards when they moved beyond the traditional static reports and into collaboration between business and IT.
Sixty-five percent of BI users across best-in-class enterprises reported that data sources were understood and documented, compared with 33 percent of other enterprises that shared that same comprehension, according to the Aberdeen report. An even wider difference was found in the development of BI skills among management, with 53 percent of best-in-class users following through on this development and only 23 percent under development from the rest of enterprises.
“Agile BI: Power to the People” prepared by David White, Senior Research Analyst in Aberdeen’s Business Intelligence practice, examines how the growth in data volume and data sources, combined with the increasing demand for timely management information, are driving organizations to seek a more agile and flexible approach to business intelligence. Business intelligence is being used to support tactical decision-making at the line of business level. For this to be successful, a flexible solution is required that can easily be adapted to meet rapidly changing business needs through a self-service BI solution. This document examines some of the key criteria required for self-service BI to be successful.
Business Dashboard Topic: Can business intelligence be compared to human anatomy?
Here’s a video from SAP that compares business intelligence to human anatomy. That’s right, watch the video and you’ll see that the parallels can indeed be made. The “foot” is equivalent to Enterprise Information Management; the “legs” and “arms” are Collaboration; the “torso” is akin to Data Warehousing, the “upper body” is business intelligence, the “ears” are Analytic Applications and Predictive Analytics and the “head or brain” is Performance Management Risk and Compliance.
BTW, the video is a recording of the Day 2 keynote presented at the 2011 ASUG SBOUC in Orlando, Florida. For more, see http://blogs.sap.com/analytics
Your company dashboard is only as good as the data it pulls and successfully interprets. Many dashboards are misleading because of flawed data or simply not useful because of the lack of good metrics.
Here is an excellent whitepaper on this subject:
Why Use Dashboard Metrics? (direct link to pdf)
“The major problem with dashboards, however, lies in the fact that many are based on flawed measurement techniques and metrics that are not predictive of success. A dashboard is only useful to a company if the data behind it are accurate measures of consumer behavior and the best predictors of organizational success.”
A useful dashboard will be tailored to the company and include unique measures that best predict its success (Harvard Business School, 2007). A “one size fits all” dashboard is not beneficial for the company. Conducting research to discover the measures that are specific to your company will create a dashboard that will provide actionable information.
Another potential problem with dashboards is focusing on only one aspect of the company, such as marketing activities. A successful dashboard will integrate all departments within the company in order to get a more complete picture of company health. Also, some dashboards only show short-term targets and results, such as sales figures, rather than focusing on critical, long-term predictive indicators (MacDonald, 2006).
The major problem with dashboards, however, lies in the fact that many are based on flawed measurement techniques and metrics that are not predictive of success. A dashboard is only useful to a company if the data behind it are accurate measures of consumer behavior and the best predictors of organizational success.
When creating a dashboard, it is not useful to throw up values haphazardly or only include those figures that make the company look good and keep the CEO happy. This way of thinking can prevent companies from staying focused on the metrics that actually predict success. In the book, “Moneyball,” baseball managers focused their decision- making on indicators that were better predictors of success rather than the traditional metrics that managers would focus on (Lewis, 2003). The author, Michael Lewis, shows how Bill James and Billy Beane transformed major league baseball by demonstrating how improving efficiency can leverage limited resources and create success. What James and Beane did was to redefine the metrics used to evaluate the value of each major league baseball player to a team. Instead of looking at traditional indicators like batting averages and earned run averages, Beane re- focused baseball on walks, on-base percentages and other metrics—because they are a better predictor of success (i.e., winning baseball games). This way of thinking can also be applied to marketing. But what are the metrics that should be measured?
Read the white paper to find out.
Dashboard Spy resource: Business Intelligence Best Practices Benchmark Report
A new business intelligence white paper has come out that’s worth getting. It features detailed examples of ROI from business intelligence projects, a summary of the drivers and obstacles of BI projects and metrics for tracking and measuring the success of BI implementations.
What’s different about this one is the emphasis on quick ROI – they stress the importance of immediate results over long drawn out perfect system architecture-type projects.
Get the white paper here: