Analytics and business intelligence systems combine data, statistics, technology and business strategy in a format that’s intuitive and easy for end users to interact with. A best practice is to develop and implement analytics and business intelligence systems using a balanced scorecard approach.
Balanced Scorecard Approach
The balanced scorecard (written about by Kaplan & Norton) is a performance management system that links non-financial and financial performance metrics and organizational goals. Strategic goals are documented in business terminology and associated with metrics. Goals and metrics are broken out and organized into four categories: financial, customer, internal process and human resources.
Business Intelligence is a broad concept with several functions including management reporting, ad hoc analysis, alerts, forecasting, segmentation and optimization. There are a number of business intelligence software/technology offerings on the market with price tags ranging for free to six figure. Examples of common business intelligence software are: Excel, SAS, IBM SPSS, Tableau, Spotfire, Rapid Miner, R, etc. Sometimes the best software to use is the one that you already have. Buying new software doesn’t guarantee success with analytics and business intelligence. A thorough financial and technology gap analysis should be done before investing in a new software.
A key component to a good business intelligence system is a collection of dashboards that visualize and make sense of a vast array of corporate, public and third party data. Highly intuitive, visually appealing, information packed agile dashboards enable managers and end users to quickly understand the most relevant facts pertaining to business performance.
Core dashboards in the business intelligence system should be built around the balanced scorecard strategy and display key metrics for visual analysis by end users. Through a series of connected dashboards, end users can take a deep dive into the key performance metrics to understand how they changed over time, how they are related and how they are predicted to perform in the future.
Supporting dashboards show the how specific projects, initiatives or functions relate to the overall performance of the company. Through data cubes and models, end users can drill down into the cause and effect relationship that every business decision has on a multitude of interconnected components of organizational operations.
Time Series Analysis, Alerts, Forecasting, Optimization
Using historical data alerts (such as emails, flags, offers etc) can be triggered to capitalized on situations by always taking action in a timely fashion. Forecasting uses historic data to predict the future, given a set of conditions. Optimization assesses a number of different scenarios to predict what’s the best that can happen.