Building the Backbone of Business Intelligence
Current Business Intelligence (BI) applications are programmed to know what information is needed to answer pressing commercial questions such as "Who are our most loyal customers?" They then search the data needed to generate that information.
For the first time, these tools are beginning to enable closed-loop decision making. They attempt to automate much of the understand-act-evaluate business process. Some of the novel capabilities which BI is starting to place in the hands of commercial decision makers are outlined below.
Changes to the Management Process Itself
Some qualitatively different management practises are being driven by BI. Knowing much more about customer and supplier behaviour means that management success metrics can be much more clearly defined and adaptable to market conditions. One example of this might be "events-based" operations in which a company, for example, can stop running an expensive e-mail campaign the moment their stock starts to run out.
Hosted Solutions and Information Portals
Business intelligence applications (for example, Business Objects and Net Perceptions) are now available as hosted solutions—often with a portal interface, and are used increasingly for the analysis of data collected from e-commerce sources. Enterprise portals on intranets are often the preferred conduit for personalized business data to be supplied directly to employees, partners, and customers.
Packaged Applications Analytics
Analysis of otherwise unused historical data from ERP and CRM solutions has created an opportunity for packaged applications. BI products are now adding workforce planning, best-practices analysis, and business process optimization, and so forth.
So-called Analytic applications tend to be BI tools that are specific to a particular business function (such as finance or marketing). They can be characterised by the following features:
- The software structures and automates the review and optimization of business operations.
- The application supports data communications with the business processes, yet can function independently of an organization's transactional applications.
- The application pulls together data from multiple sources, both inside and outside the company.
So far, there are three main arenas into which these applications fit:
- Analytic applications designed to optimize financial performance (for example, budgeting).
- Analytic applications designed to measure and optimize the production and delivery of a business's products and services (as in demand planning, workforce optimization, and pricing policy).
- CRM: analytic applications designed to record and optimize customer value, retention, and preferences.
The following developments in the field of analytics may emerge as important.
Planning and Simulation
BI tools are starting to work on the basis of more sophisticated non-linear modeling and forecasting techniques. This is more than just curve fitting to historical data and may involve artificial intelligence techniques such as modeling customers as agents. Agents are also used in the search for trends and exceptions in business data. In the most effective BI tools, rules-based software agents run in the background, monitoring data and comparing results. These tools can be applied effectively to supply chain and workforce optimization tasks.
As tools such as these advance, much more complex business issues such as the following can now be addressed at a level of detail that would clearly be impossible by using a spreadsheet to generate "what-if" scenarios:
- If the price of our product increases by 2%, how much market share will be lose?
- Should this manufacturing company acquire another plant or another company?
- What is the optimal distribution of our parts budget across five different suppliers?
Data in the form of images, sound, video, "unstructured" text, and spatial information, is starting to be usable by these BI applications. Companies that can efficiently interpret the main meanings within a database of customer complaints, each of which is geographically tagged and accompanied by time-related video footage of customer behaviour, will be at a big advantage when it comes to planning their future business activities.
The future for BI and analytics applications will be subject to many difficulties. Managers can easily be equipped now with tools, the output from which is sometimes less questioned than if it were generated by teams of data analysts. There are certain kinds of data that don't lend themselves very well to automation; for example, a deep analysis of competitor activity or an understanding of staff retention issues.
To be valuable, Analytic applications must actively guide the decision-making process towards enhanced business performance. This will involve:
- Decision-makers being in direct control of the analytic applications.
- The software must come to support collaboration within teams of "stakeholders" and to include information about the historical quality of decision making.
With the advent of artificially intelligent systems, extracting, and processing data from multiple sources, Business Intelligence is set to speed up the process of management in all sectors of industry and commerce. The real test will be whether or not it can contribute directly to a measurable, long-term improvement in the quality of those decisions.
Patrick Andrews is managing director of break-step productions, a consultancy firm specializing in designing digital businesses. His areas of interest include interactive marketing, machine intelligence and software design. Contact him at firstname.lastname@example.org.