BI on bad data is bad BI
Taking bad data and put good-looking graphics and analytic capabilities on top does not make for good business intelligence. “BI must be aligned with business strategy and backed by accurate data,” says Sean Paine, director of EnterpriseWorx.
“Companies need to be clear on their strategic objectives and implement BI systems in a methodical project-driven way. Instant BI is simply not sustainable.”
“The net result of poor data or bandage BI is the same – you get flawed intelligence that does not generate business value for the organisation.”
According to Gartner’s 2010 CIO survey, increasing the use of information and analytics is third on the CIO’s list of business priorities, while business intelligence and data management per se have slipped down to fifth and seventh place respectively on the list of technology priorities.
“The first step in implementing BI is to make it a business project, not an IT project,” says Paine. “It’s important to define the strategic objectives of the organisation, decide which business drivers are critical for achieving these, and then derive key performance indicators (KPIs) for measuring progress.
“For example, if a retail strategy is to increase market share, it’s necessary to monitor customer spend, so as to ascertain whether current customers start spending more money, or whether the firm is attracting new customers, or both. It may also be important to measure the company’s rate of growth compared to its competitors. These KPIs then form part of a BI system that measures whether or not the growth strategy is being implemented effectively.
“If, during this process, you discover that underlying systems which inform the metrics contain incorrect data, the business will be no better off. At best it will be flying blind; at worse it will be flying in the wrong direction. At an operational level, you may find for example that, because your information about purchases is incorrect, you are not providing the appropriate level of product variety and customer service.
“By implementing a BI solution, one is forced to examine the data. It’s a circular process. The organisation moves from a head-in-the-sand situation to one of possibly exposing flawed data and then being in a position to take action to remedy this.
“It’s not essential to wait until all data is accurate before embarking on BI. But it is essential to include data clean-up as part of the BI programme.
“Cleaning data must be a joint effort between business management and the IT department. Both need to ensure that standards are enforced, particularly in regard to data capture, access and modification. Only in this way will data achieve its value as an asset that can be measured in rands-and-cents terms.”
Data clean-up consists of a number of tasks:
Refining data currently stored in the database to ensure that it is consistent and accurate.
Ensuring that the database is well-organised and maintained.
Implementing effective data management to ensure that users can access data quickly and easily.
Inculcating a culture of clean data in the organisation.
“It’s a case of transforming chaos into order,” says Paine. “As CIOs play a more strategic role in their companies, they must work with the BI competency centre (BICC) as a team to drive the usage of information within the business – to take the company’s data asset and sweat it. Effective data management and BI is not simply a cost saver, but can also be a revenue enhancer.
“Strategic business intelligence and analytics backed by accurate data supports better decision making and results in a more profitable, competitive and innovative organisation.”