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How to realise BI value

The success of a business intelligence strategy will depend on the company's level of commitment.

Yolanda Smit
By Yolanda Smit, strategic BI manager at PBT Group.
Johannesburg, 12 Dec 2013

My previous Industry Insight showed the four waves of enterprise data warehousing (EDW)/information maturity. The question that may still be nagging in the background, though, is: "If the level of true maturity (wave four) is when EDW enables accelerated strategic decision-making, what strategies can the CIO utilise to facilitate the growth in information maturity to achieve this ultimate level of maturity?"

1. Get data in shape

Effective business intelligence (BI) is all about the underlying data. It must be accurate, consistent and trustworthy. Implement initiatives to retrospectively improve the quality of historic data. Improve operational system interfaces to validate data being captured and enforce high-quality data capture. Create incentives for people in the position to influence the quality of data to inspire commitment to high-quality data capturing.

2. Make data accessible

The data and information must be available, and the various sources of data across the company must be brought together. Information must not be hoarded; it must be shared across departments. However, one cannot expect decision-makers to accelerate strategic decision-making if the relevant information is not accessible.

3. Acquire the right technology

An effective BI strategy requires that the company make investments in the appropriate tools for information management. The emphasis here lies on appropriate tools. Remember that not all information users' needs will be met by the same tools. Report users require a different tool set than executives and power analysts or data scientists. The company must invest in software to access, summarise and analyse information, and to be optimally effective, this should be standard across the company for the three main user groups (report users, executives, and power users or data scientists).

Additionally, the use of advanced analytics has been proven to be a key factor in improving business decision-making and providing a competitive advantage.

Once the foundation is set with the right data and the right quality, and available to users empowered with the right technology, this unfortunately does not mean the growth to information maturity will progress spontaneously. In order to stimulate growth, the CIO also needs to consider employing some catalytic strategies to spur the growth continuously.

4. Introduce a culture shift to one of information-based decision-making

If a company has made the decision to implement a BI strategy and rely on business information to drive its decisions, the success of that strategy will depend in part on the company's level of commitment - and the degree to which management actually uses analytics-driven intelligence to support decisions.

Tangible support from executives - commitment evidenced by action - sets the tone for the rest of the company. Besides leading by example, a further culture shift can be achieved by creating key performance indicators that measure the extent to which individuals use information for decision-making as part of the performance scorecards.

5. Beef up on analytic strength

Remember, the inherent ability to look at data and make sense of trends and patterns or to effectively relate it to real life is a skill that not everybody owns. This is probably the cause for the newly evolved data scientist role that is becoming more and more popular. The data scientist role has been described as "part analyst, part artist". According to Anjul Bhambhri*, VP of big data products at IBM: "A data scientist is somebody who is inquisitive, who can stare at data and spot trends."

Information must not be hoarded; it must be shared across departments.

It is recommended that a team of data scientists are mobilised to serve as a central support function to the business decision-makers. Decision-makers can then articulate their problem statement and the data scientists can explore the analytic information available, utilise the abundance of visualisation and analytic tool capability, provide the decision-maker with the relevant information, and where relevant, also assist in interpreting the information.

6. Converge planning and decision-making platforms with BI and analytic platforms

Adoption of BI functionality will be far more pervasive and effective if the information and BI capability is accessible and embedded in the platforms where significant decision-making occurs, such as planning, budgeting, and forecasting platforms. If users are in the planning and budgeting interface and they have to switch screens or log into a different system in order to analyse the data before deciding on the new budget or plan values, this could cause a barrier to change that one wants to eliminate.

The growth to information maturity needs to be fostered through careful nurturing, just like the growth of a rose-bush needs to be honed through pruning. The strategies above are not exhaustive, but should empower every CIO and his or her information management team to spur the growth to the next level. Suddenly, real information maturity is no longer pie in the sky dreams, but a reasonable and feasible objective that can be achieved successfully.

* What is a data scientist - IBM
http://www-01.ibm.com/software/data/infosphere/data-scientist/

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