Maximise the value of your information with a data excellence approach

Information overload is a real challenge for every business, with both volume and complexity of information increasing exponentially

September 12, 2012

By Gary Allemann, MD at Master Data Management 

Information overload is a real challenge for every business, with both volume and complexity of information increasing exponentially This increases the complexity of data management and presents organisations with the growing challenge of building data quality initiatives that deliver sustainable process improvement and true business benefit. The difficulty with realising these benefits is that traditional IT driven data management approaches are typically focused on technical implementation issues, rather than on optimising data to create business value. This leaves a gap between expectation and delivery when it comes to the majority of data governance and data quality initiatives. Organisations need to shift from a cost-driven mindset to a value-driven mindset, in order to maximise the value of information. Creating this shift in mindset requires a data management strategy that is linked into business strategy – a data excellence framework.

A value-driven approach to data management is all about using quality data to unlock opportunities to increase business profitability. Ultimately this comes down to the ability to identify and measure the business impact of poor quality information and data, which enables organisations to be able to plan better and thus improve the business. It is critical to understand information and what your organisation needs to get out of this information in order to maximise business value.

Data excellence is an approach to managing data, based on the principles of business alignment, accountability and measurable business impact, in order to achieve business excellence. The data excellence approach manages the information overload by enabling a focus on critical data based on its business impact, and allows less valuable data to be treated with a lower priority.

For example, a global consumer goods company used the principles of data excellence to prioritise and address key data risks during their global ERP implementation. By focussing just on just one key ingredient, vanilla, they were able to reduce the number of specifications and suppliers – cuttings costs for this ingredient in the United States by $30million per year. On a global scale, similar operational improvements are saving billions annually.

Driving the greatest value out of data requires an understanding of which data will have the biggest impact on the business, which requires data management initiatives to be linked into business strategy, which is facilitated through the data excellence framework. The data excellence framework consists of three steps, or pillars, which together help organisations to maximise the business value of enterprise data. They provide the structure for companies to ensure that data is manageable and more importantly that value is obtained.

The first step is to secure a strong alignment between business and data. This helps organisations to gain a deeper understanding of what the business goals are, what needs to be achieved, and what data would best support this. The first step is all about creating focus, and ensuring that data objectives are aligned with business objectives. This approach puts the business transaction at the centre of data quality and data governance, helping organisations to leverage value from data management initiatives.

Next, it is critical to get a deep understanding of the value and business impact of data, in other words, what does this data mean to the business. In order to achieve this, data must be measures and then the value and impact visualised for each business context, in line with the goals and strategy of the business. Organisations can thus prioritise data cleansing and fixing data quality on key value indicators. This in turn cuts down the volume of data management and data quality transactions by focusing on areas that have the greatest positive impact first, driving business value by ensuring that initiatives are achievable and focused.

Finally, establishing very clear accountability and responsibility for data is imperative. Within any organisation it is necessary to have someone who owns the data and oversees that this data is addressed according to the data strategy. At a senior level, this accountable person needs to ensure that the data supports business objectives, and that the individuals who are responsible for data collection, data capture and data quality are working according to the data strategy. This helps to deliver a sustainable data quality culture by focusing on business value generation.

Incorporating the data excellence framework into data strategy ensures that businesses can achieve agile and effective data management that is intrinsically linked into business objectives and goals. This in turn helps organisations to develop a value-driven culture that drives sustainable business success.