Assessing your data quality - going back to basics

We all understand that quality information is an enabler for cost cutting, risk reduction and revenue enhancement. Yet companies have different approaches to managing the corporate information assets, ranging from ad hoc, tactical projects to meet specific goals, to strategic imperatives that seek to embed data quality principles across the corporate architecture.

This makes it difficult to know where to start, what is effective, or whether you are on track or not for success in meeting the data management challenge, says Gary Allemann, MD at Master Data Management.

For many organisations, data management remains a reactive process, with individual project teams or business areas identifying specific data issues that are impeding the ability of the project to achieve a specific goal. Short-term measures may include tactical data cleansing initiatives, with or without a tool, and typically do not take corporate standards and goals into account.

Reaching a mature state requires a metamorphosis from this chaotic approach to more proactive approaches that incorporate data quality metrics, standards, shared master data and data stewardship. An enterprise-driven focus on data management requires data management principles to be embedded in the corporate culture - being driven both top down and bottom up.

Just as a caterpillar does not become a butterfly in one step, it is important to build upon early successes in a sustainable way to reach data management maturity. The starting point is being able to understand the state of your data, and once this is established, planning how to improve this is the next logical step. From here, identifying opportunities for incremental improvement will allow you to maximise the value of your information with benefits such as lowered costs, improved efficiencies and customer retention, to mention a few.

A crucial success factor is to appoint custodians of the data that take responsibility, and most importantly, are accountable for the state of the data. Many organisations have failed to achieve data integrity due to the fact that IT blames business, business blames IT, and the end-user blames the line manager. Data has no boundaries and is an integral part of each and every component within the business, whether it is sales, finance or operations. Setting the company up for successful data quality means that responsibility must be clear across the entire organisation, and that the consequences of non-delivery are also clear.

Once this step is addressed, it is pointless embarking on a data quality exercise or project if there are no measurements in place. It makes sense to create a 'baseline' of the quality of your data (which is usually established with a survey and a data audit) and then key value indicators (KVIs) should be established in order to measure the improvement and success of the data quality initiative. These baselines should be linked to the business impact of failing data quality - there is no point in trying to address data quality issues that have no commercial impact on your business.

In order to fully realise the benefits of a data quality exercise, having the right tools is another fundamental. Many companies are lulled into the false sense of security that their 'stack' solution that incorporate data cleansing will suffice, but more than often, the functionality is limited. Specialist tools are purpose-built and therefore provide richer features and functionality. However, it is also important to note that technology alone is not the panacea to a business' data quality woes. It is recommended that a data quality specialist assists with the project and, in some instances, it is better to outsource the project or initiative to a specialist. They deal with data quality issues on a daily basis, which ultimately means they have more experience and insight into some of the trickier issues that need to be dealt with.

In order to tackle the first step of establishing the state of your data, take this free survey, IT Management Use of Data Quality, to assess the effectiveness of your approach against those taken by over 1 000 IT and business professionals across the globe.