In my last blog, I highlighted how a major global telco, British Telecommunications, recognised that endemic data quality problems were hurting its business. Reaching this stage of awareness is an important first step, but before things can improve, it’s vital to hone in on specific data issues that can be tackled via an improvement programme.
Any data quality initiative needs to start with the business and its needs. So the first thing we did was to ensure we understood the strategic objectives of BT as an organisation. You cannot secure the business support you’ll need to succeed unless you can demonstrate how your data quality initiative will help to improve the business.
In BT’s case, its main aspirations were to deepen the loyalty of its existing customer base, build new propositions and generate new customers, create a world-class network, and build long term partnerships with customers. It was evident that data was at the heart of all of these strategic aspirations.
For example world class network capabilities depend on accurate recording of the components that make up that network – switches, routers, cables, customer devices and so on – and so accurate inventory was key for network planning, operations and support. Our initial focus was how data was being used to support these key objectives, and to critically obtain an initial view of how fit the data was for this purpose.
How did we set about doing this? Well, one of my favourite musicals is ‘South Pacific’. I’ve always found this movie has an enduring ability to raise the spirits. The most exuberant scene is when the larger than life character Bloody Mary sings ‘Happy Talk’, a song which praises the value of talking with others. ‘Happy talk’ became a mantra for what we did next.
We quickly learned there is no better way of gathering evidence on specific data quality problems than to talk to the people who create data and use it as part of their daily jobs. So we built an initial list of the people who we surmised made a core contribution to delivering BT’s strategic goals. These included senior executives, marketers, salespeople, IT specialists, finance managers, HR specialists, engineers, account managers and so on.
It’s important to talk to people at all levels in the organisation. A day spent sitting alongside a BT call centre operative generated a long list of problems with customer and product data; a day out with a BT engineer whose job was to install new equipment at customers’ premises highlighted inventory data shortcomings. A session with a senior finance manager exposed billing and accounting shortcomings.
Gradually a picture of some of the main data domain problems began to emerge. Talking to people also generated many other insights. People who work with the data understand its context – how it is used, why, and what the impact of inaccurate or missing data is. They can also point you to others who have a detailed knowledge of the data problems, so you can evolve your stakeholder list. Many of the data experts we came across became key stakeholders, supporters and subject matter experts for the data quality improvement programme.
It’s also essential to prepare carefully for each interview or session. Ensure you have a generic list of questions (e.g. What key data do you use in your role? What are the main problems with the data?), supplementing these with specific questions relevant to the person you are seeing or shadowing.
When each data problem emerges it is vital to record it in a systematic way. This means a simple business description of the problem, ideally related to the strategic business objectives referred to above, a rough scope and scale assessment (e.g. how often is this data inaccurate or missing?), what is the business impact of the failing, ideally expressed in financial terms, the primary business process(es) impacted, and the main stakeholders affected.
Also capture initial ideas of how the problem might be improved in terms of potential people, process and technology changes. At this point don’t aim for a comprehensive analysis of the problem and potential improvements but for an initial understanding. Building a detailed case for action comes later.
This approach enables you to build a dynamic prospect bank of data quality problems. Collecting them in this way also allows you to start to look for key data issues which impact several areas of your organisation, for example poor capture of customer addresses impact marketing, sales, invoicing, operations and so on. This helps build a data centric view which transcends organisational boundaries, processes and systems.
In my next blog I’ll cover how this prospect bank can be further investigated, evaluated and prioritised and so help to ensure a focus in on the problems that really matter to your business.