How to deliver £600,000,000 of Data Quality Benefits: Interview with Nigel Turner

Nigel Turner, UK Data Quality Expert

Nigel Turner, UK Data Quality Expert

Nigel is a renowned UK based data quality expert and leader. Whilst working for BT, he led a data quality team that delivered over half a billion pounds of data quality benefits.

This is the first in a series of interviews we will be covering with Nigel, discovering what it takes to lead and deliver data quality initiatives at the very highest level.


 

it was a hard slog to get the backing for the work but bit by bit it delivered real benefits to the company

— Nigel Turner

 

Start small, focus on a few manageable and short term projects to kick start the Programme and to prove the efficacy of your structure, approaches, methods and toolsets.

— Nigel Turner

 

…you will inevitably meet with entropy and resistance in some parts of your organisation. When this happens, and your attempts to overcome this fail, move on and focus on those areas of the business which are more receptive…

— Nigel Turner

Dylan Jones: Let’s start by exploring your career progression, how did your career evolve to the point of leading data quality initiatives within BT?

Nigel Turner: I joined BT in 1981 and for the first 16 years progressed through a fairly standard IT career path through programming, business / systems analysis to project management.

In 1996 a project I managed focused on improving the quality of key data used in BT’s main customer service application systems. The project delivered significant benefits and won an internal BT award. This brought the work to the attention of BT’s Head of UK Strategy at that time.

Soon after she asked me to join her team and to lead a wider initiative to investigate and improve the quality of BT’s data across the organisation. At first I saw it as just another (more challenging) job but soon developed a personal passion for data quality.

I’ve had that passion ever since.

Dylan Jones: I meet many business leaders who are sitting on the fence regarding data quality, some may be reading this article right now, can we get these people motivated by presenting one example of a past data quality initiative you were involved in that delivered clear, tangible benefits to the business that also delighted senior management?

Nigel Turner: The best example is the Data Quality improvement programme I’ve just mentioned. It started small, focusing on a few key data quality problem areas, and improving the data and associated processes.

At first it was a hard slog to get the backing for the work but bit by bit it delivered real benefits to the company. Ultimately the programme grew to deliver over 75 data quality projects, ranging from tactical data cleanses to Master Data Management deployments.

It lasted 10 years and overall delivered benefits to BT of over £600 million. Key to this success was that no project would be authorised unless it had a business owner and a clear business case. BT’s CEO at the time became aware of the successes of the programme and became a key champion of the work.

In summary I guess it proved that investing in data quality improvement is a no-brainer, provided it has the backing of the business and its benefits are measured and acknowledged. It also showed that data quality improvement is not an end in itself; it is a way, and a very effective way, of improving the bottom line of any business.

I say any business deliberately as there is not a single significant sized business or organisation, public or private,that does not have data quality problems.

Dylan Jones: Describe how you typically structure the data governance relationship between the business, data management and IT systems teams, what structures have you seen work well on large-scale data quality projects?

Nigel Turner: That’s a big question with many dimensions so I’ll try to be brief. The first thing to realise is that the optimal structure for making data quality improvement happen is and has to be contingent on the specific context in which it happens. Businesses and their priorities change and to keep data quality relevant any initiatives need to evolve and adapt in line. Hence the structure of the BT programme changed significantly over its 10 year duration.

Looking back over that period a few key generic learning points did emerge. First, you need at least one senior business champion to give your work credibility, to help open doors and knock down barriers. With their help you then need to establish an executive steering group of senior business and IT managers who provide strategic direction and help manage the many stakeholder relationships you’ll need to develop across a large enterprise.

Sitting underneath this top level you need a Programme Board, chaired and led by the business, and project boards for each project under the Programme’s umbrella. This structure ensures both alignment of the work with evolving business goals and puts delivery of real business benefits at the heart of the programme.

Dylan Jones: The Information Quality Improvement Programme at BT generated over half a billion pounds in benefits, what were some of the most important lessons you took away from leading teams in that environment?

Nigel Turner: A few other lessons emerged from this experience.

Start small, focus on a few manageable and short term projects to kick start the Programme and to prove the efficacy of your structure, approaches, methods and toolsets. This enables you to deliver early benefits and to make the case for increased investment. It also means you can build your stakeholder community in a more incremental way.

Another key lesson is that in large organisations it is vital to acquire and deploy specialised data quality tools to support your data analysis, enhance your data and maintain the gains made. We could not have succeeded without these. Of particular value were data profiling tools and applications to enrich and enhance information.

From my own experience I also learned two key additional things.

  1. One was that you will inevitably meet with entropy and resistance in some parts of your organisation. When this happens, and your attempts to overcome this fail, move on and focus on those areas of the business which are more receptive and natural early adopters. Often I found that people who were initially reluctant to contribute come around later when they see the work is winning hearts and minds and delivering real benefits that they are missing out on.
  2. The other key lesson is that the best people to lead the early stages of data quality initiatives are people who understand data quality problems and know how to improve them. This may sound, as Basil Fawlty once said, stating the bleeding obvious but I found that several people with these skills had to be cajoled, coaxed and coached to step up into leadership roles, whether they sat in the business or in IT. People with data quality skills and experience are incredibly valuable assets to their business. The trouble is neither they nor their business always recognise it! Having specialists active from the start will ensure the initiative does not set off down the wrong road. Data quality specialists need to be leaders as well as experts.

Dylan Jones: Finally, let’s talk about the reverse scenario, the failures, with the many projects that were initiated during your time at BT and other sites, can you point to any common situations that would lead to projects failing to live up to the business case so others can learn the lessons?

Nigel Turner: After leading BT’s data quality initiative I moved to providing data quality consultancy to some of BT’s largest customers for several years. I retired from BT in 2010 and today provide that consultancy on an independent basis to other companies. I have therefore had experience of the many things that can and sometimes do go wrong.

For me the most common mistake made by organisations is to regard data quality as an IT and not a business issue. Too many are persuaded by clever IT vendors into believing that investing more in IT will itself solve the problems. This is compounded by the fact that the organisation’s IT department is all too often blamed for data quality problems. In part IT may indeed be culpable but in reality most problems are the consequence of inadequate business processes, behaviours and IT all interacting together to generate poor data quality. The only way to alleviate these holistic problems is through holistic solutions, involving changes to people, process and technology. Only the business has the leverage to do this, but IT needs to play a strong supporting role.

Another failing is that too often data quality people regard improvement as a self-justified end in itself. What seems obvious to them however is usually not obvious to the rest of the business. Unless they work to understand their business, what drives it, how poor data impedes its goals, and are then able to communicate and sell this in a way non-specialists can understand, they will inevitably fail.

To conclude I’ll cite two quotes that helped me. Both come from the political world. Karl Marx wrote that “Philosophers have only interpreted the world in various ways: the point, however, is to change it.” Andre Malraux, the French statesman said, “Often the difference between a successful person and a failure is not one has better abilities or ideas, but the courage that one has to bet on one’s ideas, to take a calculated risk – and to act.”

So finally and fatally the major mistake is to spend too long trying to get a complete picture of the problems and poring over detailed complex causal analysis. In a large organisation data quality issues can be uncovered almost everywhere you look so the complexity of such an exercise is such that by the time you’ve finished the business and its data problems have moved on, and you have to start again. It’s a data quality Groundhog Day. The key is to identify and focus in on those problems that hurt the business most and deliver tangible improvements. In other words, do something to make a difference.

Summary of key points:

  • Start small, focus on key problem areas by improving data and associated processes
  • Don’t start any data quality project unless it has a clear business owner and business case
  • Go to the top and try and obtain CEO backing for your initiative
  • Data quality is not an end in itself but part of a wider improvement strategy that can always add value to the bottom line if approached correctly
  • Business priorities change, keep data quality relevant by constantly evolving
  • Find a business champion who is willing to knock down barriers and open new doors
  • Initially deliver a few manageable and short term projects to kick start the Programme
  • Prove the efficacy of your structure, approaches, methods and toolsets constantly
  • Build your stakeholder community incrementally
  • Acquire and deploy specialised data quality tools to support your data analysis, enhance your data and maintain the gains made
  • When faced with resistance you can’t overcome, move on and focus on more receptive and natural early adopters
  • The best people to lead the early stages of data quality initiatives are people who understand data quality problems and know how to improve them, cajole, coax and coach them to step up into leadership roles
  • Data quality specialists need to be leaders as well as experts
  • Don’t label data quality as an IT and not a business issue
  • Most problems are the consequence of inadequate business processes, behaviours and IT all interacting together to generate poor data quality
  • Unless data quality specialists work to understand the business, what drives it, how poor data impedes its goals, and are then able to communicate and sell this in way non-specialists can understand, they will inevitably fail
  • The only way to alleviate holistic data quality problems is through holistic solutions, involving changes to people, process and technology
  • Don’t spend too long trying to get a complete picture of the problems and poring over detailed complex causal analysis, focus in on those problems that hurt the business most and deliver tangible improvements
  • Do something to make a difference

Nigel Turner, UK

Nigel Turner, UK

About Nigel Turner

Nigel Turner is an independent data management consultant, specialising in enterprise data quality and data governance.

Recently he authored part of the Institute of Direct Marketing’s “IDM Award in Data Management”, a new online data management training programme to be launched in April 2011. Prior to this Nigel worked for British Telecommunications plc. (BT) for 30 years. He held a variety of senior roles, specialising in information management and CRM. 

His last BT role was as an Information Management Consultant within BT’s Innovate & Design, where he worked with BT Group Human Relations to improve BT’s management of personnel information.  Before this Nigel ran BT Innovate & Design’s Customer Management practice where he led a team of 200 consultants & technical specialists in CRM and Data & Information Management, including data warehousing, data migration, data quality, data mining, data visualisation, business intelligence, Siebel, Interactive Voice Response & Computer Telephony Integration. 

Nigel also spent five years in BT’s Group Strategy unit, where he led enterprise wide business information management and data quality initiatives.  During this period he also personally provided data management consultancy to a number of external organisations including the UK Ministry of Defence, HBOS, Intel US & Belgacom.

Nigel has published several papers on information management (IM) and data quality, and is a regular speaker at CRM, Data Quality and Information Management conferences. 

He can be contacted via his expert profile on Data Quality Pro.