Data quality: banishing bouncing emails

Improving contact data quality can have far-reaching consequences for the whole business, but you need to know how to sell the benefits to the board, writes John Leonard

In a world where data has taken centre stage it is surprising that more attention is not paid to managing it properly.

Most organisations make sporadic efforts to clean their data, and many will even have a data quality (DQ) strategy in place, but unless they are properly thought through, taking the entire business with them, such efforts are unlikely to be successful over the long term.

Soft skills
In many businesses, initiatives are under way to fish in ever wider and deeper pools of data in order to hook that most elusive of species - real competitive advantage. But sometimes the simplest solutions are those that are most easily overlooked.

For example, setting up a regime to check, clean and de-duplicate contact data such as customer emails, phone numbers and postal addresses could have a profound effect on customer loyalty, new sales and repeat business - just the sort of benefits that purveyors of advanced analytics and big data claim to be able to bring.

But while the concept of data management may be simple, its execution is often far from straightforward. As with any valuable commodity, exercising controls over the collection, processing and usage of data quickly becomes political, with some staff inevitably feeling themselves to be losers in a process that releases data from closely guarded silos or compels them to operate in a different way.

"For IT professionals, demonstrating poor data quality and good data management is easy, because it's comfortable" said Stuart Morrison, database systems administrator at animal charity PDSA, during a recent Computing web seminar. "It's a hard skill.

We've all done this, and we've all bumped straight into a wall when we've done it. You need to understand the business point of view and sell those practices as adding value in their terms. That's a much softer skill."

Improving and maintaining data quality might not be the most glamorous job in the world - but for this very reason it requires a visible personality to take charge if it is to have any chance of success.

IT is often the prime mover in DQ initiatives, and this sort of engagement may not come easy to those more at home with rows and columns, as the quote from Morrison above illustrates, especially since driving up data quality is not the IT department's core responsibility. There is also a mismatch between those driving customer data quality initiatives, those responsible for managing them, and those providing the funding.

According to a Computing survey of 120 IT decision makers, responsibility for maintaining data quality was split across a wide range of job titles, including marketing professionals, heads of department and board members, while budgeting came down to executives, IT and individual departments in roughly equal proportion. A large number of respondents had no idea where these responsibilities lay.

Just 18 per cent employed a dedicated data quality manager or compliance officer, a position that is becoming increasingly important as competitive and regulatory pressures mount up.

Richard Jones, head of geographic risk and data solutions for insurer Direct Line Group, underlined how things have changed.

"For insurers, data has always been extremely important," he said. "We've moved on from five years ago where IT was the lone voice. It's now much more the business driving the need for data quality and pushing the entire company to push for more accessible, usable data."

Data quality: banishing bouncing emails

Improving contact data quality can have far-reaching consequences for the whole business, but you need to know how to sell the benefits to the board, writes John Leonard

Jones went on to explain that consolidating and integrating all the pools of data from the various services and subdivisions that Direct Line Group operates is a complex and lengthy task, and one that is very much ongoing at the firm.

Because of these changes and the timescales involved, the role of DQ manager is becoming more important. But if data quality management initiatives are not to be seen as unwanted red tape, and those championing them as interfering busybodies or box-ticking bureaucrats, they must pick their strategy carefully. It is important to be seen as an enablers rather than a policeman.

Quick wins
In most organisations the best place to start DQ initiatives is cleaning up contact data.
Contact data is the domain most likely to suffer from poor quality, according to recent studies by Computing. It is also highly visible to the organisation as a whole: the frustration of phoning someone who left three years ago, emails that bounce back or letters returned to sender are familiar annoyances to many people, not just those in sales and marketing. For most of us, contact data is the corporate information we deal with most frequently.

More than that, though, improving contact data is also likely to provide a quick win for the organisation as a whole. As well as helping to keep customers and partners engaged and on side, good quality contact data can reveal who are the most valuable customers and what products or services the business can up-sell or cross-sell.

The Computing survey shows that many firms certainly have their work cut out when it comes to the quality of their contact data, specifically their email and mobile databases (figure 1).

[Click to enlarge]

When it comes to email and mobile phone records, the sample seems split pretty much down the middle. Half of the respondents said that fewer than 10 per cent of their email records are inaccurate - which is a figure most could probably live with. After that though things go quickly awry with 11 per cent saying that more than four-fifths of their email records are inaccurate and another 16 per cent saying that more than half of all email addresses are afflicted. There is a similar pattern with mobile numbers.

Unlike transactional data, contact information is likely to be self keyed by customers without verification, or typed in quickly by busy sales staff. Out-of-date information may be allowed to remain unchecked on the system, and duplicate records proliferate as multiple departments add the same customer information, with slight variations, to the database.

Whatever the reason, the survey results suggest that half of the survey sample does not enjoy anything like a single customer view and that their interactions with their customers are likely to suffer as a result.

PDSA's Morrison explained how improving the way that mobile numbers were captured and used led to a big improvement at the veterinary charity.

"PDSA manages 2.2 million animal treatments per year, many through appointments. Failure to keep appointments can be very costly. Simple checking at point of capture enabled us to verify the mobile number meaning we can confirm bookings by postcard, then send reminder texts or calls before the appointment. This has led to a large and measurable drop in missed appointments. A simple technical solution coupled with staff training has led to benefits being available to the whole operation, " he said.

Towards a data quality strategy
Direct Line's Jones reiterated the importance of selecting the target intervention carefully and achieving buy-in from the business.

"Take your time," he advised. "Make sure you have the mandate to do the work, and make sure what you're doing is acknowledged as having value."

Sarah-Lynne Carino, principal data quality consultant at Experian QAS, went into more depth.

During the initial assessment, she said, make sure you understand the problems of each area of the business: marketing, sales, call centres, the board, then quantify the benefits to each that could be expected from a DQ intervention, putting a value on each.

"A customer who has spent £50,000 on one product line and £5,000 on another is far more valuable than one who has just bought one product," Carino said.

Armed with these numbers, as well as a firm understanding of the business issues, the task is then to sell DQM back to the business as a carefully costed way of solving the problems.

It may be worth investing in validation tools to solve addressing problems, data quality software to rectify errors in email addresses and phone numbers, and other tools to enforce and maintain data integrity - consolidating and removing duplicate records and providing a holistic view of the corporation.

As well as cleaning the data, there is also scope to augment it.

"Data is an asset," Carino continued. "Can you tack on demographic information, or grid references, or any other information that helps you grow your asset?"
Once you have looked at all of these things: data integrity, validation and data enrichment you have the basis of your data enrichment strategy, she said.

• The Computing web seminar Essential data quality: achieving long term success is available to view here