Feb 2017 | Data Quality | Data Management

Data quality

In this first of a two-part series (the second part can be found here), I share tips for increasing the success of your data quality business case. This week I’m looking at personal buyer drivers, and how they can influence the decision to approve or reject your data quality business case.

Several times in my career, I’ve created what I thought was a cast-iron proposal for data quality improvement. I made a compelling presentation, backed it up with solid analysis, only to witness our “sure-thing” vetoed or put on ice indefinitely.

What I learned is that sponsors and stakeholders are no different to anyone facing a tough buying decision. Whether you’re buying a car, house, holiday (or a complex data quality project!) – there are always wants, needs and fears that influence the overall buying decision.

Buyers are also confronted with the issue of “substitutes” which can often scupper any aspirations for change. Potential sponsors ask: “Should we remain with a slightly improved status quo or invest heavily in something new and untried?”

So how can we, as data quality practitioners, reduce the desire to maintain the status quo?

Based on my experience, and speaking to many professionals in Data Quality Pro interviews, the solution for facilitating change and persuasion lies in the importance of discovering the personal drivers of each influencer before you construct your business case.

You then have a foundation with which to build your business case, safe in the knowledge that your message will resonate with the key decision makers required for funding.

Understanding the Emotional Drivers of Data Quality Improvement

It’s relatively easy to find lots of data quality issues in most large organisations, particularly companies without a formal data quality management capability in place. The challenge comes when you try to relate your assessment results to metrics stakeholders value.

At this point, you’re often hit by the typical “so what?” response:

“You’ve found 5,000 missing data items, so what? Our business has always run this way, and we still make good margin!”

The reason for this is that, to the casual observer, there are no serious issues because the business is ticking along nicely.

Sure, there are the occasional customer complaints and perhaps a few missed deadlines, but the company isn’t on its knees. So what’s all the fuss with this push for yet another quality drive? Especially when potential decision makers claim to have so many more important initiatives to be backing.

The problem is that your organisation has learned to deal with poor quality information by adopting enterprise-wide “waste management”.

I’ve met utility firms who are adamant their data is high quality, yet they have entire teams of admin staff routinely cleansing data manually.

In one organisation, I found they were procuring millions of pounds worth of new equipment because they couldn’t source existing assets which had become “stranded” electronically and couldn’t be allocated to new services.

Poor quality data is a principal cause of wasted performance and profits so obviously one of the biggest incentives for data quality improvement. Sadly, it can be a tough sell because so many senior managers just see ‘data scrap and rework’ as the reality of running a large business.

The perception is “if it ain’t broke, why fix it?”.

The Challenge of Conflicting Motivations

Another problem is that we think we’ve found a compelling driver for change but in reality, it’s at complete odds with a buyers personal motivation.

In one organisation we made the case to a senior manager that between 10% and 20% of their team were performing administrative tasks that our data quality initiative could eliminate long-term.

We highlighted the cost benefits of reducing their team size, but they politely showed us the door.

I later discovered that they believed their personal standing and influence within senior circles was influenced by the size of their team. We were pitching to reduce the very asset they felt was vital to their position within the organisation!

As we discussed earlier, the key to a successful data quality business case is to align the wants, needs, fears of the individual with the benefits, features and experience of your data quality initiative. To do this, you need to take a holistic approach to your data quality business case, taking in the corporate and personal drivers.

To connect these drivers to the value that you bring, I recommend a ‘pilot and roadshow’ approach that highlights the value and experience of working with you and your data quality team. I will cover the ‘pilot and roadshow’ technique in detail in the next post of the series because before the pilot you need to spend time with each stakeholder and decision-maker to learn their personal motivations. This groundwork can take several weeks (or even months), but it pays off because you start to build up a detailed picture of:

Who is open to the notion of change?

Who has invested in improvements in the past?

Who is openly resistant and fearful of shifts in the status quo?

Who has an initiative that would directly benefit from greater data quality improvement?

What do they stand to gain or lose from your support?

What are the common objections we keep hearing?

How can we reposition our initiative, so our language is aligned?

That last point is probably the most important – don’t get too precious on terminology.

Terms such as “governance” and “quality” are not attractive to a lot of stakeholders who assume their data is already governed and managed by the ‘IT department’. You need to look at how you can “re-brand” your data quality initiative so that it speaks the language of the buyer.

I once pitched a data quality business case as a workforce productivity drive to an automotive research business. We completely played down the quality and governance angle entirely but “under the hood” that was our core focus – to build a data quality capability we could replicate across the company.

By focusing on productivity (a major headache for the operations director), we were able to rapidly secure funding because that was a metric they were personally measured on.

One tactic I used to employ was to download a recent annual report of the organisation and pick out key corporate drivers. I would then quiz potential sponsors on how these strategic objectives affected them personally. That way, you would always be able to demonstrate how the results you find and the improvements you recommend were going to benefit both the business and the sponsor.

A simple mnemonic I devised for these discovery sessions was S.T.R.I.F.E:

S – Stress

T – Time

R – Risk

I – Income

F – Fear

E – Expenditure

Speaking to each stakeholder, I would try and understand what driver they were focused on and how the data and its processing played a part.

Summary and Next Steps

It’s easy to think that stakeholders and decision-makers buy into your data quality business case based on cold, hard logic but in my experience the reverse is true.

There are multiple personal drivers and motivations that must be understood if you are to have lasting success with data quality management.

Many buyers are uncomfortable with change and will look to substitute your “shiny new thing” with alternatives. You therefore need to build a case against each of these substitutes in advance by discovering what they are and preparing accordingly.

Hopefully, this article has given you some simple ideas on how to get started with building a business case for a receptive audience.

Next week, I’ll explore how to de-risk your business case and increase buy-in with the use of the ‘pilot and roadshow’ technique.