Leadership Lessons in Data Quality – Part 1

I am a great proponent of data quality investments and this presents a great challenge to me when I am trying to create a new information management capability in an organization. Overcoming this challenge is essential to developing a valued analytic capability. This is what I want to talk about in my next couple of posts.

It’s a challenge because usually very few other Senior Managers / C-level Executives appreciate the value of good data. Intellectually, most usually agree with me, but when priorities are being set, data quality almost invariably gets pushed down the priority list by more ‘burning’ issues. Issues where managers see a more immediate impact on the bottom line.

The only exception to this is when a serious compliance issue has occurred or there is a serious risk of one occurring if nothing is done to prevent it. Even in these circumstances, the decision is usually made to invest in a short-term and usually one-time-only ‘fix’ rather than investing more to make a solution that continues to work over time or even goes back and fixes the underlying source of the poor data quality (often back in a source system or operational process).

So the …

I am a great proponent of data quality investments and this presents
a great challenge to me when I am trying to create a new information
management capability in an organization. Overcoming this challenge is
essential to developing a valued analytic capability. This is what I
want to talk about in my next couple of posts.

It’s a challenge because usually very few other Senior Managers /
C-level Executives appreciate the value of good data. Intellectually,
most usually agree with me, but when priorities are being set, data
quality almost invariably gets pushed down the priority list by more
‘burning’ issues. Issues where managers see a more immediate impact on
the bottom line.

The only exception to this is when a serious compliance issue has
occurred or there is a serious risk of one occurring if nothing is done
to prevent it. Even in these circumstances, the decision is usually
made to invest in a short-term and usually one-time-only ‘fix’ rather
than investing more to make a solution that continues to work over time
or even goes back and fixes the underlying source of the poor data
quality (often back in a source system or operational process).

So the challenge when constructing a program of work to deliver a
new analytic capability is to build into the solution an appropriate
and sustainable level of data quality.

Couldn’t be easier, right?

Remember – the Board is not going to appreciate explicit data
quality goals unless they think that they will not delay other (and in
their minds more valued) goals. In other words they want it for free.

Tricky to do but here are some actions that I have successfully used in multiple large projects/programs:

Establish Business Ownership

If you can’t find the formal business owner of the data then you
can’t fix the quality issue in a sustainable way. It may be the case
that your work delivering a new data warehouse or other data store may
solve today’s quality issue – but if no one in the business owns the
data then the quality will inevitably decline over time. This will
usually happen a lot faster than anyone predicts.

The more usual situation is that the quality issue remains and
everyone continues to work around it. Often the dashboard, analytic
reports, or data extracts you deliver from your shiny new platform will
also have one of two of the following consequences:

• The data quality issue becomes masked so that people look at the
  shiny new analytics and forget that it is still based on the same lousy
  data, and/or
• Users continue to not trust the numbers, and/or
• Areas of analysis that require the poor quality data are excluded
  from the analytic platform – making your investment much less valuable.

All of these are negative consequences that can seriously devalue you and your teams hard work.

The
best business owners are those who are directly and negatively affected
when quality declines. They are also normally (but not always) the ones
who most benefit from improved data quality as well. Why are they the
best business owners? Because they will care the most.

Once the correct owner(s) are agreed with the business, I also
recommend that data quality improvement of their data be made a
measured part of their performance incentive program – but more on this
in my next post.

Manage Data As An Asset

Manage your data assets just like you do capital and equipment. If
it is worth something then at the very least you need to manage it to
protect that value.

Would you leave a valuable printing press or lathe outside to rust?
Data is the same. If you don’t look after data, it will decline over
time just as capital equipment depreciates over time.

The best data asset managers are your senior information management
experts. Why? Because they care deeply about data quality and should
already be closely engaged with the business and IT owners of data.

Link Cause To Effect

Explicitly link data quality issues to known problems that need to
be solved. If 1% of your customer records have invalid details, who
cares? Why do they care? And most importantly: how much do they care?

If you can identify the department suffering most, then go to that manager and work with them to quantify the dollar cost. Agree with them
also about the importance of the resulting problem(s). If it is
important and causing the company to lose sufficient dollars then can a
solution be devised that will save money and deliver this benefit in a
reasonable timeframe? Reasonable in my experience is almost always
within 6 months.

Sidebar: The Value Of Program Management

If you structure the
delivery of a program of work to spread delivery evenly throughout the
life of the program, you can often buy yourself up to 12 – 15 months of
time to solve complex data quality issues. Again, this is a much wider
topic than data quality that I will leave to another post.

Work Closely With IT

Business ownership is great – but most of the data the business
cares about is electronic. If IT can’t help deliver the solution then
there is little hope that the beautiful analytic platform you build
will operate to maximum efficiency over time.

IT has to be a full and active partner in any data quality
initiative. Even if initially it is only to educate IT and to develop
their own information management capabilities. In my experience, many
IT people will enthusiastically take on IM/DQ roles when they know that
people in the business care.

If IT doesn’t care about data quality, then at least some of the
blame for this sits with the business leaders. In data quality, just
like most areas of modern business, IT and business managers are very
dependent on each other. The safest way to ensure a successful analytic
outcome is close cooperation.

Link to original post