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Why do data quality? A case study

And while the consequences of non-healthcare data quality errors aren’t usually quite as dramatic as “someone might die,” they are causing problems, right? Otherwise why are we even doing this?

- from Don’t trust the dashboard: A key question for hiring DQ team members

Why are we doing data quality?

For some of you, the answer might start with “we’re legally required to”: GDPR, among other up-and-coming data privacy regulations, requires that covered companies maintain a data quality program.

Is it worth going beyond the minimum to achieve compliance, though? And if you don’t have to have data quality, is it worth pursuing?

It is. Here’s a case study in why (plus what we did to make it happen).

The situation

We were working with Deutsche Telekom (DT). DT is the leading German telco, with more than 151 million mobile customers, 38 million fixed-network lines, and 15 million broadband lines. Currently, they have $115 billion in annual sales.1

It’s a safe guess that a company that size probably hasn’t achieved perfect technical unity. And indeed they hadn’t.

In fact, DT had evolved a markedly decentralized approach to IT, where each department had full responsibility for its own data systems, and was wholly focused on its own niche and own customers.

As you can imagine, these departments all developed their own operational practices too.

The corporate-level documentation of all these independent operations didn’t reflect the actual day-to-day IT activities of individual departments, and not all those activities were aligned with DT’s overall understanding of its data and processes.

In 2007, DT introduced a new and ambitious master data management plan. Responsibility for data quality was given to a central data quality department, and new data governance, quality, and quality assurance protocols were introduced.

The problem

As a typical example of what the new DQ team faced, take the revenue assurance system.

Revenue assurance systems are supposed to identify billing problems so that customers are billed exactly the right amount. Underbilling results in missing revenue, while overbilling reduces customer satisfaction and, for some enterprises, can have legal implications.

Major challenges facing data quality for the revenue assurance system included:


So let’s skip to the end: did DT’s revenue assurance actually need data quality?

Yes. We found that 4% of billed contracts were underbilled.

The total missing revenue: $50 million/year.

There aren’t many companies where an extra $50 million annually isn’t worth it, and even if data quality requires an up-front investment, it’s usually not a $50 million investment, and costs don’t continue at up-front levels.

(If your data quality costs are that high on an ongoing basis, please get a second, third, and fourth opinion on your solution.)

In addition to the cash savings, Deutsche Telekom was also able to start catching data quality errors earlier, and customer satisfaction measurably increased. And as the cherry on top, the improved data made it possible for DT to automate many of its processes, which resulted in additional cost savings.

What it took

Clearly, increasing revenue, savings, and customer satisfaction took more than the minimum-effort “put up a dashboard and watch it” approach.

Instead, DT used our MIOvantage software platform to produce these results:

Data quality that involves active intervention to improve the data is the kind that will get you the big returns.

What it didn’t take

But that’s not to say that active data quality needs to actively disrupt operations--despite what all the ‘touching other systems’ described above might imply.

Operational disruption increases the opportunities for data quality failures, not to mention fomenting undesirable effects like decreased productivity and decreased customer and employee satisfaction… which is all exactly the opposite of what we’re trying to achieve in the first place.

So, MIOvantage data quality fits in around existing IT.

DT was able to take advantage of that:

So in conclusion

Data quality beyond the dashboard can produce major positive impacts without turning your existing data ecosystem upside-down.

It's a worthwhile investment, and one that you should definitely explore if you're already having to implement some level of data quality for compliance reasons.

Obviously, not every vendor is going to be able to deliver the kind of big-upside, low-disruption results we did here.

And of the ones that can, not every vendor is well-suited to every single project. So be sure to shop around for a data quality solution that's going to be able to do these things for you.

So far, the industry has mostly focused on providing data quality for large enterprises: if that’s you, you’ll have the biggest choices of data quality provider.

If you’re coming from a small-to-medium business (or a small-to-medium department within a large enterprise), you’ll have fewer choices.

But your options are starting to increase, like with our new offering, so don’t give up if the first few data quality solutions you come across don’t look like what you need.

(PS Big or small, we’re happy to talk to you about MIOsoft data quality options no matter where you are in your data quality journey.)