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The cost of Poor Quality Data

Consider this figure: $136 billion per year.

That’s the research firm IDC’s estimate of the size of the big data market, worldwide, in 2016. This figure should surprise no one with an interest in big data.

But here’s another number: $3.1 trillion,

IBM’s estimate of the yearly cost of poor quality data, in the US alone, in 2016. While most people who deal in data every day know that bad data is costly, this figure stuns.

The Data Analytics vs. Data Quality Dilemma

So here’s the question: Why do we see so much emphasis on data analytics and so little focus on data quality?

For many, analytics is the first thing that comes to mind when they hear about data management. However, those involved in delivering meaningful insights understand the complexity involved in understanding, assessing, integrating, and preparing data for analysis. Data quality issues, data integration challenges, and the struggle to find accurate data all add risks and costs to the analytics processes.

The Challenge for Data Scientists

Here’s another striking figure: 60%!

That’s the amount of time that data scientists spend trying to get data into a usable state for analysis. In conversation with a data scientist at a South African corporate, she laughed when this number was mentioned. “More like 90% of my time,” she responded.

Hidden Costs and Challenges

Unfortunately, these costs and challenges are often hidden from business users, including senior technical management. In some cases, data professionals may be apprehensive that their insights will not be taken seriously if users understand how much data manipulation was necessary to derive the insights. As a result, senior staff may ignore concerns raised by data scientists until they can no longer be overlooked.

The “Sizzle” of Graphical Analytics

BI vendors often sell the “sizzle.” Modern BI tools offer a plethora of captivating graphics and charts to represent insights in compelling ways. These graphics are like condiments that allow us to spice up our data “steak.”

Prioritizing Data Quality

However, just like any good chef will tell you, the quality of the raw ingredients matters. A good quality steak, simply seasoned with salt and pepper, will deliver a far better eating experience than a poor-quality cut.

Similarly, for analytics, data is the raw material. Investing in improved data quality, including tools, training, and process improvements, has a direct operational impact and can save businesses trillions annually.

Data Quality Deserves Its Own Focus and Budget

Time and money spent on enhancing data quality should not be hidden or absorbed into analytics or data integration budgets. Data quality deserves its own focus and dedicated budget.

The Long-Term Benefits of Data Quality

Importantly, the benefits of improving data quality go far beyond cost reduction. It is hard to imagine any sort of future in data when so much is so bad. Thus, improving data quality is a gift that keeps giving — it enables you to take out costs permanently and to more easily pursue other data strategies. For all but a few, there is no better opportunity in data.

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Source: Bad Data Costs the U.S. $3 Trillion Per Year