Forbes Insights Research - The Data Differentiator: How Improving Data Quality Improve Business
It’s no coincidence that the topic of data, including its quality, is everywhere. That’s because data is everywhere.
Technology in large part has unleashed a giant data beast that is propagating exponentially, leaving businesses unsure of how to wrangle the best data available for their competitive advantage.
It’s top of mind for many business leaders. In the Forbes Insights and KPMG 2016 Global CEO Outlook, data and analytics capability ranks the highest of the top five investment priorities for CEOs today. The report also found that 84% of CEOs are concerned about the quality of the data on which they base their decisions.
One of the reasons it’s such a focus for business leaders is because it’s costing them money. Gartner measures the average financial impact of poor data on businesses at $9.7 million per year.
To help businesses get a practical handle on their data with a focus on quality, Pitney Bowes enlisted Forbes Insights to dig into the topic. The resulting research paper The Data Differentiator: How Improving Data Quality Improve Business tackles these four areas:
- What is Data Quality, and What Does it look Like?
- Data is a Differentiator
- Bringing Outside Data In
- Choosing a Data Partner
As part of the research process, Forbes Insights interviewed Dun & Bradstreet’s Chief Data Scientist, Anthony Scriffignano, PhD, who framed his views on the definition of data quality and why it’s so important – particularly in today’s digital world.
He also offered these three helpful pieces of advice for companies looking for the best data partners:
- Never lead with a data set – lead with a question. Get the question right before you start interrogating the data. Understand what the problem is that you’re trying to solve.
- Understand the data quality drivers important to you. Don’t let data providers tell you how accurate their data is. You can consider their input. But understand what’s important to you.
- Always have some sort of closed-loop process. Don’t fall victim to the “dipstick test”— taking a convenient sample to reach a conclusion. It doesn’t work well, especially with complex data sets. Rather, work with a data provider to create a closed-loop process to get to a steady state. Communicate your needs, get a representative data sample, test, and share results. Then decide whether the data is good enough.
Attaining data quality in the face of what seems like a data monstrosity doesn’t have to be daunting. With a strong business objective for growth defined, businesses can harness the best data available to take full advantage of game changing technologies like Artificial Intelligence and the Internet of Things.
Get the full read on making data quality a top priority: