We talk a lot in this space about big data in the context of its effect on marketing, covering everything from marketing-technology buying strategies to marketing opportunities presented by the Internet of Things. Just last week, we looked at the subject of customer data unification and the valuable, often unexpected, insights that unified data can deliver. But while marketing is most often the focus here, it’s worth remembering the bigger picture: the powerful force that big data can exert throughout a company.
I was recently reminded of the broader value of big data in business today when I ran across “Measuring the Business Impacts of Effective Data,”a report on the results of a study that a team of researchers at The University of Texas at Austin conducted for Sybase. The first chapter in this three-part study focuses on the impact of effective data on financial performance. The results present a compelling set of conclusions that are relevant both generally and to groups like marketing that operate within a business.
The study’s focus isn’t specifically big data; it’s effective data. But I would argue that the more data you have and the more complex it is, the more important it becomes to use it effectively — and the greater the impact of doing so. The first chapter of the study points to several specific ways in which data can have an impact on financial performance, and I think the results are well worth sharing here.
1. Data usability and employee productivity
The principle is pretty simple: The more usable you make data, the more productive you make the people who work with it. And the more productive you make people, the more money you’re likely to make. I was especially impressed with the claim that
“If the median Fortune 1000 business … increased the usability of its data by just 10%, it would translate to an increase in $2.01 billion in total revenue every year.”
And while revenue of course isn’t profit, it’s where profit starts.
So what exactly is this “usability” that can have such a profound effect on revenue generation? It’s one of several attributes of data that the study authors considered in their research, and it refers to how concisely data is presented, how easily it can be processed and how consistent it is across multiple sources. Those criteria should strike a chord with every marketer who’s ever struggled with multiple silos of data and with the time and effort it takes to correlate information from so many sources. Technology that brings the data together and makes it more accessible, usable and insightful will make people in marketing — and across the business — more productive.
2. Data quality and growth
Quality is another attribute of data that’s considered in the study, and the authors list the following as the defining characteristics of data quality: accuracy, scope, timeliness and recency (or how up to date the data is). Their premise is basically that improvements in data quality can improve a business’ return on equity (ROE), which is a metric that reflects the business’ ability to grow.
And how does a business increase the quality of its data? The researchers say that reducing data errors is an important step. I think this, too, resonates with some of the specific data challenges that marketers experience. When the marketing organization is trying to manually correlate massive amounts of customer information from multiple silos, for example, errors are inevitable, and the ability to use the data effectively suffers. That’s why unifying and automating correlation is so important. This is where a customer data platform comes in, defined as a marketer-controlled system that integrates all sources of customer data to create a complete view of the customer and provides actionable insights to support timely external marketing execution.
3. Data intelligence and ROA
Return on assets, or ROA, is the measure of a company’s ability to use resources efficiently to generate income. Data intelligence is a data attribute that can positively affect ROA, according to the study. Data intelligence in this context relates to a business’ understanding of what’s important to customers. Being able to understand and respond to what customers want and need is, of course, a principal tenet of customer-centric marketing today.
The study holds that improving data intelligence requires improving the accuracy of data predictions, analysis and recommendation. “For example, what percentage of recommendations made by a business intelligence application results in cross-selling? How much more revenue can a better recommender system bring?” Those are key questions for marketers, and this study provides good guidance toward improvement.
I’m looking forward to the next chapter in this three-part study, in which the researchers turn their attention to an area familiar to every marketer: the customer-focused impacts of effective data. I’ll let you know what I find out.
This article was written by H.O. Maycotte from Forbes and was legally licensed through the NewsCred publisher network.