Forget Flashy Technology: Here are 3 Data and Analytics Best Practices Any Company Can Use Right Now
Practically everyone is talking about using data and analytics to succeed today in business, but surprisingly companies are only deriving a fraction of the value that’s available to them in their data when they’re making decisions. The reasons for this vary across organizations, but often times it comes down to budget constraints, talent constraints, or lack of recognition from leadership that analytics will help their business to run better. During an interview in 2009, Google’s Chief Economist Dr. Hal R.Varian predicted, “The ability to take data—to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it—that’s going to be a hugely important skill in the next decades.”
Let’s take a look at some of the highest-performing companies out there today. Over the past 5 years, there have been 13 companies that have managed to outperform the S&P 500 each year. And when you take a look at this elite group—which includes companies such as Facebook, Amazon, and Google—you find that the majority of these businesses are algorithmically-driven. These companies take in data constantly, and use this data in real time to update the user-experience. In their 2012 feature on big data, Andrew McAfee and Erik Brynjolfsson shared findings from their research that “companies in the top third of their industry in the use of data-driven decision making were, on average, 5% more productive and 6% more profitable than their competitors.” It is hard to deny that success in our respective businesses is not a function of how well we make use of the data available to us.
So how does Human Resources (HR) fit in to this picture? HR may not be the first group that you think of when considering who should have a strategy around using data. However, HR has the weighty responsibility of managing the top expenses of a company—salaries, healthcare, and benefits. The 2018 Milliman Medical Index estimates that the cost of healthcare for a family of 4 this year will be upwards of $28,166. Yet approximately 20% of employer-sponsored health care spending is wasted each year due to unnecessary or preventable costs across the continuum of care. The rise of high deductible health plans mean that decisions made within HR on health plans and benefits are decisions that weigh heavily on their employees pocketbooks as well. When we look at HR through the expense-management lens, we see that HR carries the company’s fiduciary responsibility to manage these expenses not just for the bottom line of the employer, but also for the sake of their employees’ wallets.
We often see companies who make the decision to start using data and analytics immediately start shopping for a tool to make use of their data. While this step may be right for some companies, there are a few foundational analytics best-practices that we recommend companies have in place before making any analytic technology investments.
- Understand the quality of your data. One of the biggest mistakes we see companies make is that they assume that just because a report comes from I.T. or from a vendor, that the data is correct. However, very rarely is the data captured by a company in “ready-to-use” form. IBM estimates that poor data quality cost American companies $3.1 trillion in 2016 alone. A recent study of 75 executives who assessed their own organizations data quality found that only 3% of their companies’ data met basic quality standards. Furthermore, understanding data quality is a fundamental issue within organizations, executives are more informed to understand how data quality affects their vendor partners as well. Every bit of data that we review is a piece of a much larger picture, and understanding the limitations of the quality of your company’s data helps to make a more accurate assessment of its insights.
- Develop your data strategy. Take a step back from day to day operations to decide how to data can help to inform your decisions. This affects what metrics you’re looking at, and how often you’re receiving it. Many companies are surprised to find that the process of developing a data strategy often means reducing the amount of reports people are looking at. A common assumption is that the more data we’re looking at, the better off we are. In reality, when decision-makers are inundated with extraneous reports, they may miss valuable messages that they need to see. What goals is your division working towards? Which pieces of data most closely track progress to these goals? The best way to guide a strategic process for looking at data aligns your business goals with a limited number of key metrics to indicate when changes are needed to reset course.
- Identify a data “expert” on your team. Given the issues that exist in every organization with data quality, it is valuable to identify someone who is intimately aware of the source and limitations of the data your company assesses. This person can answer questions on why particular data might be wrong, if duplicate records are skewing the data, or how outliers are affecting results. Your data expert can help to tell the story of your organization’s data to better frame what actions are needed to meet your operating goals.
Using data to make better business decisions does not need to be cost-prohibitive for your company. Before investing in any data and analytics tools, implementing these foundational best practices lays the groundwork for a sound approach to using data. They can be used by any company, regardless of size or budget. And the best part is, you can start to use these best practices today.
Bob Selle has led culture change and organizational design for America’s most recognized retailers. He is currently the Chief Human Resource Officer for the northeast’s premier close-out store Ocean State Job Lot, leading a transformation that has named them a Forbes Best Midsize Employer two years in a row.
Shannon Shallcross is a data expert who believes that data interpretation holds the key to solving healthcare’s toughest challenges. As the co-founder and CEO of BetaXAnalytics, her company uses the power of data “for good” to improve the cost, transparency and quality of healthcare for employers.