The following is adapted from Guaranteed Analytics.
Analytics is the process of sourcing data, turning that data into information, using that information to generate insights, and then implementing those insights to monetize your data. For this process to go right, it’s essential that you approach your data capture with tremendous care. Otherwise, even the smallest error can cause things to get messy fast. Garbage in, garbage out.
One common downside of poor data sourcing is scope creep. If you find your analytics projects are never-ending, or if you are drowning in a data sea of everything but the kitchen sink, these data sourcing best practices are for you.
Sourcing the Right Data: Three Questions
You might want someone to tell you what data you should be sourcing, but there’s no magic answer that fits every business. Your enterprise is unique, and you’ll have to decide which data can give you the answers you need to solve problems, make decisions, and take action for the future.
However, there are three questions you can ask to help narrow down your choices. First, what value (according to your defined goals) are you trying to capture? Second, what actions must you take to capture that value? Third, what insights are needed for that action to occur?
Examples of Scope Creep
If you study examples of scope creep, you’ll facilitate an understanding of just how easy it is to fall down a data rabbit hole. Let’s say you want to reduce 80 percent of the financial analysts’ time spent assembling data at your business. Your data needs are likely to include:
- Last week’s sales
- Product allocated costs
- Margin estimates
- Key attributes like reporting calendar
But many companies are going to go too far with their data collection, thinking more is better. They’ll look at unnecessary data like:
- Accounts receivables aging
- Sales rep tenure
- Cost coefficients
Don’t let curiosity lead you down the path of collecting data you don’t need to achieve your analytics goals. It’s not about how much data you can get; it’s about getting only the data that drives value for your business. More is rarely better (the law of diminishing returns).
Knowing Where to Find Data
There is a lot of data available for virtually every business in existence. A common mistake is to look at competitors first, rather than looking at your own internal data.
There are two major flaws with this thinking. One is that your own data is low-hanging fruit. Why not grab it for little or no time and cost?
Focusing on your own data is nearly always inherently richer because it’s so accessible. With a competitor, you’re only seeing the tip of the iceberg. Therefore, your own data yield more value.
The other flaw is that almost every business makes the mistake of assuming they know everything about what’s going on in their own company. This is 100 percent untrue all the time--until you make the effort to measure and analyze your data.
Proper Input for Efficient Data Use
One more important caveat to note: you can collect just the right data from the right places and avoid scope creep, but if you input it incorrectly, it will give you skewed information that denies you hard-earned insight. Sorting and storing information can help or hinder your analytics tremendously.
As a simple case in point, think about a survey where the end result doesn’t sound realistic for the situation. Say you want to know how many cigar smokers there are in your town to promote a new club, so you send out a survey.
The final tally shows 20,000 cigar smokers in a town of 30,000. Hmm… that can’t be right.
Only once you look at how your data were input do you realize that every time someone answered they were simultaneously “an occasional smoker,” “a regular smoker,” and “a cigar fiend,” they were input as a separate individual. There was no accounting for the fact that many survey respondents considered themselves all three types of cigar smokers based on fitting into each category at various times or locations.
Therefore, in addition to capturing data as close to the source as possible, you need to make sure it’s properly contextualized. You want to get as granular as possible to rule out confusion. By its very nature, analytics automates the data collection process, but there has to be human intervention somewhere to correct for mistakes like the survey mentioned above, whether that’s in aligning the output with the input to check for nonsense outcomes or in creating the input program to screen for likely error.
Your data is the foundation on which you build your analytics program. Take the time to collect from the right sources for your needs and input the data properly. You’ll get the insight you need to answer vital questions, make important decisions, and hopefully, boost your bottom line in the end.
For more advice on sourcing data for analytics, you can find Guaranteed Analytics on Amazon.
Jim Rushton began his career in analytics working with some of the biggest consulting companies in the world, including Accenture, Deloitte Consulting, and IBM Global Services. Jim then moved to an executive position with Verizon, where he oversaw the company’s customer and marketing information. Leveraging his experience across corporate America, he helped found Armeta Analytics, and in the past decade, his team has helped dozens of Fortune 1000 companies learn how to monetize their data.