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  • Writer's pictureJerry Olson

Fluff or Facts: The Role of Data Analytics in Business Decision-Making

Updated: Jun 21, 2023

When you make business decisions using subjective reasoning that’s based on hunches and gut-feelings, your outcomes are bound to disappoint. Like baking a cake without knowing and measuring the ingredients, it’s more likely to turn out poorly than positively.

But say you do happen to end up with a positive outcome: well, it won’t be repeatable! You weren’t tracking the data used to create that outcome, and therefore don’t know what recipe you used to make the cake!

That’s where data analytics comes in. Data-driven decision making is defined as using facts, metrics, and data to guide strategic business decisions that align with your objectives and initiatives.

Reliable valid data with good analysis allows you to define your goals and measure your progress using real key performance metrics (KPIs). Most importantly, data takes the emotion out of decision-making. And we’ve found that in business, that's usually what separates a well-baked success from a half-baked disaster.

How Do I Start Making Data-Driven Decisions?

According to a McKinsey Global Institute survey, data-driven organizations are not only 23 times more likely to acquire customers, but they’re also six times as likely to retain them. They’re also 19 times more likely to be profitable.

With numbers like that, incorporating data analytics into the decision making process is one of the most important things you can do as a business owner. When choosing which data to include in your decision making process, here are some key things to consider:

  • Does the data relate directly to your goals? Think carefully about which data actually indicates progress toward your overall vision.

  • Is the data understandable? Will your team know what the data mean?

  • Is the data reliable? Make sure the data actually measures what you think it’s measuring (e.g., the number of sales calls may not be an accurate metric to measure future conversions).

  • Is the data timely? Is it measuring something you can currently impact, or is it just a report of the distant past?

  • Is the data actionable? Track metrics that you know you can take action on if changes are needed.

When it comes to data analysis, my motto is KISS: Keep it Stupidly Simple, at first. Start by tracking numbers that are readily available at least on a monthly basis. Then ask yourself what you can track within that number weekly. And make sure the data is actionable — can you actually move that number?

What Categories of Data Can I Track?

When it comes to data analytics, monthly financials are a favorite of the business owner. While financials are certainly important, they shouldn’t be the focus of your data analysis. You want your data to provide a balanced picture of what’s going on in the organization — not just how profitable it is.

Here are some common types of data you should be tracking:

People Data: In today’s tight labor market, having good “people data” can be a game changer. How do you measure whether or not you’ve got the staff you need, both in numbers and in characteristics? Examples include forward looking staff projections, hiring needs over the next year, and turnover rate. Additionally, you’ll need an objective method of tracking the staff performance.

Rear-View Mirror and Windshield Data: Rear-view data are indicators of things that have happened, like last week's sales number, last month's turnover data, trailing four weeks of time-to-hire, etc. On the flipside, windshield data looks at what's coming for the business and can include sales/customer visits, future projections of orders for the next quarter, and any kind of marketing data such as numbers of leads or social media impressions.

Data That Reports on Functions: Every part of the operation should have some data that they’re tracking to make decision-making more objective. At The Resultants, we often talk about the “three-legged stool” on which every business sits. The legs include sales, operations, and finance/administration. You should be tracking data that paints a balanced picture of each of these functions. Find a few key data points in each that measure progress, and help your decision-making be more objective.

Examples of sales data can include average customer lifetime value (CLV), cost of selling, and the average length of sales cycle, while operations metrics might include through-put or production numbers, COGS (costs of goods sold), and gross margin (often by line of product or service). Additionally, operations metrics that report on quality should also be tracked, including rework, corrective action reports, unbilled time, and other labor costs.

Financial Data: These metrics should include your organization’s balance sheet, P&L statement (especially gross margin), and cash flow. Businesses function better when leaders can predict cash flow, but we often see leadership teams that lack visibility and understanding of this metric. Aim for a full 13-week projection of cash flow, and start working toward that by predicting the next two weeks. Then add additional weeks onto the projection. Overhead percentage is another easy, meaningful metric to measure in this category.

How Often Should I Be Tracking & Reporting Data?

The best way to approach data tracking is to figure out what data you’re going to track, at what frequency, and in which format (preferably the format that tells the clearest story). It’s important to note, however, that the optimal frequency with which an organization tracks data will depend on the type of business you’re running, as well as what your product or service is.

For example, if monthly data is important to you, I’d suggest tracking a trailing four week average. A good starting point, however, would be to track weekly data. This frequency allows you to respond quickly to any changes that arise.

Trending data (tracking data by the quarter, or every 13 weeks) can also be critical. Trending data illuminates “trends” that you might miss looking only at weekly or daily data.

For example, you could say, “Wow, we did $40,000 of production today. I feel good about that.” But four weeks ago the numbers were at 43, and the week after that was 42, and the week after that was 41… Sure, you might feel good about 40 now, but if you aren’t trending that data, you’ll miss a big-picture problem that needs your attention.

Final Notes From Jerry:

A note about accountability: Each member of the leadership team should be responsible for tracking the data in their area. They might not do the actual tracking or reporting, but they should at least understand the data enough to know where to get it and make objective decisions with it. (There’s nothing worse than being in a leadership meeting and having a supervisor shrug their shoulders when asked about their department’s data!)

So, ditch the guesswork, hunches, and gut-feelings — they have their place in this world, but in business decision making, they make for some unsavory results. Data analysis, on the other hand, will make sure the cake is well-baked, every time.


Track Your Way To Success

At The Resultants, we give our clients the tools to track Key Performance Indicators directly related to their business. These “Scorecards” give clients a bird’s eye view of their organization’s performance. Interested in hearing more? Shoot me an email and let’s chat about which data your organization needs to be tracking to measure success.


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