Our world is being overrun with data. In 2020, new data generated per second for every human being reached 1.7 megabytes. Consider there are over 7.7 billion people on the planet, the amount of new info generated is equivalent to more than 25,000 one-hour videos – per second!

Summary

  • Companies now have access to more data than ever before. But what are you doing with this data? Do you let it degrade over time or are you using it to fuel business growth?

  • Companies that leverage their data to drive decision-making gain a competitive advantage, reduce business costs and increase profit. Sounds great – now what?

  • We explain what data-driven decision-making is, what you can use it for, and how it can positively impact your business. Plus, we share a five-step process that you can use to create better business decisions.

All of our digital behavior is recorded. But for many companies, this data sits in pretty dashboards and or even worse, databases, never to be used. The good news is that instead of letting your data degrade, you can use it for your business’ growth strategy to make better decisions.

What is Data-Driven Decision-Making?

Instead of going with the decision you think is best, data-driven decision-making is a strategy that uses data to inform business decisions. In data-driven decision-making, you group together historical information to analyze trends and make decisions for the future based on what’s worked in the past – rather than make decisions based on gut feelings, opinion or even experience.

“Best-in-class” companies position data at the core of every decision they make.

For example, imagine you want to roll out a new line of services for your MSP. Instead of starting from scratch and hoping a new line of service works, take a look at your previous lines of service. What’s profitable? Replicate it. Don’t offer more of something that didn’t work.

Put simply, do more of what worked and less of what may or may not work – all based on the data you have collected to make better business decisions.

Research backs this up model, too.

Businesses leveraging their data experienced a profit increase of 8–10%, and a 10% reduction in overall cost.

If you’re still not convinced, consider this:

While 91% of companies say that data-driven decision-making is important to the growth of their business, only 57% of companies said that they base their business decisions on their data.

How to Use Data to Make Business Decisions

Before you analyze your company’s data, it’s best to start with a plan of action that details how you’ll find the right data and, more importantly, interpret the data to make the right business decisions.

Here’s a five-step process you can use to get started with data-driven decisions:

Any decision you need to make starts with a business goal at the core. So, start by asking yourself: “What areas do you want to improve”? If you aren’t sure, you can compare your data against your peers (benchmarking) to get an idea of where to start.

Once you’ve identified the problem you want to solve and the decision you need to make, find and present relevant data. I cannot stress relevant enough. Don’t spend hours looking at pretty data or get lost in data that’s just interesting. Just because it’s interesting doesn’t mean it’s useful. So, keep the data relevant and only review data that relates to your decision.

Example: Let’s say your customers are complaining that when they call in, they have to re-explain the same problem. What data should we look at?

  1. Does the CSAT data show that?
  2. What could cause needing to re-explain already reported problems? Time entries?
  3. Who is putting time and notes in?

How quickly are they putting time and notes in?

Take a look at the historical data you’ve collected and try to identify patterns or trends.

If we use the “explaining the problem” example from above, you might consider rewriting your policies and procedures around time entry expectations.

For data-driven decision-making organizations, this means looking at historical data to see if there’s any indication that a rewrite would perform well. How effective are we at rolling out new policies? Where does the current policy fall short?

Throughout this process you might find:

  • Engineers aren’t putting in time consistently; some do it same day, some do it same week.
  • Engineers enter time, but no notes in the ticket – or notes that only they understand.
  • Timesheets aren’t submitted on time, or at all.

In this case, you could conclude that rewriting your time entry policies and procedures to include measuring with a daily review is a safe bet because the data indicates it could be successful. And that would be a smart decision!

Now compare this to a non-data-driven decision-making example.

You’d like to reduce customer complaints about repeat issues, so you decide to rewrite the script your dispatcher says to ask if this is a repeat issue. Instead of looking at historical data, your rewrite mostly consists of changes in words the customer hears – but you end up keeping the same problem and your dispatcher is now getting cussed out!

A few weeks pass by and there’s no difference in customer complaints. So, you decide that it’s not the dispatcher that is the problem, but something else. So, you take a guess and move on.

See the difference?

Now imagine taking a data-driven approach for every business unit in your organization and it’s easy to see why companies that use data-driven decision-making are a lot more successful.

You’ve found the goal you want to improve and analyzed the data to make a decision on whether you’re going ahead with a new strategy.

Next, you’ll need to create a plan of action to put your decision into practice.

The key at this stage is to make clearly defined goals on what needs to be done and when, by whom, why you’re doing it and what is the outcome you expect – rather than creating vague goals that “need to be done before the end of the year”.

For example, you might use data to conclude that a rewards program will help with prompt time entry. In this case, your clearly defined goal would look something like this:

“Tom and Heather will set up a points-based rewards program to increase time entry timeliness within the next 30 days. This will reduce CSAT complaints around repeat issues to 5%.”

Simple, but is surprisingly effective.

Your decision has been made and your results are in – well done!

But that doesn’t mean your decision-making process is over.

Look at the data you originally reviewed and based your initial decision on. Then, as the deadline for your goal arrives, compare the historical data with the new data you have collected and ask:

Did your data-driven decision have a positive impact on your business?

If your decision was successful, congratulations! Keep a process in place to continue to measure this.

But if the change you implemented wasn’t successful, that’s okay. Sure, your decision might not have had an immediate impact, but at least you now know what doesn’t work. And sometimes that’s just as important as knowing what does work.

As Thomas Edison was constructing the light bulb he said: “I haven’t failed, I’ve just found 10,000 ways that didn’t work”.

Next Steps

What other business problems should you be able to answer with data?

Know Your Data

  • How profitable is that customer?
  • Is this technician pulling his weight?
  • Are my rates the right amount?
  • When should I make my next hire?
  • Are my customers happy with service?
  • Are we getting paid on time?
  • Are we billing for everything we’re supposed to?
  • What type of projects should I sell?
  • Are tickets bouncing around?
  • Who should I give bonuses to?
  • What type of customer is our best customer?

Know How Your Data Stacks Up

  • Use the same metrics above to compare to best-in-class MSPs

Know What to Do About It

  • Learn best practices – join peer groups, read blogs, attend conferences
  • Align with a consultant
  • WATCH THE DATA CHANGE
  • Constantly revisit (either directly or through delegation).

Conclusion

There’s no doubt that data is a valuable tool for any MSP. In fact, MSPs that use data at the core of their decision-making reduce costs and increase profit.

The next time you need to make a decision, base it on the data you already have. It could be the technique you need to fuel growth, retain staff, take over your competitors, and acquire long-term, loyal customers.

Aaron Kennedy, CEO, Cognition360
Aaron Kennedy, CEO, Cognition360

Aaron Kennedy has worked in Managed IT Services for over 20 years. As a consultant, he has worked with a wide array of MSPs on operational soundness and efficiencies which ultimately drive the bottom line. Read bio

Unlock the value in your ConnectWise data with Cognition360

The gateway to Cognition360 is a FREE trial for 30 days. The sign-up process is fast and easy and can be completed online in a matter of minutes. The faster you contact us the faster you will be up and running.

Sign up for a trial now, or get in touch to find out more. 

Start your trial
Find out more