Three Quick-Start Steps for Making Data Driven Decisions

“Big Data” has become a buzz phrase today.  How can companies use the volumes of data to that they have to make data-driven decisions? 

Using data to make decisions seems logical.  Yet, turning data into a compelling story is not always easy. 

Why is that?

According to a recently-released executive survey from New Vantage Partners, among the 85 Fortune 1000 firms who answered this survey identified “culture – people, process, organization, change management – as the biggest impediment to becoming data-driven organizations – 92.2%.” (page 8)

This Harvard Business Review article suggests that companies who want to get past the cultural barriers to using data should focus on high impact business problems, re-frame thinking of data as a business asset, and remain patient when results are not immediately apparent.

Those are great suggestions!  However, for many businesses they seem somewhat nebulous. 

Here are three concrete steps we recommend to get started using data to make decisions.

1. Review Sales Data for the 80-20 Rule

Recall the 80-20 Rule from “Why Pareto Should Be Part of Your Lexicon” and that it is not uncommon for  80% of your sales come from 20% of your clients. Pull your sales data and determine if this is true for you.  It may not be exactly 80/20 (possibly 90/10 or 70/30), but there is a good chance that you will find a customer or group that represents most of your sales.  If you are a larger organization and looking at *everything* feels too daunting, it is completely reasonable to start by considering just one product or service line.

2. Analyze this Data to Make or Justify Three Decisions

Is there a risk with too many eggs in one basket? Do you want to treat some of your customers differently than others in terms of customer service, pricing, or lead time commitments? Figure out what is compelling about this information and make or justify three decisions that everyone can get behind based on this data.

Here are three example decisions, assuming you have something akin to an 80/20 rule.

  1. Develop and Deploy a Customer Service expectation for your Top / Key Customers to retain these accounts.
  2. Create an Operational Prioritization strategy around pricing or lead times that resonates with your Top / Key Customers to grow these accounts.
  3. Analyze and search for New Sales prospects that match your Top / Key Customers’ profiles to diversify your organization’s accounts in areas where you already shine.

Note: When you analyze the data, it is common to discover the need for data clean-up. For example, your team may have entered sales for the same customer in three different ways in your order entry system.  For example, “Acme,” “Acme, Inc.”, and “Acme Co” may all be the very same company – so take this opportunity to clean up and standardize your nomenclature so it is easier to analyze your data going forward!

3. Tell Your Organization a Compelling Story Around the Data and Decisions

Time to share your data visually and tell a compelling story for why this data has been used to make these decisions.  Describe the vision of what your organization will look like when these activities are done well.  Prepare some forecasts for what the data will look like in a quarter, six months, and/or a year of this work being done. Facilitate a discussion with the groups responsible for the decisions you have made on what they can do to support the decision, and what their concerns are.  Paraphrase the concerns back to ensure you understood them and follow up on anything you cannot answer immediately.  Ask people managers to develop and track specific actions around these decisions.  Collect the same data at regular time intervals (i.e., quarterly) and show the team how things are going.

This may not be Big Data and establishing AI to follow those trends, but in mid-to-large sized organizations where data has not historically been part of the decision-making process, following these three steps is a tangible way to get started.