Your company’s data is at the heart of your success. A healthy heart enables us to function well and rise to new challenges. Having healthy data management practices will help your company be efficient and deliver personalized experiences.
How healthy is your data? Here’s a quick check-up you can perform!
Step 1: Map your data
If you are like most companies, data is spread across many separate locations and software platforms. The first step toward a strong data heartbeat is to build a data map that represents the several types and locations that your company has.
We all trust that our doctors understand what our circulatory system looks like before they operate on us. This applies for data management. Before you can operate on your data, you need to know what you have!
Perform an audit to identify what data is being collected across the company. Your goal is to identify all the data collected, which means you need to investigate data collection practices across teams and departments. Do you collect customer contact information? Is your customer information in one place, or do several teams collect their own information? How about buying history? Where do you track marketing campaign performance or current inventory? Where do you keep track of your sales leads? What sales information are you tracking?
Once a data map is formulated, it will be easy to answer each of these critical questions:
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What personally identifiable information (PII) is my company collecting and storing?
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Why are we collecting and storing PII and other data? Who is asking for it?
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Where is it stored? Is the data centralized or siloed (stored in multiple, disconnected locations)?
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How are we using the data that we are collecting and storing? Where is it sent? Who is accessing it?
Step 2: Centralize your data
Just as blood needs to reach the heart, you need to centralize your data before using it to drive insights. Data map in hand, you know have a clear picture of data you are collecting, the places it’s stored, and who needs access.
The next step is to start reorganizing and centralizing your data into a healthy data architecture.
A healthy data architecture is one in which the data collected is working for you—you know what you have and how you are going to use it. Having a healthy data architecture also means that you are protecting your customer’s privacy:
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Only collecting the information needed
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Securely storying it
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Limiting access to the data to those who need to work with it.
This may sound overwhelming, but there are many different solutions available to this. Spending time reviewing your company’s goals and pain points will pay off! You will want to adopt a software solution that scales according to your needs. Here are some common options:
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Customer Relationship Management systems (CRM): track all your customer data so that you can retain, build, and develop strong customer relationships.
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Data management platform (DMP): used in advertising to store and activate third party data.
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Customer Data Platform (CDP): used to route data sources into one central location.
Step 3: Clean your data
To get the most out of your data, you need to be sure that it is clean. This is like the importance of having plaque-free arteries to supply the heart with blood. Clean data includes several components:
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Irrelevant data removed (this includes outliers).
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The data has been de-duplicated.
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The data stored in a consistent format (think of phone numbers: What are you using to separate the numbers? The formatting should be consistent for every phone number you are storing!)
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Missing data filled in where possible.
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Data validated (this step involves checking the stored data for accuracy).
Step 4: Find the pulse of your data
Now you’re ready to put your data to work! The challenging work mapping, centralizing, and cleaning your data pays off when you can start analyzing and leveraging it. This step will look a little different for every company, it revolves around what you collected and the questions you want to be able to answer. You might leverage your data to answer questions related to customer behavior and buying patterns, tracking, and predicting sales, streamlining processes and operations, or budgeting and forecasting.