Digitalization is happening at an even faster pace these days. This means there’s more data than ever before. Organizations can capture all this data to optimize their businesses. In theory, every business can capture data. For example, a hospital captures data about its patients and their diseases, treatments, and much more. A pharmacy can collect sales data and customer data. Any type of data collection is possible!
If your organization doesn’t manage data properly, then the collected data will be scattered and hard to analyze. These problems can be especially tricky if you’re trying to DevOps your data.
To prevent disorganized, inaccurate data, it’s important to learn about data management and its importance. We’ll also discuss common data problems and how to solve them.
A Definition of Data Management
Data management focuses on capturing, validating, storing, and protecting data. Besides that, a lot of effort goes into processing the data to ensure its quality and reliability. Big data is becoming increasingly important for organizations to make more data-driven decisions. This data helps organizations understand their customers, spot new trends, improve their existing services, and even develop new services.
Therefore, data management is more important than ever. Let’s take a look at why data management matters to your organization.
Why Take the Time to Manage Your Data?
First of all, data is the most valuable asset in the world. When your organization captures the right data, you can find valuable insights by analyzing it. High-quality, useful data is a centerpiece of DataOps.
Here are a couple of reasons data management is important for your organization.
Use Data for Marketing
By its nature, marketing is a data-driven department. When you collect data about current customers, you can develop insights into what your customers actually like. You can use this data to offer new products or services that your customers will probably like. Also, you can use the collected customer data to help you find prospective customers. Large retailer websites such as Amazon use this data to offer customers cross-selling opportunities by learning about the linked interests of buyers.
Analyze Product Usage
Great tools like Hotjar help you track user habits on your website. They can answer questions like these:
- How much time does it take for a user to finish the checkout process?
- On which page does a user spend most of their time?
- Which flows or processes are unclear in the product?
- What impact does a larger “buy” button have on the buying behavior of customers?
All these questions can be answered by tracking data about the users’ product usage. Some organizations underestimate the number of data points that they can measure. The above questions show that tracking users’ behavior can help you answer many types of questions.
Extend Your Organization’s Data History
When digitalization was not yet a big thing, companies often threw away paper files that they didn’t need anymore. Also, because they needed a lot of space for storing all these paper files, they stored only a few years of data at most.
Now with digitalization sweeping across all industries, it’s easy to store data in a digital format. This means you can store much more data and therefore prolong the data history of your company. Improving how you treat data is one of the success patterns of DataOps.
Improve Data Quality
When you define data management policies, data becomes more accurate and complete. Besides that, data management tries to define standards in a way that reduces data errors. It’s important to define how to categorize data, who can access it, and where the data needs to be stored. If everyone in your organization has the same understanding of data needs, then your data quality will increase.
Stop Searching Fruitlessly for Data
When you’re dealing with scattered data, you and your team lose valuable time searching for all related data points to construct the data object. It can be a confusing or frustrating search to find the right data. Data management tries to solve this problem. 
Two Common Problems With Data Management
Not everything goes well when managing your data. Let’s dive into two data problems organizations often experience and how to solve them.
1. Hard-to-Aggregate Data
You’ll likely have experienced a situation where you urgently need a specific type of data, but you can’t find it. The data is scattered over multiple databases or lives in other departments where you don’t have permission to retrieve the data.
It’s a common problem and also why data management was invented.
What Is the Solution?
To put it simply, manage your data properly. Here are some easy tips to make data more accessible:
1. Change Company Culture: Departments often form data silos. Instead, avoid data silos, and create a culture of open data sharing. In the end, you and your colleagues all want the company to grow, so try to share data across departments. If you want to learn more about the importance of company culture, make sure to check out this blog.
2. Review and Revise Permissions: Permissions are worthwhile when they restrict access to sensitive data. However, a permission-based data management system can quickly become messy. Try to implement as few permissions as possible, protecting access to sensitive information only. As stated above, you’ll want to encourage an environment of data sharing, not an environment where everyone needs to ask permission for accessing another department’s data.
3. Streamline to Avoid Technical Problems: Even when a company has a great company culture and few permissions, departments often experience technical difficulties when sharing data. Consider choosing one tool or database system for managing all your data. It’s adverse for your data to live in different data structures. When trying to aggregate this data, you’ll have to overcome many technical challenges to construct the data object you need.
Pay attention to technical barriers teams might experience as you consider whether to use one data management system for everyone in the organization.
2. Low-Quality Data
Many organizations have problems with low-quality data. This data often includes double entries, missing information, or incorrect information. You can’t trust or rely on this type of data.
Low-quality data is one of the worst-case scenarios because it prevents your employees from performing at their best. How? Imagine you receive an order for a customer, but you can’t find their delivery address.
Loss of data can even cause reputational damage. Why would a customer want to buy from you when you can’t even handle their data?
What Is the Solution?
Consider using tools for deduplicating data. Such tools can automatically spot duplicate entries and resolve them into one record. NetApp is a great tool for deduplicating data.
Next, think about using data synchronization tools that can help you to match up different data sources. For example, a data synchronization tool can help you to collect customer data from many different departments and merge it into one large, aggregated database.
Data Management Summarized
Data management might not seem to be an important topic for your company—until you experience data issues. When your business loses data or can’t retrieve it, this can harm your organization. For example, let’s say you lose the data about a client order. This can have quite an impact on the way the client perceives the quality of your company.
Therefore, implement an open data culture, and support this culture on a technical level. Make sure not to over-permission your data management solution, as you’ll want to create an environment where departments can openly share their data.
Good luck with starting your data management journey!
This post was written by Michiel Mulders. Michiel is a passionate blockchain developer who loves writing technical content. Besides that, he loves learning about marketing, UX psychology, and entrepreneurship. When he’s not writing, he’s probably enjoying a Belgian beer!