Data management helps you and your organization capture data in a structured and organized way. Also, data management helps improve data quality and makes the data easier to discover. Correct data management implementation brings many advantages to your organization, allowing you and your team to make more informed decisions and improve inefficient processes. But what does data management include?
Data management tackles topics such as data collection and data processing. Let’s take a deeper look at data management. In this article, you’ll find out some of the most important data management best practices and pitfalls.
What Does Data Management Include?
Data management is concerned with the following data domains:
- Data gathering
- Processing of data
- Data protection
Although these are the main focus points, data management also touches on data preparation, data profiling, and many other data-related processes. Let’s explore the above four domains.
Data gathering focuses on collecting data for further processing. In theory, anything can be valuable data for your business. Let’s say you want to collect data about the number of issues that arise with a certain service. Your organization can use this data in many ways:
- Calculate on a quarterly basis whether the number of issues decreases.
- Find major problems for a particular service that need to be resolved.
- Calculate the efficiency of customer support.
Often, the data collected during the data gathering step is raw and needs to be processed for you to use it. Therefore, let’s tackle data processing next.
Higher Quality Data Through Data Processing
It’s not easy to draw conclusions from raw data. Data processing helps with formatting and standardizing data. The goal of data processing is not only to compile the data but also to improve its quality. By quality, I mean its accuracy and accessibility. For example, data that uses different formats and standards isn’t very accessible for analysis. Therefore, it makes sense to use data processing techniques to improve data quality.
Data processing is especially useful when you want to merge data from different sources into one dataset. For example, if you work for an online retailer, you may want to merge retail data for different years into one dataset to help you forecast future buying behavior or revenue. Another example includes merging customer details with information gathered through customer support to build customer profiles that are richer. Doing this allows the marketing team to target customers more personally.
When you’ve processed the data, you’ll want to think about storing it.
The Art of Storing Data
Anyone can dump data in a database without thinking about the bigger picture. However, storing data in an organized way is a true art. It’s vital to store your data in a meaningful way. For example, you may want to create a data architecture that defines data domains. A data domain groups data into logical groups, such as customer-related data. Next, the data architect defines the links between the different data domains.
In short, carefully structured data storage helps make your data more discoverable.
Securing Your Data
Data protection or data security is an important element of data management. When you’re handling personally identifying information (PII), you must secure this data. Many organizations use data masking to protect PII data.
An important part of data protection is encryption, which scrambles data. If a data breach should happen and your data is encrypted, then that data is useless for the attacker. The attacker needs the encryption key to decrypt the data to a readable format. Therefore, data encryption is often the last line of defense in case of a data breach.
Next, let’s learn about obstacles you may face when trying to manage all this data.
Data Management Challenges
Your organization may excel at collecting data, but make sure you collect only the data you need. Always justify why you’re collecting a specific type of data. Data storage is expensive. Therefore, collecting large amounts of data increases costs for your organization. Collecting data without justification might contradict your goal of improving processes to reduce costs.
In short, data must be relevant. Data can help you validate a business case or business requirements. You can also use data to improve inefficient processes or debug problems with current processes.
Data management can be successful only when you first think about how you want to use the data you capture. Without clear plans or goals, there’s no need to capture data. Doing so adds overhead for employees because they have to spend valuable time on capturing this data.
Now that you’ve learned about the challenges of data management, let’s explore some problems you may encounter.
Data Management Pitfalls
Even if you follow the steps in the data management process scrupulously, you may encounter some common pitfalls.
The advice to capture only data that’s relevant to your organization also applies here. Many organizations start out planning to capture as much data as possible. Only after capturing data for a couple of months do they begin to assess the data and try to find opportunities to use it. This approach is misguided and wasteful. Instead, have a clear plan or goal up front.
Second, don’t capture data without a deep understanding of the data your organization already holds. Many organizations start to collect data like headless chickens, without any sense of what they really need and why. To avoid this situation, always start with data profiling.
When you understand the basics of data profiling, you can communicate with employees, leads, or managers to understand all their processes and the data they handle. From this understanding, you can start to define important types of data that are worth capturing.
We’ve discussed why certain techniques are shortsighted or even dangerous. But what positives can come out of a dedication to data management?
Benefits of Data Management
Data management can bring substantial benefits for your organization. Let’s go over a few:
- First, data management helps you create higher-quality data.
- Also, data becomes easier to explore and more available.
- Data management helps you improve data preparation. Moreover, data preparation helps you combine data from multiple sources, letting you create a high-quality dataset to verify a business case.
- It allows you to create a data-driven culture in which departments openly share data with one another. Let’s say multiple departments interact with customers, which allows them to capture information including email addresses, personal details, or problems customers experienced. Your marketing department can use this information to target clients more personally. In many organizations today, departments fail to share this valuable information that should be available.
- Data management helps you avoid data silos. For example, if each department in your organization has its own way of tracking customer info (such as in an Excel sheet), but they don’t share it with other departments, then your organization has at least one data silo—and there may be many more.
- Last, data management helps you align data tools across departments. For example, one department might use Excel for capturing data, while another department uses a specific customer tool. Data management helps you align all processes and define a single tool or approach for collecting data.
Finally, let’s learn about the optimal techniques for managing your data.
Data Management Best Practices
Keep these approaches in mind as you strive to improve data management.
Document! When you introduce data management to your organization, you’ll likely introduce new processes. It’s a best practice to keep track of all these processes by documenting them. In case an employee decides to leave the company, you can always fall back on the documentation that describes this process. The documentation should also describe how to capture data.
Besides documentation, spend time educating your employees. Education is a logical step after documentation. In fact, you can use the created documentation as the basis of your education. It’s important that everyone in a department has the same understanding of processes.
Next, define clear roles and responsibilities. Every process needs a clear owner. The process owner is responsible not only for collecting data but also for verifying whether that data is correct. Each process owner acts as the gatekeeper for their specific data domain.
And finally, if you have a choice between using tools and aggregating data manually, then choose tools. Humans tend to make mistakes, especially with tedious or repetitive processes such as data aggregation. Therefore, try to use tools that allow automation. This will reduce the stress on your employees.
Conclusion: Implementing Data Management in Your Organization
Data quality is more important than data quantity. Start with defining why you need certain types of data. Then only collect the data you need. When you do data management right, you’ll end up with high-quality data that’s easy to explore. This allows you and your employees to improve processes, measure key metrics, and make more informed business decisions.
When you’re implementing data management, consider how you can make sure your organization is ready for this change. Most importantly, your organization needs to prepare itself on a technical level, installing new tools and educating employees to start using them. Therefore, you or your colleagues should make sure the IT department assesses the feasibility of this. Introducing data management brings major changes, and you need to be certain your organization can handle them.
Remember to stick to the best practices defined above. Documentation and education are key for the success of your data management implementation.
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!