What’s the most valuable resource in the world? According to The Economist, it’s data. If you haven’t noticed, data is all around you. Major corporations are investing in tools that help them capture and process data and then classify what they’ve harvested. And with the rising importance of data comes a need to understand the steps in the data management process.
For example, when you browse a typical website, the site might be capturing your behavior. Which links do you click? Where’s your mouse located on the screen? All this data is of great importance for optimizing a website and leading visitors to key pages, such as an online store.
In short, data can help your organization increase profits and reduce costs. Want to learn more about data operations? Learn the basics of DataOps here.
This post will explain why data management matters and which steps you’ll need to take to implement a successful data management process. Let’s start by exploring the importance of data management.
Why Is Data Management Important?
Data is increasingly seen as a valuable asset for a business. Organizations use data to make important decisions, improve existing services, or even find trends that lead to the creation of new products. Data is everywhere!
Because data is so widespread and so valuable, you have to take care of the data you harvest. Simply dumping all your gathered data into an Excel sheet or database is unacceptable. Instead, you have to treat your data properly. For that reason, you need a data management framework that describes how to collect, process, and classify data. A data management process helps to raise your organization’s standards and keep them consistently high.
Moreover, a data management process helps improve the quality of your data. Many organizations struggle with this data quality metric because they don’t have a data management process in place.
How can you and your organization get better at this important task? Let’s look at 10 steps you can implement in your data management process.
10 Steps to Implement a Successful Data Management Process
Data management describes how people and organizations collect, process, and store information. Also, people use the terminology of data management to describe data governance and how to format data. In short, many aspects are involved in the data management process.
Without further ado, let’s take a look at 10 data management steps.
1. Define a Data Architecture
First, it’s crucial to define a data architecture. It acts as a blueprint that defines all databases and data tools that you want to use as an organization. Without this blueprint, you’re creating a process without knowing where to store your data and without knowing which data is related.
Creating a data architecture is especially important in larger organizations that handle huge amounts of data.
2. Assign Responsibilities
When you’re implementing a data management process, be clear about who’s responsible for each step. A lack of clarity in a group’s roles and responsibilities often leads to poor data quality or uncertainty about the data. To avoid this, decide which people in your organization will be in charge of capturing specific data. For example, someone might be in charge of capturing all customer-related data, and someone else might be in charge of collecting data related to finances.
3. Define How You’ll Name Things
The definition of nomenclature is part of setting standards.
Define standards for naming files, and decide how changes will affect a file’s name. If you do this clearly and consistently, then a user should be able to figure out what the file is for and what data it holds just by looking at the name. This saves time and effort.
4. Collect Data
Of course you’re going to gather the data you need, but you must also define what data you need and what output you expect. Since you may make important decisions based on this data, this could be your most important step in this process.
Data collection happens through:
- Surveys or questionnaires to gather insights about product trends.
- Customer or employee interviews so you can better understand their needs.
- Tracking user behavior when using an application to improve the layout or flow of your site.
- User testing or A/B testing to help you figure out what’s most effective.
Data collection methods are almost endless and can even happen through the day-to-day operations of a business.
5. Prepare Data
Once you’ve gathered all the data, you can start preparing it. Data preparation is the first part of data manipulation. It’s impossible to process raw data, so you must validate the data and check its accuracy. You can do this by checking the data against another source. If this isn’t possible, you can do an exploratory data analysis to check the accuracy.
The outcome of the data preparation step should be a dataset that you can use for further processing. A dataset often includes data from multiple sources.
6. Process Data
Next in your data strategy comes the data processing step. Here, you’ll want to convert the constructed dataset into data that a specific piece of software can understand.
The data processing step usually includes tasks such as formatting data. For example, you’ll want to make sure that all timestamps are in the same format. Or you have may have different field names for customer data. Let’s say the field that holds the name of a customer is represented by customer_name, fullname, or name. By using data processing, you can merge all these field names into one field, fullname.
7. Analyze Data
The magic happens during the data analysis step. At this point, you’re trying to examine the data and collect meaningful results. Often, a specific piece of software will analyze the dataset. A computer program has better capabilities for processing data and finding patterns than a human brain does. For example, a computer might find patterns in customer behavior that you didn’t see before. Using only human processing is a slow and error-prone process.
When you’ve analyzed the data, those results can help you improve existing services or processes. For example, you might find out that customers actually want a functionality that you haven’t previously offered.
8. Interpret Data
The interpretation step helps your organization document all the previous steps and their results. You can summarize the gathered results in a report, video, or presentation. Make sure to store those artifacts using the nomenclature you worked out in step 3.
9. Share Your Documentation
Data collection is a continuous process. Share documentation about your data management process with team members. A data management process can succeed only when all employees understand the standards and associated responsibilities.
Education is also part of this step. Whenever your employees need to learn a new tool that’s part of your data management process, make sure they understand it fully.
The final step is the key to the success of your data management process.
For larger organizations, cross-department collaboration and communication are especially important. For example, different departments might have different data management processes. Therefore, make sure to communicate your processes with other departments that handle the same type of data.
Creating a cross-department data management process can further optimize efficiency. This will avoid confusion and uncertainty about data management processes between departments. A cross-department data management process will also improve your data quality.
Besides implementing a data management process, it’s also worth understanding data security management.
Let’s wrap up this topic with a few final thoughts.
What About Your Data Management Process?
The above tips should help you implement a data management process. I recommend starting with one small data management process before using multiple ones. It’s important to learn how a data management process works. Starting small means you’ll be able to optimize those processes.
Also, don’t forget to educate employees and share documentation about the new processes. Consider having an informal meeting where you explain new processes and employees ask questions. In the end, the success of your data management process depends on your employees’ willingness and ability to collaborate with one another.
Good luck with implementing your first data management process!
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!