Data management specialist: Role and skill set for success

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Table of contents

  • Role of data management specialist
    • Data analysis
    • Data storage
    • Infrastructure maintenance
  • 12 key skill sets for success in 2022
  • The world is overflowing with information and with that comes the rise of a job with the responsibility for keeping it straight: the data management specialist. Someone must organize the files, curate the databases, synchronize the feeds and handle all of the tasks essential to building trust in this data. 

    The job itself is new and it shares the role with a number of other titles that sound similar, such as data scientist, customer data analyst or business intelligence specialist. There are often subtle differences and the roles are evolving, but all bear responsibility for making sure that their enterprise is able to make sound decisions from accurate information. 

    Many surveys show that jobs with titles like “data analyst” or “data scientist” are some of the hardest for organizations to fill, making data management specialists an in-demand skill. 

    Role of data management specialist

    The need for data management specialists arose when businesses realized that they needed more people to take responsibility for the quality and permanence of the data. Their relationships with their customers and suppliers are stored in the data files and preserving these details is essential for maintaining the enterprise. 


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    Data analysis

    At the same time, many new ventures for companies begin with finding better ways to analyze the data. The marketing team wants to understand how customers are making decisions by looking at all of the digital clues available. The websites, advertising companies and storefront systems generate many digital details that marketing teams want to use to find the best way to inform end users and convert them into customers. A good data management specialist often sits in the nexus that supports these efforts. 

    Data storage

    New products and services often have a substantial data storage role that’s part of the product. Many of the devices that are part of the internet of things (IoT) report back to their home company with telemetry, and the data management specialist must find efficient ways to store and analyze the information. Often, a substantial part of the value of the product depends upon the extra insight that comes from the data storage. 

    Infrastructure maintenance

    Another important function of the data management specialist is maintaining the infrastructure of the enterprise. Warehouses and assembly lines depend upon good data management specialists to track all of the company’s assets. Supply chain management and manufacturing support are essential functions because these jobs can’t be done without software that organizes and guides the workflow. 

    In many companies, there are often different types of data management specialists that are taking on the responsibilities. Some are more technical than others. Some have a long career working with marketing teams. All bring something to the table. 

    There are also often people who play a key role, but work in other parts of the organization chart. Data management specialists must often work closely with the other parts of the IT structure, including programmers and devops teams. 

    “I see the development community as being an absolutely essential stakeholder to all of this,”  explains Ryan Fleisch, director of product marketing for profile and activation at Adobe. “It’s not like marketers are doing these things, start to finish.” 

    Also read: Don’t take data for granted

    12 key skill sets for success in 2022

    As the role of data management specialists evolve, the required skill set is also changing. Many need to grow into the role by acquiring these skills on the job. Here are 12 desirable skills for the role: 

    • Detail focused: The databases are filled with millions or billions of records and the job demands that they be as accurate as possible. The role requires a dogged determination to gather the data quickly, efficiently and correctly. 
    • Database administration: Some of the data is stored in traditional databases and the job of managing the databases is one that database administrators (DBAs) know well. 
    • Data warehousing: Much of the newest data feeds aren’t stored in traditional databases. Understanding newer data storage architectures such as data warehouses and data lakes is important for managing data. 
    • Traditional programming: The records are generally kept digitally, so being adept at writing instructions for computers is ideal. Still, many data managers are not expert programmers. They often work with programmers for specific jobs, but they are more focused on ensuring that the data is well curated. If they’re able to help with some programming tasks, that’s a bonus. 
    • Scripting languages: Much of the work for storing and backing up data is done with scripting languages like the BASH shell script, Python or Perl. A working knowledge of these languages is helpful. 
    • Statistics: Much of the analysis is done with statistical algorithms, so understanding this branch of mathematics is often helpful. But as with programming, data managers can often rely upon professional mathematicians when the analysis is complex. 
    • Data science languages: Much of the analysis today is done with languages like R or Python. Having a working knowledge of them and the systems that support them like PyCharm or R Studio is a good foundation for some of the analysis. 
    • Business reporting: Some of the best analysis is done with business intelligence and business reporting tools. Understanding how these systems work and how they can be customized or configured is essential for ensuring that the right reports summarize the right data for the right stakeholders. 
    • Customer-tracking software: Many businesses are turning to products that are sometimes categorized as customer management software or customer data platforms. These tools gather data to follow how customers are reacting to marketing with purchases. Understanding how this software works is essential and it often helps to have an agile familiarity with the platform chosen by your business. 
    • Privacy rules: Customers are often increasingly skittish about trusting their personal information to companies. Managing the data requires an understanding of and a sensitivity to these feelings. Also, many industries are drafting and adopting privacy rules that codify the obligations. Data management specialists should be aware of them. 
    • Legal regulations: Some governments are making strong laws that control how and when data is collected, analyzed and stored. Data management specialists must be aware of them and when they apply to the data at hand. 
    • Encryption and data security: Protecting the information from unauthorized intrusion is an essential part of maintaining data warehouses and data lakes. This often requires understanding what encryption algorithms can and can’t do to protect unauthorized disclosure. A good understanding of computer security is also important. 

    This is a long wishlist and no one person can deliver all of these skills. Managers would want to assemble a team that complements each other so they can work together to get the best data to help make better data-driven business decisions.  

    Enterprises are also encouraged to work with outside vendors to pair their data management specialists with the best tools available. This allows the in-house team to focus on better using and applying their data. 

    “I think the ability for a [marketing team] to actually do these sort of more advanced things with the data, though, is often very limited by the resources that they have available,” noted Kevin Yang, co-CEO at Idiomatic, a company that specializes in using AI (artificial intelligence) to understand customer data. “To do what we do in-house would require a team of machine learning people and other engineers. Some people have built classifiers in house to do what we do, but that’s only at the very largest companies.”

    Read next: How AI could help enterprises to reduce data storage costs

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