Every business in the world has to contend with data. From a single-person LLC to multinational enterprises, data is everywhere, and it needs to be properly managed to be an effective business tool.

Data isn’t just customer data and other externally sourced information, though — employee records, reference data, network maps, research data and results, payroll data and other forms of external and internal information all fall under category of a data asset that has to be managed.

It takes a lot of work to turn data into something usable. Without proper management, you can end up with duplicate records, incorrect information, wasted time and storage space, and a host of other problems that come with poor organization. Digital data is a lot more complicated than paper, so it requires specialized skills to organize it.

Enter the world of data management. Here are the essentials about data management, including models, software, implementation, data sharing and more. This article is also available as a download, Cheat sheet: Data management (free PDF).

 

What is data management?

Data management is “the development and execution of architectures, policies, practices and procedures in order to manage the information lifecycle needs of an enterprise in an effective manner,” according to DAMA International, a consortium of master data management professionals. In other words, data management is multidisciplinary and keeps data organized in a practical, usable manner. At its most fundamental level, data management works to ensure that an organization’s entire body of data is accurate and consistent, readily accessible and properly secured.

Along with being a way to eliminate duplicates and standardize formats, data management also lays the groundwork for data analytics. Without a good master data management plan, analysis is practically impossible at worst and unreliable at best.

 

 

What is involved in a complete data management model?

If the definitions and descriptions of data management make your head spin a bit, it’s understandable–there is a lot within the data management body of knowledge.

DAMA International breaks data management down into 11 knowledge areas:

1. Data governance, which is the planning of all aspects of the data management. This commonly includes ensuring availability, usability, consistency, integrity and security of data managed by an organization.
2. Data architecture, or the overall structure of an organization’s data and how it fits into a broader enterprise architecture.
3. Data modeling and design, which covers data analytics and the design, building, testing and maintenance of analytics systems.
4. Data storage and operations, which is concerned with the physical hardware used to store and manage data.
5. Data security, which encompasses all elements of data protection and ensuring only authorized users have public access.
6. Data integration and interoperability, which includes everything to do with the transformation of data into a structured form (i.e., in an organized database) and the work necessary to maintain it.
7. Documents and content, which includes all forms of unstructured data and the work necessary to make it accessible to, and integrated with, structured databases.
8. Reference and master data, or the process of managing data in such a way that redundancy and other mistakes are reduced by standardizing data values.
9. Data warehousing and business intelligence, which involves the management and application of data for analytics and business decision making.
10. Metadata, which involves all elements of creating, collecting, organizing and managing metadata (data that references other data, like headers, etc.).
11. Data quality, which involves the practices of monitoring data and its data source to ensure quality information is being delivered and integrity is being maintained. Data quality is all about ensuring poor quality data is being filtered out.

All of these elements have to be included in a total data management model; if even one element is missing, some aspect of managing data is complicated, if not damaged entirely. For instance, if you get rid of metadata management, you lose the ability to easily categorize data. Without data quality being ensured, the entire structured data becomes suspect, and analytics become useless. Eliminating data integration and interoperability would make it nearly impossible to combine disparate forms of data into a usable whole.

 

How does data management fit into a larger big data model?

If an analytics model is the product made from a business’s data, then data management is the factory, the materials, the supply chain–everything that goes into making the product.

You can’t have a big data model without a data management strategy — trying to do so would be like saying your messy desk is perfectly organized chaos in which you can find anything; in time, you’re bound to lose something important. You must make an informed decision.

Data management is a total lifecycle information system that follows data from the moment it’s created until it ceases to be useful. Data management tracks the data from place to place, monitors the transition of data from one form to another, and ensures that nothing important is left out of a business analytics model.

In short, data management doesn’t just fit into a big data model –it’s the umbrella under which all big data falls.

 

What skills do data management professionals need?

There’s no mistaking the essential role that data plays in the modern business world. Big data professionals need to have particular sets of skills that make good data management possible.

A data management team needs several people who are adept at certain elements of the entire end-to-end management chain. The skills a data management professional should be trained in include:

• General computer science: A qualified data management professional should be trained in the basics of computer science–they’re going to be spending a lot of time using basic skills to organize data.
• Database programming: Some of the most important database languages in the data management world include SQL, Python, R, Hadoop, XML and PERL. Be sure to learn at least one of these languages and get familiar with its corresponding database platforms.
• BI/BA: Business intelligence (BI) and business analytics (BA) are at the core of why companies do data collection and organize data. Data management pros should be able to understand the hows and whys of analytics.
• Cloud computing: Data hosting can take up a lot of storage space, which is why many businesses turn to the cloud to host, manage and analyze their data. Skilled data management professionals should be familiar with AWS, Microsoft Azure, Google Cloud, IBM Cloud and other major platforms.
• Machine learning: Data analysis, in particular its later stages like predictive analytics and prescriptive analytics, make extensive use of machine learning technology to decrease the computing time needed to deliver results.
• Data management certifications: Data management is a science in and of itself, and there are several certificates that data management professionals can pursue. DAMA International offers the Certified Data Management Professional (CDMP) certification. Oracle, IBM and others also offer certifications.
• Soft skills: Making use of data requires a lot of collaboration with non-IT departments to plan and execute a data strategy. Good writing, speaking and innovative thinking are an essential set of skills for successful data management professionals.

 

 

 

What data management software is available?

Data management can’t be done in a haphazard way — organizations need to invest in data management solutions that can deliver all the results they need to be successful in managing and using data.

There are a number of data management tools, each with its own unique features and industries in which it fits. Some of the top platforms include:

• SAS Data Management
• Adobe Data Management Platform
• Salesforce Audience Studio
• IBM Data Management
• Oracle BlueKai

Some platforms, like Google Cloud’s big data analytics software, aren’t specifically built to do data management, but that doesn’t mean they can’t do it. In the case of Google Cloud, all the necessary software is present, but it needs to be configured to function as a data management platform.

As with any major software platform, choosing the right one from the onset can make a huge difference in an organization’s success. Make sure that when deciding on a platform, your data management team has a good understanding of the kind of data you have, how you want to host it, and what your end goals for data management are. Armed with that information, a data management team can make the best choice possible for the needs of their organization.

 

How can organizations get started with data management?

There may seem like a million and one pieces to planning a data management initiative, but don’t get bogged down in the weeds: Planning to integrate data management into your organization is just like any other business transformation project.

First, make sure your data management initiative has a clear goal: To what end are you trying to organize your data? A business that wants to use data to make internal changes, for example, will have different data management needs than a company that wants to use its data to increase sales.

Once you have a stated goal, it’s time to think about what will be needed to make it happen. If your data exists entirely as unstructured files and documents, you’re going to have a different starting point than an organization with large Hadoop databases filled with well-organized records.

Consider all the possible needs: Reassignment of employees, new hires, training, software platforms, budget, timeframe, the types of data already on hand, the kinds of data that are needed, and more. Having all these elements in mind will help you when you actually start planning in earnest.

Next, it’s time to put your talent in place. Hire new employees, reassign those who are going to start working on your data management project, and get the team acquainted with your data management goals.

Once your data management team is in place, it’s time to start the planning phase. Outside of how the team is going to accomplish its goals, this is when a data management platform is chosen, training can be undertaken, and the whole model starts to come together.

After this, your data management team should be well on their way to building, testing and implementing a full data management model. When all of those prerequisites are in place and data management is an integrated part of your business, it’s time to start thinking about what comes next: How all of that well-organized data can help transform your organization, internally and externally.

The entire process of building a data management system can take a long time, and even then, data management is just the groundwork for further use of big data.

Data management isn’t an end in and of itself: It’s the house in which an organization’s data lives. It’s up to that organization to make use of the house it built by putting that data to work.

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