Data Management Guide

Here’s everything you can learn about data management.

data management

What Is Data Management?

Whether we like it or not, giving a clear data management definition is not an easy task. There are as many ways to define it as there are to approach it (and that means “a lot”). In essence, however, data management is the umbrella term that boils down to collecting, cleansing, implementing, and managing data in a way that helps businesses draw actionable insights.

Good data management is multidisciplinary and it helps businesses keep their data in place, as clean as possible, and as usable as possible.

If you want to define data management in a more relatable way, think of it like this: if Big Data is the bread and butter of every modern business, data management is the health inspector making sure the bread and butter have been manufactured according to the latest health compliance rules and with the best of ingredients, at the right time, and according to the best recipe.

Data Management: Benefits for Businesses

The benefits of a data management system are quite clear. When all the data you have collected (through various means) is neatly organized and cleansed, you can easily access it and transform it into proper insights for your business. This stands just as true for the Business Development team as it does for Marketing, Product, Development, and Human Resources.

Why is data management important, in more detail?

Here are just a few reasons to convince you of the importance of data management:

Better data quality.

When you manage your data correctly, it will be of a higher quality — and thus, it will provide you with better insights for your business.

data collection quality
data compliance 123 form builder

Compliance

From GDPR to HIPAA and CCPA, more and more sets of regulations regarding data privacy have cropped up in the last few years. That might sound like a headache, but data privacy compliance was long coming — and yes, you do need to abide by it no matter who you are, as long as you are subjected to these regulations, of course. Good data management might not ease off all the pains that come with compliance, but it can definitely make things a lot easier!

data compliance 123 form builder

Reducing costs

When you have good insight, you can invest better. You can develop better products that fit your target market like a glove, create marketing campaigns that speak to them, and make sure you attract top talent to keep the ball rolling. Even more, good data management will help you lower the costs of all these endeavors — precisely because it will help you make better, more informed decisions from the get-go.

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eliminating human error with data

Eliminating human error

We are human, therefore, we make mistakes. With proper data collection and data management best practices in place, however, eliminating human error is a lot easier. In turn, this will help your business save money, grow at a healthier pace, and eliminate various kinds of risks.

eliminating human error with data

Of course, these are just some of the main benefits of doing proper data management for your business. Many more can branch out from what we mentioned above — but in the end, data management importance narrows down to a very simple fact: in the age of Big Data, you simply cannot afford not to manage your data correctly.

Data Management Challenges and Concerns

Data management challenges are just as important to consider as the benefits. As they say, your insights are only as good as your data management is. If you don’t have quality data, if you don’t organize it properly, and if you don’t have the skills to run proper data management and analytics, you’re only sitting on a pile of bits of information that are not very useful.

Data collection is one of the primary data management challenges, particularly because this is where it all starts. If you know how to collect data in a neat, organized, and mindful manner, you have already set yourself up for success.

Data organization is equally important as well. It’s not enough to just collect lots of data, you have to know how to categorize and organize it. More specifically, all your data organization needs to happen with your primary questions in mind. Knowing what specific insights you are looking for should be the main guideline in organizing your data and making sure it stays clean, relevant, and usable.

Aside from these two key concerns, it’s also important to consider the following:

  • Lack of insight and scaling options
  • Maintaining performance levels in data management
  • Compliance with the ever-changing data requirements
  • The ability to easily process and convert data
  • Proper data storage
  • IT agility and costs

Looking for a data collection &data management tool for your business?

Data Management: Best Practices

One could write entire libraries on data management best practices and on how to create the perfect data management process. We won’t attempt to cover everything in this section, but we’ll make sure to cover the essential data management concepts every business should master.

Data Management Principles and Methods

There are several ways to look at data management principles and various schools of thought categorize them differently.

In essence, however, there are seven main data management principles you should absolutely keep in mind:

  1. Create a clear and well-defined data management plan
  2. Make sure you implement data lifecycle control
  3. Identify data ownership
  4. Ensure the security of your data
  5. Maximize the usefulness of your data, as this will help you avoid collecting and processing the same types of information multiple times over
  6. Establish clear data quality standards (which should include clear standards for accuracy, relevancy, updated status, completeness, accessibility, consistency across different sources, and reliability).
  7. Always make sure you have proper documentation and data tracking processes in place

Additionally, you might also want to consider these data management best practices and tips:

  • Start small. Don’t allow yourself to be overwhelmed by the complexity of setting up a proper data collection and data management plan. Start small and work your way up from there.
  • Don’t measure everything, just your metrics. Otherwise, you might end up drawing in a sea of data and chaotic insights that do you no good.
  • Make sure your data management and data governance system are clear to everyone. This means you’ll need to have great communication skills to make sure everyone in the company understands what data management is for your business, why it’s important, and why they should abide by certain rules and processes.
  • Help everyone understand that data management and stewardship is not solely a matter related to the IT department, but to everyone else.
  • Perform regular data management assessments. This will help you make sure your databases don’t grow into a jungle of information that’s impossible to use.
  • Create a Master Data Management (MDM) file — a point of reference for all other pieces of data, which can be accessed by all the interested employees to help them navigate your database. This MDM should include Master, Reference, Transactional, and Analytical data. Furthermore, the MDM should be seamlessly integrated across all other platforms and sources.
  • Avoid data silos (data stored in just one department). This kind of data “blockages” can hurt the entire company and are quite easy to avoid once you have established a data integration procedure.
  • Always look for the best and easiest to integrate tools for data collection and management. They will save you time, effort, money, and help you avoid human error, regulatory risk, and more.

Each and every single one of these data management principles are essential to healthy, insightful, helpful business insights. Therefore, you should make sure you don’t overlook any of them and that you provide each of them with the same level of importance.

What to Include in a Data Management Model

A data management model is a framework that explains and describes the different processes involved in data management. Data modeling is, in itself, a subcategory of the data management discipline (which, along with others, is part of a data model).

The most important elements to include in a data management model are:

  • Data governance (the planning of data management from an availability, usability, consistency, integrity, and security perspective
  • Data architecture (the structure of the data)
  • Data modeling and design (which deals with data analytics systems, as well as their design, building, testing, and maintenance)
  • Data storage and operations (dealing with the hardware part of data management)
  • Data security (handles the means by which data is protected)
  • Data integration and interoperability (which transforms data in a structured form, an organized database, etc.)
  • Documents and content (including unstructured data and the processes needed to make it structured and incorporated into databases)
  • Reference and master data (which deals in reducing redundancy and mistakes)
  • Data warehousing and business Intelligence (which deals in managing and applying data for decision making)
  • Metadata (everything related to collecting, organizing and managing data that references other bits of data, such as headers, for example)
  • Data quality (which handles monitoring data, its sources, and the quality of the information delivered)

Data Management Life Cycle

Data life cycle is also a very important part of data management. In essence, there are five stages of data life cycle, as follows:

  1. Creation (which can be manual, captured from other external acquisition, or captured from devices)
  2. Storage (which involves Security and Backup & Recovery as well)
  3. Usage (Data viewing, processing, modification, etc.)
  4. Archival (archiving data and protecting it, but making sure it’s available for use)
  5. Destruction (which refers to purging data that isn’t up to date anymore)

The Future of Data Management

With Big Data come big responsibilities and special data management challenges. As more and more data is collected from multiple sources (including smart devices of all kinds), data management analysis, collection, and maintenance will require more computational needs and talent able to make sense of all the information flowing in.

Without a doubt, there’s no future without data management — but the exact specifics of the future of data management are still shaping. Automation and the cloud are keywords for the further development of this field, and more technology will become more accessible, allowing businesses to collect, manage, and interpret data in more efficient, smart, and insightful ways.

Data Management Frequently Asked Questions

What does data management mean?

Data management encompasses the design and management of data. The job of a Data Manager is to sort through all the different types of technology used in an organization and come up with a system that can be implemented to safely store, manage, access, and share information.

What are the data management skills?

A Data Manager must be able to separate the technical skills related to the management of data from the business skills of marketing, project planning, and development, etc. A system developed for building model airplanes is not likely to work well for storing and managing financial records.

What is an example of data management?

The simplest example is when an organization stores data on a server in a database – say, HR. The data can be stored in a relational database, a flat-file, or even a spreadsheet.

Why is data so important?

Data is important because without it, an organization will struggle to make decisions. For example, how do you know if you are getting the best ROI from your marketing budget? You need data.

Who is responsible for data management?

Data Management is a very broad field. You could find yourself doing data management and never know it. Have you ever given someone directions to your house? That’s data management.

What is the role of data governance?

Data governance is the process for managing data. This includes processes, policies and procedures that ensure that information is appropriately collected, validated, stored, and disposed of.

What is data management good for?

Data management is very useful because it helps an organization make important decisions.

What is the difference between data management and data governance?

Data management is the process and policies used to keep data safe and secure. Data governance is the process that supports that same data management. Data governance concerns itself with how the data is used, who has access to it and what should be done with it after it’s been collected.

What is data management strategy?

The data management strategy is a collection of rules, processes, and best practices that allow organizations to collect, manage, and use data properly, as well as dispense it according to specific criteria and in specific ways. 

How do you build a data management system?

A data management system has to be built taking all the stakeholders into account and making sure that every single step of the way in the data life cycle is fully covered and documented. 

What is an example of Big Data?

Machine data, streaming data, sensor data (from mobile devices, airplanes, sensors), unstructured data, and multimedia content are all examples of big data – information that is too large to be processed with traditional database management systems.

What are the data management tools?

A Data Manager can use a variety of tools, from spreadsheets and databases to enterprise resource planning (ERP) systems, form builders (for collecting data), and so on. 

Which software is best for data management?

It depends on a number of factors, but the best software is one that provides familiar and intuitive controls so that data managers can focus on their work. 

What is the best way to manage data?

There is no one best way to manage data. However, there are some best practices you can use as a framework around which to build your system. Please refer to the “Best Practices” section in the article above to learn more. 

What are the tools of data analysis?

There are a number of tools, including (but not limited to): 

  • Data mining – which uses the process of analyzing data to find patterns and trends that can be used to improve the organization.
  • Data visualization – which provides a graphical representation of data so that it can be understood easily by non-technical users.

What are master data management tools?

Master data management tools are used to manage the key business entities in an enterprise, and they include (but are not limited to) tools such as ERP systems, data warehouses, and business intelligence (BI) applications.

What are the components of data management?

Data management encompasses a wide range of processes and tools, all of which contribute in some way to the safety and security of data.

What is a data component?

A data component is any individual unit within the larger data set. For example, a restaurant’s employees, customers, suppliers, locations, and products could all be considered distinct data components.

What is framework data?

The purpose of framework data is to govern the development of non-productive data when it’s first collected. This eliminates the need for organizations to create huge databases with data that doesn’t serve any useful purpose.