What Actually Is Data Management?
Simply put, data management is just what it is managing data.
However, to be more technical, data management is the IT administrative process and set of technologies to more efficiently, securely and cost-effectively gather (input), store, and use (output) data.
In today’s digital environment, data is a very important asset for any businesses, and the goal of data management is so that anyone that is eligible (according to policy) can use the data easily and securely, and on the other hand can prevent anyone outside the regulation on accessing the data.
Why Data Management Is Important?
It the past half decade or so, there has been a massive surge of data volume far surpassing what we had in, say, the early 2000s. Today, there are more than 2.5 quintillion bytes of data generated every single day.
On the other hand, data has become really important for any business, often dictating growth, productivity, and innovation, as well as being a key factor to competitive advantage these days. Companies like Amazon or Google, among others, now have tremendous power and influence thanks to their data and their data strategy.
Data can provide useful insights both about our customers and clients, and about our competition. Knowledge is power, and more knowledge is more power.
Data is growing not only in volume, but in at least three major areas we know as the 3Vs: volume, velocity, and variety. Not only more data is generated every single second, information comes in a very diverse variety (images, videos, sound files, texts, and so on) and from many different sources at once.
In turn, collecting, storing, and extracting the data are also far more challenging than ever, and this is where Data Management comes in.
Data Management especially focuses on providing the definition of regulations, roles, responsibilities, and processes throughout the organization regarding data governance. In short, providing a working structure on how to manage the data.
Data Management VS Data Governance
The terms data management and data governance are often used interchangeably, and might create a confusion especially for beginners.
First, Data Management is better seen as an IT practice, with the goal to control the accessibility,timeliness and reliability of data collection, storing, and extraction.
So, Data Management is mostly about logistics, encompassing the entire lifecycle of the said data: from the initial generation of the data to the final retirement.
On the other hand, if Data Management is about logistics, then Data Governance is a more holistic, strategy encompassing the data management process.
The main purpose of Data Governance is for financial and economic reasons: how an organization can determine the real financial value of data, minimize the financial risks of non-performing data and prioritize valuable data assets.
Data governance does not only involve IT personnels, but must also include stakeholders from all business aspects throughout the organization.
As a holistic practice, Data Governance might overlap with other processes including Data Management, and will be involved in various data areas from compliance, security-privacy, data integration, and usability among others.
The end goal of Data Governance is to determine an all-encompassing, holistic method to control data so the organization can extract the best possible value from the data functional-wise, but especially financial-wise.
Another key difference here is that Data Management is solely about, and defined by technology. How well we can store the data will depend on, for example, the technology behind the server or data warehouse.
In Data Governance, on the other hand, technology is just an aspect to support it, together with the people (stakeholders) and business process.
So, to summarize, Data Management and Data Governance are not interchangeable. They are intricately tied to each other, and both are equally important. However, they are very different in principles and functions.
Why Data Governance Is Important?
There are several key trends causing a greater need for data governance, such as:
- Similar to Data Management above, the increase of 3Vs: Volume, Velocity, and Variety, causing numerous data inconsistencies. Proper investigation of these inconsistencies is necessary, as well as a proper (correctly informed) decision making process.
- The introduction of new regulatory requirements, especially GDPR in recent years. This, increase the importance of knowing where a specific data is stored, and how it’s actually used to avoid breaking the rules.
- It’s easier for everyone to get analytics reports for any data—data democratization—, so it’s necessary to maintain a common standard of data (common understanding, common values, etc.) throughout the organization.
- As data becomes more diverse, the necessity of an agreed protocol and common business language is more important to allow cross-departemental discussions, analysis, and joint decision making process.
Data governance is especially important in three main factors:
- Accessibility: the ability to get data when it’s required, and also the ability to protect the data from breach.
- Authority: how we can maintain the quality, accuracy, and security of the data.
- Activation: how the organization can act on the stored data, and extract the best possible value from it
The Benefits of Data Governance
Once your Data Management process has been properly established, technology-wise you wouldn’t have any issue with data generation, storing, and extraction.
Implementing Data Governance after this, can add various benefits to the organization, and here are some of the important ones:
- Ensuring your organization’s compliance to data regulations and policies (both internal and external), such as GDPR.
- Decreasing costs of the data management process, as well as other related data processing aspects.
- Increasing the overall value of your organization’s data, and can increase revenue while maximizing cost-efficiency
- Standardization of your company’s data policies, procedures, and systems throughout the whole organization
- Improving the overall quality of the data, while filtering out irrelevant data
- Increased transparency of any activities related to the data
With data continue to become an integral asset for many businesses, and will continue to grow in importance, data governance will be more and more relevant.
Data Governance Processes
Due to the holistic nature of the Data Governance field, it can involve various different processes and thus, roles.
Some of the key processes that are essential in a Data Governance strategy are:
- Planning the overall strategy, and defining the scope of the program
- Finding and/or develop effective methods to improve data security, accessibility, and quality
- Research and evaluate the validity of new data sources, and integrate valid data sources
- Develop and manage metadata
- Monitor your organization’s compliance to relevant data regulations and policies
- Conduct training and aim to improve the data literacy across the organization
- Finding out key data-driven opportunities
There can be other tasks that might be required according to your organizational needs and its regulatory environment. Also, some tasks might be more important than the other depending on your organization’s specific data-driven goals.
Best Practices for Data Governance
Here are some best practices to follow when implementing data governance in your organization:
1.Gradual, not instant
Implementing effective data governance can be a long-term process, and will require the commitment of a number of different stakeholders, employees, business processes, and technology. Thus, it’s better to start small and approach the implementation gradually.
Start by conducting training to improve the data literacy of the human factor. Then, develop the right process to suit them, and find the right technologies to help with the process.
2.Define measurable goals
What are the goals of the Data Governance process? Define specific and realistic goals so you can prioritize your resources.
Also, make sure to assign the right KPIs and metrics to these goals to evaluate the progress of the Data Governance, and find ways to improve it continually.
Considering you might involve executives and higher ups in the Data Governance process, having a clear way to demonstrate results is very important.
3. Maintain communication
One of the key aspects of Data governance is constant communication, input, and approval of various employees and executives throughout the organization.
Make sure everyone is on board, as they understood the possible benefits of the Data Governance program, and also to commit in overcoming all the challenges.
Stay in touch, gather valuable inputs from the stakeholders, and improve the program in the process.
4. Start With a Specific Problem
It’s easier to implement data governance to address a specific problem occurring in the organization, because you can have a more focused implementation before expanding Data Governance gradually (as discussed above).
This will also help in convincing executives and stakeholders, as you can have an easier time to demonstrate the value of Data Governance when it’s able to address a specific case.
The importance of both Data Management and Data Governance cannot be understated in this age when we are so reliant on data.
In the near future, we can certainly expect data to become even more complex, and on the other hand more important for any businesses.
Data Governance is an integral part of data strategy, to ensure that data is used effectively and properly within an organization to maximize the potential value of data.