Data Management Best Practices and Strategies

Today, small businesses and large enterprises worldwide recognize data as their most valuable resource.

Some data management best practices, strategies, and guidelines can guarantee your data is accurate and aligned with your business objectives.




The true value of data hides on what you do with it. Data has an enormous potential. If you want to unlock its potential, you must manage it properly.

On this page:

  • What is data management?
  • Best practices for data management including data governance, data stewardship, data integration, data quality, and enterprise master data management best practices and strategies.
  • Infographic in PDF

A variety of companies struggle with handling their data strategically and converting the data into actionable information.

What is data management?

data management categories and topics - infographic

Data management includes all of the activities relating to the planning, implementation, development, and control of the information generated by an organization.

In other words, data management is the development and realization of all the processes for the collection, storage, security, quality, dissemination, and changes of data.

From data creation to their retirement, data management is responsible for the end-to-end lifecycle of data.

Typically, data management includes multiple topics and categories such as:

  • data security
  • data sharing
  • data integration
  • data governance
  • data architecture
  • database management
  • master data management
  • data reference
  • business intelligence
  • document and record storage
  • data quality management
  • data warehousing

Data should be treated as a strategic resource. Well-managed data is data in the right place, at the right time, in the right format.

There are fundamental strategies and best practices for improving the way companies manage their data. We grouped the best practices, according to the key categories of data management.

Data Management Best Practices

Data Governance Best Practices

In short, data governance (DG) includes the management of the usability, availability, consistency, integrity, and security of data in a company.

DG ensures data meet the business rules and standards and thus enable companies to control the management of data resources.

There are many challenges that affect directly the success of your data strategy. From cultural challenges within your organization to political and organizational matters, there are many obstacles you need to face while moving forward with the governance initiatives.

Here are some best practices to help you address and overcome the above-mentioned issues:

1. Define your data strategy and goals

It is not about a data strategy. It is about a clear and achievable data strategy for your business. A good data strategy requires a deep understanding of your data needs.

There are some key questions you need to know the answer when you are building the strategy:

  • What exactly are you trying to achieve? What problem does it solve?
  • What are your data needs – where and when?
  • When do you need real-time data?
  • What data skills and knowledge has your company?
  • What is the value of your data? For example, how much one email address worth?
  • How do you integrate and transfer data silos?

A well-developed data strategy has a strong data management vision, clear goals, well-defined metrics to measure the success, and a strong business reason.

2. Start small and think of the big picture

DG involves people, processes, and technology solutions.

It is a best practice to start with the people and culture, and then gradually move on to the data governance, stewardship processes, and technology.

You need to suitably design roles and responsibilities and build a team of data managers, including experts from all business areas.

Without the right people, the process will be ineffective.

So, begin with the right people, then define the process, and end by defining and providing the technology.

Never forget the big picture and the big idea. Remember, start with an end in mind.

3. Set the right metrics. Do not measure everything.

Setting the right metrics and focusing on those of them that matter is a whole other science!

Metrics are the core of measuring any progress.

We work in a world where data-driven decision making is what makes your business more effective. Today, we cannot successfully run data management without first identifying the metrics that matter most.

The truth is that you can set metrics to everything related to your data and track different tools available in your company. But, does it worth the efforts?

The best practice here is to identify what the most important metrics are. This way, you would be more focused on optimizing data performance.

Enterprises and businesses that are implementing data management best practices often struggle while developing performance metrics.

Setting the right metrics starts with understanding what your data organizational goals are, and what’s the plan for achieving them.

Choosing the vital KPIs is an interactive process that involves feedback from different departments and specialists – e.g. analysts, managers, you!

Thus you will get a better understanding of which data management processes need to be measured and with whom they should be shared.

4. Build effective communication. It is a must.

Do not make data governance full of bureaucracy and never look at communication as something that’s not a real work. To handle DG effectively, communication is just a must.

Managing data governance involves strong communication skills and dealing with different people issues. That’s why effective communication is one of the top gates to success for data governance managers.

How to build an effective communication for DG?

Here are some key points and best practices:

  • Introduce your DG program early (even before its launch, if possible) via your most-effective communication channels such as employee emails, meetings, town halls, social media and etc.
  • Describe clearly the expected benefits to all involved parties. When all parties see the benefits of a DG, it breaks down barriers to understanding and participation.
  • Set clear roles and expectation to the DG team.
  • Use jargon-free messages to keep the employees’ attention and understanding.
  • Use a wide variety of communication channels such as intranet, emails, any internal social platform, chats and etc.
  • Communicate on a regular basis. Develop a timeline and a schedule for your communication activities and channels.

The more you understand and implement communication in DG, the more successful data management efforts will be.

Data Stewardship Best Practices

Data is one of the most valuable enterprise assets and must be actively managed.

Data stewardship is the management, collection, use, and storage of data. The goal is to provide businesses with high-quality data that is easily accessible.

Data stewardship involves activities such as creating and managing core metadata (DMBOK); managing data quality issues; documenting standards, guidelines, and rules around data; performing operational data governance activities.

Data stewardship focuses on ensuring that data is treated as an organizational resource.

Here are some best practices to help you handle your data stewardship and to maximize the benefits of data stewards work:

5. Data Steward should be a part of the business, not only of the IT

You need to clearly realize that data steward is a business role. Many organizations view data stewardship as an information technology (IT) field. However, it is not only limited to IT.

Most of the data stewardship issues and solutions are linked to business processes or to the business rules. To be truly effective, stewards should be familiarly knowledgeable of the data and its utilization in the business.

The tech interface and background of a data steward is always a preferable advantage, but it is the business knowledge, which is the core. Ultimately, the majority of the root cause analysis tools of data quality are linked to the business.

6. Stewards must have clear and specific goals for data quality progress

Not having well-defined and SMART goals will lead to lack of understanding and less focus.

You, as enterprise or data manager, must define clear goals for data quality improvement based on some relevant and measurable metrics.

When data managers define metrics and targets, make sure the stewards are an integral part of this process.

They must also participate in monitoring the progress and the success of the data quality management and improvements.

7. Stewards should be visible, influential, and accountable

It is not only important for data stewards responsibilities to be well-defined before they start doing their job.

The key moment here is that the stewards should have a fairly high level in the enterprise organization. They need a big vision to understand the impact of data quality on the overall business objectives and success.




Stewards must be empowered to make specific business process shifts and assign assets to address quality problems and situations.

Also, they need the power to influence how their colleagues perform business processes to achieve improvements. Moreover, stewards should be made accountable for data quality improvement.

8. Provide the right culture of stewardship

Even if you have the best stewards with clear goals and well-defined responsibilities, your stewardship can be an absolute fail. That’s why data managers need to create a culture that views data as a key competitive resource rather than a necessary everyday job and tasks.

At all levels, the people must realize the impact of data quality and to act on it. Without this type of culture, stewardship will not achieve the goals and the desired success.

Data management best practices related to data stewards can ensure a data quality improvement effort and maximize the results.

Data Quality Best Practices

To effectively meet the growing challenges of our data-driven age, businesses should strive for the highest data quality.

In our modern ages, data quality is a critical science – it’s a discipline all its own.

Data quality is a kind of measurement of the adequacy and usefulness of given data sets from different perspectives. In the business world, data need to be high quality in order to be used as a basis for business intelligence and for making business decisions.

The key data quality metrics that companies can utilize to find out if their data satisfies its purposes include:

  • Accuracy
  • Relevancy
  • Completeness
  • Update status
  • Reliability
  • Accessibility
  • Consistency across data sources

In fact, data quality is the main reason why companies perform data management programs.

So what are data quality management best practices and guidelines?

9. Regular data assessment and constant monitoring and reporting

In order to determine a problem from its beginning (before turning into a big issue), companies need to lead regular assessments of their information and periodically checking up on it to ensure data accuracy.

Do not forget that data is very prone to change. For example, every time clients change their email address, their contact information must be updated.

So, it’s vital to perform constant monitoring.

Furthermore, provide time series trend charts or other relevant types of graphs to show progress over time.

10. Establish data quality metrics and a measurement scale

How to track your quality of data?

With metrics and a measurement scale, of course.

In order to assess your business’s ability to improve the quality of data, you need the right metrics.

Data quality metrics can include different things such as information like the number of incomplete entries in a database, the number of missing entire records, the number of errors from manual entry, or the amount of data that cannot be analyzed due to some incompatibilities.

Your data metrics may vary. What’s crucial is to have established key metrics for assessing data quality.

11. Educate your entire organization

Could you find an employee in your company who doesn’t have some kind of a role to play in data management?

If you think deeply, it is hard to find.

Whether your employee realizes it or not, he/she has an impact on the overall data management program. It is no need he/she to be a data scientist.

For example, your administrative assistant regularly enters manual data in an appointment book.

Even websites designers make a website that automatically gathers specific data about customers.

Everyone in your organization (from the top to bottom) should be educated in the basics and importance of data quality.

They should know the consequences of letting data inconsistencies and errors.

12. Put employees in charge and choose data stewards

Your personnel should accept accountability for their own data-driven activities.

Employees have to take responsibility for data quality. Define the right people who should become “data stewards.”

Moreover, data stewards should take ownership of data quality metrics.

You can consider tying their compensation to the targets of data quality metrics in order to increase their responsibility and accountability.

It’s becoming absolutely clear to a great number of business leaders worldwide that it’s important to have high data quality and to follow good practices in this area.

Master Data Management Best Practices

Master data management (MDM) includes creating and managing processes, standards, governance, and tools that form the data of an organization.

MDM is about linking all the crucial data to a single file (known as the master file) and thus ensuring a common point of reference.

In other words, MDM is a process for creating one master reference source for all critical business data, leading to eliminating inconsistent and redundant versions of the same business data in an organization.

MDM can significantly facilitate managing data in multiple system architectures and platforms.

The users of master data are customers, employees, vendors, and products.

In most cases, MDM is of interest to large enterprises, and highly data distributed organizations.

Typically, the data that is mastered include:

  • Master data
  • Reference data
  • Transactional data
  • Analytical data

Let’s see some master data management best practices

13. Identify the business value of MDM

Keeping the overall business goals in mind is a crucial point.

You need to have a clear answer to the question “How MDM strategy will help your company handle all the disparate sources of data ?”

Additionally, it is also vital to identify your business value to ensure a high ROI (project’s return on investment ) after the process.

Defining objectives and business value of MDM project will help you to justify the budget, motivate the people, and to focus on the progress.

In fact, one of the main reasons why a lot of MDM projects fail is because the ROI, is not linked to the business value.

14. Create a good IT strategy for MDM

Your IT strategy for the master data management initiative has to involve two crucial things.

First, it should be sustainable. To put in other words, the IT platform has to be reusable and scalable.

Second, real-time availability is a key point. Your IT solutions should not only do the typical cleansing, validation, integration, and etc., but they also must be capable of working in real time.

So take your time to research what are the best IT solutions for MDM. They can make a huge difference in the sustainability of your project. A good technology platform will help your organization grow.

15. Ensure seamless integration

It is essential for successful master data management to have seamless integration of the master data across different platforms and sources within and outside your organization.

Without an easy integration solution, your MDM efforts will be limited and unsatisfactory.

In addition, there are a lot of high-quality external data that can bring new insights and many values to your own information, but only if you have a platform flexible enough to accept it.

16. Test

Your MDM environment will be unique, with no analog.

Your company has a unique variety of source and systems – sales force automation, CRM, ERP, customer service, and etc.

With the continuous increase in MDM complexity, your business and technical professionals must be trained very well.




Using untrained system integrators and untested deployments or outsourcing attempts can cause serious issues for master data management users.

These 4 tips do not aim to be a long list of master data management best practices, but they should get you thinking and help you avoid some typical mistakes businesses make.

Data Integration Best Practices

Data integration is the process of retrieving and combining heterogeneous data from different source systems.

The primary goal of data integration is to combine data from a variety of sources in a way that it can produce meaningful and valuable information for business reporting and data analysis needs.

Nowadays, data integration involves much more than retrieving and combining processes – it brings together a wide range of tools, techniques, and methods supporting real-time integration.

Today the powerful data integration tools can transform easily structured and unstructured data and deliver it to any system.

What are some best practices for data integration?

17. Make data integration an important part of any business strategy

It doesn’t matter if your company is performing a merger or entering a new market, the data integration activity should have a key place in the success of any business strategy.

Normally, business strategies consist of a large amount of data, and data integration is vital to unlocking the true value of these data.

18. Use new generation technology solutions

New advanced and powerful data integration technologies can bring enterprise data integration to the next level.

Modern technologies like the cloud, mobile, big data, real-time analytics, and new gen databases all work together to help you improve business processes, customer care and more.

Always aim to implement the latest technology solutions as they are designed to meet today’s data challenges and trends.

19. Reduce or remove data silos

A data silo is something like a repository under the control of a particular department.

The data in a silo is isolated from the remainder of the company.

This practically means that data isn’t shared with others in the company.

Silo also means that data cannot easily be integrated. Data need an agile, integrated environment.

Silos are commonly found in large enterprises, although they can be found in small or medium sized businesses as well.

20. Keep in mind long-term benefits

While searching for an optimal data integration strategy and solution, don’t look at the short-term advantages and don’t aim for a quick ROI.

The true value of data integration hides in the long-term meaning.

You know that data needs are constantly changing at a fast pace and your data integration solution must be able to go with those changes in order to define and resolve challenges that might appear in future.

Data integration software tools must be flexible enough to easily meet those changes.

Download the following infographic in PDF for free

Data Management Best Practices - infographic

Conclusion

Data hides enormous value.

To unlock its power, it is critical to understand how modern data management works and how businesses are using it to refine business processes and achieve success.

Any business process can benefit from effective data management best practices, simply because all processes rely on data.

The benefits of effective data management include almost everything you can think of – improved decision-making, improved customer satisfaction, better operational effectiveness, and finally higher revenue and profits.

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