Data Management Strategy: All you Need to Know in Simple Words

From financial forecasts to supply chains, marketing, and product sales, in many cases, data plays a critical role in the work of modern corporations. This is the blood of any business, without it, it’s impossible to make decisions, both strategic and operational. Data itself is just a bunch of numbers. It needs to be collected, analyzed, stored, and used effectively—there are even narrowly focused data science services for all this. Implementingdata management is not a luxurybut a necessity, and the more efficiently you can do it, the greater your competitive advantage.

If your business is dealing with tons of data and you’re looking for ways to organize, store, and use it, you’ll find today’s article useful. Today, you’ll learn the answer to the question, “What is a data management strategy?” as well as consider its components and best practices for implementing this strategy in your business.

All internal mechanisms of different organizations, whether it’s the operation of applications, analytical solutions, or algorithms, are directly related to the availability of data for all stakeholders. But if this data is huge, then it can no longer be called traditional. It’s not so easy to manipulate and manage with conventional data processing software. As a rule, more important information can be extracted from it, and the benefits are much wider.

The term “big data” can refer to both a huge and complex set of data and the methods used to process it. Big data has four main “V” characteristics:

• Volume: Big data is directly related to the emergence of huge amounts of data, measured in terabytes, petabytes, and even exabytes, whose processing requires powerful tools.
• Variety: A big data set typically can include structured data (usually stored in relational databases), partially structured data (data not suitable for tables but can be organized hierarchically), and unstructured data (does not have an organized structure—audio and video materials, and images).
• Velocity: Big data is generated quickly and is often processed in real time. If one machine is enough to process data, it’s likely not big data. The number of servers in a cluster is always more than one.
• Veracity: Veracity or simply credibility refers to the data quality. Data with high veracity is valuable for analysis and makes a meaningful contribution to the overall results.

Big data opens up huge opportunities for companies, including important insights into customer behavior, more accurate forecasts of market activity, and overall efficiency gains. Without proper management, such data becomes useless. To ensure that all data management processes run smoothly, a data management system comes to the rescue. It is designed to ensure the accuracy, availability, security, and timeliness of data. Thanks to this, all employees can perform tasks and solve problems efficiently. But this is not all of its benefits. The best data management practices example allow you to achieve the following:

• Since the data is well-organized and does not need constant transportation, and because employees with the appropriate authority have access to it, productivity improves.
• Employees do not have to perform the same tasks or process the same information, as data is not duplicated, which means increased cost efficiency.
• Big data is a big responsibility, so it’s very important to ensure the security and reliable preservation of data. With the help of a data management system implemented—for example, through data silos—companies can give access to certain types of data, such as bank card numbers, payment information, etc., only to those who have the appropriate authority.
• The success of any business largely depends on the ability to make quick decisions when necessary—otherwise, there is a risk of losing money or missing opportunities. Access to data should not be limited to team leads when you have to spend a lot of time trying to contact colleagues and get the information you need. The data management process ensures that data is received on time, which is especially useful when working remotely.
• The more accurate and up-to-date the collected data, the more complete the team sees the picture and makes better decisions. The use of a well-designed data management tool ensures this. This also works in the opposite direction: with erroneous or inaccurate data, it won’t be possible to make a correct and profitable decision.

What Is a Data Management Strategy?

A data management strategy is a plan drawn up for a specific organization and aimed at the consistent implementation of data-driven operations to achieve the organization’s business goals. With a data management strategy at hand, organizations clearly understand how to use this data, how to collect it, how to share it, how to store it, filter it, sort it, etc. 

At the same time, adhering to these guidelines ensures compliance with standards—both generally accepted and those that have been approved internally, and also provides cost-efficiency, high productivity, and simplicity.

In general, the presence of such a strategy allows organizations to overcome such challenges as the use of low-quality data, duplication of data, and unjustified resource consumption of data-related operations.

Thus, a data management strategy can become both part of a global digital transformation and a separate independent element that opens up new opportunities for scaling a business.

Implementing Data Management

Implementation of a data management strategy always implies the revision of both internal and external data sources, tools and technologies for collection, processing, storing, and sharing this data, as well as the whole data infrastructure and processes within it adopted by a specific organization. Compliance standards and levels of access to data within the organization’s departments, individual employees, and external parties are also taken into account.

After this, when bottlenecks are identified, specialists select new solutions and create a step-by-step plan for their implementation that meets the current goals of the organization. Then, a data backup occurs (to eliminate the risk of its irretrievable loss), and the strategy is gradually brought to life.

What Are the Components of a Data Management System?

The basic functionality of a managed data infrastructure depends on the specific application, whether it is data in educational processes, medicine, online sales, or the provision of other services. Even the smallest companies usually have their websites, purchase or order history, user information, etc.—a certain set of data that can be collected and analyzed. But there is a basic list of components that make up a data management system:

• The introduction and use of data by users includes viewing, creating, modifying, and deleting records, as well as the ability to search for data, sort it, and save query results.
• Data structure management includes data classifications, lookup tables, and the creation of complex data structures.
• Reconciliation and normalization of data are responsible for the identification, verification, and removal of duplicates.
• Data integration with company information systems includes automated importing and exporting of changes from local systems, comparison of data between local and central data structures, and provision of a set of services for applications and users to access and manage data.
• Administration of the process of data management is the management of user roles and permissions, maintaining the history of data exchanges, performing exports and imports, as well as appointing a chief data officer.

What Is Enterprise Data Management?

The variety of raw data that is received and stored in business organizations is constantly growing and changing. How do you deal with them, organize them, extract useful information, and be sure of their accuracy? For this purpose, there is the implementation of specialized enterprise data management software.

Enterprise data management (EDM) is the process of extracting and storing data for both processes within an organization and external communications. In other words, EDM is not only the process of managing data but also people. This means that all employees of the company receive accurate, timely, and necessary information.

The main purpose of this is to create conditions for the exchange of data while maintaining confidence and trust. This allows an organization to better understand its customers, develop new products, and make critical financial decisions based on comprehensive data analysis.

There is a wide range of data that can be managed, including:

• Basic data. This is not transactional business data
• Transactional data. This is information about purchases, production, sales, etc.
• Analytical data. These are generated reports on transactional data
• Reference data. This is used as instructions to maintain the general rules
• Metadata. This is descriptive information about other business data

When organizing and analyzing all of this data seems like an impossible task, enterprise data management comes to the rescue. Well-designed software guarantees organization, accessibility, and structuring. With enterprise data management, organizations can easily achieve:

• Availability of high-quality data for accurate analysis
• Data security and compliance
• Consolidation of data from multiple sources, thereby achieving increased efficiency
• Consistent data architecture that scales easily with your business

To make sure that all data management tools and techniques work correctly and that all information is used with maximum efficiency, creating an enterprise data management strategy lends a helping hand.

A data management strategy (DMS) is the process of creating strategies for handling the data created, maintained, managed, and processed by an organization. Its main goal is to develop a business strategy to ensure that the data is:

• Collected, stored, consumed, and processed
• Controlled and protected
• Classified and standardized

Since it’s so often the case that companies are already hugely successful, it’s difficult for owners to understand why and how the data management process will improve what is already working great. However, there are many examples where the results of reporting and analytics have served as a kind of secret ingredient in many new business initiatives. Сompanies develop data management strategies to:

• Think about future trends and how best to manage them
• Continuously improve the decision making process in the organization at all levels
• Manage the data flows that are critical to business success
• Form a culture of data
• Develop a sustainable competitive advantage given the volume, depth, and accessibility of digital information

In simple terms, data management strategy is GPS navigation for data usage. They ensure that the process of data management follows generally accepted rules that are efficient, useful, and simple. This homogeneity enables effective communication throughout the enterprise for effective data-driven decision making. Building an effective data management strategy is valuable to every business.

What Are the Initial Steps to Implement a Business Data Strategy?

To be ready for a more manageable environment, there are some transition steps. Of course, it is difficult to fit everything into a few steps, since the implementation of a high-quality and reliable strategy will take time, but here are examples of where you can start your journey.

1. Determining the Goal to Follow

The first one of five data management steps from our example is to determine the goal. If you want everything at once and see a blurry horizon in front of you, then there is a risk of directing resources in the wrong direction, storing and processing unnecessary data, and wasting time, money, and effort.

It’s much more reasonable to single out two or three global and priority areas for the sake of which data will be stored and processed. After that, build smaller goals around them, which will build a strategy brick by brick, define business processes and management methods, and help you determine which of the tools is best for the company.

2. Creation of Data Processing 

Once you’ve determined the purpose for which you collect and process data, you have to define its data capabilities. Newly collected data tends to be unstructured, of unknown quality, unrelated, and inconsistent. As a rule, a narrow circle of specialists is responsible for their storage, use, and transformation. At the same time, they’re incomprehensible or inaccessible to other employees.

Consider the following:

• Data research: How to collect it and from what sources.
• Data maintenance: Where it’s stored and in what form (structured or unstructured), how to improve its quality, integrity, availability, and security.
• Use of data: Who owns this or that data, in what form it needs to be delivered to the receiving party, and for what purposes. Consider automating data processing wherever possible.

3. Definition of the Organizational Model

These are the principles of the formation of divisions, the delegation of authority, and the vesting of responsibility. In essence, the organizational model shows how to form a unit. There are three main principles among which you can choose the one that suits your business:

• Centralization is a process in which all important decisions and actions at the lower level are subject to approval by top management.
• Decentralization is the systematic delegation of authority at all levels. Top management retains the authority to make important decisions. The rest of the powers can be delegated to the middle level and the lower level of management.
• With the hybrid principle, different structures are observed in different departments, and these changes depend on changes in internal and external conditions.

4. Choosing a Reliable Data Management Service Provider

It’s important to find specialists who can meet all your data needs, from consulting, strengthening your team with experienced developers, and migrating local data to the cloud—specifically with implementation of proven data as a service examples—to complex implementation of data management solutions.

If you haven’t already chosen a team with data engineering expertise to take care of all aspects of the data lifecycle, our team will be happy to provide you with big data management and all related processes.

5. Employee Training

A data management strategy will make no sense if all policies are not followed by employees. They must be aware of all data management processes and work as a single mechanism. That’s why it’s important to train all employees on how data is managed based on their responsibilities as well as potential problems and how to respond to them.

In addition, after the implementation of a data management strategy, the best practice to create a common policy and improve the understanding of techniques and processes is to conduct company-wide educational training. Otherwise, different parts of the team may have different views, which can lead to conflicts.

Final Thoughts

There is no one-size-fits-all data management strategy for every business. However, nothing forbids creating one for each business separately so as not only to successfully collect, analyze, and save data, but also to adapt to future needs without reducing profitability. Once the data management system is built, the work is not over. After that, a long process of testing and implementing new integrations begins.

Building a data management system can take a lot of time, but it soon becomes an integral part of how your business functions. Trust the professionals. We’ve put in place proven data management processes so you don’t have to worry about the inner workings of data management and can instead focus on new opportunities.

Contact us for a consultation on big data management. NIX will help you determine the most appropriate approach, tools, and technologies tailored to your specific business needs.