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 is impossible to make decisions, both strategic and operational. The data itself is just a bunch of numbers. It needs to be collected, analyzed, stored, and used effectively. Implementing data management is not a luxury, but 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 are looking for ways to organize, store and use it, you will find today’s article very useful. Today you will learn what the process of data management is, what components it consists of, and best practices for implementing this strategy in your business.
The process of data management is related to the accumulation, organization, updating, storage, and retrieval of data. But don’t mix data governance and data management. These are different concepts, although they are related. Data governance consists of organizational structures, data owners, policies, rules, and processes, and terms for collecting, storing, using, and protecting data, while data management is the technical implementation of the management process. The first without the second is just documentation.
All internal mechanisms of different organizations, whether it is 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 is 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:
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 practices in a data management strategy example allow you to achieve the following:
The basic functionality of data management systems 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 variety of 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 an 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 the analysis of large amounts of corporate data.
There is a wide range of data that can be managed, including:
When organizing and analyzing all 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:
To make sure that all data management tools and techniques work correctly and that all information is used with maximum efficiency, creating enterprise data management strategies lends a helping hand.
A data management strategy (DMS) is the process of creating strategies/plans 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:
Since it is so often the case that companies are already hugely successful, it is difficult for owners to understand why and how the data management process will improve what is already working great. But 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:
In simple terms, data management strategies are GPS navigators 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 a data strategy is valuable to every business.
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.
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 is much more reasonable to single out 2-3 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 processes, and management methods, and help you determine which of the tools is best for the company.
Once you have determined the purpose for which you collect and process data, it’s time to think about how to achieve these goals. 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 are incomprehensible or inaccessible to other employees.
Think about the following questions:
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:
It is very 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, 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.
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 is why it is very important to train all employees on how data is managed based on their responsibilities, what problems can be, and how to respond to them.
In addition, after the implementation of data management strategies, the best practices 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.
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 begins a long process of testing and implementing new integrations.
Building a data management system can take a lot of time, but it will soon become an integral part of how your business functions. Trust the professionals. We put in place proven data management processes so you don’t have to worry about the inner workings of data management, but instead focus on new opportunities.
Contact us for a consultation on big data management. We will help you determine the most appropriate approach, tools, and technologies tailored to your specific business needs.
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