The term "data management" refers to the set of practices needed to build and maintain a framework/framework for importing, storing, mining, and archiving data that is necessary for business operations. Data management is the backbone that connects the different segments of the data lifecycle in the enterprise.
7 types of data management
In general, data management experts focus on certain specifics of the domain, namely :
Master Data Management - Master Data Management (MDM) practices ensure that the organization always works with a single, up-to-date and reliable version of the data, enabling it to make better decisions. It should also be noted that data management operations (importing data from different sources, presenting it in a consistent and reliable form, propagating it to different systems) require appropriate tools.
Data Governance - The data steward is not responsible for developing data management policies, but rather for their deployment and enforcement across the enterprise, including policies for data collection and movement, implementation of best practices, and compliance.
Data Quality Management - While the data steward is akin to a police officer policing the digital world, the data quality manager is more like a court clerk. The DQO is responsible for carefully examining the data collected to identify underlying problems such as duplicate records, inconsistent versions, etc. The DQO is responsible for ensuring that the data are accurate and complete. Data quality managers are involved in the operation of the data management system defined in the company.
Data security - One of the most important aspects of data management is security. Although emerging practices such as DevSecOps incorporate security mechanisms at all levels of application development and data exchange, security specialists are still responsible for a number of operations, including managing encryption, preventing unauthorized access, protecting against accidental movement or deletion of data, and other key concerns.
Data governance - Data governance defines the laws that shape the state of data in the enterprise. A data governance framework/framework should clearly describe the policies to be followed in the collection, circulation and protection of institutional data. Data governance managers oversee a network of actors: data stewards, quality management specialists, security teams, etc. These various operations must be defined within the framework of a governance policy that facilitates the management of the company's reference data.
Big data management - Big data is a catch-all term used in conjunction with practices aimed at improving business operations: the collection, analysis and use of large volumes of data. More broadly, this area of data management focuses on importing, integrity checking, and storing a tsunami of raw data that other specialists then use to improve operations and security and to feed into the company's business intelligence environment.
Data warehousing - Data is the most valuable resource for successful businesses. The ever-increasing volumes of data pose a very obvious challenge: how do you manage all this e-ffi-ca-ce data? Managing a data warehouse enables the deployment and administration of a physical and/or cloud infrastructure to aggregate raw data and analyze it in depth to generate knowledge that can be used by the business.
The specific needs of companies practicing data management may require a mix of all or some of these approaches. An excellent knowledge of the different types of business activity allows data managers to gain the experience they need to develop solutions adapted to each environment.
Data Management - Key Benefits
Data management helps companies identify and resolve internal challenges and deliver a better customer experience.
Data management allows companies to measure the volumes of data to be processed. In any enterprise, a myriad of interactions occur in the background between network infrastructure, applications, APIs, security protocols, and more. An incident in one of these interactions can cause serious problems for overall operations. Data management provides managers with a comprehensive view of the business, making it easier for them to see the big picture, set goals and plan ahead.
When data is managed effectively, it becomes a wealth of knowledge and information for business intelligence. Data management helps companies in many areas, in particular :
-Intelligent advertising that targets customers based on their interests and interactions.
-Holistic security that protects critical information.
-Alignment with applicable compliance standards, saving time and money.
-Machine learning that takes the environment increasingly into account over time, leading to automatic and continuous improvement of its results.
-Reduction of operating costs by limiting processing power and storage capacity to the values required for optimal performance.
Consumers and Internet users can also benefit from efficient data management. By gradually discovering their customers' habits and preferences, retailers can offer them faster access to the information and products they are looking for. Customers and prospects can enjoy a more personalized shopping experience while ensuring that their personal and payment information is stored in secure environments, which encourages repeat purchases.
Leading retailers such as Office Depot leverage data management to define sales cycles that measure visits, purchases and delivery in seconds, giving them the ability to satisfy customer demand in near real time. This kind of result is made possible by an efficient data management solution.
Data Management - Key Challenges
However, these benefits are not acquired "by blowing on it". The IT landscape is constantly changing and evolving, and data managers should expect to face many challenges in their business.
The volumes of data to be managed are often daunting. And because it can be difficult to anticipate these volumes, when thinking about systems and processes, think big. And even "big"! Third-party partners who specialise in integrating big data or making it available on a suitable platform are essential allies.
A number of companies still store their data in silos: the Dev team works from dataset A, the Ops team from dataset B, and so on. High-performance data management solutions rely on easy access to all available data to fuel an effective business intelligence environment. Real-time data platform services enable the dissemination and sharing of clean data between teams from a single, reliable source.
Moving from unstructured to structured data can be difficult. Data often enters organizations in unstructured form.
In order to be exploited by the company's business intelligence environment, this data must be prepared: structuring, deduplication and more generally "cleansing". In many cases, data managers rely on partnerships with third parties to assist them in these processes and use tools designed for on-premise, cloud or hybrid environments. Another solution is Talend Data Preparation, which seamlessly integrates with the Talend platform so that data preparation is neither siloed nor outsourced to a third party.
Corporate culture is critical to data management. The best systems and processes in the world won't do any good if the people managing the data don't know how to use them or, just as importantly, don't know what they're using them for. By educating their team members about the benefits of data management (and the difficulties that may result if they don't), managers can convince them to become essential parts of the process.
These (and other) challenges characterize the transition from old practices to harnessing the power of data for business intelligence. With careful planning, appropriate practices, and skilled partners, technologies such as the accelerated learning machine can turn challenges into bridges to deeper knowledge and a better customer experience.