A Complete Guide to Data Management

Data management is a big question that has been pondered in different industries. Data-driven insights as a crucial component of machine learning made big businesses take a step forward. Do not be naive and think that data management is reserved for big enterprises only. For a small team, the quality of data management is equivalent to their improvement in fields such as revenues and productivity. Before we get into data management specifically let’s first understand its importance. One must know that it takes various data management tools and strategies utilized for collecting, storing, and analyzing data. Good data management refers to the effective practice of these processes, including protection, delivery, and data processing. From a business angle, data is related to clients, customers, finances, the market, etc. Effective data management undoubtedly enforces better business decisions thanks to data-driven insights. However, do not neglect security during different processes of data handling and management, especially privacy and protection from ransomware attacks. Let’s look at some of the key data management processes that small businesses can use to grow sales and increase revenues.

Data management
Data Management

Data Lifecycle Management

Data Lifecycle Management is a term that defines stages of information flow, including the creation of policies used for the management of different stages. The framework aims to maximize the data life.

Data Processing

Once collected data is translated into useful information we are talking about Data Processing. So far, there are three known methods of data processing, where many businesses rely on electronic and automated data processing.

Data Modeling

Simple system diagrams with all the data on them are referred to as Data Models. The role of data modeling is to enable teams to see the way data flows through different systems and business processes.

Data Architecture

Data strategies are supported by certain structures that help the team operate easily. In that sense, Data Architecture reveals not only the way your business obtains data, but also where that data further goes. Particularly, most data strategies start with data architecture and include the processes of storage, usage, and security of data.

Data Integrations

Combing data from different sources to make a unified set is data integration. In rare cases, data is collected by one system. Rather, there is more than one application in charge of the different specialized processes. It is important to allow separate teams to operate with their databases.

Data Migration

Data Migration is viewed as a single process of relocating data from one database to another. Data migration often happens if businesses add new data locations or systems. It can also refer to the shift of data formats.

Data Catalogs

Different inventories of data resources are Data Catalogs. The resources are organized thanks to the use of metadata, and a catalog’s role is to make the business both more visible and searchable to users. For instance, Google provides data catalogs as products used for data management, where each product is a search bar. Organized as such, data assets can be easily found and categorized.

Data Governance

A crucial component that imposes rules and procedures which define data management in any business or enterprise is data governance. It can be both a team or an individual in charge of data governance. Such responsibilities include the definition of column names, access requests, and record maintenance.

Data Security

As one of the most important aspects for any company, data security is in charge of protecting data from being stolen, corrupted, or misused within the data lifecycle. Data security covers the aspects of hardware, software, backups, user access, storage, and others.

Data Storage

Preservation of data and reorganizing it for further use is in hands of data storage. As opposed to manually storing paper documents, electronic storage minimizes the data volume.

Data Testing

Making assertions regarding data and testing their validities is defined by data testing. Any source data can be tested for its quality. Moreover, it is possible to validate the code within data models.

Conclusion

With all being said, it is hard not to realize how many benefits come with effective data management. Quality data-driven insights will help any company improve its decision-making process, increase productivity and grow revenues.