According to Forbes, around 53% of companies worldwide have already adopted the data processing and analytics culture to streamline their data and operations to the best. So far, financial service providers and telecom businesses are leading the race to adopt these changes fastly and bringing a change in their work operations.

Before moving on to data analytics and management’s importance, let’s sneak into its literal meaning first.

What is data management?

It is a thorough process of gathering, storing, organizing, and preserving the data developed or collected by the organization. An efficient and detailed process acts as a crucial part of any organization, especially in the IT sector. The data management process proves helpful for an organization’s decision making and strategy planning aspects. As a result, many organizations include it in their overall work operations. Generally, business managers, project managers, and corporate executives remain a major part of data processing.

This process encompasses a blend of multiple functions that collectively ensure the data has been entered into the corporate systems accurately. With this, IT and data management teams’ responsibility is to ensure complete availability and accessibility of gathered data for permitted individuals in the organization. Plus, before anyone could use the collected data, it is necessary to ensure if the piled up data meets the governing guidelines or not.

Significance of data management –

With time, companies have started to see data as a progressive corporate asset that helps to make firm decisions for a better future. From optimizing the business operations to enhancing the marketing campaigns’ potential, there are numerous reasons for making the process a vital part of any business.

Negligence in this process can result in incompatible data silos, data quality, inconsistent data sets, analytics applications, and sometimes even faulty leads. Initially, these factors may remain hidden, but with time, it starts to take a toll on the analytics applications and business intelligence to the worst.

Its concept and analytics has got serious by the time businesses are subjected to accelerate and abide by the regulatory compliance requirements such as – protection laws, data privacy GDPR, and the California Consumer Privacy Act. Many companies are capturing large volumes of data with a variety of data types to support the big data system often deployed in MNCs. Here, appropriate management with detailed synchronization of data makes navigation easier.

Types of data management functions –

There are multiple disciplines in the overall procedures. All these disciplines work as a line of steps to process and store the collected data following the governance. This encompasses everything from operational to analytical system processes. Here, the first step is to draft the data architecture, which effectively simplifies the process.

In most of the large organizations, preparing the data architecture, drafting the blueprint for the database, and deciding on the number of database management tools required are some aspects to be discussed before commencing the process. A detailed architecture offers a wireframe for the data platforms to be used, how data will be deployed, what technologies will be used for a particular database management application.

In general, databases are the most typical digital platforms encompassing corporate data. It contains different sets of collected data segregated into their type, size, and nature. This makes data accessibility, upgrading, and management hassle-free for the IT team. Apparently, this format is applied to both analytics and transaction processing systems.

The transaction processing system develops the base for operational data, sales orders, customer records, and data warehouses. It stores the sets of data from BI and business systems used in the organization.

Role of database administration?

Database administration plays a critical role in the overall process. Soon after the database has been set up, it requires to be monitored for performance and tuning up. This improves the complete response time and database queries stored in the backend. It includes a string of administrative tasks including – configuration, database design, data security, installation and updates, database backup and recovery, updates, security patches, and software upgrades.

Multiple parts of the data management process –

Data architecture – The data architecture decides the base design be deployed with the database systems. It includes the other types of repositories from the data of an organization.

Data models – These are drafted to create multiple map workflows and strengthen the relationship between the set of information and business requirements. Many MNCs appoint a specialist to create data models.

Data generation – The overall data is fetched and collected at one place to be stored in the main database. It consists of file systems, data repositories, and cloud object storage services.

Data collection – Crucial data information is collected from multiple sources. Generally, the IT and data management team gathers it from different transaction systems and other vital sources, which are integrated with the data warehouse or the data lake, which is often used for analysis.

Data quality – Running a rigid data quality check at multiple levels is necessary to ensure data errors if any. This process detects and resolves the data inconsistencies, which are often sorted through data cleansing processes.

Data governance – There are many data governance programs developed to define data and usage policies. It ensures accurate data consistency throughout the system.

One of the highly preferred technologies to administer and deploy the database is DBMS (database management system). This software works as an interface among the database, applications, and end-users. It includes data platforms that store data in a structured manner while mainstreaming it for the database. This function offers multiple types of data and flexibility to work as a good fit for transactional applications.

The last line –

As the corporate world has started to rely on digital platforms, its importance and analytics are surging high. It not just helps with better decision making but synchronizes the data for recovery purposes. From MNCs to small or mid-sized ventures, almost every organization type has started to use analytics effectively.

About the author

Mark Coleman

Mark Coleman is the editor at MarkupTrend. He is also a technical writer and digital marketing expert. He handles all marketing, advertisement related activities at MarkupTrend along with his team.