Managing Data Resources: The need for Data management, Challenges of Data management

Data management is the process of ingesting, storing, organizing and maintaining the data created and collected by an organization. Effective data management is a crucial piece of deploying the IT systems that run business applications and provide analytical information to help drive operational decision-making and strategic planning by corporate executives, business managers and other end users.

The data management process includes a combination of different functions that collectively aim to make sure that the data in corporate systems is accurate, available and accessible. Most of the required work is done by IT and data management teams, but business users typically also participate in some parts of the process to ensure that the data meets their needs and to get them on board with policies governing its use.

This comprehensive guide to data management further explains what it is and provides insight on the individual disciplines it includes, best practices for managing data, challenges that organizations face and the business benefits of a successful data management strategy. You’ll also find an overview of data management tools and techniques. Click through the hyperlinks on the page to read about data management trends and get expert advice on managing corporate data.

Need of data management

Data increasingly is seen as a corporate asset that can be used to make more-informed business decisions, improve marketing campaigns, optimize business operations and reduce costs, all with the goal of increasing revenue and profits. But a lack of proper data management can saddle organizations with incompatible data silos, inconsistent data sets and data quality problems that limit their ability to run business intelligence (BI) and analytics applications — or, worse, lead to faulty findings.

Data management has also grown in importance as businesses are subjected to an increasing number of regulatory compliance requirements, including data privacy and protection laws such as GDPR and the California Consumer Privacy Act. In addition, companies are capturing ever-larger volumes of data and a wider variety of data types, both hallmarks of the big data systems many have deployed. Without good data management, such environments can become unwieldy and hard to navigate.

Challenges of Data management

While some companies are good at collecting data, they are not managing it well enough to make sense of it. Simply collecting data is not enough; enterprises and organizations need to understand from the start that data management and data analytics only will be successful when they first put some thought into how they will gain value from their raw data. They can then move beyond raw data collection with efficient systems for processing, storing, and validating data, as well as effective analysis strategies.

Another challenge of data management occurs when companies categorize data and organize it without first considering the answers they hope to glean from the data. Each step of data collection and management must lead toward acquiring the right data and analyzing it in order to get the actionable intelligence necessary for making truly data-driven business decisions.

Data Management Best Practices

The best way to manage data, and eventually get the insights needed to make data-driven decisions, is to begin with a business question and acquire the data that is needed to answer that question. Companies must collect vast amounts of information from various sources and then utilize best practices while going through the process of storing and managing the data, cleaning and mining the data, and then analyzing and visualizing the data in order to inform their business decisions.

It’s important to keep in mind that data management best practices result in better analytics. By correctly managing and preparing the data for analytics, companies optimize their Big Data. A few data management best practices organizations and enterprises should strive to achieve include:

• Simplify access to traditional and emerging data
• Scrub data to infuse quality into existing business processes
• Shape data using flexible manipulation techniques

Data management platforms enables organizations and enterprises to use data analytics in beneficial ways, such as:

• Personalizing the customer experience
• Adding value to customer interactions
• Identifying the root causes of marketing failures and business issues in real- time
• Reaping the revenues associated with data-driven marketing
• Improving customer engagement
• Increasing customer loyalty