Data management is an administrative process by which the required data is acquired, validated, stored, protected, and processed, and by which its accessibility, reliability, and timeliness is ensured to satisfy the needs of the data users.
Companies today are faced with the challenge of consolidating and managing large volumes of data that are lying in disparate systems. A robust data management practice is essential for deriving meaningful insights in a timely fashion. It can help companies in making informed decisions, gather deep insights about their customers, trends and opportunities, and deliver a quality customer experience. But a good data management practice is not as easy as it looks. It is a tardy process as the data is fragmented and managed by multiple stakeholders. This leads to large quantities of inaccurate and disparate data along with many challenges towards its maintenance.
Let’s turn our attention to the top five data management challenges faced by companies today.
1. Massive Data Volume
About 2.5 quintillion bytes of data are created daily. This implies companies will continue to have issues in aggregating, managing and deriving value from data. Looking at the volume of data created and the numerous data sources, data management should be on the top of the list of companies.
2. A Reactive Data Management Strategy
A big issue that is often seen in companies by large is their inability to realize that there is a problem with their data. This implies that companies adopt a reactive strategy to data management and will generally wait until they identify any specific problems that need to be fixed.
3. Insufficient Processes and Systems
It’s been observed that once data is extracted from disparate sources, it leads to inconsistencies in data that can’t be trusted by anyone. This generally happens due to lack of processes, data management systems and insufficient data strategies that result in inaccurate data.
4. Scattered Data Ownership
Data ownership is a major problem faced by several companies today. Data is mostly scattered and the data quality management is done by several stakeholders, and generally measured at a department level than across the business as a whole.
5. Lack of Right Skills & Knowledge
Several companies are unable to get sufficient support to improve their data culture. This is primarily due to companies not having sufficient knowledge or skills towards data management along with the resources needed for proper data management.
Best Practices for Data Management
The shift towards becoming a more data-centric company requires giving due importance to quality data and a better approach to data management. A great way to manage data and obtain relevant insights required to make data-driven decisions is to ask a business question and get the data that is relevant for answering that question. Companies need to collect large quantities of information from several sources and adopt best practices while storing, managing, cleaning, and mining, and then analyzing and visualizing the data to make business decisions. Data management best practices lead to better analytics. Through proper management and preparation of data for analytics, companies can get optimum benefit from their Big Data. Some great data management best practices companies can adopt is a simplified way of accessing traditional and new data, scrubbing the data for infusing quality into prevailing business processes, and shaping the data through flexible techniques.
Investing in a data management platform is definitely the logical way forward owing to the plethora of benefits it offers. Apart from the above, it simplifies processing, validation along with other functions and saves time. You can manage data from all data sources in a central location and get accurate business and customer information.A data platform like DataBlaze offers a 360-degree view of your customers and helps you in acquiring in-depth insight into your customers’ behavior to stay ahead.