Data Management Challenges (And Advice for Overcoming Them)
By Indeed Editorial Team
Published April 8, 2022
The Indeed Editorial Team comprises a diverse and talented team of writers, researchers and subject matter experts equipped with Indeed's data and insights to deliver useful tips to help guide your career journey.
Data management is becoming a bigger and more complex task for businesses and organizations. Data management plays a crucial role in business decisions, task management, customer support and many other functions. Learning about data management and the challenges it presents can be integral in creating a data management system that is best for a business or organization. In this article, we discuss data management challenges and solutions for overcoming those challenges.
What is data management?
Data management is the set of practices and policies that control, protect and enhance the value of data assets and information. Companies and organizations use data management to collect, store, organize, update, protect and use data for analysis or reference. Data management programs aim to complete these tasks in a cost-effective way and maintain the security of the data they manage.
As technology progresses, organizations and companies generate and consume data at unprecedented rates, so data management processes are essential for turning large quantities of data into useable information. Successful data management systems ensure data is reliable, up to date, accessible to those who use it and secure from attacks and leaks.
Read more: What Is Data Management?
Data management challenges
Here are some common challenges that data management professionals face and some advice on overcoming, mitigating or avoiding those challenges:
This challenge involves the ability to keep information consistent across separate systems as information is input to the different systems. This issue especially affects organizations that use legacy mainframe systems, which often run old, custom-built applications that can rarely handle the demands of modern data management and put out data in outdated formats that require transformation or deciphering. Keeping data updated consistently across all systems requires real-time communication between those systems, which can be difficult for older or slower systems.
One solution to this challenge is real-time data streaming. Updating storage and operations to cloud-based systems can help because data in the cloud can update simultaneously. Companies that use legacy systems can also create mobile and web applications that allow consistent data access for customers or employees.
Within a single organization, confusion can occur when variations in data modeling or data input occur. Different data organization programs often use different methods to display and categorize data. For example, a business selling to other businesses keeps accounts with information on customers, prospects and vendors, along with an inventory system for its stock. Different management programs sort the accounts in different ways, leading to crossed or lost information when collaborating with other businesses, and different inventory systems use various metrics to sort stock. Along with sorting data differently, inputting variations on an account name can also create separate accounts.
This system challenge requires a system solution. Organizations can use analytics software to sift through existing data, find duplicates or incorrectly formatted data, work to correct inaccurate data and merge duplicate data. Streamlining the data input and organization method can help ensure uniformity and accuracy, even as new data comes in. Companies can also work with existing employees to update their skills with data analytics and organization or create a new role dedicated to maintaining analytics systems.
Often, relying on flawed, outdated or simply incorrect data increases an organization's challenges. As with many business systems, databases can become outdated, inefficient or improperly managed if they don't receive regular maintenance. Many businesses increasingly rely on data-based decisions and making those decisions based on flawed data can have negative effects on the business.
This can be a relatively simple challenge to overcome. Regular checks of database health and frequent updates can improve data accuracy, quality and consistency and optimize database performance. Updating all databases, whether frequently or infrequently used, can make keeping track of data easier and improve the reliability of data-based business decisions.
Governance and storage
Another data management storage comes from data governance and storage. Data governance is the management of data availability, usability, integrity and security based on standards and policies that control data usage. Inefficient data storage and governance can lead to several issues, including data not in compliance with current regulations or standards, uncertainty about data definitions and lack of clarity about who holds responsibility for data. This can lead to confusion or larger consequences for improper data management.
Overcoming this challenge involves finding and using a solid data governance program and standardizing the data entry and storage process. Standardizing these processes and actively overseeing their governance helps clarify data definitions, roles and responsibilities. It also brings transparency to the storage and management system, including who changes data in the system.
Data security remains a consistent challenge as both data systems and hacking methods advance. Data often comes into organizations from various outside sources, and not all of those sources can ensure security or compliance with standards and regulations. Sometimes improper data retrieval happens as well, through attacks or scam attempts. Data management systems consistently require security checks and updates to maintain their integrity.
In working to solve this challenge, organizations can evaluate their priorities and put data security at the forefront. Organizations can research and implement the security best practices that best fit their data systems and ensure that data management and protection personnel know how to repair faults or prevent breaches to the systems. Organizations can also work with encryption programs that help protect data and use secure storage methods. Authentication services exist to ensure the correct people or programs have data access as well.
As the scale of data creation and usage increases, so does the demand for people who have the skills to manage data. Unfortunately, many organizations face the challenge of a shortage of people skilled in handling this scale of data management. Highly skilled people can be difficult to find, and training new people can be expensive.
This solution requires a variety of technical skills. Organizations can use machine learning and artificial intelligence (AI) to assist with data management. As technology becomes smarter, it develops a higher capacity for handling complex tasks. Many organizations are turning to machine learning and AI to help manage data storage, organization, regulation, updates and usage. This allows organizations to use both their personnel and technology to their full potential and find solutions to their challenges.
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