High Quality Data Management Improves Productivity

High Quality Data Management Improves Productivity

In recent years, data has become more fundamental and linked directly to companies’ success. With high volumes of data flowing in from multiple sources, managing this data has become a complex task. To make faster business decisions based on more confidence and knowledge organizations need access to high-quality, actionable data. Using Data Management system is a major part of the solution to this issue.

Let’s define “Data Management” and why it’s so important

According to Gartner Glossary, Data Management (DM) consists of the practices, architectural techniques, and tools for achieving consistent access to and delivery of data across the spectrum of data subject areas and data structure types in the enterprise, to meet the data consumption requirements of all applications and business processes.

In simple words, DM is how organizations manage their central data asset. DM includes collecting, storing, organizing, protecting, verifying, and processing essential data and making it available to all the employees. DM is aimed to increase the quality and accessibility of the data used by the company. As the volume of data and the number of sources increase, there’s a growing need to successfully manage and maintain the large volumes of data that companies produce daily.

Since most organizations rely on their data to moderate the risks of their business decisions, DM becomes critical to increasing productivity, creating digital experience and building successful applications. Business decision-making should be based on high quality information. To make data turned into information it needs to be managed, processed and made meaningful and relevant.

High quality DM systems ensure access to complete and accurate data. With no such reliable systems, according to Dun & Bradstreet, nearly one in five (19%) of businesses have lost a customer by using incomplete or inaccurate information about them. In addition, machine learning/personalization/AI cannot be executed without an effective DM in place.

Big amounts of data raise the need for an effective Data Management System

In recent years organizations are subject to a growing number of compliance regulations, large rises in information storage capacity and the big amount of data and documents being generated. This degree of growth is not expected to slow down – International Data Corporation (IDC) predicts the amount of information generated will increase 10-fold by 2025. This large amount of data from ERP systems, CRM systems, and general business documents is often referred to as big data and it makes DM almost a must.

On this big data environment, a good DM enables businesses to rely on a single source of information which can prevent approaching outdated leads by the sales teams and build better customer relationships with relevant potential leads.

Key Data Management Advantages

  1. Trust and availability – Employees should trust the available high quality data for making decisions. Having the data/information distributed in a simple user friendly manner to the relevant employees makes it easier to use.
  2. Data Compliance practice – Having DM system makes it easier for the company to ensure that the data is collected in one system and fits the compliance practices like the European GDPR. Validating data confirms that data comes in an anticipated constant format across the whole organization which makes it quicker and easier to work with.
  3. Data Security – Another issue of having a lot of customer data is the security risks engaged in it. A strong, effective DM system helps protecting the information from cyber-attacks or other kinds of abuse. DM system reduces the risk of losing vigorous company information. It also ensures that this info is backed up and can be recreated if required. Companies invest time and money into data compliance since almost 77% of consumers would stop using a brand if that organization lost their personal data or used it irresponsibly.

Data Management implementation strategy

DM implementation strategy should be thought as a roadmap for the company to use the data to achieve its business goals. To become a data-driven company, the implementation DM strategy includes 4 steps:

  1. Find and pick the right DM software tools for your business.
  2. Identify how to use the data collected to achieve the company’s goals. The way the data is collected, processed and transformed should fit the company’s business model and objectives.
  3. Create a DM team that can analyze data and understand technologies associated with DM.
  4. Ensure employees have access to the relevant data according to their role. Different permission levels should be defined. Each department should have access to a different level, quantity and type of customer data to be able to provide a better service.

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