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7 Data Management Challenges Faced By Enterprises


Businesses are becoming increasingly dependent on data to make decisions. They are hiring data management consulting companies to devise a framework for handling their information assets. In this article, we are discussing major data management challenges that corporations can face if their initiative is not being run efficiently. Data has emerged as an invaluable resource for improving the performance of corporations but the asset can easily turn into a liability if the whole strategy for its management is flawed.

The huge amount of information being accessed by organizations while providing an opportunity for getting valuable insight into business aspects also poses the challenge of sifting through it to find useful elements quickly. Let’s take a look at the challenges associated with data management.

1. Ensuring The Collection Of Accurate Data

One of the biggest challenges faced by corporations is to ensure that only correct and useful information is accessed by them. The huge amount of information originating from multiple sources can easily lead to duplicate data or useless elements entering the digital ecosystem of the company.

This can lead to incomplete, inconsistent, and outdated data becoming a part of the management program which will ultimately harm the accuracy of the decision-making process. Enterprises have to ensure that the time between actual collection of an information element and its intended use is kept to the minimum possible level. They also have to identify dependable sources and frame appropriate guidelines for data collection.

2. Efficiently Managing The Unstructured Data

Technological advancements have led to the creation of new types of data at a rapid pace. Organizations are capturing information about different aspects of the business from a variety of sources. Most of the data entering the digital environment of corporations is unstructured.

Comments on social networks from clients about the service are an example of such data. Companies have, as part of their data management strategy created infrastructure in the form of database and software solutions for handling structured data.

They face problems, though in handling the large volume of unstructured data which possess valuable insights. Modern enterprises have to invest in building an infrastructure for analyzing unstructured data as part of their information management program.

3. Drawing Incorrect Inferences From Data Analyses

The main objective of a data management program is to provide analyses that can be used for informed decision-making. Most organizations make the mistake of misinterpreting these evaluations or trusting them blindly. An analysis only shows an interconnection between two elements or events which needs to be studied further. Corporations often misread the interconnection as the cause which can lead to incorrect decisions.

An assessment needs to be backed by more evidence gathered from in-depth studies to prove that there exists a causal relationship rather than a simple association. It will be pertinent if decision-makers do not base a decision only on the findings of an evaluation and instead use it as a starting point for the whole process.

4. Protecting Decision-making From Human Bias

The origin of one of the biggest data management challenges lies not in technical imperfections but human nature. No matter how efficient and accurate technological solutions have been used for managing and analyzing information elements, the bias of the human user can easily creep in while interpreting the evaluation results.

More often than not human beings will gravitate toward information which is in accordance with their beliefs. This sort of prejudice can easily affect the quality of the final decision-making process. Enterprises must have a comprehensive data governance strategy for overseeing the whole management program. This will ensure that the initiative remains free from errors.

They must also educate their key decision-makers about the hazards of human bias to avoid this problem.

5. Security Of Data Assets

Data has become a valuable asset and companies have to take necessary measures to ensure its protection. The leakage of sensitive information related to any aspect of an enterprise can be damaging for its future and can also have possible legal repercussions. One of the basic features of any effective information management initiative is to provide easy and quick access to data elements.

Companies though must invest in a system that makes information available only to authorized users in a secure environment. They must be careful while assigning ownership and stewardship roles as well as identifying other users.

6. Treating Data Management As A Fixed-term Project

Many organizations make the mistake of treating data management as a fixed-duration project. Managing the information assets of a company is a constant process which needs to be modified according to the changing requirements or realities. Once corporations start treating data as an asset, they have to ensure that it is efficiently managed continuously.

There are stated objectives of every information management program but the main idea behind starting such an initiative is to bring about a change in the thinking of the workforce. Every staff member needs to understand that maintaining the sanctity and quality of information elements is the responsibility of each one of them.

7. Ensuring Compliance With Legal Regulations

The increased focus on data has resulted in companies trying to access as much personal information about their clients, vendors, distributors etc. in order to improve their services. This has led to concerns about the safety of the personal data of individuals. Many jurisdictions around the world have enforced legal regulations for protecting such data.

These laws make the organizations accessing the information liable for its protection. Corporations have to make changes in their governance and management structure as well as educate the employees about the issue.


Organizations which understand that the prime motive of managing data is to improve their decision-making will find ways to overcome the data management challenges and enhance their performance.

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