The world today is a capitalist obstacle course, with competitors struggling to make it past the finish line first. Companies and Businesses are in fierce competition with each either, to gain the slightest advantage over one another. Companies are fervently using every available asset to increase their market value and appeal, and one such asset happens to be Data.

In today’s world, data flows into companies from a variety of sources, like transaction systems, operations, applications, scanners, and sensor systems, social media, videos, and other potent sources. This diverse data, managed, interpreted, and analyzed properly can help companies make intelligent decisions, and monetize this data for valuable profits. However, for a company to extract value from data, they need to manage this data effectively.

What is Data Management?

Data management is essentially the process of collecting data across different platforms, integrating it, storing it securely, and making it readily available at any instance for analysis via AI systems, scientists, and engineers. This can thus be used for several important business decisions. Today’s world creates and destroys data at an exponential rate, and the growth of data is often too vast to effectively utilize, with time constraints, and limited resources.

With an increasing requirement for data managing, state-of-the-art software is being developed which efficiently manages data, providing the best for business decisions. Data governance, storage, cataloging- there is nothing that these advanced software data management systems can’t do!

Data Mesh versus Data Fabric

Data management techniques can be of various types, from data preparation to ETLs to catalogs and governance. Data architecture is an increasingly popular technique due to its comprehensive nature of data management, as it helps in providing a systematic approach to organizing data flow and organization. There are several different types of data architecture software, however, data meshes and data fabrics have been experiencing a surge in popularity. Well, what is data mesh? The term data mesh is often used interchangeably with data fabric. Both are software that help in integrating data across different platforms and sources, however, data fabrics tend to be more about the architectural organization, whereas data meshes connect users and processes. If data fabrics are more about technology, data meshes are more about the organization.

Four Problems that Can arise if Companies Don’t Manage their Data Effectively

Businesses need to sort through their vast pools of data and monetize it to take well-informed financial and organizational decisions. These are several problems companies can run into if they don’t manage their data effectively:

1. Difficult Management.

The amount of data flowing into companies is increasing in magnitude day after day. Their is data flowing in from new applications, databases, mergers, websites, and consumers. There is expected to be an exponential increase in the information storage capabilities of companies over the next few years. The IDC even predicts a 10 fold increase in inflowing data within the next 10 years.

This massive amount of data takes a lot of resources and time to sort through, However, the companies have neither the money nor the hours to sort through this vast pool. Thus ineffective data management leads to fewer desirable results over longer periods of time and this results in companies being unable to utilize their data effectively.

2. Data Loss.

At the end of the day, data is a very valuable asset, and is crucial for the development and profit of a company. Data, when well utilized, can be a powerful weapon, helping a company strike down fearsome competitors. This data is often the lifeline companies cling to in times of crisis.

Research shows that of all the companies that misplaced their data for more than 10 days, a whopping 93% went bankrupt within the year. Similarly, more than half of the businesses fold up shop every year, due to data mismanagement. Valuable data and intelligence that had once been collected with time, effort, and resources, need to be collected again. To avoid such losses, data needs to be managed properly.

3. Reduced Productivity.

Employees will have a cumbersome time with data that has been inefficiently managed. They will take longer periods to locate and understand the new data they need to complete their job effectively, and be unable to cross-check their results swiftly. Information that cannot be stored easily, cannot be shared easily, and thus poor data management, will lead to employees being unable to carry out their assigned tasks to their fullest potential.

4. Security Risks.

Data needs to be stored safely and securely. Data. that is valuable, in the hands of the wrong people can lead to disastrous consequences. Can we ever forget the cyber-attacks on market giants like Sony and Facebook, that led to the theft of the data of millions of users?

In conclusion, companies need to manage their data effectively and emerge victorious and unscathed, or fall victim to this harsh capitalist world.