Data is to be managed. But why?

The reason lies in this fact.

Now that you know how quickly the corporate world is changing, it is not only artificial intelligence, or AI, but also data-driven decisions that make the information useful. Simply put, AI requires accurate, reliable, and fresh datasets to introduce transformation to its life cycle. If seen simply, data management is the process of defining, validating, and streamlining neat and clean datasets into comprehensive structures. Actually, it’s a broad concept that involves data migration, conversion, cleansing through enrichment, de-duplication, normalization, and standardization.

Overall, this process has a number of components that are all advantageous for companies of all sizes, including small, large, and medium-scale enterprises.

Why is it so- have you ever thought?

This post will help you understand why people bother about strategically handling information and why they use or integrate it. Certainly, multiple benefits are the reasons. These benefits complement the overall business performance, its productivity, and also help in winning the cutting edge over competitors. But, some mistakes can hamper and lead to bad decisions.

 5 Common Data Handling Mistakes

Let’s discover some common mistakes to avoid during data management.

1.      Ignoring the Main Objective

Creating a data management strategy involves more than just collecting and streamlining data. It’s main objective is beyond them. Whatever you do with the data, it must resonate with your business objectives. Otherwise, optimizing resources, prioritizing data initiatives, and preparing a workforce won’t be aligned with the objectives of these data management-driven practices. With the collected details, business entities can make informed decisions about how to allocate resources efficiently and manage the end goal. Well-managed data helps in discovering actionable insights, which guide how to drive personalized client experiences to the next level and achieve business objectives with minimal threats.

Many strategists think of multiplying revenues and enhancing the customer experience, but without considering data handling issues. This mistake can result in more data loss incidences, which eventually disturb business continuity. It is simply because the lost data might allow data management services providers understand the voice of half data, which leads to impractical decisions.

All in all, the data management strategy should be derived after understanding all aspects of the collected data. Otherwise, your decision won’t be strong and realistic.

2.      Underrating Unstructured Data

The voice of data is always powerful, provided it has insights from all sources (structured and unstructured data). Simply put, you must not forget to collect customers’ voices from messages, audios, social media, images, and other data formats. These resources of information can help your strategists understand customers and market trends. All in all, underestimating the power of data resources can be a big flaw.

Mostly, we underestimate unstructured data. This is actually a big mistake because it can show your insights that drive innovation and add value to your business. This kind of data can be leveraged to define clear goals, which can be associated with revenue or market growth, enhancing cost efficiencies, etc.

However, the entire unstructured data may not be of the greatest value. So, you should understand them, handle them well, and filter out unnecessary details over time. And, data managers should segment sources of that unstructured data for continuous monitoring and improvement. It requires a proper strategy to determine and draw insights, which must include crystal-clear privacy protocols to be defined and regulatory compliance to be followed. This clarity can help in preventing legal battles and penalties for neglecting compliance policies and privacy.

3.      Creating Data Silos

Data silos refers to a data repository that is proactively controlled by an individual department. Overall, it defines limited access to crucial details. It means that other departments, business units, or groups won’t have access to it.

Let’s say a company has restricted its lead data to the hands of its owner. The sales and marketing departments won’t be able to discover how many inquiries came during a specific span. And the business owner may skip its insights that might be helpful in building a more powerful sales and marketing strategy. It clearly shows that the insight drawn won’t be as effective and valuable as it could be. It could reveal inconsistencies in results and operational inefficiencies.

The limited access to a particular set of data by departments that work together may generate different conclusions and recommendations. With a centralized database, the company can integrate and manage data across different systems. It also enables its various departments to access crucial details, which leads to significant decisions.

4.      Decentralizing Datasets

Decentralization of data refers to distributing data across various departments. It triggers similar kinds of challenges, as aforesaid. With the flows of data to multiple departments, it won’t be easy to handle them effectively. The chances of errors are higher because the hands used to handle them will be different.

In order to overcome this challenge, data handling teams should be centralized. Or, they should be physically co-located while aligning with similar objectives. This is how a unified database can be created, wherein data handlers can integrate more datasets, share them, and collaborate across the entire organization. This practice will create a collaborative environment where concerned departments can discover insights continuously, increase efficiency, and reduce friction across all associated departments.

5.      Underestimating Poor-Quality Data

Bad data refers to poor quality data. Error-free and real-time datasets are luxurious. They can prevent bad decisions, and you can discover thousands of ways to make customers happy.

Today, artificial intelligence is dependent on excellent data handling to produce flexible and actionable models. These models make a neural network active. Today, we can see its refined version, which is called generative AI. It is effective in delivering spontaneous responses to a certain extent. But poor or bad data leads to its failure.

This is where data hygiene, or cleansing, emerges as a true savior. It helps in removing noises or imperfections, which lead to a well-managed data management strategy encompassing various attributes, relationships, or data types for a common model.

Summary

Data management mistakes are mainly concerned with silos, mismanagement, forgetting or ignoring objectives while handling data, and introducing errors. These mistakes can directly impact your decisions, which leads to impractical solutions.