Why is Data Management Essential for Data Science?

Why is Data Management Essential for Data Science? We will answer the question in this article.


All analytics tools and machine learning algorithms are built around data. It enables the executives to discover what actually affects the client and moves the needle.

Simply put, when used wisely and efficiently, data is an asset to any organization. The time when businesses lacked access to data and were not aware of its potential benefits is long gone.

Recent events have demonstrated that many firms have advanced past the data limitations and have a plethora of data to begin the analytics exercise.

The availability of data does not, however, solve all of the problems that organizations encounter when they embark on their digital transformation journey.

They must put in place data management solutions that are the result of the union of business and IT teams.

Data Management

As the name implies, data management encompasses everything related to data, including how information is ingested, stored, structured, and maintained inside an organization.

Although traditionally held by IT teams, efficient data management can only be achieved through cross-collaboration between IT teams and business users.

Since the business has a better understanding of the final result the firm is trying to achieve, a business must offer IT the data needed.

The data management team is entrusted with carrying out a number of tasks, including those listed here, in addition to developing policies and best practices.

Let’s examine the range of topics covered by data management:

  1. Data Storage and updation
  2. High Availability and Recovery from Disaster
  3. To comprehend the data inventory and its use, one must understand data archival and retention policies.
  4. Multi-cloud and on-premises data storage
  5. Last but not least, it’s crucial for data security and privacy to follow legal standards.

Self-Serve Analytics: An Accelerator for the Creation of Business Value

Easy data access and self-serve analytics, which form the foundation of data democratization, greatly speed up the production of business-impacting insights and conclusions.

Let’s go into further detail about this. Consider a scenario where a business analyst delivers a report to the company’s executives that focuses on achieving a certain goal, such as customer segmentation.

Now, the company must pass this request back to the analyst via the complete data cycle and wait for the revised results before they are in a position to take action if it wants to know certain additional facts that were not included in the initial draught of the analysis.

This results in an unwarranted delay in getting enough information on the table to enable all leaders and executives to accept the data and analysis and develop the company strategy, as should be clear at this point.

The report and the data also grow outdated by the time they are extensive enough for the company’s demands, which results in the lost business opportunity in terms of competitive edge.

That’s great—now we know what the issue is. Let’s change gears and consider how we may bridge the gap between the analysis provided and the business needs.

One problem is now apparent in the scenario described above: the existing state of the data, which is mostly handled and used by analysts, or tech users.

Non-technical business users (data consumers in general) can easily access the analysis of their needs and make timely decisions thanks to well-managed data systems.

Data Management in Data Science

Since we now understand data management and its importance, the equation also applies to data science teams and projects.

All machine learning algorithms are built around data. The most common consumer of organizational data is data science.

The phrase “data science does not own the data” needs to be emphasized more because it is the consumer of the possible (and ideally!!!) well-managed and organized data.

Why potentially controlled data?

Because data is frequently missing in the proper form and shape. Data challenges are what keep data scientists on their toes the majority of the time, echoing the voice and concerns of the data science community.

To ensure that the key strategic asset to the business data is adequately cared for and exploited, data management teams and the entire organization must adopt the data-first culture and promote data literacy.

When Should An Organization Claim To Have Well-Managed Data Systems?

That is a difficult question to answer, to be sure. It is imperative that the data science team begin ingesting the data into their machine learning pipeline as soon as the data management teams give the go-ahead.

Laying solid foundations for strong and well-managed data teams would be a practical approach, keeping in mind that this is an iterative process.

Yes, the underlying data management is a lifecycle approach, exactly like the iterative nature of machine learning algorithms.

It keeps changing as data science collaborates with the data management teams to enhance and improve best practices and standards.

Despite this, the data management team is the exclusive proprietor of all data-related policies, procedures, and access protocols with reliable data governance frameworks.

Many organizations are actively seeking to monetize the data as a result of the increased data created during the pandemic era, including but not limited to a better understanding of the end user, increasing operational efficiencies by comprehending internal processes, or by offering a better end-user experience.

As a result, during the past few years, there has been a rapid increase in the focus on data and data governance frameworks.

Combining teams from business, data management, and data science

Effective data governance regulations are the single most important factor in making this alignment happen. Strong communication and feedback channels are required for all three teams.

The major accelerator of the organizations’ successful digital journey is the teams’ openness to iterate and enhance the current data procedures.

In reality, data culture itself affirms that the duties related to data are not only the responsibility of one team or individual. Each company employee has a shared responsibility to build data procedures that are of the highest caliber.


All things data were the focus of this article. It began with an awareness of the general tasks and roles of data management teams.

The post’s focus in the second part is on the value of data management for data science teams and how cross-team alignment can be extremely beneficial for developing efficient data processes inside an organization.

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