Data Management: The Next Step on Your Path to Analytic Maturity

3 min read
Apr 26, 2023 9:53:28 AM

When organizations reach a certain level of analytics maturity, they often face the same challenge: How can we get more value from the available data?

Answering this question is a complicated task, even more so when we consider that when organizations grow and expand, they usually include new business units and multiple sources of information that make the use of data even more complex. 

At this point, it is essential to establish policies and guidelines that allow the information collected to maintain a certain level of coherence. 

However, there is a point where the volume of information is such that it is necessary to use cloud-based services and tools to make the most of the organization’s resources.

Next, we will dwell on the possibilities that data management brings through the cloud and the advantages of unifying all the data sources in a Data Lake or enhancing the organization’s analytical capacity through a Data Warehouse. But first, let’s see the key advantages of using the cloud for data management.  

Why is the cloud essential for data management?

When we talk about using the cloud for data management, analysis, and storage without worrying about the limits of an on-premises server is just the beginning. 

The most attractive factors of this technology are the possibility of automating processes and enhancing the data strategy through technologies such as Artificial Intelligence and Machine Learning. 

Among the benefits of data management in the cloud, we can highlight the following:

  • Eliminating information silos: The cloud allows data from various sources of information to be unified in one place. This, accompanied by good ingestion and processing policies, guarantees that all the information can be used to achieve business objectives. 

  • Creating a unified source: When all the organization’s data is suitable and is treated under the same parameters, all business units can base their decisions on the data with absolute confidence.

  • Having access to a single view of the user and the business: The entire organization can access data in real-time, allowing for accurate decisions based on all available data. 

  • Gaining access to deep analytics models: Cloud computing makes it possible to profile users to create personalized campaigns rapidly. 

The advantages of cloud data management are often those of cloud services, including pay-as-you-go pricing, scalability, anywhere access, automated backups, and a single source of truth for organization-wide data.

What is a data lake, and why does your organization need it?

A data lake is a secure and flexible repository that stores data from different sources of information in the cloud. 

Thanks to this technology, we can store, govern, share and analyze the entire organization’s data to benefit all areas. 

Organizations that decide to create a data lake manage to eliminate their data silos and optimize the use of information through visualization tools capable of displaying information in real-time and highly customizable filters that allow queries to be precisely aligned with the needs of each area. 

It is worth mentioning that cloud service providers, such as AWS, have created tools to facilitate both the creation (Amazon Lake Formation) and governance of data lakes (S3).

Using these tools, you can simplify some of the most complicated processes when implementing this type of solution, such as:

  • Creating data ingestion pipelines. 
  • Integrating tools for viewing or using data.
  • Managing different layers of permissions to access information. 
  • Sharing the insights from the analysis with other areas or business units. 

A data lake is essential for an organization that handles large volumes of data to take advantage of them efficiently. However, many organizations go one step further and create a data warehouse to take their analytics strategy to the next level. 

What is a Data Warehouse, and when should it be used?

First, we must clarify that although a data warehouse may be the natural evolution of a data lake, we can find cases in which data warehouses are implemented from on-premises databases or even hybrid solutions. 

Whatever the origin of the information with which you work, a data warehouse always fulfills the same function: It organizes the data to take the analyses we can perform from them to the next level. 

In a data lake, we had a pile of information from different sources and had been treated through policies and guidelines to make it “speak the same language.” Conversely, in a data warehouse, it would be as if we had taken all that information and organized it in a vast library that makes it easy to consult and use the latest technology tools to obtain actionable insights. 

A clear example of the above is the ability of data warehouses to drive the organization through advanced analytics. In these cases, learning algorithms are created to review the data and identify patterns representing risks or business opportunities. This is one of the most advanced phases of an organization’s analytical maturation process. Still, it is undoubtedly the horizon that should be aimed at to overcome future challenges. 

Are you looking for a partner to help you take advantage of the full potential of your data?

At Pragma, we have years of experience working with companies at various levels of analytics maturity, and our teams of data professionals are ready to get down to business. 


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