Data Democratization


Data democratization refers to the practice of making data accessible and usable by a wide range of users, regardless of their level of technical expertise. In other words, the term refers to the creation of a data ecosystem where information is accessible and understandable by anyone, not just IT professionals or data scientists.

The immediate benefits are that democratization improves transparency, accountability and efficiency of companies and society as a whole. For example, in the sector of PA services to the public, it can favor the reduction of inequality in access to information and promote transparency and accountability of the PA towards citizens.

But what are the steps needed to achieve these benefits?

Data Democratization is a business process first and foremost. According to Gartner Group: “It is a process that means everybody has access to data and there are no blockers and the goal is to utilize data without any barrier. Data Democratization creates more data-driven organizations. Moving forward, data democratization is defined as the top tech trends for enterprises”.

Certainly, nowadays, information technology favors this process through Data Virtualization, Artificial Intelligence, Cloud Computing and Big Data Analytics, since thanks to these technologies, it is possible to process large amounts of data and offer useful and relevant information to anyone who needs it.

In this article we focus on two aspects that directly concern us because, as Humanativa Group, we are directly involved in supporting our Clients to make their Organizations Data-Driven:

  • the Client’s point of view in dealing with this process of democratization of data
  • and the point of view of Humanativa’s Technological Services to support the process itself.

The Client’s perception of data democratization

Client perception certainly depends on the right balance between specific business needs and market circumstances. But democratizing data can deliver significant long-term benefits for companies that adopt it, enabling them to use data more efficiently and develop new solutions to meet end-customer needs. Let’s examine the key factors from the point of view of the Business:

  • Costs: Investments in technologies and infrastructure for Data Governance, such as Data Virtualization, and value-added services such as Artificial Intelligence, can result in high upfront costs for the company. However, democratizing data will reduce long-term costs, as it allows data to be used more efficiently and made more informed decisions.
  • Benefits: Making information accessible has two aspects:
    • Within the organization: how to improve collaboration and share information.
    • External to the organization: such as allowing you to identify new business opportunities, develop new solutions and improve customer satisfaction.
  • Constraints: Starting the process also means having the determination as management to enforce data protection, privacy and data security regulations. At the same time, there is a need for greater transparency and accountability in data management, which could place additional constraints on the business. Most importantly, a collaborative organizational culture needs to be fostered, which means investing for cultural change within the organization as it requires data sharing and collaboration between departments. This can be difficult for some companies that have a traditional organizational culture or a “siloed” mentality, where departments work in isolation and don’t easily share information.
  • ROI: Data democratization has a positive and indirect impact on a positive long-term ROI, as it allows you to use data more efficiently in order to make more informed decisions and develop new solutions. Achieving a positive ROI takes time to achieve, varies depending on the industry and specific business needs and, above all operationally, may depend on the effectiveness of the technologies used for Data Democratization.

Humanativa technologies to support the Client process of Data Democratization

There are two enabling technologies for Data Democratization that in this article we would like to underline, as Humanativa’s competence in leading enterprises towards Data Democratization: Data Virtualization and Artificial Intelligence.

  • Data Virtualization: As a Denodo partner, we use Data Virtualization technology, as part of Data Governance services, to integrate and combine data from different sources, without the need to physically move data from one location to another. This is the first fundamental step for the democratization of data since Data Virtualization aims to:
    • Improve data access and use
    • or help simplify the data analysis process,
    • or improve the efficiency and rapidity of data analysis.

Data Virtualization is an enabling technology because it can be used to create a virtual view of data, integrate data from different sources, share data between different applications, and access data in real time. Some examples:

  • Access integrated views of Sales Data, CRM and ERP to allow users in Sales and Marketing, but also Management Control to access sales, product and Customer data, CRM and ERP data without the need to access different applications or databases.
  • Enable the construction of Advanced Analytics, allowing users to perform advanced analysis on customer and sales data.
  • Access real-time data: allowing users to use the most up-to-date and relevant data. For example, customer transaction data.


  • Artificial Intelligence and Machine Learning. We provide Machine Learning Services and AI services that are an added value towards the democratization of data, especially if it draws on the data made available in the Data Preparation phase thanks to Data Virtualization. In this context, ML and AI are enabling in the democratization of data because:
    • extract knowledge from the “integrated” Data
    • improve the quality of data: identifying errors and identifying hidden trends. A knowledge certainly difficult to locate manually.
    • improve the user experience: e.g. from Real-time ML Model Queries, to Advanced Analytics Dashboards, to Chatbots.


ML algorithms are therefore useful for the democratization of data. Some examples that aim to improve the user experience:

  • Clustering algorithms: explore data in greater depth and identify hidden trends.
  • Classification algorithms: to classify data according to different categories, making it easier for non-specialized users to analyze the data.
  • Regression algorithms: to predict future outcomes, helping users make data-driven decisions.
  • Deep learning algorithms: to create advanced predictive models on structured and unstructured data.

How does Humanativa accompany the Client to promote Data Democratization?

Through the skills of our Data Stewards (for Virtualization) and Data Scientists (for Machine Learning) we provide the fundamental roles in Data Democratization and carry out some actions to foster it for clients. The 5 fundamental levers we offer in our services of both Data Governance and Machine Learning are:

  • Communication and Training: Our management can communicate the importance of Data Democratization and information sharing to end-customers and employees of the Organization. In addition, we provide training and support to ensure that our Clients understand the benefits and risks associated with data democratization.
  • Creating Data Governance: Our Data Stewards have the expertise to collaborate with Clients in creating data governance that defines the responsibilities and procedures for managing, protecting and sharing data within the company.
  • Use of virtualized data access tools. Our Data Stewards create and manage the Data Catalog and provide integrated data standardization and dissemination, enabling the Client to access and use data easily and securely, up to and including making data views available for custom dashboards or APIs for data access.
  • Data Visualization & Advanced Analytics: Our Data Scientists develop machine learning models to help Clients make more informed decisions. They deliver in output Advanced Analytics based on ML Models by increasing knowledge about the data, helping the Client to better understand the data and identify new business opportunities.
  • Data protection: Through standard tools and operating procedures, the competence of Data Stewards is also in the use and proposal of security protocols and data access to ensure that only authorized persons can access sensitive data. At the same time, our Data Scientists ensure that machine learning data meet with data privacy and security regulations.

In summary, through these specific actions, we contribute to promoting a more efficient and responsible use of data and to favoring greater collaboration and sharing of information within the Organization and with end customers.

To conclude, this article is inspired by an interesting exploratory survey carried out by our Partner Denodo which, in collaboration with IKN Italia, published the results of an exploratory survey on the priorities that Italian companies have for Data Democratization in 2023, analyzing how Italian companies are interpreting, living and implementing the democratization of data.

Article written by Maurizio Mansueti

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