Data is essential for organizations that are looking to maintain a competitive edge enabling them to pinpoint potentially profitable areas that they may have been missing for a long time: to drive down costs, and to make decisions based on fact and not the fallibility of gut instinct.
Data democratization allows the average end users to be in a position to assess the data in a digital format without requiring help from the outside. It is a source of decisive competitive advantage in any organization.
The way of thinking and culture of an organization is driven by people who are passionate about data. Data democratization can be seen as a game-changer today as it makes it easier, faster and simpler for the people in a company to get access to the insights they need. Data democratization protects the organization from becoming a top-down company where the opinion of highest paid person wins. The people are given more ownership and responsibility with data democratization. Let us see how this happens.
In an organization, the data democratization works on four major pillars: tools, data, people, and training.
In most organizations, a considerable fraction of data exists in silos spread across flat files, Microsoft SQL Servers, with partner companies, and in the private folders of the people. As one would expect, this is not conducive to allowing one to view the ‘big picture.’ The creation of a cloud-based data warehouse is being done to tear down the silos. The cloud acts as a consolidated, solitary source of truth for data analytics. Companies are regularly sharing anonymized or aggregated data with independent third parties to increase their transparency.
Tools and Training
All types of data cannot be processed by a single analytical tool. For powerful and accessible self-service analytics, we can use Tableau Server and Tableau Desktop. We can also consider open-source alternatives like caravel of Airbnb or Apache Zeppelin. For heavy data processing and more involved analyses, we can utilize Python and its remarkable PyData stack that runs on an in-house JupyterHub set up based on Docker.
Data democratization can be stated as a process of empowerment. In order to make sure that data democratization doesn’t come with data misinterpretation, the people of a company train each other with the creation of self-study training material by ensuring efficient dissemination of expertise through mailing lists, HipChat channels, seminars, and even ensuring that learners and experts are sitting next to each other in the office.
Companies are determining that there is a large group of business users who need to explore the data more deeply and freely on their own. A combination of training and analytics tools is needed for these business users. Rather than limiting the analytics to providing just raw or summarized data, a multi-tiered approach is recommended so different users can move towards the right depth of data depending on their analytical skills and needs. After dashboards and static reports, the next tier might be interactive, dynamic dashboards where the users can drill into different areas to get incremental insights.
The next tier is the guided analysis experience that the analyst prepares for a group of business users or a particular user. The analyst essentially creates a safe and rich environment in which a lower number of technical users can follow the analysis process through annotations and explanations. The last tier is the access to a visual data discovery tool where the business users can explore a broad set of data instead of via less intuitive means like data tables and SQL queries. To allow the users to impart the data in a simpler way into an analytical tool that is more familiar, like Microsoft Excel, is another alternative. As a greater level of data access is provided to the business users, an internal certification course can help avert the users from misusing and misinterpreting the data.
Expertise in data analytics is closely associated with a specific type of mentality – that of open, persistent, positive, and inquisitive people. Companies are ensuring that those kinds of people are rewarded during their appraisal or hiring processes. Besides, they are trying to keep people motivated and engaged to think and play with data and ask questions. Experts are invited to deliver seminars on key concepts, tools, and new technologies.
Challenges Faced During the Implementation of Data Democratization
Now that we have learned how data democratization is implemented in an organization, let us learn about the challenges that it is facing. The main challenge is that data teams are struggling to keep up with the rising hunger for data from all areas of the organization, mainly when throughout the organization, the understanding of data demands more complex analysis. For many organizations, this has created a conundrum – they want to provide each and every employee with a chance to be data-driven, but they are short of resources to deliver.
The emergence of self-service analytics has solved this problem. It enables everyone in the organization to become a data scientist. This is through making data available to anyone who needs it at any given point of time. Usually, this is carried out via dashboards, which can provide real-time data but can also be wholly implemented by analysis tools in some cases. Simply putting the technology in place is not enough to make it work. The training that is necessary for staff must be provided to bring real value. Thus, self-service analytics is making data democratization highly efficient.
In your current organization, if you want to increase data democratization, you must keep in mind that this is a slow process in which small wins are brought through incremental transformations in culture, which in turn boost the next cultural change. Today, more and more organizations are trying to give access to data to all of their employees through data democratization, and this is helping them enhance job performance and the overall health of the organization.