Unlocking the Power of Data Democratization: Empowering Your Entire Organization with Access to Data

Data democratization entails making data accessible to all employees within the organization,  regardless of their technical proficiency, and doing away with gatekeepers who would otherwise create a bottleneck at the data’s entry point. The objective is for anyone to use data at any time to make decisions without understanding or access restrictions.

The democratization of data facilitates quicker decision-making, operational effectiveness, financial success, and improved customer experiences.

Data Challenges

We recently spoke with a customer about their challenges, and the most frequent ones were:

• Don’t have access to data.
• No integrated views of the data
• No trust in the data
• Not sure how to use the tools.
• Data SME availability

Why Data Democratization

Data Democratization will enable access to data for different departments or roles. Also ensures users can understand and visualize data in the context of their specific use cases. Some of the following users heavily rely on the data to do their regular activities.

• Sales
• Product and Engineering
• Marketing
• Customer Service
• HR
• Executives

Self-service is essential so users from organizations can explore, visualize and search for information without relying on IT-Teams who are busy and expensive resources.

Also read: Certainty in streaming real-time ETL

Data Governance and Security

Data democratization allows many users from the organization to access the data, and this leads to a potential risk of data security. So, we need to maintain control of the data platform to ensure high security and data governance. Every organization has their own unique method of security and access policy. The policy needs to be updated and kept up to date to have better data governance. Some of the key processes for better governance are:

• Create a guideline for who can access data and the type of data.
• Set the role-based access.
• Perform data discovery and profiling and create the policy based on the profiling reports.
• Set the policy for PII, HIPA and GPDR data.
• Enhance the base policy with encryption, masking, and tokenization across each data service accessed.
• Monitor the data access.
• ·Audit the data access.

Democratizing Data

Here are some of the steps that can be considered for implementing data democratization.

• Study the existing data landscape:
  • Is data on-premises, in the cloud or both?
  • Tools and technologies used to build the data platform.
  • Tools and technologies used to analyze data.
  • Volume of data managed.
  • Number of source systems
  • Level of data maturity

• Understand the stakeholders’ needs, business goals or objectives.
• Involve the business and end users in various implementing phases to achieve their decision making process
• Enable self-service analytics tools where IT involvement is very minimal and makes data governance easy
• Assess the data solutions.
• Address the following in data analytics solution.
• Descriptive Analytics — What Happened:
  • Uses historical data to measure performance. For example, tracking sales revenue.
  • Diagnostic Analytics —What Caused This?
  • Incorporates pattern recognition to help users diagnose why something is happening.
  • Predictive Analytics —The following events will take place:
  • Uses historical data, current trends, and modeling techniques to predict future performance.
  • Prescriptive Analytics —What Can We Improve?
  • Prescriptive analytics suggests future steps you can take to influence the outcomes predicted.

• Provide training to users for newly implemented democratization toolset.
• Make data accessible.

Benefits of Data Democratization

• Deliver trusted data to all employees and help on improved decision making.
• Data team can focus on strategic solutions rather than spending internal processing, which saves time and effort.
• High level data quality makes greater trust in the data and decisions
• Everyone in the organization understands the data and data becomes a second language in the organization.