Data is a valuable asset in today’s digital age. Enterprises that capture and analyse data get rich insights rarely discernable in plain sight. They gain a deep understanding of business operations, customer behaviour, and market trends. Businesses leverage such insights to make informed decisions and reap competitive advantages.

Benefiting from data, however, depends on rank-and-file employees having access to the insights. IT has been the traditional gatekeeper of data in the enterprise. Data analytics has been the exclusive domain of data scientists and a few employees with technical expertise.  Business units needing data to make critical business decisions must wait for the IT team to make the data available. The process is time-consuming and inefficient. The data analysts may not appreciate the sense of urgency, and even if they do, they may have competing priorities to handle. By the time the analysts offer the insights to the business executive, the customer might have moved on, making the insight redundant. Or a competitor might already have unlocked the insight and converted the customer. 

Enter data democratisation.

What is data democratisation?

Data democratisation makes data accessible and available to the rank-and-file. Employees without expertise or analytical skills access, analyse, and use data without expert help. 

Data democratisation goes much beyond making data accessible, though. The approach ensures the lay enterprise user can understand and derive actionable insights from the data. The best data democratisation initiatives make employees confident in making data-driven decisions. It enables self-service analytics with no code to low code tools that feature user-friendly elements such as drag-and-drop modules.

Why data democratisation?

Data Democratisation ensures fast access to data. The reduced wait makes business processes more efficient. Employees can act fast to seize opportunities within the short window of opportunity.

In many enterprise settings, even accessibility is an issue, leave alone speed. Much data resides in siloes owned by functional bosses or individual employees. The use and analysis of such data depend on such owners, limiting the opportunity to share such data with other stakeholders. And when sharing such data, the lack of a proper platform leads to varied interpretations and incorrect decisions. Data Democratisation ensures a “single source of truth.” It offers access to the right data, to the right user, at the right time.

Resiliency is essential for success in today’s uncertain and fluid business environment. Resiliency comes with a self-reliant workforce. One critical element of self-reliance is data literacy. Data literacy means the ability to access, analyse, and interpret data to make data-driven decisions. The prerequisite for data literacy is data democratisation.

In the ideal scenario, data democratisation allows everyone in the enterprise to access and analyse data. Such easy and immediate access improves collaboration and makes informed decision-making viable. Empowering non-technical users reduces the need for professional data scientists and saves costs.

The self-service analytics made possible by data democratisation enables business users to customise the information they need, to work optimally. For instance, a finance executive may fill his data mart with relevant information to make annual reports and projections. A sales executive’s data mart may have resources for fast pipeline analysis that make explicit fast-selling and dud products. 

How to Implement Data Democratisation

Data democratisation does not do away with data scientists or the IT team handling data. Rather, their role changes. Instead of analysing data for others, they enable the workforce to access data easily. They put in place tools for the rank-and-file employees to access context-driven relevant analytics.

Understand the enterprise data usage patterns.

Develop a data strategy that outlines the goals, objectives, and roadmap for data democratisation. Understand the data needs across the enterprise.

Assess the current data capabilities of the enterprise. Review the data generated and captured and how users use such data. Take an inventory of data sources and identify access and usage patterns. An enterprise will have varied needs and collect and store data in different ways and formats. For instance, sales teams use CRMs and may store data in CSV format. Marketing teams use automation tools to distribute PDF and JPG content. Support teams may use helpdesk systems to engage with customers and use emails, logs, and other data types.

Find out the tools and resources currently available and the adequacy or inadequacy of such resources. Identify the gap between available and needed resources to implement the data strategy.

Invest in data infrastructure.

Data democratisation leads to increased data volumes as more and more users grapple with data. Traditional BI tools, which are rigid and not scalable, cannot keep up with such increasing data volumes. In any case, these tools are for data scientists with the technical know-how to extract insights from data. The latest data tools that further data democratisation enable self-service analytics.

Choose platforms that offer:

• Ease of set-up and use.
• Flexibility and scalability to adapt the platform per the enterprise’s specific needs.
• Natural language capabilities. Users may ask questions in human conversational language and get answers likewise. 
• Simple yet powerful user interfaces that empower business users with self-service capabilities.
• Resources to integrate data from disparate sources. Pre-built connectors and embeddable APIs make data integration seamless and fast.  
• Data visualisation capabilities, with the option to customise the visualisations.
• Actionable insights, interact with data stories and share findings easily. 
• Automated data catalogue for efficient data management and observability
• Easy-to-author policies and audit logs for effective data governance. 

The latest tools also co-opt AI-powered intelligent search and interactive audio-visual stories.

Ensure effective data governance.

Implement robust data governance policies to ensure the accuracy and integrity of collected data. The best governance systems co-opt robust access control frameworks that make available data on a need basis. It allows supervisors to set granular policies to ensure that the right people can access the right data. 

Challenges that impede data democratisation

The obvious benefits notwithstanding, several enterprise-level obstacles become stumbling blocks to data democratisation.

• Overcome resistance to change. Implementing data democratisation initiatives necessitates change. Like all change initiatives, data democratisation also meets stiff resistance from the rank-and-file. Some employees wield power by hoarding data. As such, they resist democratisation initiatives. Identify such resistance and take focused initiatives to overcome it.
• Simplify the organisational structure. Rigid power structures and hierarchy-driven enterprises work counterintuitively to data democratisation. Encourage a culture of sharing insights and data-based collaboration. Promote transparency and openness of data.
• Provide training and support. Data democratisation initiatives often fail as the rank-and-file employees lack competencies. Institute training to ensure familiarity with the data platforms. Make targeted interventions to improve data literacy. Offer ongoing support to help users troubleshoot issues and get the most out of the available tools and resources.

Data democratisation is still in its early stages. But there is huge pent-up demand. In a recent Harvard Business Review and Google Cloud survey, 97% of respondents believe organisation-wide access to data and analytics is critical for business success. But only 60% of respondents believe their enterprises offer such access. Enterprises can leverage data democratisation to optimise their data assets and become more competitive.