Data democratization definition
Data democratization is the process of empowering everyone within an organization to access, understand and act on data – regardless of their role, location or level of technical expertise. It covers both desk-based and deskless (or frontline) workers, is a culture as much as a strategy, and involves the way data is collected as well as the way it is shared and used.
There are three pillars of an effective data democratization strategy:
1. Trust: For employees to trust data, the data itself needs to be reliable. Accurate and repeatable collection of data is central to data democratization.
2. Access: Employees need access to data tailored to their needs, through self-serve analytics tools specific to job function.
3. Confidence: Employees need to have confidence that they understand the data sufficiently to act meaningfully upon it. This can happen through training, but preferably is achieved via intuitive analytics tools that enable self-discovery. These need to be developed through user-centered design.
But before we get into these, let’s look quickly at why data democratization is important in the first place.
What are the benefits of democratization of data?
Imagine a political democracy where you were only allowed to vote if you had studied politics at university. In truth, it wouldn’t feel very democratic at all.
Instead of restricting the use of data to experts, data democratization presents it in ways that are easy for everybody within an organization to understand and act on.
Expanding the availability of data beyond the departments that have traditionally owned it allows more agile responses to changing situations.
The democratization of data can also highlight and solve problems that wouldn’t otherwise be visible. For example, if retail associates have real-time insight into customer footfall, there is an upward feedback loop which makes it easier for head office to get a view “from the floor”. They can then work with associates to change store layout quickly if a drop in traffic is detected.
The rewards for implementing such an open culture can be significant. Businesses that capture and aggregate data intelligently can grow revenue by over 30%.
The three pillars of a data democratization strategy
Data democratization depends on three core principles. Though exact data requirements differ from department to department, from role to role, and from frontline to desk-based workers, the same three pillars still apply when developing a data democratization strategy.
Pillar 1: Trust
For employees to trust data, the data itself needs to be reliable. This means accurate and repeatable collection of data is fundamental to data democratization.
For most companies, this needs to encompass not just digital assets but also data capture from tangible assets and physical operations. In a retailer, the digital marketing department would likely rely on a web analytics package, while collection of data in store (such as inventory levels) would utilize smart data capture technology.
Companies then also need to make data available for analysis – through a data warehouse such as BigQuery or Snowflake and other elements of a modern data stack. This process is typically highly technical.
Pillar 2: Accessibility
To have access to data tailored to their needs, employees need accessible, self-serve analytics tools specific to their job function. These also need to serve them data on a one-to-one basis.
This may sound obvious, but, while access to laptops is rarely a problem for desk-based workers, frontline workers often have to share devices. This can limit their ability to access self-serve analytics.
The types of tools employees need vary widely. A product team, for example, might run sophisticated data visualizations through a business intelligence tool such as Tableau or Looker. This allows them to analyze product adoption, buying and demand generation trends, progress towards targets, gaps and improvement areas.
In contrast, frontline workers are often away from a desk, interact principally with real-world objects (such as packages in a van out for delivery) and need quick, actionable insights.
They require mobile tools – typically smart data capture apps running on smartphones – that can identify real-world objects, connect them with real-time system data, and provide easy-to-use insights.
The toughest challenge can be this “final mile” of extending the availability of data to frontline workers. However, overcoming this barrier is essential to helping the physical industries that make up 75% of the world’s $100 trillion GDP access the benefits of data democratization.
What is smart data capture?
Data democratization in traditional industries relies on smart data capture.
Pillar 3: Confidence
Data democratization is intrinsically linked to data literacy. But data literacy doesn’t necessarily mean extensive training, or understanding data more deeply than is required to perform your role effectively.
In the context of data democratization, data literacy means feeling confident in interpreting and acting on data in a way that’s appropriate to your role – whether that is as a CEO, a product manager, an operations director or a frontline worker.
One of the best ways to improve data literacy is through the development of intuitive tools that enable self-discovery, benefit day-to-day work and fit seamlessly into it.
This means designing for humans. Individual employees and departments are likely to be the best people to decide what data would be useful for them, and how they would like it presented. User-centered design is vital to instill confidence in data among employees at all levels of an organization.
Data democratization is an ongoing journey
Technology is always evolving, and data democratization involves constant change. New ways to gather, process and share data will be developed continually. Customer demands will evolve, competitor standards will improve.
As we all get more sophisticated with our use of data, we need to bring more and more of our colleagues along for the ride. Data democratization is, and will always be, an ongoing process.