Understanding Data Democratization: Definition & Strategies

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.

Data democratization doesn’t just involve empowering every worker to understand and act on data, but also includes creating the systems and adopting the tools necessary to achieve these goals.

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: In order to foster an environment that facilitates data democracy, 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.

Key benefits of data democratization include eliminating barriers between teams and individuals, allowing everyone to access and interpret the same data. Doing so can help foster innovation by tapping into the collective intelligence of individuals and teams. It can also help teams make faster, smarter decisions based on the data that is captured.

But before we get into more about how companies can benefit from data democratization, let’s look quickly at why data democratization is important in the first place.

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 the 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%, according to research by IDC.

Improved data literacy

One of the key benefits of data democratization is that it helps improve employees’ ability to interpret and use data effectively. This improvement allows for decisions to be made faster and smarter. When employees at every level of an organization can read, analyze and interpret data, outcomes tend to improve.

It’s worth noting that for the best results, different data literacy levels are required at different levels of the organization. For instance, data democratization looks different for a store floor associate compared to a CEO.

Empowered workforce

Data democratization empowers employees with the tools and information that they need to take initiative and innovate. It can help them make decisions based on evidence rather than intuition. This can be true no matter the level of the employee, from the front lines to the C-suite.

Smart data capture solutions are an example of one tool that can be utilized to facilitate a more empowered workforce. By providing real-time insights at the point of data capture, it empowers frontline workers to take data-based decisions in the moment. For example, a retail associate could scan product barcodes in-store and see real-time stock levels on an augmented reality overlay.

this is a test

And this is domething else

i am a link

Expedited decision-making

The more quickly you’re able to access data, the faster you’re able to make educated, data-based decisions. For example, in last-mile delivery, smart data capture applications surface rich, real-time data such as delivery zones and address changes. These help drivers to optimize sorting and van loading and make faster decisions en route.

Enhanced operations

Time is money in any business — and democratized data can help improve efficiency and effectiveness when used correctly and to its full potential.

One example is Airbnb and its Dataportal, a data discovery solution self-service tool that any employee can access to better discover and understand data to make better decisions and streamline operations.

In retail environments, shelf intelligence solutions can deliver accurate, comprehensive, accessible shelf-level data and insights in near-real-time. These empower associates to take immediate action to optimize on-shelf availability and planogram compliance.

Better customer experience

Businesses wouldn’t make a profit if they didn’t have customers who purchased their product or service — and data democratization can help benefit the customer as well.

Apart from the indirect benefits to customers through more efficient operations, customers benefit directly through being given direct access to product data that helps inform their purchase. An example of this is free mobile app Yuka, which enables consumers to scan product barcodes and obtain instant evaluations of the nutritional value of food products or the healthiness of cosmetics ingredients.

Despite its benefits, data democratization also faces some notable challenges — specifically when it comes to technical, cultural, and even security-related problems.

Data literacy and training

Even if data is easily and widely accessible, it doesn’t mean that employees know what to do with it. That’s why ongoing training is so important in such organizations. It can be challenging when you consider the different levels of workers in any company and that they may need to access data differently.

Choosing tools that automatically surface the right data, to the right employee, at the right time, also mitigates the need for extensive training. For instance, a smart data capture app running on a smartphone might help a field service engineer to identify which parts to prioritize for maintenance, with minimal training required.

Cultural shifts and adoption

Any significant change in an organization can be met with resistance — and changing to a data-driven culture is no exception. It may be difficult for workers to change from a strategy they believe is working effectively to a new approach, even if sold on all of its benefits.

The adoption of any new mindset or strategy must start from the top with buy-in from leadership. It also helps when the tools involved have a short learning curve and are easy to use.

Governance and security

Democratizing data may also come with security concerns. When company and customer data is more accessible, it may be more accessible to everyone — not just employees. This could increase the risk of data breaches, data misuse and unauthorized access to your company’s data.

Because of this, robust data governance policies are required. Part of this should be ensuring that any providers have their own robust data governance policies in place. (Scandit, for example is ISO 27001 certified.)

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 (also known as intelligent data capture).

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.

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.

1. Secure leadership commitment

Any paradigm shift in strategy has to come from the top. If you can’t get leadership buy-in and members of leadership to communicate why a company is evolving in this way, workers aren’t likely to follow suit.

Some tips to get leadership to buy into data democratization include:

• Focus on the benefits and how it can improve operations.
• Focus on the ease of use and short learning curve of data democratization tools and technologies.
• Find advocates who can support and champion the initiative.
• Choose the right time to make the change.

2. Assess your data ecosystem

Successful data democratization largely involves accessing the current data ecosystem to find opportunities for improvement and fill in any gaps. Do this using key performance indicators (KPIs) to help ensure that any metrics align with your objectives.

Look for things like quality of data, how accessible the data is, and cost efficiency, among other KPIs.

3. Unlock legacy data

To fully democratize data, a company has to unlock its “legacy data.” Legacy data is defined as data that is either outdated or stored in obsolete IT systems. This legacy data still has value, even while accessibility may be a challenge.

Integrating legacy data begins with evaluating capabilities for integration. Depending on this, integration may be something that’s able to be done in-house or it may require the help of an external service provider.

4. Promote self-service

Enabling self-service data access helps improve decision-making, reduce costs and help streamline a more data-driven culture.

There are a multitude of tools that can help support self-service, from Tableau to Power BI to smart data capture tools that democratize access to data for frontline workers.

5. Provide ongoing education and support

Data literacy is one of the key challenges of data democratization — and one of the best ways to overcome it is to provide ongoing training and education.

Provide webinars, hands-on and online training on how to use tools. Encourage continuous conversations around data, reward data-driven decision making, and establish a culture of continuous improvement.

6. Encourage collaboration and knowledge sharing

One of the big advantages of data democratization is breaking down silos and ensuring that workers at every level have access to the data and insights that help them excel at their jobs.

Encouraging collaboration should start at the top with leadership. Other strategies for encouraging collaboration include using accessible tools and facilitating environments for collaboration where different perspectives are welcomed and employees feel empowered to take data-driven decisions.

Data capture tools

There are many data capture tools that can help with data democratization. At the most basic level, meaningful data democratization depends on accurate data being input into the system in the first place.

For a digital company, this step is relatively straightforward. However, for any company with a significant physical presence (such as retailers, manufacturers, logistics companies, airlines or healthcare providers) accurate capture of data can be a major challenge.

Ensuring that core data capture technologies and workflows such as barcode scanning and passport and ID scanning are fast, accurate and reliable is a critical first step. If employees don’t trust the data, they will never even take the first step towards data literacy.

Advanced smart data capture solutions are also a key data democratization tool for frontline workers. Smart data capture gets actionable, real-time insights into the hands of frontline workers at the point of data capture in accessible, intuitive ways, empowering them to take data-based decisions in the moment.

Data analysis tools

Good data analysis and data visualization tools should integrate with your existing infrastructure and offer ease of use. Some solutions include Tableau, Excel, SAS, Python and Looker.

These tools are designed to integrate with data capture tools, analyze multiple data sources, and grow and evolve over time.

Data governance tools

Data governance tools are solutions that administer, monitor, control and report on data throughout its entire lifecycle — from creation to duration to archival. They also help firms establish policy-based principles. Some tools include Atlan, Erwin, Collibra, Alation and SAP.

One of the best real-world examples of data democratization involves Pandora, the jewelry company that operates with nearly 30,000 employees throughout 100 countries.

Pandora worked to successfully merge data from all of its online and brick-and-mortar stores. They used tools like data science workspace and data warehousing to meet the needs of the organization’s three main personas and overcome silos and operational bottlenecks.

Another example is VF Corporation, one of the world’s largest apparel, footwear and accessories companies. They deployed smart data capture to give store assistants full inventory visibility.

Store assistants now know exactly where an item is in store, even if it hasn’t been fully processed and moved to the shop floor.

Technology development by non-IT professionals

In the past, IT professionals were synonymous with developing and using technology. Increasingly today, it’s every employee who can benefit from using technology by encouraging data literacy and putting the right systems in place.

Data democratization is also making its way to the frontline. The Scandit Smart Data Capture Platform plays a significant role in this, empowering frontline workers with actionable insights that helps complement their existing skillset.

AI’s emerging role

Artificial intelligence (AI) is already revolutionizing the way people view and interact with data — and it’s only going to play more of a role moving forward. Smart co-pilots will build data into every workflow by delivering dynamic, automatically prioritized actions and insights using whatever device and platform is appropriate for different employees.

Innovation across industries

One of the key goals of data democratization is breaking down barriers between departments within a company. On a grander scale, however, it can also be used to break down barriers and encourage collaboration and innovation between industries.

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.

What is data democratization and why is it important?

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.

How can data democratization improve decision-making in my organization?

The more quickly you’re able to access data, the faster you’re able to make educated, data-based decisions. Democratized data results in distributed decision making within organizations, where employees at all levels are empowered with the right tools and levels of data literacy to take informed decisions aligned with company priorities.

What are the main challenges of implementing data democratization?

Key challenges include data literacy and training, cultural shifts and encouraging adoption, and governance and security involved with such environments.

How does data democratization impact data security and governance?

When data is more accessible, it may be more accessible to everyone — not just your employees. This could increase the risk of data breaches, data misuse and unauthorized access to your company’s data assets.

What tools are essential for successful data democratization?

Data capture tools, data analysis tools, data virtualization software, and data governance tools are all essential for successful data democratization.

How can organizations encourage a culture shift toward data democratization?

Various strategies for a shift toward data democratization include securing leadership commitment, unlocking legacy data, promoting self-service, providing ongoing education and support, and encouraging collaboration and knowledge sharing.