Data Democratization

Data Democratization | Top Tips for starting your own Data-Driven Revolution

Data Democratization is a common boardroom priority across a number of industries, from Financial Services to Healthcare.

It was one of Gartner’s top 10 strategic technology trends in 2020 and will continue to be a huge focus for organizations in 2021.

The democratization of data has been described as the “ability for information in a digital format to be accessible to the average end user”. In other words, the goal of data democratization is to allow non-data specialists to be able to gather and analyze data easily to aid them in their role. It’s the freedom, equality and true team culture that comes about by empowering every employee — not just data scientists and analysts — to make decisions informed by data.

The opportunity

Data, and the universal access to it, is key for today’s organizations to solve problems, create new opportunities and unlock the value embedded within their organization – all of which can positively impact a company’s top and bottom line.

It pushes organizations to re-think and maybe even restructure, which often means driving a cultural change in order to realize financial gain. It also means freeing information from the silos created by internal departmental data, customer data and external data, and turning it into a border-less, integrated ecosystem of information.

The current state of play

Disappointingly, very few businesses are living and breathing the core principles of data democratization currently. Our research last year initially suggested senior decision-makers were confident that they were opening up access to data sufficiently. However, when we scratched a little deeper, we found almost half (46%) of respondents believed that the democratization of data wasn’t feasible for them.

IT infrastructure challenges were cited by almost four out of five respondents as a blocker to democratizing data in their organization. Performance limitations, infrastructure constraints and bottlenecks were also standing in the way.

This tells us that on a major scale around the world, many valuable insights from data aren’t being gathered quickly enough, projects are being stalled and the competitive edge is being lost.

So, what steps can organizations take to create an effective data democratization program of their own? Here are my top tips:

Assign a leader responsible for data

Organizations should consider recruiting a Chief Data Officer (CDO) to take ownership of its data. According to KPMG, organizations with a CDO are twice as likely to have a clear digital strategy in place. The role of the CDO is to drive the business forward on multiple departmental levels – from revenue growth and advancing internal innovation to improving operational efficiency.

Achieving true data democratization relies on having the right skills in your people and triggering their imagination and innovative ideas. The CDO is one of the best-placed individuals to make knowledge of data an integral, normal part of the everyday life of an organization.

Develop an overarching data strategy

Organizations also need a coherent data and analytics strategy in place to extract all of the insights they need. The most effective data strategies are integrated within the overall business strategy and establish common and repeatable methods, practices and processes to control and distribute data business-wide. Also, if the whole organization is involved from the beginning, they will be more inclined to help drive a strategy forward.

A robust data strategy and culture that harnesses data democratization also requires the right infrastructure to support it. When choosing a deployment model, organizations need to consider factors such as speed, cost, future requirements and the types of workload expected. Therefore, it’s important for businesses to make this decision after the data strategy is in place – to fully evaluate whether on-premises, cloud or a hybrid approach is the right option for what they want to achieve.

A hybrid cloud approach can often be the most efficient. It allows organizations to manage sensitive workloads on-premises, but also utilizes the cloud – which is powerful when it comes to delivering large volumes of data to lots of people in real-time.

But no matter how brilliant an organizations strategy and infrastructure, it is worthless if its employees don’t buy into it.

Develop an employee education process

As organizations provide more teams and departments with access to data, they’ll need to build training into the process. Championing data literacy and trying to teach data as a second language within the business will be critical. Some firms go further and develop a Data Centre of Excellence (CoE) too.

Regardless of their level of technical expertise, everyone working with data can gain confidence by familiarizing themselves with the components of the analytics stack in their organization and the best practices that come with it.

AirBnB is a great example of a company doing this well. Despite having a data science team of more than 100 people, the company’s fundamental belief is that every employee should be empowered to make decisions informed by data. In an effort to scale its skillset, AirBnB has developed its own in-house Data University and curriculum for its team.

Implement the right tech stack

Getting the tech stack right – or as right as possible – will help organizations ensure the resulting solution is fit-for-purpose. When scoping out options, they should keep in mind where they want to get to in the future and how they can enable more people to work with data to drive insights and support decision-making. During this process they should test the use cases of different departments and rather than giving demonstrations, let employees experiment with their own data and the tools to give them a clear idea of what’s in it for them.

The co-existence of multiple analytics tools within different teams and departments can add a layer of technical challenge but it’s one worth solving. Having teams with various tools at their disposal will provide greater opportunities for the right tools to be used at the right time for the right purpose and outcome.

Learning from the leaders

With all of this in mind, there are a couple of companies who are doing data democratization well that demonstrate its value.

Firstly, digital banking alternative, Revolut. The UK fintech knows exactly how important data is to its success and reputation. It is an extremely data-driven company, maintaining around 800 dashboards and running around 100,000 SQL queries on a daily basis across the organization.

By embracing analytics and implementing an improved data management foundation, Revolut was able to unlock the true value of its data. Queries that used to take hours are now completed in seconds, enabling self-serve data analytics for all employees across all business functions. This is despite data volumes increasing 20x over the past twelve months.

The company wanted to ensure everyone has access to the data they need for their daily work in a simple and efficient manner. On top of this, the data science team uses the central database as a single point of truth, from which it can download real-time extracts and insights from at any time.

Revolut can now optimally analyze large datasets spanning several sources to assist in fraud detection, improving customer satisfaction and financial reporting.

Not-for-profit healthcare provider Piedmont is another great data democratization example. It has successfully turned a massive 555 billion data points into an actionable source of information for its employees. By replacing its data warehouse and its core data repository with a high-performance in-memory analytics database, it has opened up access to data to more decision-makers who are now much more informed and able to improve the running of the company.

Hospital care quality, operation outcomes, and patient satisfaction have all improved as a result of Piedmont transforming into a data driven healthcare provider.

Data democracy in 2021 and beyond

Organizations are under enormous pressure to become data-driven, and as a result should be ramping up their efforts to democratize data access across the entire business. For many organizations earlier in the data journey, 2021 will be a year of continued investment in data literacy and organizational structures at a basic level. Businesses that are serious about maximizing the value of their data will push to help employees of every seniority understand and utilize the data at their disposal to the best of their ability.

Data Democratization to Bridge the Data Science Talent Gap

Data Democratization | The demand for Data scientists is at an all time high

So much so that a recent report by Indeed identified a 344% increase in job postings for data scientists since 2013.

This need comes at a time when data analytics is becoming mission-critical to more and more businesses. New data is constantly available, volumes are increasing and businesses need to use the data to drive deeper and more meaningful insights as they look to become more competitive.

Data scientists are key to unlocking the story behind this data. These highly-skilled professionals interrogate and identify key patterns and trends within the data available to them, making a significant contribution to a company’s overall performance.

However, as alluded to above, data science requires a broad array of complex and scarce skills including (but not limited to) quantitative disciplines such as statistics, machine learning, operations research and computational linguistics. And, unfortunately, as the market currently stands, there simply aren’t enough skilled and qualified people to fulfill this demand.

That said, there is a middle ground where the gap can be massively reduced. Organizations can up-skill and train existing employees to ensure everybody can handle data and prove effective in the data value chain – particularly in lines of business where its reach and impact can be most felt, for example in marketing and product development.

The rise of citizen data scientists

This is where “Citizen Data Scientists” come into play. According to Gartner, “citizen data science bridges the gap between mainstream self-service data discovery by business users and the advanced analytics techniques of data scientists.”

This allows organizations to incorporate data science more easily and more broadly within the business. It is a complementary role to the expert data scientist who is typically a coder with a deep involvement in the development, training, and use of algorithms and models. Citizen data scientists, on the other hand, bring business and industry vertical domain expertise that many data science experts lack.

While this is an emerging role and title within organizations, its presence is growing at speed. Gartner predicts that the number of citizen data scientists will grow five times faster than the number of expert data scientists through 2020. This is because more and more organizations recognize that leveraging citizen data scientists can be an effective way to start bridging the current skills gap.

It’s also important to stress that citizen data scientists may already exist in many organizations, but just not with this specific job title. Therefore education continues to be a priority to ensure businesses understand the value of leveraging citizen data scientists as part of their data-driven culture.

A promising future for data science

One way organizations can enable these citizen data scientists is by bringing data science and business intelligence (BI) practices together, providing them with timely access to insightful, trustworthy and governed data.

Due to significant enhancements in open data frameworks, senior managers can now leverage insights directly within their familiar visual BI tools, allowing them to exploit complex data science algorithms under the hood. This means they can self-serve reports whenever they need to.

With the significant rise of data analytics in organizations, we’ve also seen the democratization of BI dashboards for knowledge workers, analysts and senior staff, giving them customized up-to-date reporting on key business metrics.

The next evolution will be for BI reporting to become fully self-service, empowering all employees with the latest up-to-date metrics that are relevant to their job. This is thanks to ever-more powerful and intuitive BI tools that are augmented with self-learning features and functions sitting on high-performance analytic databases – databases that can even cope with the Monday morning workload without a hint of slowdown.

Given the increased demand for a “data-driven” approach to business, leading organizations are ramping up efforts to democratize data access while increasing their use of data science disciplines to enable them to be more forward-looking and competitive organizations. As such, the citizen data scientist role will only continue to become intrinsically woven into organizations as a way to bridge the current data science talent gap.