Discover the biggest challenges and benefits of data democratization.
In the era of big data, an unprecedented amount of data is available to companies to drive growth.
Yet up to 73% of companies’ data never get used.
What are the bottlenecks to accessing data? And why is there such a wide gap between the data we have in our data lakes and data warehouses and the data we end up using for making business decisions?
The smoking gun is in the hands of data democratization.
Bernard Marr provides a very succinct characterization of data democratization:
“Data democratization means that everybody has access to data and there are no gatekeepers that create a bottleneck at the gateway to the data. It requires that we accompany the access with an easy way for people to understand the data so that they can use it to expedite decision-making and uncover opportunities for an organization. The goal is to have anybody use data at any time to make decisions with no barriers to access or understanding.”
But why would you want your company to become a data democracy? And what are the tangible advantages to being data democratic?
There are 4 main advantages companies get when striving for data democratization.
Business decisions are your main competitive advantage. Making the right choices in what to do next and how to solve problems takes you ahead of the competition.
But if all your choices are based on gut feelings instead of data, you are competing against chance. Trusting the future of your company to instincts - instead of data-driven decisions - places your future in the hand of chance - will your gut be right or wrong? This increases the risk of making the wrong choices.
Giving everyone data to base their decisions on diminishes the risk of wrong business decisions running your company astray.
When every worker has all the data to do their jobs, they are more empowered to do what you hired them for. Drive company growth.
Instead of waiting on other departments to provide them with data, they can be independent, explore the data themselves and resolve business problems faster.
Data departments are often burdened with an endless backlog of data requests. Democratizing data access unburdens their talents.
This means they can focus on more revenue-generating activities, such as driving data science and machine learning initiatives that give your company a competitive advantage.
Having data about every customer touchpoint allows you to optimize the customer experience.
When dealing with disgruntled buyers or trying to upsell existing customers, comprehensive data access allows you to see the big picture for every customer. So you can quickly determine the best points to showcase and the traps (unresolved support tickets!) to avoid.
So how do you become more data democratic? By removing the hurdles in the way to data democracy.
Companies that aim to offer data to all their end users to help foster and drive decision-making face 5 challenges.
As a rule, data is viewed to belong to a selected few individuals in the company.
Traditional enterprises place the responsibility (and privilege) of data into the hands of the IT department.
Modern startups and fast-growing companies have devoted data departments: data engineers who take care of data integration and ETL pipelines, data scientists who compute metrics for business users’ decision-making, and data analysts who provide data analysis and dashboards with data visualizations to drive companies’ business decisions.
What is missing here is unfettered data access.
Non-technical users cannot unlock data for their decision-making without first waiting on the gatekeepers to provide that data.
End-users need to either wait on a dashboard to be built or an entire data pipeline to be integrated into the data warehouse before they can have data with which they make their decisions.
Which slows down processes, burdens the data department, makes the data people the culprit for delaying decisions, and causes missed opportunities.
Often important decisions need to be made in real-time on the spot, not 5 weeks later when data becomes available.
Self-service analytics seems to be the easy solution to this prerogative, but self-service analytics cannot unlock data democratization without first solving data silos.
Data is often locked in different business units without any cohesive connection. Finance has its contractual data spread across different spreadsheets. Marketing tracks leads using SaaS software that is not integrated into the data warehouse. And so on.
Data silos act against data democratization whenever there are interesting questions to be asked.
For example, “Which leads result in the highest paying contracts?”.
Breaking data silos is part of furthering data democratization. But breaking data silos can lead to security issues - giving everyone access to every data set can become a dangerous game of “who leaked it first?”.
Data management guidelines and data governance frameworks often limit data access to safeguard data privacy and uphold regulatory compliance.
But oftentimes we throw the baby out with the baby water.
Under the umbrella term of data security, we limit access to data preventively, causing decision-makers to make guesstimates on their gut feelings instead of data-driven decisions.
That is not to say that you should sacrifice data security for data democratization or vice versa. This is not a tradeoff.
Instead, you should build processes and provide tooling that allows you to both secure your data and offer data access at the same time.
Secure data access allows everyone in your company to play with the data. That is if they trust it.
To build a true data democracy in an organization, people need to trust the data assets they use.
What impedes trust?
First, the lack of transparency of how the data sets, metrics, and visualizations are built diminishes trust. When data gatekeepers are the only people who know how data assets are built, non-technical end-users often mistrust the assets they work with.
Second, inconsistencies. A company with data silos often has duplicated and inconsistent views of the world. The metric “number of new leads this month” differs between sales and marketing reports. Inconsistencies arise whenever data silos cause unaligned data sets and metrics.
So data quality is a necessary cornerstone of data democratization. You can have all the data sets, metrics, and dashboards available to everyone. But if they mistrust the data, they will not use it.
So how do we help people understand and trust the data? The answer is data literacy.
Even when end-users can access data, in a secure way and data is validated and trustworthy, data literacy prevents them from unlocking the data’s full potential.
One of the main hurdles to overcome is data literacy. Depending on the engineering and analytic tools your company uses, non-technical users might have problems accessing and understanding data.
For instance, if your business intelligence tool needs knowledge of DAX to build a dashboard and associated metrics (PowerBI anyone?) non-technical users will not be able to make their own visualizations. Or if your data pipelines rely on Scala jobs deployed on AWS, end-users will not be able to use data integration by themselves when needing a new data set.
Data literacy should inform your tools. But beware. The tools you provision should satisfy your non-technical users, but also your technical experts.
A drag-and-drop report tool will be great for non-technical Tony from sales. But it might also be inadequate for all the data engineering tasks Jane needs to do as the data engineering lead.
The answer to this dilemma is what separates successful data democracies from siloed companies. The majority of lagging organizations opt for an ecosystem of technical tools and rely on Tonys to learn SQL and other languages by themselves.
Successful companies build around their workforce strengths, not weaknesses.
Instead of trying to cover for all the technical weaknesses of their workforce, they pick the right tools for each job. Non-technical Tony gets a drag-and-drop interface so he can focus on selling to potential clients, while technical Jane gets advanced data engineering platforms to build sophisticated pipelines.
And the right tool for the job of data democratization is Keboola.
Keboola is a Data Stack as a Service platform that allows you to plug-and-play different tools into a unified ecosystem:
Ready to move from theory to practice?
Keboola has an always free, no-questions-asked plan so you can move from traditional siloed data operations to a data democratic future. Try Keboola for free, today.