Data democracy provides businesspeople with the tools they need to independently understand data and make insights-driven decisions. Learn to successfully implement data democratization at your company by aligning analytics with clear business outcomes, ensuring sound foundational data, and focusing on the user experience. Enable free data exploration and approachable analysis, and employees will be empowered to move the needle on business growth without struggling against technical learning curves and insights bottlenecks.
Business data is more abundant than ever. Whether this data is collected first-hand or purchased from a third-party or syndicated source, it must be properly managed to bring companies the most value.
To accomplish this task, companies are investing in data infrastructure and platforms, such as data lakes and data warehouses. This investment is critical to extracting data insights, but it’s only part of the solution.
All too often, data becomes locked away in these platforms. Technical gatekeepers, such as data scientists and analysts, can access the data, but key decision-makers are barricaded. When data doesn’t flow downstream to the businesspeople who’d derive value from the information it holds, businesses can’t fully capitalize on their infrastructure investment.
Self-service BI and analytics solutions can address this challenge by enabling businesspeople to access data directly and gain the insights they need. However, simply providing self-service analytics doesn’t guarantee that a company will become insights-rich and that key stakeholders will be able to act on insights without involvement from technical team members.
The transition to truly insights-driven decisions requires a concerted leadership effort, investment in the right tools, and employee empowerment so that leaders across functions can consult data independently before acting.
In other words, companies must strive for data democratization: opening up access to data and analytics among non-technical businesspeople without technical gatekeepers. In data democratization, the user experience must align with the behaviors and needs of businesspeople to ensure maximum adoption.
Let’s further break down this definition and discuss the value of data democratization.
What Is Data Democratization?
As we mentioned above, data democratization is about access and enablement. Pragmatically, businesspeople, such as marketing and sales professionals, should be able to analyze data without support from a data analyst or scientist.
True data democratization solves for three key business challenges:
- It allows business users to access information that would otherwise be confined in a data lake or data warehouse.
- It makes it simple for non-technical users to search data and find the answers they’re looking for.
- It produces actionable analysis and insights that are easily understood by the average end user, not just technical professionals.
Data democratization is generally facilitated by advanced self-service analytics tools designed with business users in mind.
Strengthened by the advent of AI in business intelligence, advanced analytics “combines technologies like machine learning, semantic analysis, and visualization to automate analysis” that “leads to better, faster decision-making,” according to the Advanced Analytics Guide.
However, it’s important to recognize that advanced analytics alone won’t enable data democratization, which brings us to our first tip for successful adoption.
1. Start With the Business Problem
Before starting the process of data democratization, companies should first determine the business outcomes they want to achieve. What problem needs to be solved? How can data democratization arm them with the answers to drive these outcomes?
If the ultimate goal of data democracy is to empower employees to advance the strategic objectives of the company, then the solution must be designed around specific enterprise goals and KPIs.
For example, a top-level business problem could be declining revenues. In further dissecting this complex problem, a company might decide they need to improve their understanding of sales team performance, the competitive landscape, marketing effectiveness, and so on.
Identifying the facets of the large problem leads to more concrete guidance around the data and analysis needed to solve it.
The goal of data democracy, in this case, would be to enable the business to monitor revenue performance, conduct root cause analysis, and identify key opportunities for improving revenue generation. To do so, executive, sales, and marketing teams need to understand and act on relevant business data to move the needle on driving top-line revenue.
When employees understand how data democratization gives them the answers they need to be more effective in their roles, they’re more likely to adopt the best practices that ensure continued usage of analytics tools.
Additionally, employees who understand the value of data democracy can act as champions for other use cases. As such, starting with a focused business outcome helps companies build momentum and scale data democratization into other areas of the organization.
Once companies determine a specific business problem to solve, they’ll need to formulate a plan to gather and assess the quality of their data, which brings us to our next point.
2. Ensure the Foundational Data Is Sound
In order for users to extract value from data, the data itself must be clean and accurate.
Incongruous, inaccurate, and low-quality data will produce analysis and insights of the same caliber.
That’s why it’s critical for companies to clean and model data before connecting to an analytics or BI solution. Part of this process involves determining consistent data definitions that will be agreed upon and used throughout the organization.
As discussed previously, data democratization should be focused around a specific business outcome. Starting with a small, focused project can help companies successfully implement data democratization without waiting for perfection in their data. Accurate, quality data is key, but perfection is unrealistic.
That said, to ensure that good quality data flows into an analytics tool, companies need the proper systems and support in place, including:
- Clean, organized, high-quality data.
- A common data lexicon to be used throughout the organization.
- Data security protocols and clear access guidelines, so that classified or sensitive data remains secret, and departments only have access to pertinent information.
- For AI enablement, businesses need AI expertise either in-house or through vendor relationships.
The process for completing these steps will be dependent on a company’s pathway to data democratization — whether that’s developing an in-house solution or working with a vendor.
Companies who plan to work with analytics vendors should determine the depth of the implementation process and who will be responsible for which aspect of ensuring sound foundational data.
Now, let’s discuss critical components of an analytics or BI solution to facilitate independent data exploration.
3. Enable Users to Easily Explore Data for Analysis
To truly empower businesspeople with data, analytics solutions need to meet businesspeople where they are. That means analytics should mirror and enhance a natural human workflow — not bog down the process with complex tools or technical learning curves, such as requiring users to understand SQL queries and data organization.
Analytics should allow users to freely explore their data, iterate on their ideas, and go deeper with each piece of information they learn.
Analytics solutions can enable this process with the following features:
Natural Language Processing (NLP) and Natural Language Queries (NLQ)
NLP allows users to ask questions about their data in conversational language. For example, users can ask NLQs like, “What were sales by category last year in the southeast?” or “How did Mega Gum do last year?”
Being able to ask questions in their own words means that users don’t have to adapt to convoluted keyword queries and parse their thoughts into specific, query-friendly phrases.
NLP is also a key component in augmented analytics solutions, which you can learn about in this article.
Analytics solutions that enable data democratization benefit from functionality that mimics everyday technology. Businesspeople are used to Googling questions from their cell phones while on-the-go. Why should an analytics solution be any different?
A solution that supports mobile offers an incredible competitive advantage.
Ideas and questions can strike at any time; a good analytics solution allows businesspeople to seize that momentum with immediate answers.
Visualization is perhaps the most intuitive way to understand data. At a baseline, visualizations should be easily comprehensible and offer some interpretation to the user, usually in the form of insights that surface notable findings, trends, and outliers.
Users should also be able to click on interesting data points within a graph or chart and drill down into deeper analysis. This functionality allows businesspeople to fully grasp and contextualize analytical output by streamlining follow-up research.
These features assist users in examining their data, but just as important (if not more), is understanding the resulting analysis and insights.
4. Deliver Approachable Analysis for Data Democratization
The output of an analytics solution must be clear and consumable to the average end user. If too much onus is placed on the user to interpret and hunt for insights within the analysis, they may miss the key findings and proceed on to make ill-informed decisions.
True data democratization equips businesspeople with the meaningful insights they need to quickly and effectively take action.
Approachable analysis should produce findings that make sense, place information into full context, and prioritize insights so that users are guided towards making smarter, informed business decisions.
To do so, analytics solutions should have the following capabilities.
Natural Language Generation (NLG)
Whereas NLP applies AI techniques to understand natural language, NLG leverages AI to produce natural language narratives.
In BI and analytics tools, NLG is used to explain analysis and surfaces key insights in plain English.
Together, NLP and NLG allow users to ask questions in their own words and get answers that make sense.
One of the biggest benefits of AI and advanced analytics technology is the opportunity to automate routine tasks. According to Gartner, AI augmentation will create $2.9 trillion of business value by 2021.
Automation in the context of data democracy refers to analysis and insights generation. That is, the user simply asks a question, and the solution does all the work of answering the question.
The insights produced through automation should be contextual and prioritized. For instance, if the analysis shows that your market share increased by 6%, it’s important to know how that increase compares to your competitors, which factors most drove that increase, and where there are more opportunities to capitalize on the gains.
Automated analysis delivers this complete narrative to the end user, so they don’t have to piece the story together themselves with disjointed insights. With actionable insights in hand, businesspeople can act quickly on the information they learn.
Understand Why, Not Just What
At the heart of analytics is the need to answer “why” questions. Whether a key metric increased or decreased, understanding why means knowing how to proceed.
A solution that simply focuses on descriptive analytics — or reporting on what happened — leaves too much guesswork on the table for decision-makers. A solution that explains why — and delves into diagnostic and predictive analytics — enables more immediate action.
Data democratization means businesspeople can act with critical, insights-driven information. Advanced analytics solutions allow data democracy by aligning their features to the needs of businesspeople — those that can accelerate the answering of complex “why” questions are primed to deliver the most value to organizations.
Ultimately, data democratization benefits businesses by allowing employees to make faster, insights-driven decisions without intervention from technical team members.
By ensuring access and enablement to self-service advanced analytics and employing proper data management techniques, companies can capitalize on the competitive advantages of this disruptive technology.
About the Author
Prior to AnswerRocket, Pete founded and led Retality, a firm focused on helping companies conceive, build and bring new technologies to market. Before founding Retality, Pete was a founding team member and SVP/GM of BlueCube Software, where he led the Workforce Management business unit before the company was sold to RedPrairie. BlueCube was a spin-out from Radiant Systems, where Pete spent eight years driving the development and market introduction of new products at the company. Pete got his start at Accenture working with Global 2000 organizations. Pete has a B.S. in Computer Science/Economics from Union College in Schenectady, NY.