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Maximizing Business Value By Democratizing Data

We shall begin with here an interesting question – should the ideals of democracy be limited to just polity? Democracy, in its truest essence, is allowing everyone equal opportunity to form and express opinions. However, without access to information, the opinions established are ignorant at best. With businesses dealing with copious amounts of data, democratization of data as a concept is taking strong roots in organizational governance. There has been a visible shift of culture from “data owning” to “data sharing”, ensuring everyone has access to data despite technical know-how.

Five factors contribute heavily to the democratization of data, putting critical capability in the hands of professionals.

  • Automated machine learning: 80% of time spent by data scientists is on repetitive tasks that can be fully or at least partially automated. Tasks such as data preparation, feature engineering and selection, and algorithm selection and evaluation can be easily automated using various tools and techniques, leaving more time for employees to focus on strategic tasks.
  • No-code App Development: Low-code or no-code platforms can make software development up to 10x faster. Graphical user interfaces, drag-and-drop modules, and other user-friendly structures allow even non-technical users to accelerate AI-led app development and delivery.
  • Pre-trained AI models: One of the core responsibilities of data scientists is to build machine learning models. However, with pre-trained AI models that effectively package machine learning expertise into a product, time, and effort put into training can be slashed into half and generate specific insight almost instantly.
  • Self-service data analytics: As the number of tools to deliver data-based insights, self-service analytics tools offer several business intelligence features to augment and automate data analytics and discovery. Features such as natural language query, search, visual data discovery, and others help users find, visualize, and narrate data findings easily.
  • Accelerated learning: People with basic mathematics and coding background can be trained with basic data science skills in a short span. These training programs arm professionals with basic skills that they can implement in projects quickly.

What is Data democratization?

Data democracy is an ideal scenario where every employee within the organization has equitable and timely access to data. However, providing raw, intelligible data to non-specialists is not data democracy. It is an ongoing process where everyone in the organization is comfortable accessing and understanding the data, without seeking help from IT administrators, to build well-informed decisions that drive innovation, digitization, innovation, and transparency.  A survey by Gartner showed that data-sharing practices that effectively broke down data silos were linked to high-performing data and analytics teams and that by 2023, organizations that promote data-sharing will significantly outperform their counterparts. The IT department and the gatekeeping processes to safeguard data affect the decision-making process within different business and support units. Though there remain those who deem it necessary, after the unprecedented demand for data analytics during the Covid-19 pandemic, the traditional “don’t share unless” mindset is being replaced with “must share unless”. The status quo is unlikely to change overnight, yet, leadership has started to recognize the importance of establishing trust-based mechanisms based on shared data.

Why should Data be Democratized?

The concept of data democratization evolved to turn data into a valuable business resource. To successfully operate in today’s fast-paced and competitive environment, everyone in the organization has an accurate view of available information to quickly respond to the rapidly changing market situation. 

Some of the most common data challenges include limited or no access to the data, understanding the data, trusting the data, or that data experts are too busy to help decode information. Organizations cannot afford to have these bottlenecks anymore. Allowing equitable access to data across all tiers of an organization empowers individuals to continue specializing in their domains while keeping a window open into business performance. 

To ensure that data is genuinely democratized, organizations need a three-pronged approach – 

Accessibility:- Ensuring employees feel comfortable accessing data and asking questions
Education:- Arming them with proper tools and training to understand and analyze data
Empowerment:- Understanding that democratization is an ongoing process and would require an overall culture shift

Concerns with Data Democratization

Data is frequently used to understand the behavioral patterns and therefore the psychology of choices that people make. A huge challenge when it comes to democratizing is using data to alter people’s choices, especially the use of big data. Though data democracy has its benefits, there are a few concerns over ensuring data democracy within the organization. A major concern is regarding data quality, integrity, and security, especially in a world driven by data privacy regulations.

Another concern is the misinterpretation of data by business functions, leading to bad decisions. After all, not everyone is a data scientist. The current supply and demand trends show that there could be a shortage of 250,000 data scientists by 2024 in the United States alone. The need is to create a more data-literate organization using modern data architecture such as self-service business intelligence tools and dashboards.  

The role of AI in Democratization

Five factors contribute heavily to the democratization of data, putting critical capability in the hands of professionals. The goal is to accelerate the adoption of data science and analytical capabilities through tool simplification and training resources. These tools and training will then enable an array of professionals from various backgrounds to gain better insight into data and learn key data science skills. 

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