Head of global hospitality and strategy at Airbnb, Chip Conley, recently shared his experience of staying at Airbnb. He stood firm that data analytics is a critical factor in understanding and connecting the customer’s voices to the Airbnb product line. Back in 2011, Airbnb experienced multiple challenges with the team’s collaboration and business transformation. Ultimately, the company made it through the challenge by democratizing data practices which abetted them to move quickly into the decision-making process.

Today, every industry is highly exposed to digital disruption and the global economy is discerning the business of data as an emerging sector. According to Forbes, the data economy is projected to surpass 35 zettabytes, and to analyze this massive heap, organizations need analysts, as the aptitude to analyze data and excerpt valuable insights are still a niche in the industry. Many organizations consign data and analytics tasks to the core team. However, the challenge is how to scale and stay sustainable for the long-term.

Another key challenge is teaming and communication. To identify a problem, data scientists and business teams waste a massive amount of time on debates that end up nowhere. Such inadequacy of data science literacy leads to conflict between teams which also affects the business’s decision speed and its ability to react on the fly, to both internal and external factors.

How to fill this gap?

One of the effective means to fill this gap is to build a data-driven culture where everyone deeply understands data and has a basic knowledge of data-driven outcomes and we can not deny the importance of Big Data in today’s enterprises. Moreover, democratization allows you to share tools, knowledge, and skills to consume data and support your decisions with data insights. Considering the current scenario, most of the industries hold a substantial amount of data. Small primary team of data and AI specialists bring calibration, democratize tools, and skills to experts across the organization which might bring empowerment and the ability to utilize data rapidly at a large scale.

List of Effective Ways to Democratize Data Science

Without data, there’s no concept of AI. To democratize data science for business, the initial step includes democratizing data. This means, if employees can access data and have the ability to explain it, they only require a tool to create data models and apply data science techniques.

Understanding Data

To work with data, business users must understand the power of data. They should be skilled in problem-solving with data or have an intermediate level of SQL skills and exploratory data analysis. Several data training programs can upskill users at low-cost, but with a major impact. Therefore, a teamwork environment may act as a source of FAQs, tutorials, and peer-to-peer discussions.

Identifying Personas

Organizations usually start with an assessment to scrutinize multiple roles and capture methods for data-consumption – the aim is to describe personas, comprehend what they’ll look in data, and create a data access framework. For example, the CXO layer requires a dashboard for storytelling with data and visual insights. Similarly, business analysts perhaps require data models for ad-hoc analysis to report, based on accurate insights.

Dispensing Toolkit

Apart from specialized AI tools, centralized data science teams can also share tools that can help to connect to data systems and run elementary analytics. In this way, data science experts won’t be hampered by ad-hoc data requests from the business. Albeit from a standardization perspective, organizations also set a self-service data platform which is a low-code platform with drag & drop features, auto ML functions, and data viz aptitudes.

Creating AI Elements

The democratization of data science begins with self-service data and further develops with the appropriate use of AI. Data scientists can issue basic pre-trained ML models that can combine with other products. Such packaged solutions allow quick innovation, saves time by evading rework, and bring standardization.

Introducing New Culture

Disruption transforms culture by stimulating conventional processes and democratization is another form of disruption that is often perceived with disinclination and reluctance. Therefore, organizations should relay the broader vision with the employees for promising outcomes.

 

Conclusion

To make data democratization effective in a VUCA world, organizations must understand that a centralized data policy is crucial in breaking data silos into a unified data platform such as data lake. Robust data governance in a constant flux data lake ecosystem assures the availability of data, verifies data quality, and adds consistency in architecture. Companies that seek to start their democratization journey must reflect on their digital strategy and assess data alacrity. So, start with a few business teams, target quick solutions, share tools and strategies, and then do something big with evangelization!