Many organizations in today’s marketplace realize that data and analytics are essential to meet business goals while also predicting future trends. What’s more is that democratization of data has also become a large part of success for organizations, allowing them to be more data-driven in every capacity of their business initiatives.
Gartner predicts that by 2023, organizations that promote data sharing will outperform their peers on most business value metrics. With industries imbued by rapid change and market disruptions, it’s not enough for organizations to bottleneck data and data science capabilities to the few. Essentially, not sharing data across an organization can lead to harmful outcomes and siloed data in today’s marketplace.
Democratizing data science starts with the democratization of data itself. If employees have access to data and the ability to understand and explain it, they can then act upon that data.
Here’s how to democratize data science in your organization to take advantage of insights and act in real-time.
A Culture of Data
Organizations seeking the democratization of data science (and data in general) need to understand the importance of how their culture is focused. Well-established enterprises leverage data culture and technology to become truly data-driven.
Encouraging the use of data to make better business decisions comes from the top. Although it’s not easy to change the status quo, leaders within organizations must focus on fostering a culture of shared data, not data ownership.
It all comes down to the “bigger picture” when democratizing data science. Expert data scientists are highly sought-after in today’s marketplace, and enterprises may struggle to find talent. Therefore, it is important for business leaders to reflect on their digital strategy, and see how they can foster a collaborative culture.
In learning how to democratize data science in organizations, business users must be educated adequately. And in today’s marketplace, there are an increasing number of training courses and “bootcamps” that accelerate the process of learning for users.
With proper training in areas such as problem-solving with data, SQL skills, and exploratory data analysis, users are empowered to make better data-driven decisions.
The rise of the “citizen data scientist” is nothing to be feared. Non-technical individuals who use data science tools to solve business problems are not the enemy. Instead, the combined efforts of data scientist experts and those users equipped with the know-how and tools equate to increased efficiency.
Taking a strategic approach to introducing simple tools and training can encourage users to actually use those tools in their day-to-day. Additionally, in taking steps to democratize data science, organizations can utilize personas to define roles and responsibilities to hone in on what group of users needs training in specific areas.
Defining personas, which are an employee’s context mapped to their access rights, are essential to democratizing data science. The objective of defining personas is to build a data access framework that encompasses each role.
While the data should all be the same, some business users do not need as much in-depth access as others. Each data persona has a different relationship with data and requires different competencies to be empowered to work with data. For example, a marketing analyst who regularly works with Excel may need the training to learn basic coding and AI tools, while a business leader may only need to know how to make informed decisions with data.
This also plays a major role in governance and security. Improper data governance and defined roles can lead to large bottlenecks. Eventually, those bottlenecks can produce data silos, poor data analysis, and a lack of accountability.
Expanding Artificial Intelligence Access
With the rapid expansion of data democratization across businesses came the rise of useful tools and solutions to assist non-technical users to understand insights. Self-service analytics continues to be popular as it does not involve analytics specialists, especially data scientists.
Many cloud-based providers such as Google Cloud have launched pre-trained artificial intelligence (AI) models. Pre-trained AI models allow for developers to train high-quality models specific to their business needs with minimal ML expertise or effort. Essentially, these models can cut down on training time and also start procuring specific insights immediately.
Alongside leveraging AI comes automation, which has obvious benefits like saving time and increasing productivity. Many tools are available today that automate many aspects of data science. Nowadays, that includes automation tools that assist with massaging data, creating algorithms, and creating code to deploy a model into production.
How to Democratize Data Science
Every organization is different in terms of business objectives and goals, and their journey to data science democratization will also be different. It also should go without mentioning that change, especially regarding data culture, will not happen overnight. Instead, business leaders should strategically plan how to democratize data science within their organization and take action on that plan over time.
At Pandera, data science experts can help you better understand your customers, personalize the customer experience, automate, as well as improve business processes.
In the end, our industry-veteran data scientists can help to begin the steps to not only democratize data science in your organization but continue the journey to unlocking powerful processes. From marketing analytics to anomaly detection, data science plays a major role in the success of predictive analytics efforts and beyond.
Our work speaks for itself. Get in touch with us today to learn more about how to democratize data science in your organization!