Why are Your Data Scientists Leaving Your Enterprise?

In 2019, the Data Scientist was named most promising job by LinkedIn. From Fortune 500 companies to small enterprises all around the world, building a team of data science professionals was a priority in their business strategies. To support this claim, the year 2019 broke all records of AI & data science investment.

Despite all of these positive trends, Data Scientists are quitting and changing companies at a rapid pace. How come? Let’s analyze the situation.

They Don’t Spend Their Time Doing What They Were Hired For

Unfortunately, many companies that hire data scientists do not have a suitable AI infrastructure in place. Surveys still suggest that roughly 80% of data scientists’ time is spent on cleaning, organizing, and finding data (instead of analyzing it), which is one of the last things they want to spend their time doing. In their article “How We Improved Data Discovery for Data Scientists at Spotify”, Spotify explains how in the beginning their “datasets lacked clear ownership or documentation making it difficult for data scientists to find them.” Even data scientists working for Web Giants have felt frustration in their data journey.

Most data scientists end up leaving their companies because they end up filtering the trash in their data environments. Having clean and well-documented data is key for your data scientists to not only better find, discover and understand the company’s data but also save time on fastidious tasks and produce actionable insights.

Business and Data Science Goals are not Aligned

With all the hype around AI and Machine Learning, executives and investors want to showcase their data science projects at the forefront of the latest technological advances. They often hire AI and data experts thinking that they will reach their business objectives in double the time. However, this is rarely the case. Data science projects typically involve a lot of experimentation, trial & error methods and iterations of the same process before reaching the final outcome.

A lot of companies increase their hiring of data specialists in order to increase the research and insight production across their company. However, this research often only has a “local impact” in specific parts of the enterprise, going unseen by other departments that might find it useful in their decision making.

It is therefore important for both parties to effectively & efficiently work together by establishing solid communication. Aligning business objectives with data science objectives is the key to not lose your data scientists. By using a Data Ops approach, data scientists are able to work in an agile, collaborative and change-friendly environment that promotes communication between both the business and IT departments.

They Struggle to Understand & Contextualize Data at Enterprise Level

Most organizations have in place numerous complex solutions, usually misunderstood by the majority of the enterprise, making it difficult to train new data science employees. Without a unique centralized solution, data scientists find themselves going through a various different technologies, losing sight of what data is useful, up-to-date, and of quality for their usages.

This lack of visibility on data is frustrating to data scientists whom, as mentioned above, spend the majority of their time looking for data in multiple tools and sources.

By putting in place a single source of truth, data science experts are able to view their enterprise data in and produce data-driven insights.

Accelerate Your Data Scientists Work With a Metadata Management Solution

Metadata management is an essential discipline for enterprises wishing to bolster innovation or regulatory compliance initiatives on their data assets. By implementing a metadata management strategy, where metadata is well-managed and correctly documented, data scientists are able to easily find and retrieve relevant information from an intuitive platform. Empower your data science teams by providing them with the right tools that enables them to create new machine learning algorithms for their data projects and thus, value for your enterprise.

About Actian Corporation

Actian makes data easy. Our data platform simplifies how people connect, manage, and analyze data across cloud, hybrid, and on-premises environments. With decades of experience in data management and analytics, Actian delivers high-performance solutions that empower businesses to make data-driven decisions. Actian is recognized by leading analysts and has received industry awards for performance and innovation. Our teams share proven use cases at conferences (e.g., Strata Data) and contribute to open-source projects. On the Actian blog, we cover topics ranging from real-time data ingestion, data analytics, data governance, data management, data quality, data intelligence to AI-driven analytics.