How to stop wasting the talents of your data scientist
Data is transforming our world. Governments across the globe are examining data and how it can be used to serve their citizens better. Businesses, both old and new, are making the shift towards data-driven business models. With these changes come the growing demand for data scientists skilled in data analytics and adept at delivering insights critical to supporting organizations and businesses.
The reality is that data scientists spend a greater chunk of their time on data administration.
Limited time is spent on analysis and creating meaningful, actionable insights from the organization’s data. With the COVID-19 pandemic looming over the world, the time spent searching for and preparing data could be used to generate actionable insights critical to supporting the business in this current climate.
Sadly, if businesses continue on this trajectory, the monopolization of data scientists’ time will only get worse as the volume of data grows. As teams struggle to manage volume, data professionals are confronted with more issues. With data siloes and incomplete data sets thrown into the mix, IT teams find themselves standing at the top of a data chaos heap – a chaos that data professionals will find themselves responsible for and, perhaps, have to spend even more of their time sorting out.
Data is the lifeblood of digital transformation, and businesses of all sizes should have access to all of their data, not just some of it. Yet, many businesses are unaware of the state of health of their data.
Having insufficient data can be worse than having no data at all. If a business has only a partial view of their data, the insights delivered by data professionals will not reflect the business as a whole – leading to poor decision-making that could have a negative impact on the bottom line. With a full view, data scientists can deliver accurate and actionable insights that will provide the business with clarity and confidence to make the right decisions.
The severe data talent shortage that the world is currently facing is only further monopolizing data professionals’ time. Without a full and developed team of data professionals, data administration will fall to a few individuals stretching their time even further.
With so much time being spent on data admin, key activities including data analysis and strategy are neglected. Data scientists’ inability to focus on value-adding tasks has resulted in lower revenues, fewer opportunities for innovation, and poor customer experiences.
By prioritizing data intelligence, data professionals will no longer have to sacrifice hours finding, sorting and preparing data from across the business. Data professionals will be able to look at business data as a reliable source from the start and focus their efforts on delivering key business insights.
Businesses should invest in tools that accelerate the data-to-value journey with easy and searchable dataset documentation, quality proofing and promotion. Deploying these tools effectively will mean data is pinpointed across data sources, even those that are siloed. With a single point of governance and access, data can be turned into reusable and shareable assets. This will free up data professionals, and allow them to work on analyzing the data and delivering actionable insights to direct business strategy.
When deciding which tools to invest in, businesses should look for a system that facilitates access to data and checks data relevance and trustworthiness immediately. Applications that provide instant assessments of data health and accuracy based on quality, popularity and user-defined ratings will reduce the time that data professionals spent searching and preparing data.
Data is one of the most valuable commodities for businesses; however, many are failing to take advantage of data’s full power. Data scientists are bogged down with data administration tasks and left with little time for analysis to support business strategy. Investing in the right tools will help data professionals address efficiency and productivity problems from the outset, and enable them to leverage trustworthy data quickly and effectively.
Author: Stu Garrow
Source: TechWire Asia