Charting Business Success with Data Scientists

By CIOReview | Tuesday, July 4, 2017
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Apart from deploying advanced analytics tools as part of a concerted big data program, the other key criteria for an IT enterprise is to build a team of data scientists. Rated as one of the most desired jobs of the 21st century, the position of a data scientist has gained popularity within a short timeframe. The relative ease, with which the mid-level professionals and even fresh graduates can acquire the required skills and get hired, is alluring more people toward data scientist position. The popularity of the data scientist jobs can also be attributed to a flurry of emerging technology trends that are dictating the analytics efforts today. This has eventually resulted in the urgent need for skilled workers with analytics expertise who are adept at deriving valuable insights out of the huge volumes of data.

Skills for a Data-driven Culture

As the demand for data scientists continues to outpace the availability of skilled professionals for the job, there are certain skill sets that can be acquired by IT professionals to fill in this gap. For someone who wonders what differentiates the role of a data scientist from a Business Intelligence (BI) or analytics expert, the answer lies in how data science seamlessly intermingles with big data and IoT (Internet of Things). The rise of big data technologies has also made it necessary for the data scientists to have working knowledge about big data frameworks such as Apache Hadoop, Spark, Solr, and Kafka. A data scientist is expected to possess skills related to machine learning, statistics, data analysis, and programming languages such as Python. As the data scientist’s job revolves around working on analysis and data modeling, they must be proficient in working with data visualization libraries as well.

Skills that Add Value to the Role of Data Scientist

While data scientists are expected to have a thorough understanding of statistical research techniques, applied mathematics, and data mining, they are also expected to possess in-depth industry knowledge to discern patterns that could be used to discern risks and opportunities. Data presentation and interpersonal skills are a beneficial add-on to the repertoire of skill sets offered by the data science professionals as analytics communication to make the trends easily understandable for the C-suite, adds value to an enterprise’s Analytics program. Also, a data scientist must possess proactive leadership qualities. To clearly trace the data sources and identify the data bias are some critical abilities a seasoned data scientist must possess in order to advise companies in their analytics efforts.

Tapping the Data Science Talent Market

Companies are often advised to append data scientists to various departments to gain a business context with regards to their data sets. To set data culture in motion, enterprises must use recruiting tactics to tap into the potential offered by the ever-evolving, skills-based markets. Additionally, to counter the gap in the availability and requirement for the right talent, enterprises can create in-house analytics courses as a cost-effective strategy to fill in the data scientists posts. The key lies in creating an in-house data science and analytics talent pipeline to build a robust data science team. For instance, a competent coder who is enthusiastic about attaining proficiency in new coding languages and programs may be an ideal choice for someone who can be trained to become a data scientist. The introduction of an apprentice-mentor style to training will allow the high potential employees to effectively contribute to the enterprise’s data analytics program. Employees who are already familiar with the industry and the business are the best fit for the position of data scientists as this allows the organization to reap a significant return on their investment with a short timeframe.

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Analytics

Business

Business Intelligence

Hadoop

IoT

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