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Few would argue against the importance of data in today’s highly competitive and evolving marketplace. The techniques used to transform data into actionable insights are crucial to the performance of an organisation. In the booming market for data scientists, the challenge is to meet the shortfall of resources with adequate skills. Let us first try to understand what it takes and means to be a data scientist.

Data Scientists must master advanced statistical and quantitative methods and tools, have a keen business acumen and curiosity for what lies beneath the numbers and solutions, obtained as a result of running these sophisticated analytics tools. They must also understand how to integrate large datasets using fairly new technologies and computing environments. Domain knowledge is also a must whether its retail, finance, telecom or ecommerce. Broadly Data Scientists are data crunch experts, computing experts, statisticians and business intelligence experts with excellent communication skills.

While training one individual with all the above skills is not impossible, it may not be a very practical solution to meet the businesses immediate needs. Hence a good solution is to build data scientist teams with people proficient in one subject matter or another, and then collaborate their efforts. Many top companies have built up a team of data crunchers, statisticians, business analysts, computer programming experts or data engineers, data visualization experts and navigators who can explain the findings and interact between the teams.

Although, it’s good to have subject matter experts that ultimately contribute as data scientists, whether a company needs to have an exclusive data scientist team, depends upon the scope of their projects. Small businesses particularly, may use a data scientist’s skills to arrive at good business decisions but may not need specialized and sophisticated analytics methodologies to come up with business solutions. All business problems do not require analytics to tackle their needs.

There is also a possibility that there may be existing teams in disguise already executing the role of a data scientist in essence. For example all companies who deal with large datasets, already have data crunchers, analysts and business insights teams. They only have to leverage their existing resources wisely. Data Visualization itself can be a very powerful technique to analyse the data and represent the analytics solutions in a presentable format that clients can easily understand.

To summarise if an organisation needs to have a data scientist team at all, then it is best to create subject matter experts or SME’s and then integrate their efforts to produce best business solutions. Not all companies need to hire a full data scientist team and may use existing resources optimally to come up analytics solutions for growth and profitability. However, given sufficient time and resources it is best to leverage on data science expertise to come up with objective insights from the data validated through sophisticated analytical approach.

 Related searches :
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Interested in a career in Data Science?
To learn more about Jigsaw’s Data Science with SAS Course – click here.
To learn more about Jigsaw’s Data Science with R Course – click here.
To learn more about Jigsaw’s Big Data Course – click here.
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