While early adopters and proponents were quick to frame Big Data as having the potential to provide miraculous reservoirs of insight and functionality, today’s business leaders are under intense pressure to address the most challenging technical components of data-driven projects. C-Level executives such as CEOs, CTOs, CIOs and CMOs are thus focused on moving beyond the buzz, with the aim of finally turning investments and promises into solutions and profits.

Leveraging Data: The Rise of the Data Scientist

As a result of this push to improve profitability by generating top-line growth and reducing costs, board level executives are pressuring their IT organizations to find data scientists. An indicator of the growing prominence of data scientists has been the introduction of new data-centric roles such as chief data officer and data scientist into businesses that are realizing that data is a real asset that needs to be leveraged.

 

Executive Decisions: According to some studies, 2017 could be the year of the data driven CMO (Source: https://which-50.com/good-news-cmos-data-driven-marketing/).

 

As a result of this surging demand, data scientist was ranked as one of the top 5 highest paying IT jobs in a recent survey. Data scientists test and tinker with processes until they find a solution or formula that works best. By analyzing the data a data scientist could thus determine whether the information gathered is relevant for the realization of a large enterprise’s strategic goals.

With analytics having gone mainstream, the role of the data scientist has evolved. Whereas once upon a time statisticians were hired and trained to address business problems, today’s business professionals are hired and provided training in analytics.  Initially, the data presented by analysts was not clearly understood by business officers. As such, the talent pool has been expanded to include MBA graduates, engineers, physicists and even psychologists.

Today’s multi-faceted data scientist is a rare combination of a few must-have skills. While mathematical and analytical expertise are required, business acumen, intellectual curiosity and strong communication skills are no less important.

 

Looking for a Career? Look No Further! The tremendous growth in Big Data is creating job opportunities across various domains
(Source: http://www.datasciencecentral.com/profiles/blogs/10-reasons-why-big-data-analytics-is-the-best-career-move).

 

The rise of the data scientist is corresponding with and contributing to a fundamental shift in how a modern business’s value is assessed: entirely on the value of its data. Cases in point include the flotations of Twitter and Facebook and the impending IPO of Snapchat; reputed to be somewhere between $25 billion and $40 billion.

And data creation is only increasing. According to one recent study, global data could grow ten times, to 163 zettabytes (zb), by 2025. In addition, there are some predictions that enterprises will create 60 percent of the world’s data by 2025. Such a development presents businesses with a golden opportunity to embrace new opportunities, but will require strategic choices on data collection and utilization, which is where data scientists enter the picture.

Behind the Curtain: Big Data Analytics Technologies

Big Data technologies are what’s enabling businesses to wade through the unprecedented data deluge. To gain the competitive advantage that Big Data holds, analytics must be infused everywhere.

It is the IT leaders’ responsibility to make sure that data scientists have the technology and infrastructure required for the latter to be able to deliver actionable insights to C-level executives. In turn, these business leaders will use this information to decide upon which strategies to pursue.

The selection of analytics technologies is crucial – making speed a differentiator, not to mention cost, and exploiting value in all types and scales of data. This requires an infrastructure that can manage and process exploding volumes of data, without becoming an IT-focused entity as a result of complex implementations or overly intricate management of Big Data analytics technologies.

Not a small challenge for IT leaders.

In response, businesses are increasingly partnering with Big Data Analytics vendors such as SQream Technologies to make it easier to implement and manage the proliferation of data across systems, with minimum infrastructure changes involved.

SQream’s GPU-infused analytics engine is capable of processing and analyzing extremely high volumes of both historical and fresh data, delivering a high cost/performance ratio, ensuring simplicity and enhancing IT’s ability to implement and maintain the technology that data scientists can then harness to access the actionable insights they seek.

Specifically, SQream’s next generation GPU database provides a comprehensive solution for data scientists to easily and flexibly ad-hoc query hundreds of terabytes and beyond in near real-time, resulting in faster and more trusted data-driven insights.

Such technology allows a more dynamic, data-driven approach to business.

 

A Win-Win-Win Scenario

For companies to get a handle on Big Data and use it to boost their productivity and drive revenues, IT must collaborate with the business side of the organization to develop a strategy that will deliver the infrastructure required for data scientists to work their magic. Data scientists are crucial to a Big Data platform’s successful implementation since they perform the experiments that prove which hypothetical models perform best.

 

Conclusion: A Business’s Secret Weapon for Success

A data scientist needs to have a deep understanding of a business’s goals in order to effectively experiment with the relevant data, by calculating the odds for success of various business scenarios. He then needs to be able to successfully communicate his findings to business executives, empowering them with the knowledge needed to make optimal business decisions.

However, data will only remain a potential, untapped asset for an enterprise to exploit, unless IT is able to deliver technological agility and flexibility to the data scientist.