Cloud technology and the growing need for data scientists

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Cloud technology and the growing need for data scientists
Cloud technology and the growing need for data scientists

As many more companies around the world embrace cloud technology to manage their data, the importance of data scientists have come to the fore.

Many jobs around many multinational companies are reserves for data scientists to fill even as companies scramble for the available professional in data science.

Recruiting website, ‘Glassdoor’, has cited data scientist as the highest-ranked job in the U.S. based on salary, job satisfaction and the number of job openings.

In Africa, it is rapidly becoming the most sought after job due to the digital transformation happening in the continent.

According to the Africa Data Forum, there is a growing demand for predictive analytics for use in decision making in Africa but unfortunately, the continent has a shortage of competencies in data science.

Basically, data scientists help organizations shift from relying on instinct and experience to using data for new, transformative insights.

Yet the role of “data scientist” was not identified as a profession until a decade ago.

Data scientists are in great demand because of the volume of data that organizations are dealing with, due in part to the explosion of data streams now enabled by cloud technology.

About 20 per cent of this is structured data that businesses have historically collected, but the other 80 percent is unstructured data, which comes in the form of emails, social media, images or videos, and can be much harder to manage, collect and analyze.

Additionally, recent survey data highlights cloud growth in several areas, which means data scientists will need to grapple with new workloads from AI, analytics and IoT devices.

Regional representatives at market analysis and research firm IDC anticipate growth in cloud services in 2018, driven by issues such as the increase in hardware prices and investment by top tier cloud providers in Africa. Access to data in the cloud is critical to today’s data scientists, as they need a centralized and accessible platform across all teams — especially data science teams.

As digital transformation drives more companies and industries around the world to the cloud, there is a constantly growing need to capture and manage both new and legacy data.

As long as a data scientist has easy access to this data, he or she is already equipped with the skills to analyze the growing volumes through cloud technology to turn information into insights that can transform businesses and industries. The problem is, there’s just not enough data scientists to handle current, let alone future demands.

The lack of skilled labour in the data science and software engineering sectors is a problem worldwide but it is especially true in Africa. Most organizations hire data scientists to develop algorithms and build machine learning models, which is typically the part of the job that they enjoy most.

According to a report from CrowdFlower, however, at most companies, there’s an “80/20 rule.” Data scientists spend 80 percent on finding, cleansing, and organizing data, leaving only 20 percent to actually analyze data.

For these reasons, organizations need to provide new cloud services and technology to provide data scientists with the tools they need to rapidly find and organize growing volumes of data.

This leaves them with more time to focus on where their skills are most valuable: analyzing and working with the increasing volume of datasets being generated by everything from sensors to devices and users.

This can include tools to automate and simplify data discovery, curation and governance, as well as intelligent search capabilities to help data scientists find the data they need. Metadata, such as tags, comments, and quality metrics, can help them more quickly decide whether a data set will be useful.

Integrated data governance provides data scientists with confidence that the models and results they produce from data sets are used responsibly by others in the organization.

The goal is to give data scientists the time needed to build and train multiple models simultaneously, rather than being limited to working on one model at a time. This approach spreads out the risk of analytics projects, encouraging experimentation that yields breakthroughs, instead of focusing resources on a single approach that could be a dead end.

Cloud is the foundation of such a strategy, and it gives data scientists the ability to easily save, access, and extend models, allowing them to use existing assets as templates for new projects.

A Juniper report forecasts that cloud services will be adopted by 3.6 billion consumers globally by end of 2018. Additionally, over the next five years, it is predicted that Africa will see rapid growth in its cloud service adoption, with a forecast of 42% growth a year.

Disruptive technology is available to eliminate the “80/20 rule,” and provides data scientists with the tools to reclaim much of the time that they’re currently wasting on discovering and cleansing data.

Instead, data scientists can produce innovative work that provides competitive advantage for organizations and will help them transform their businesses and industries.

By Dipo Faulkner, Country General Manager- IBM Nigeria

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