We need data scientists to unlock the Power of Data?

Last week I highlighted the need to unlock the power of data; not surprisingly, readers responded and addressed the significant rise in demand for data specialists and the resulting talent crunch.

As the talent crunch is intensifying, it is essential to equip the people in our organization with the necessary digital skills for tomorrow, enabling them to design impactful, innovative solutions to manage our data inventory and data analytics, risk assessments, consumer rights requests, privacy notices, sale of data, consent, incident response and more.

As compliance is such a strategic issue it deserves top management’s utmost and undivided attention, and willingness to make resources, like automation tools, available.

It was interesting to read the report of the Philippine Institute for Development Studies (PIDS) which outlined that the Philippines badly needs data analysts.PIDS added that the Philippines labor force still lacks employees with Data Science and Analytics (DSA) skills. The findings show an undersupply of functional analysts and data stewards. What kind of skilled people are those?

Data engineers design, construct, test and maintain data infrastructure while data scientists use statistical techniques to create models needed to obtain new quantitative and qualitative data. 

Functional analysts, meanwhile, use data and leverage insights to help institutions to make better decisions.

Data stewards develop, enforce, and maintain an institution’s data governance process with the aim of ensuring that the data used is of high quality.   Studies show employers are looking mostly for functional analysts comprising 66% of the DSA demand. It is strongly suggested that industry players encourage their own employeesto be trained and then train others.

According to the Gartner Data and Analytics Summit, by 2020, 40% or more of data will be automated, presenting great opportunities for Filipino data scientists.

Given the data revolution, predictive analytics and data automation will be some of the hottest topics for businessthis year. As companies are realizing the unprecedented opportunities ‘data mining’ creates, there will be a shift to more predictive analytics to assess future economic conditions, risk areas, climate trends, infrastructure maintenance, and investment needs.

Corporate IT and data science departments will begin to integrate the various pieces of analytics into an organized whole. There is the baseline of rudimentary analytics, and then there is the possibility of augmenting these analytics with machine-generated data queries through artificial intelligence (AI) and machine learning (ML).

Both AI and ML "learn" from data analytics repositories by observing repetitive patterns of data, processing, and outcomes, and then posing derivative queries from what is learned. AI and ML will augment-not replace-human creativity in terms of framing unique analytics queries. Because AI/ML can rapidly perceive repetitive patterns, they may be able to deliver faster times to market for certain business insights.

As mentioned above already, it becomes obvious that companies have no choice but to train their staff in data analytics so that they can respond to market opportunities faster and – at the same time – avoid disruptive innovation affecting their business by competitors. If assistance is needed, let me know.

I am looking again for your feedback – contact me at [email protected]