In the digital age, digital data lakes have replaced file cabinets, and every organization is sitting on an astounding amount of data. With this increase in generation of data, businesses are reeling under pressure to reduce time to garner insights from this data for a definite competitive edge. There is 2.7 Zettabytes of data existing in the digital universe today¹. At any given minute, US alone generates more than 2,657,700 gigabytes of data².

This is the golden age for data scientists and engineers, as there are a host of things to accomplish and an overwhelming amount of data at disposal. One might think this to be an ideal situation where organizations have what they want and know what to do with it. But it’s far easier said than done. For starters, there is a huge dearth of qualified professionals in the business to deal with the data. Mckinsey believes there will be shortage of 250,000 data scientists by 2024³, and Forbes opines that it takes 51 days on average to fill a position of data scientist or advanced analyst in professional services⁴. The demand-supply mismatch remains a pivotal challenge for business leaders everywhere.

To add to this challenge, analytical teams are unable to build and test big analytics projects as fast as they would like. This can be attributed to the fact that the data with any enterprise is both complex and unique. The exploratory data queries by data scientists tend to include advanced statistical methods, which are repetitive and time-intensive. Having a meaningful impact with data involves innovation, thorough experimentation and time. Whereas majority of the time is consumed in tasks that serve as a precursor to actual data analysis such as making the data fit for analysis, identifying important variables etc.

These two aspects of demand-supply mismatch and time-consuming tasks need to be addressed to unravel the full potential of big data. There is now, more than ever, the need to accelerate. The primary focus is to accelerate productivity of data scientists by providing them a user-friendly data platform that can help analyze datasets quickly.

Speeding up the data-to-insight journey

The start can be with automating and simplifying all repetitive tasks such as data ingestion, schema recognition, automated variable selection, model creation and validation. The automation of above processes provides more time to the data scientists for application of their thought to reach actionable insights. The models created can be stored and reused by citizen data scientists as and when the need arises. Combined with the power of the Cloud to easily scale based on the amount of data, models can be trained effortlessly, and deployment can be automated. This will dramatically accelerate the time needed to build, scale and deploy a data analytics project.

These steps are not exhaustive by any means; innovation knows no bounds but starts with a simple step. For the world of data science, things have started to move in the correct direction, it is now upon the big data fraternity to accelerate the processes to be future-proof.

References

¹ https://www.forbes.com/sites/bernardmarr/2018/05/21/how-much-data-do-we-create-every-day-the-mind-blowing-stats-everyone-should-read/#9ceee2560ba9

² https://www.iflscience.com/technology/how-much-data-does-the-world-generate-every-minute/

³ MGI – The Age of Analytics – Executive Summary

⁴ https://www.forbes.com/sites/louiscolumbus/2017/05/13/ibm-predicts-demand-for-data-scientists-will-soar-28-by-2020/#27b440e37e3b

Samir Bansal

Consultant - Data, Analytics and Artificial Intelligence, Wipro

Samir, a technology enthusiast, likes all things new about technology. He has been actively involved with various analytics-based projects essentially finding actionable insights from Big Data. His area of work includes social media analysis, text mining and natural language processing.