| | AUGUST 20218CIOReviewIN MY OPINIONACHIEVING BIG DATA ROI THROUGH DATA SCIENCE The ROI Challenge of Big DataThe race to leverage big datato address the new generation of business opportunities has become accelerated in this new COVID-19 environment. When big data started, the original three V's ­ or attributes that defined big data ­were volume (increased amount), velocity (speed by which data is generated), and variety (various types). Since then, an additional nine V'shave been identified: variability, veracity, visualization, value, vinculation (with social media), validity, vulnerability, volatility, and viscosity.Data quality and governance are challenging enough. But with big data in the mix, companies are presented with an even more daunting task to manage and control data. As we have seen the amount of data increase exponentially through decades of dizzying transformations, the challenges ­ along with opportunities ­are increasing as well. Data quality is a continuous journey for those involved. While there is a common belief that the more data we have, the better, finding more of the "right" data is key. The right set of "small data" can sometimes be more impactful and compelling than big data to the overall business strategy and mission. The combination of both big and small data's potential and promise could bring tremendous positive impact to the bottom line.But, chasing ROI to address all 12 of big data's attributes is challenging and difficult to achieve .In this article, we will explore how data science can help the industry realize big data's full impact.Monetizing Big DataThe inception of big data coincided with the emergence of data science and machine learning. Data scientists began navigating the complex landscape with advancements in the way data was leveraged to achieve actionable insights.But data science turned out to be the missing link that helped businesses use big data to achieve ROI.Enlightened executives have now started to understand that in order to differentiate their companies in the market and continuously innovate, they must be able to estimate the inherent value of their company's data. They understand that data must be treated as an asset, not just as a technical repository of information with unknown, unquantifiable value.Monetizing data requires an entire reframing of our mindset: Big data needs a purpose to exist. And data science provides the scientific rigor and framework that enables value extraction from imperfect data. The best data scientists can extract effective intelligence and insights to drive actionable insights with a highly-targeted level of personalization to the business need. That is what data monetization is about.Case Study: Tenets of the Lincoln Financial Distribution BusinessLincoln Financial has one of the most powerful By Anjna Kumar, Vice President, Data & AnalyticsBernard Ong, Assistant Vice President, Data ScientistJudith Shepherd, Assistant Vice President, Enterprise Architecture for Lincoln Financial GroupAnjna Kumar
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