| | December 20208CIOReviewIN MY OPINIONACHIEVING BIG DATA ROI THROUGH DATA SCIENCEBy Anjna Kumar, Vice President, Data & Analytics, Bernard Ong, Assistant Vice President, Data Scientist, Judith Shepherd, Assistant Vice President, Enterprise Architecture, Lincoln Financial GroupTHE ROI CHALLENGE OF BIG DATAThe race to leverage big data to 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 arepresentedwith 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 B2B distribution networks ­ Lincoln Financial Distributors (LFD) ­ in the insurance industry. LFD is comprised of many strategic partners, broker dealers, and over 300,000 financial professionals who provide clients and investors with Lincoln's broad portfolio of retirement and insurance solutions. LFD had the foresight to build out a Data & Analytics (D&A) team eight years ago, to help drive sales effectiveness. Since then, Lincoln's D&A team has been on a journey to build clean, consistent and accurate data for analytics purposes. Partnering closely with Lincoln's IT team, the business revampedits data governance processes to target sales pipeline data as a priority. LFD enhanced key analytics capabilities, working with distribution leaders to create assets for Lincoln'sdata scientists.LFD's investment in data & analytics paid off this year, as the company faced a new environment: precipitated by COVID-19, Anjna Kumar
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