Achieving Big Data ROI through Data Science

Bernard Ong, Assistant Vice President, Data Scientist, Ent and Anjna Kumar, Vice President, Data & Analytics and Judith Shepherd, Assistant Vice President, Ent

Bernard Ong, Assistant Vice President, Data Scientist, Ent

The ROI Challenge of Big Data

The 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’s have 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 Data

The 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 Business

Lincoln Financial has one of the most powerful B2B distribution networks – Lincoln Financial Distributors (LFD) – in the insurance industry.

Anjna Kumar, Vice President, Data & Analytics and Judith Shepherd, Assistant Vice President, Ent

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 revamped its data governance processes to target sales pipeline data as a priority.

In this “Black Swan” scenario, the best recourse to address a surprising event with a potentially significant impact was to wait for new data to come in. This is where “small data” analysis became even more critical to the business

LFD enhanced key analytics capabilities, working with distribution leaders to create assets for Lincoln’s data scientists. LFD’s investment in data &analytics paid off this year, as the company faced a new environment: precipitated by COVID-19, the selling environment became completely virtual. Because of its investments, the distribution team was ready to leverage data and analytics to target financial professionals with the greatest propensity to sell Lincoln solutions.

The data science team formulated a strategy that resulted in highly-sophisticated predictive and prescriptive models that help businesses personalize their targeting and engagements.

What LFD’s salesforce ultimately uses is one simple metric that encapsulates the intelligence and predictive power to determine highly personalized engagement prioritization, sales propensity, and cross marketing opportunities. As a result, LFD has witnessed more effective and productive use of its salesforce’s time, greater consistency in how it targets and segments financial professionals, an increase in advisor satisfaction, more efficient use of company resources, and an increase in wholesaler capacity – all of which drive a great amount of incremental sales.

The COVID-19 Black Swan

During the pandemic, Lincoln’s data science team re-calibrated its algorithms to address the unforeseen and sudden change in environment.

In this “Black Swan” scenario, the best recourse to address a surprising event with a potentially significant impact was to wait for new data to come in. This is where “small data” analysis became even more critical to the business, as new data was carefully incorporated into Lincoln’s models.

Lincoln leveraged the opportunity to extend models to include more prescriptive recommendations on how to engage financial professionals in a virtual setting. As a result, Lincoln has found innovative ways to segment advisor engagements based on their affinity to engage in virtual outreach versus in-person meetings.

What the Future Brings

during COVID-19 – and is finding innovative ways to get to the right data to reveal the story unfolding, and to surface compelling insights.

Lincoln is working on opportunities to leverage new factors to more deeply understand financial professional sentiment using advanced embeddings and “affective computing” methods. Lincoln is also formulating strategies on how to more effectively leverage the data around financial professionals’ virtual engagement in a post-COVID-19 world.

It’s critical to recognize that data has inherent value. Data scientists at Lincoln are continuously discovering more ways to capture that value and apply it to our suite of products and services to better serve our customers for the long-term.