Data & Decision Science — Fueling Augmented Business Results
On August 27th, 1859 in the town of Titusville, Pennsylvania, an entrepreneur struck oil for the first time on record. This single event changed the course of history and generated unprecedented wealth for those holding hydrocarbon reserves.
For the past decade, C-Level executives adopted “Data is the New Oil” as a mantra to justify underperforming investments in Advanced Analytics. Traditional corporations like Coca-Cola and Walmart currently sit on top of Arabian sized reserves of data that still hold tremendous untapped value.
Across the board, organizations have embarked on Digital Transformations designed to make them Data Ready. In an age where personalization is the norm, whoever can interpret the most data points on a particular customer wins.
How companies extract and interpret data has evolved. Today, companies have deeper access to billions of data points that provide better insights. With broader applications of Data Science, organizations are able to optimize, refine and automate many of their processes.
In parallel, the evolution of Decision Science as a new discipline allows companies to bore further into their data. Decision Science leverages existing data points and Augmented Intelligence to make optimal choices. This is a powerful tool when finding the best solution to a determined problem.
Unlocking some of these opportunities is bound to change the way organizations operate in a historic way and really make data as valuable as oil. Let’s look at how companies can apply Data Science and Decision Science in tandem to improve business outcomes with quantifiable ROI.
Digital Transformations — Making Companies Data Ready
The journey for an organization to run predictive analytics of any sort generally begins with a Digital Transformation. These strategic initiatives usually start after executives come back from a visit to Silicon Valley where they are blown away by innovative and exponential ways of doing business.
Upon returning from San Francisco with some beads around their wrists and a deeper understanding of corporate nirvana, top consultants are summoned to put an action plan together. Large checks are written and perfectly aligned powerpoint presentations are reviewed to reach the same conclusion: the company must become a data driven organization.
Cloud infrastructures are created and more consultants are hired to build the infamous Data Lake. Suddenly, everyone; from the newly minted Chief Data Officer to the marketing intern, seem to be talking about NoSQL Databases and Dockerized services by the water cooler. Management is eager to show the Board that the field trip to Singularity University was not in vain and pressure is on to monetize all the hype.
The large investment in these initiatives makes everyone anticipate a Houdini-like act of magic to happen. In reality, the results at this stage are rare to increase sales or substantially move the needle on any financial KPIs.
Next step: find some Data Scientists to make sense of all the data.
Data Science — Creating Analytics for Actionable Insights
There are few professional paths out there more in vogue than Data Science. This is due to the high demand of these roles at virtually every Fortune 500 company and the short global supply of experienced talent. Qualified Data Scientists are not only tough to find but even harder to appease into a 9–5 badge swiping corporate existence.
Like most rockstars, the best Data Scientists want to play by their own tune. This bodes for a big incongruence between strategy recommendations and practical business execution at large organizations. In a nutshell, Data Scientists integrate math and tech to extract insights from large datasets using analysis, visualization, and mathematical computations.
Machine Learning models can be trained to predict certain patterns and make systems smarter within a company. Powerful algorithms can have tremendous value in many industries; ranging from Healthcare to Finance where Data Scientists use them to provide better insights.
Things are different in the convoluted jungle of sales and marketing where decisions require more than mere knowledge and handling of numbers. Sometimes other business variables must be contemplated in order for Advanced Analytics to produce meaningful results.
Enter: Decision Science.
Decision Science — Using Advanced Analytics for Optimal Choices
The success of a business is largely dependent on making accurate decisions. Business leaders are measured on the quality of their decision-making, and the human element to running a business is largely irreplaceable. However, developing hypothesis built on logic and probability using data bodes for sharper decisions.
This is the intersection where Decision Science is brewing as a powerful concoction for organizations to monetize on the promises surrounding Advanced Analytics. Through the interdisciplinary application of math, business, technology, design thinking and behavioral sciences organizations are better prepared to consume analytics that satisfy the accountability for results.
The analytics that Data Scientists create are best consumed with the thorough analysis of skilled Decision Scientists who understand the business constraints and qualities of a particular problem at hand. The end goal of Decision Science is to leverage Data & Statistics to improve the business decision making process and, as an example, enhance marketing campaigns and budgets.
Here is what it looks like in practice using Fligoo SharpAI Churn.
Case: Reducing Client Churn
Many organizations face the issue of churn: customers at high risk of leaving a business due to inactivity or worse, losing a sale opportunity to a competitor.
This happens because of a failure to identify risk patterns and re-engage the customer with a relevant action. In this case, our Data Scientists analyze thousands of behavioral variables of customers including demographic data, current and past products data, contact channel data, transaction data, and other variables that are relevant to every specific case.
Using Fligoo’s proprietary technology framework, our Data Scientists calculate a statistical probability of retention of each client with respect to each product or service at any point in time. Taking into account business constraints, our Decision Scientists then generate next best action recommendations that can be executed through the optimal sales & marketing channels to maximize retention rates.
The end result is increased sales through personalized content that is specific to each client. This initiative led to a 8x return-on-investment for one of our Financial Services clients by retaining customers and engaging them with the correct Next Best Offer. By leveraging both Data Science and Decision Science the value this client gained from their data is certainly analogous to striking oil.
Ignite Your Data
Your organization is not alone in making sense of what to do with data overflow. In order to use Advanced Analytics appropriately, it is important for executives to understand the power, limits, and relationship between Data Science and its lesser known cousin Decision Science.
The interplay of these two disciplines will be paramount in the next decade for data to really become a valuable a resource as oil that companies can harness to fuel new growth opportunities.
