Why Creating Predictive Analytics Models is Called a Team Effort?

By CIOReview | Wednesday, July 20, 2016
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In today’s hyper-competitive business environment, companies as well as market research analysts and professionals seek more accurate, reliable tools that help them dig deeper into the data. Insights derived from data analysis reveal better perspective and findings to forecast customers, products, and partners, and unexpected opportunities for informed decisions making. Predictive analytics models are born out of collaborated approach of data scientists, analysts and researchers to effectively interpret big data and create predictive intelligence by uncovering patterns, irrespective of the data type—structured or unstructured. Drawing on a combination of data mining, statistical modelling, and operations research, the predictive analytics models help reduce errors, fraud possibilities, automate manual processes, and also facilitates, smarter and timely decision making. Simply put, building predictive analytics involves team effort.

For enterprises to effectively leverage on the tools and software for creating befitting models derived through predictive analysis, a cohesive environment is needed to bring together the capabilities and goals of data experts and predictive modellers.

Team-centric Approach to Predictive Modelling:

According to a recent study at Forrester Research, the greatest impediment to a successful predictive analytics project is the lack of approach that fosters team-centric practices amongst the data analysts. For analytics to work, businesses do not require theories of individual data analysts working on models in isolation. Instead they prefer to collective ideas and findings of data scientists and analysts to build high-quality models with lasting value and also to ensure that organizations reap huge rewards. As per the Gartner’s research, at the top of the big data spectrum is a team- centric predictive analytics model, providing foresight and the knowledge required to create workflows and operations. In a nut shell, a smart data scientist team and predictive modellers would generally help simplify complex credit risk modelling, create targeted marketing campaigns and conduct web data analytics to optimize business outcomes—in short; this would be ideal, forward-thinking business intelligence (BI).

Additionally, a team approach toward predictive modelling determines strong conditions for problem definition to ensure that the analytics created are solving the right problem with the methods resulting from joint efforts of the smart minds in the room as possible. Many business managers have an opinion that to excel at analytics, companies need to hire a bunch of statisticians who are capable of understanding the nuances of sophisticated algorithms and also give them high-powered tools to crunch data, resulting in recruitment and retention challenges. The question of how to hire data scientists is coming to the forefront, vexing the management personnel in companies to zero in on the skills of individuals that translate to cogent set of analytics models. The art of analytical modelling involves soft skills, strong communication, and business acumen more than mere technical capabilities. Inevitably, organizations need to know how to obtain, manipulate, and analyze data that are often found in very large volumes and build high quality models which reflect their IT managers' perceptions of business and technical realities. In short, the efforts invested in building predictive analytics models does not end once the code is written.

Central Question: Do Models Perform within Established Threshold Criteria

In the course of building predictive analytics model, business managers tend to believe that it is certainly an art of testing analytical models, to quickly obtain results with the help of randomized samples from data sets and rework on it until they are fully reliable. Unlike other BI technologies, predictive analytics model is forward-looking; using the past events to anticipate the future and capitalize on emerging new trends or market opportunities.

A real-world scenario that analysts from Upworthy, a New York-based online media company emphasized during the 2015 Big Data Innovation Summit, forces an organization to rethink their method of cranking out algorithms and implement advanced predictive models for analyzing the behavior of media customers. In general, the analyst team found that most end-users and customers find it arduous with the long page load times in websites, ultimately leaving the site before perusal of their preferred article. With the company’s predictive analytical model and team-centric approach, the firm was able to predict how likely a reader engages with the content by deriving maximum potential from all the available data. This assessment of data ensures that each aspect of the model is systematically addressed to realize game-changing decision-making capabilities. Results falling outside the established performance thresholds are then flagged for further investigation, leading to possible model recalibration.

As a result, to generate useful analytics results, predictive modelling team need to work in unison to drive timely business strategies. Overall, the goal of building the best predictive analytics model will benefit organizations manage their workforce aspects, such that every analyst will be able to defend the decisions made during the development process through what-if scenarios and risk assessment.