Predictive Analytics for the Masses – Data Science-as-a-Service

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JSHRIBER_Headshot.C9In this special guest feature, Justin Shriber of C9 advises how data science-as-a-service opens many doors previously closed to companies wishing to capitalize on the power of predictive analytics. Justin Shriber is Vice President of Products at C9 where he is responsible for Product Management, Product Marketing and Strategy. For the past two decades, Justin has focused on helping companies accelerate growth and profitability by building and executing strategies that align marketing, sales and service with the needs of the customer.

Historically, the cost and effort associated with data science have placed it beyond the reach of most firms. Curating a robust data repository, deploying sophisticated analytics packages, and building a team of data scientists are daunting challenges that few companies are well equipped to address.  Fortunately, DSaaS (data science-as-a-service) mitigates many of these issues. A new generation of applications, born in the cloud and driven by data science, will allow virtually any company to subscribe to predictive services that span a broad range of disciplines from sales and marketing to supply chain optimization.  This movement will expose firms to new levels of insight that will fuel the next era of business productivity.  Outlined below are three aspects of DSaaS that will make data science far more accessible than it has been historically.

Domain Specific Replaces General Purpose

Traditionally, companies have taken a generalist approach to data science. Vendors like SAS and SPSS offer multi-purpose tool kits that must be adapted to specific problems by an army of internal analysts and external consultants. The effort is time and resource-intensive—often requiring budgets in excess of seven figures. DSaaS flips the generalist model on its head by offering an array of specialized services designed to address specific needs, much like the specialization of the industrial age. Today, for example, a given company may work with three different firms to score marketing leads, anticipate customer churn, and optimize inventory based on future demand. These “off the shelf” services deliver actionable results in less time and for less money. That’s because the purveyors are experts in their respective fields and have built specialized models to more efficiently generate and deliver insights (e.g. lead scoring services can sit inside of Marketo or Salesforce and be used to efficiently promote leads to opportunities).

Purpose-Built Data Repositories Replace Ad Hoc Data Collection

Not even a great team or great predictive algorithms can compensate for poor data. That’s why data curation usually represents the most costly part of a data science project. Companies must first identify the data they need and then, secondly, determine how much of it is available internally vs. externally. Lastly, they must implement a data warehousing strategy to consolidate it. It’s an iterative process that often continues for months, if not years. DSaaS offerings alleviate these challenges in several ways. The best services provide a punch list of required data, are pre-wired into essential external data sources, and offer pre-built connectors to the customers’ internal data. Additionally, they’ve developed a standardized schema to thread these data elements together quickly. This approach significantly reduces discovery and deployment time and yields reliable results without the need for continual iteration.

Machine Learning Replaces Manual Re-Tuning

Historically, predictive models have required extensive ongoing maintenance. As businesses evolve and as marketplaces shift, analytics tools need to be constantly updated to reflect these changes. This reality has turned most data science projects into multi-year commitments. DSaaS changes that. These offerings don’t require manual re-calibration. Instead, they rely on machine learning to automatically adapt to change. To do so, algorithms constantly evaluate the correlation between incoming signals and desired outcomes. For example, if an algorithm is predicting opportunity win rates and a company decides to move into an international market, an attribute such as geography may suddenly transition from immaterial to highly correlated with winning or losing. In an environment where the rate of change is constantly accelerating, machine learning is one of the most compelling aspects of modern data science.

As companies large and small become more focused on developing “Big Data” strategies, the services that harvest insights from that data will become increasingly important. In such an environment, historical approaches to data science won’t measure up—generalized tools, bespoke data aggregation, and highly manual processes don’t mesh well with most companies’ cost and time constraints. DSaaS offers a viable alternative. By capitalizing on specialization and economies of scale, it will deliver a new generation of applications to allow virtually any company to subscribe to predictive services that span a broad range of disciplines from sales and marketing to supply chain optimization and foster a new level of intelligent productivity.


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  1. You nailed it.