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Leveraging Value from Statistical Analysis Software

Badri Raghavan, CTO & Chief Data Scientist, FirstFuel
Badri Raghavan, CTO & Chief Data Scientist, FirstFuel

Badri Raghavan, CTO & Chief Data Scientist, FirstFuel

With more data available than ever before– coupled with the emergence of new technologies and techniques to easily access and effectively leverage that data–today’s CIOs have boundless opportunities to innovate and deliver value to both their customers and their own organizations. An increasingly pervasive way that organizations leverage value from this data is through sophisticated statistical analysis software.

“Cloud enables convenient access to a shared pool of resources that can be utilized by CIOs with predictable overhead”

Today, statistical analysis software looks radically different than it did just a few years ago. This can be attributed to a few major industry shifts— the explosion of sophisticated and reliable open source software, the large-scale move to cloud based infrastructure, and the continued integration of predictive analytics into core business processes, in addition to retrospective style data mining. In this article we will explore the influence of each in some detail.

The Explosion in Open Source Statistical Analysis Software

For several years now, corporations have been increasingly adopting open source software within their IT stack. Continuing this trend is the increased use of open source statistical software packages like R and SciPy. Not only does open source statistical analysis allow CIOs to reach a level of reliability previously unseen, it also has clear cost benefits. This cost-effective approach enables the democratization of data–and how that data is used.

In the past, only large corporations could afford and benefit from statistical data analysis because of the high cost of proprietary software packages. Open source software, however, allows small and medium sized businesses to tap into helpful information and use that content to innovate, boost cus-tomer relationships, and/or improves their companies’ bottom lines—all at a reasonable price point.

Take Kaggle as an example. Kaggle is the world’s largest community of data scientists and statisticians that hosts regular competitions to solve the most difficult problems companies face related to data science. The Kaggle community relies on an open-source platform to participate in these competitions and solve real-world problems across a diverse array of industries. As a result of the organization’s open source statistical analysis approach–and that of other similar open source communities–we have seen major innovation in fields spanning energy, financial services, life sciences and retail. One of the most famous examples of this approach was the $1 Mn Netflix prize instituted on Kaggle for coming up with a significantly better recommendation algorithm.

The Move to the Cloud

Not only are we seeing a shift in the way that data is secured, we’re also seeing a transition in the way it is stored. Increasingly, statistical analysis systems are becoming deployed to the cloud, as opposed to deploying them locally behind a company’s firewall. This is a logical move since data and the cloud have similar focuses on scalability, agility and on-demand availability. The cloud enables convenient access to a shared pool of resources that can be utilized by CIOs with predictable overhead.

Due to the move to the cloud, businesses have more and more opportunity to extract value from data resources, ensure timely, appropriate responses to business opportunities and challenges, and manage one view of various data sources. As another example, FirstFuel has been at the forefront of this trend, leveraging algorithms to analyze data in the energy sector and drive improved customer intelligence for the utility industry on a 100 percent cloud-based SaaS platform.

A Shift from Retrospective to Core Operational Systems

In the same way that trends toward open-source statistical analysis and the cloud have opened doors for securing more and better data, the shift of analytics to core operational systems is enabling CIOs to harness optimal, timely information that enhances customer relationships.

Historically, CIOs have leveraged desktop back-end analysis systems that presented information in a retrospective way. Now, CIOs have access to deployable systems that can run sophisticated predictive analytics algorithm in real-time. This brings huge opportunities to CIOs, who can leverage real-time data to secure and share accurate, relevant customer insight, as well as provide more customized customer experiences powered by intelligent statistical software.

At FirstFuel, for instance, we provide utility customers with near real-time insight into how their business customers are using energy. The utilities can then deliver these personalized insights to individual customers through multiple engagement channels, along with actionable next steps and guidance.

And it’s no secret that personalized customer experiences are crucial for ensuring business success. Today’s consumers expect products and services to best fit their individual needs and interests. By securing insight into customer trends and behaviors, businesses can respond with timely, relevant, and well-informed recommendations–whether that’s suggesting a new shirt that resembles a past purchase, or identifying a low-cost opportunity reducing energy spend via simple operational changes. And when businesses start to deliver more value to customers, they will likely start to see impact to their own bottom lines.

The Rise of Systems Integrated with Domain-Specific Solutions

Companies and their CIOs are increasingly moving away from horizontal, all-purpose statistical analysis tools to solutions that more industry-specific. Obviously, data can have very different implications across industries. By embracing domain-specific solutions, CIOs can better relate what data means within the context of a specific industry. This helps them further enhance opportunities for personalized customer interaction and boost their own business performance.

In view of the ever-emerging data solutions and techniques, CIOs may become overwhelmed in choosing the products and approaches that will yield the greatest value. However, those that embrace the solutions and statistical techniques that give them deep insight and optimal leverage from the right data will find themselves in an increasingly differentiated position within their markets.

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