SAS to Open-source Migration
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SAS to Open-Source Migration – Modern Analytic Practices

The visibility and remit of analytics has changed over the years as companies increasingly understand that future success depends largely on the use of data to make optimal business decisions and drive efficiencies.

This may require a change from reactive statistical analyses around a narrow range of challenges, to more proactive open-source data science methods to drive business decisions in a repeatable and scalable manner.

In this whitepaper, we look at the drivers for change for SAS to open-source migration.

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Building an effective analytics & data science capability

In today’s digital world, organisations are increasingly looking at analytics-led strategies to implement data-based decision-making and leverage insights for strategic business gain. Whether the business goals are client acquisition, a reduction in churn rate or the optimisation of store locations, a data-driven approach can deliver critical competitive advantage.

At Mango Solutions, we know a thing or two about the issues faced by the implementation of data analytics and the importance of building an effective analytics capability to meet those challenges; one such step towards tackling these is through building an internal analytic community.

 

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The Utilities Analytic Challenge

Like many other industries in the modern world, utilities is a sector that’s now led by data.

The utilities sector has distinct pressures and opportunities that are driving analytic changes. Regulation from government combined with environmental considerations, technological advances and consumer churn mean that analytics is at the forefront of creating data-driven businesses.

“Data analytics is key to unlocking the benefits of Smart technology” Andrew Coleman, Head of Smart Metering Transition, Bristol Energy

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The UK government is committed to having driverless vehicles on British roads by 2021. It’s exciting, but what does it mean to the insurance industry as organisations are forced to think about risk and claims management  – who carries the risk? Is it the car manufacturer, the software designers or the engineers? These questions can only be answered if the insurance organisation become truly data-driven.