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Predictive Modelling Infrastructure: 3 Ways Forward

Organisations in every industry are attempting to get ahead by harnessing the power of predictive modelling. Research from Gartner reveals approaches to data analytics are becoming more holistic, and predictive modelling will soon drive all critical business operations.

However, with great power comes great risk. While 43 percent of businesses have experienced a significant positive impact, badly executed predictive modelling can leave lasting damage.

Many of these problems come from building and deploying models on legacy infrastructure that cannot support the model’s goals. These issues include:

  • Wasted IT labour from prolonged infrastructure maintenance
  • Slow run times that result in costly downtime
  • Poor elasticity and economies of scale

 

Thankfully, the cloud offers affordable and flexible solutions, meaning the power of game-changing predictive modelling is no longer reserved for elite corporations.

3 Ways Your Predictive Modelling Infrastructure Is Holding Your Business Back

Predictive Modelling Infrastructure fail #1: Prolonged Maintenance

It can be tempting to build and maintain your own predictive modelling infrastructure. However, this will eat up your time and your budget and could jeopardise your modelling efforts.

A successful predictive modelling project requires the right resources at the right time. You cannot afford to waste your analyst’s precious time with routine infrastructure maintenance.

These hours would be better spent building and deploying the predictive models your business needs - don’t waste your star striker by making them mow the field.

Predictive Modelling in the Cloud

By migrating distributed computing processes to the cloud, our clients have seen a dramatic reduction in IT labour expenditure. For example, following property management company, Telereal Trillium’s migration to Azure, their IT team was free to focus on the core business.

As a result, their productivity rose by 200%.

When you are working with a fully managed environment like Azure, your data science team are free to do what they do best, and your predictive modelling efforts have a better chance of success.

Predictive Modelling Infrastructure fail #2: Slow Run Times and Costly Downtime

As predictive forecasting becomes more mainstream, building competitive predictive models is an essential step for many critical business decisions.

For this reason, organisations can’t afford inconvenient and costly infrastructure downtime. In fact, 24 percent of businesses say an hour of downtime costs up to £300,000. While 14 percent state it can cost as much as £3.5 million.

Screen Shot 2018-05-04 at 12.12.12(The statistic shows the average hourly cost of critical server outages, according to a 2017.)

Predictive Modelling in the Cloud

Moving predictive modelling platforms to the cloud can reduce the run time of batch and intraday processes to a sixth of their original timeframe.

In fact, by migrating a leading financial firm to a more flexible cloud environment, we were able to cut key process run times from 18 hours to less than three.

Predictive Modelling Infrastructure fail #3: Lengthy Time Constraints on Scaling

Building scalable models should be at the forefront of your predictive modelling strategy. However, certain legacy predictive modelling applications can be inflexible and difficult to scale.

Many organisations find their on-premise estates reach maximum capacity when running complex workloads and the potential for real-time analysis is lost.

Predictive Modelling in the Cloud

Deploying predictive modelling applications in the cloud makes it possible to provision resources in minutes and can help your business to hyperscale on-demand. By running predictive models in the cloud, you can take advantage of scalable processing, without paying over the odds.

Since moving mission-critical workloads to the cloud, the same financial firm can now deploy an additional 1000 compute cores in less than 11 minutes, making it easier to manage peak loads and meet fluctuating business demands.  

Building New Capabilities on Old Ground

As we’ve seen, there is more than one reason to move your predictive modelling infrastructure to the cloud:

  • Reduced IT labour costs
  • Accelerated run times
  • On-demand elasticity

Yet, most software vendors estimate that only 20 percent of organisations will run full cloud analytics in 2020. While this state of affairs is expected to change in the next decade or so, in the meantime, enterprises need a way to experience the best both on-premises and the public cloud have to offer.

With a hybrid cloud solution, you can still benefit from the elasticity, flexible costs and supported maintenance of the cloud, without needing to migrate your entire predictive modelling stack. As Big Data and advanced predictive analytics continue to revolutionise the way we make critical business decisions, freeing your organisation from the constraints of legacy infrastructure can help you stand out in an ever-changing marketplace.

For a more in-depth look at the way the cloud is boosting forecasting and predictive analytics, check out the resources on our predictive modelling page.

 

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