Cloud Predictive Modelling: 4 Tips to Building a Business Case

[ServicesExperts]

‘Business analytics or predictive modelling is a $100 billion industry, and $41 billion is spent on outsourced business analytics every year. I think that's about twice the size of the movie industry - it's really big.’ – Anthony Goldbloom, CEO of Kaggle

Predictive modelling is well on its way to becoming a business necessity. Without it, organisations may fall behind their competitors and fail to invest in the right business opportunities.

However, traditional on-premise predictive modelling solutions, such as Excel-only models, are often:

• Slow
• Costly
• Inflexible

For businesses that require streamlined forecasting applications, these outdated models simply aren’t enough.

With this in mind, many organisations are setting their sights on the cloud for more accurate modelling and forecasting processes. According to 71 percent of strategic buyers, the most important driving forces behind their move to cloud infrastructure are scalability, cost and business agility.

To build a watertight business case for cloud predictive modelling, you need to focus on specific business outcomes. These include:

• Cost reduction
• Increased scalability
• Better reliability
• Accelerated innovation
• Improved security

Let’s breakdown how a predictive modelling migration helps organisations achieve all of these goals.

4 Reasons to choose Cloud Predictive Modelling for your Business

When you’re looking to gain stakeholder buy-in for an IT project, it helps to know exactly how a new technology will affect ROI.

1. Cutting Predictive Modelling Costs

With an on-premise predictive modelling infrastructure, your business not only has to pay for maintenance and IT labour costs, but also the cost of managing and scaling physical servers to store large amounts of predictive modelling data.

However, by investing in cloud predictive modelling, your organisation can cut the budget constraints that are holding back your data-driven efforts.

Here are some key cost benefits to predictive modelling migration:

• No upfront capital infrastructure costs
• Reduced IT expenditure (which 52 percent of businesses cite as the biggest benefit of cloud computing)
• Lower IT labour costs, with fewer on-premise servers to maintain
• Lower storage costs
• A pay-per-use model (making it easier to track and reduce operational expenditure)

2. Scope for Scalability

Organisations who use out-of-date legacy systems become bogged down restrictive servers and inflexible IT infrastructure. After all, without the money, capacity or maintenance resources, it can be a difficult and time-consuming process to scale up your predictive modelling efforts.

It’s no surprise, then, that 52 percent of businesses believe the biggest benefit of cloud computing is greater scalability.

52 percent of businesses cite ‘greater scalability’ as a public cloud benefit.

With a predictive modelling migration, you can secure your cloud data centre and deliver the elasticity to scale your processes up (or down) rapidly on a pay-as-you-go basis. Without the time-restraint and cost of on-premise workloads, your business can:

• Deploy new predictive models within minutes
• Cloud burst on-demand
• Run complex predictive modelling workbooks in parallel, twice as fast

3. Better performance and reliability

For some businesses, downtime can make or break predictive modelling performance.

Picture this: your insurance risk model requires more processing power to upscale your workloads during periods of bad weather, but your on-premise predictive modelling infrastructure isn’t powerful enough to rise to the challenge.

However, with the help of the cloud and Azure SQL Data Warehouse’s guaranteed 99 percentavailability, your predictive applications can benefit from increased availability and higher processing speeds.

As a result, your organisation can stay productive at all times and, in the case of one client, increase efficiency output by as much as 200 percent.

4. Innovating with predictive modelling in Azure

When it comes down to building your predictive models and analysing your predictive analytics and forecasting, only the best tools will do.

Although a third of businesses still rely upon on-premise tools, such as Excel, for the brunt of their modelling processes, these programs are out-dated and inherently flawed. Without the processing power to handle demanding computations or real-time models, these applications can limit the potential of your predictive modelling efforts

By re-housing your predictive modelling infrastructure in Azure - using tools such as our Azure Calculation Engine or Azure Machine Learning - your business can harness the power of the cloud to:

• Upscale data-driven decision-making
• React to near real-time forecasts and predictions
• Deploy predictive models within minutes
• Invest in better, more engaging data visualisation
• Spend more time and resources on innovating and turning your predictive forecasts into actionable business insights

Securing your Data

Understandably, housing your predictive modelling workloads on-premise can offer a sense of physical security. But, just because you can see your servers, doesn’t necessarily mean they’re protected.

One unlocked office door, and your data could fall into the wrong hands.

However, by moving your workloads to the cloud, your business can take advantage of a dedicated, secure data centre environment. With Microsoft’s watertight data centre security policies and intuitive threat detection, your most sensitive data will always be closely monitored and protected.

It’s no surprise, then, that 90 percent of users trust the cloud with at least half of their sensitive information and 83 percent of users believe cloud security is as good as, or better, than their on-premise security.

With a hybrid approach, you can even use cloud predictive modelling to run mission-critical calculations and store the outputs in your own datacentre.

Are you ready to make the move to cloud predictive modelling?

The Big Data and predictive analytics market is projected to generate $210 billion (£154.4 billion) in revenue by 2020.

Revenue from Big Data and business analytics will reach $210 billion by 2020.

With so much scope for business innovation, competitive advantage and customer satisfaction, it’s no surprise businesses are focusing their efforts on improving their predictive modelling performance.

As more and more organisations seek a better alternative to their outdated, restrictive and costly legacy systems, predictive modelling migrations are helping them to:

• Scale predictive modelling infrastructure quickly and easily
• Reduce the TCO of their modelling environment
• Improve productivity and reduce run-times for critical processes
• Act upon key business insights and drive better business outcomes

We hope this guide to building a business case for predictive modelling has been useful starting point. If you’d like to find out more about the RedPixie approach to data analytics, head over to our predictive modelling page.