How Predictive Analytics Solutions Disrupt the Future

Finding ways to generate more accurate predictions and make better business decisions.

Finding an Edge with Predictive Analytics Solutions

Current analytics and business intelligence arrays closely examine events in the past. The information is valuable. But it’s simply giving organizations a more detailed view of what’s already happened. Businesses today that only look backwards run the risk of being left behind. By deploying predictive analytics solutions, companies are able to focus on their future and disrupt it.

Think about a credit score. The reporting companies look at a buyer’s payment history and current financial situation and give creditors a numerical representation of how likely it is they will pay their debts on time in a way that measures the risk of granting more debt. That’s a very basic predictive analytics solution. The methodology takes disparate data sets, analyzes them, and generates a numerical degree of certainty that something will or won’t happen in the future. 

That’s why predictive analytics isn’t just forecasting. These solutions may combine many sets of structured (transactional data for example) and unstructured (social posts, image) data and scrutinize it in real-time. Unlike legacy business intelligence approaches, information will be stored differently, analyzed in new places, and searched for everywhere. Applications have the potential to move closer to data for real-time edge processing with IoT and the cloud. Predictive analytics is known to spur improvements both in business unit collaboration and decision-making.

Obtaining information is only part of the equation. Seeing correlations where your organization hasn’t found them before is what transforms the data into a catalyst for valuable and actionable insights. The resulting changes can lead to better customer experiences, products, and services across the entire organization.

Get the Intel perspective behind each week's IT headlines. Sample a bit of Intel's insight on this week's developments and then subscribe today.

By signing up, you agree to our Privacy and Terms of Use.

By signing up, you agree to our Privacy and Terms of Use.

Solutions in the Real World

Analytics solutions help your company beat competitors to market with new products and services. They delight customers with thoughtful communication that connects with them. Analytics in the real world is all about data. When you have more data available to process, you will generate more accurate predictions. By collecting new data streams, you build a comprehensive view of the business, and a stronger starting point for the analytics journey.

A case in point is the UK’s North East London National Health Service Foundation Trust* (NELFT*). The system cares for more than 1.5 million diverse people over the age of 65 who spend an average of four days a year in the hospital for unplanned admissions. Too many of these admissions were preventable and they needed a way to reduce them.

After partnering with Intel and Santana Natural Language Analytics*, the NELFT* implemented a new pilot program that created huge amounts of unstructured natural language into helpful coded medical data without any new data entry requirements. So clinicians were able to predict which patients showed the highest risk for life-changing events and disorders and were able to intervene earlier and keep patients out of the hospital in the first place.

This program didn’t just involve new systems to build on an existing solution. It required a deep understanding of the business problem, an examination of all the information available to best determine the correct factors, new branches of data analysis, and the willingness to build a technological environment that was secure, nimble, and scalable to many new tasks. Predictive analytics solutions often start outside the box, and build to adapt to the new environment.

People: The Start of Your Solution

Leadership is a necessary starting point for any predictive analytics solution. A frank assessment of current infrastructure and employee skills are crucial first steps to any successful deployment. It’s no surprise that predictive analytics requires people such as analysts with special skills such as modeling, statistics and data science.

In terms of new people skills, data scientists have the specialized skills to guide and structure an analytics solution from the ground up. Their knowledge of different algorithmic approaches and data structuring techniques is essential during the early stages of any new predictive analytics solution. Proper data science and data placement can spur new insights borne of coupling existing data stores with real-time sources stemming from places such as the Internet of Things (IoT).

It’s equally important to understand the various predictive analytics platforms that are available to your organization. Many of the major suppliers already in your enterprise support predictive solutions with upgraded software suites, and are also likely to have the capability to incorporate unstructured data, either directly or via a connector to an open-source solution. Identifying the skills you have on hand and addressing any skills gaps can help to jump-start your efforts.

Obviously, the need for administrators, app developers, and other common IT skill sets does not disappear. And many of the advanced skill sets involving virtualization, network topologies, and storage will be necessary. The use of public and hybrid cloud resources should also be a consideration, and the skills that any modern enterprise is building in those areas will be very useful for a large-scale predictive analytics solution.

The Infrastructure Evolution

Adoption and expansion of analytics solutions may lead to an infrastructure build-out, especially if data is moved closer to its source or must be processed in-memory or in real-time. Data security and access management are of utmost importance.

Since analytics focuses so much on data, and potentially involves moving data from edge sensors to the cloud and the data center itself, there are points where it’s vulnerable, and many places where regulation will have an effect. For instance, data from multiple IoT sources may need to be made anonymous to protect the individual end points. In a possible future addition to the NELFT* example, the organization might provide a variety of home health sensors to monitor at-risk patients. This would then necessitate end-to-end data security to protect patient information.

Processing speed is essential—the value of data can rapidly decay with time. A business will be collecting and analyzing data from many sources almost simultaneously, and needs to extract the most value from it. That data will understandably be more complex, and will need to be parsed in a variety of ways. Even pre-existing data that appeared redundant or useless —so-called “dark data” that the business has but isn’t using — could likely be of value.

Infrastructure flexibility will need to increase, too. An IT department ready for analytics may consider SAP* HANA* in-memory solutions, design an architecture for streaming analytics, or program a big-data intensive Hadoop* deployment. Many deployments entail moving significant resources in the cloud, and require new software skills that provide edge and network processing.

Steps to Implementation

Once the right infrastructure and skills are in place, and the business problem to be solved is chosen, it’s time to begin implementing.

Organizations are often faced with the choice between a long view and solving short-term problems. Consulting with a trusted partner who has the necessary expertise to help design a viable solution may help you settle on a good starting point and possible immediate wins.

Where and how to store the new data the business will utilize is a major consideration. On-premise and cloud solutions both have their merits, and a hybrid of the two often accomplishes the goals of speed, security, stability, and scalability.

Likewise, new software platforms may be worthy of consideration. Open Source solutions continue to play a major role in analytics clusters. Having a community innovating on a shared platform may spur significant advances for your organization. Apache* Spark* and Hadoop* are two essential analytics solutions that are open source, with more options coming as needs are better defined.

Soon your organization will have more actionable information than it ever imagined, and dealing with unstructured data is wholly different than traditional information stored inside a relational database. Cloud Platform-as-a-Service (PaaS) solutions can help build skills and accelerate progress early on.

Information will end up in what’s often called a data “lake,” since it’s so much information in one place. However, this “lake” is actually a series of “puddles” and it’s the job of the organization’s scientists and architects to connect the “puddles” and make sense of it all. This includes discerning which bits of information are related enough to look at together, as well as which data sets seem dissimilar but could reveal valuable insights under the lens of analytics. Software-defined storage initiatives could be necessary to aggregate data in a single logical “place” and format it for a variety of different analytics engines. Some storage solutions even have enough capability to build analytics engines that run directly on the storage node itself.

Getting Support for Your Analytics Solution

Undertaking any analytics cluster requires backing from upper management. An early and ardent supporter in the C-Suite is an invaluable asset, because there will be uncertainties and there will be challenges. But analytics is too important to be ignored any longer. If competitors in your market haven’t yet begun to harness the power of analytics, they will soon.

To assuage concerns about potential drawbacks to an analytics cluster, emphasize partner expertise and highlight an approach that values speed, stability, and scalability. Intel can be an excellent partner for you. Intel® architecture is the basis of nearly all the compute, network, and storage infrastructure that you need to deploy nimble and robust solutions. The resources available from the Intel ecosystem enable you to build a secure foundation for all of your predictive analytics needs.

Business Intelligence systems of the past will remain valuable. But they will foster higher-level insights and innovation when coupled with a powerful data driven analytics solution. By shifting their focus from the past to the future, businesses with predictive analytics will have the power to disrupt their own futures in new and fascinating ways. This is the message that will carry businesses to the forefront of their markets in the 21st century.