Business Intelligence and the Role of Predictive Analytics



March 24, 2015

We know that the big buzz in the business intelligence world is cloud and big data. We also know that one can only get to the intelligence to apply to the business issue at hand by applying some form of analytics. Now, throw in the growing world of the Internet of Things (IoT) and the challenges in creating, managing and exploiting business intelligence start growing at an exponential rate.
Predictive analytics are not new to this world, but I would say their value in delivering business intelligence is growing. I found this article, “What is Predictive Analytics” , by Jeffery Strickland Ph.D., insightful.  One of Dr. Strickland’s comments really resonated with me that predictive analytics are not just for operational engineering and asset management type applications that I am most familiar with in the SLM world, but also business applications.  A few ideas around predictive analytics in a business environment are:

  • – Insurance underwriting and actuarial risk assessments
  • – Marketing / social media trends
  • – Financial institutions in fraud detection, investment opportunities and global monetary trends
  • – Utility demand and network risk
  • – Weather risks and trends
  • – Retail consumption and logistics
  • – And many more

Another interesting point the article made was that deriving business intelligence through predictive analytics is not just a statistical exercise.   As Dr. Strickland stated; “Unlike the statistician, the analytics professional—akin to the operations research analyst—must understand the system, business, or enterprise where the problem lies, and in the context of the business processes, rules, operating procedures, budget, and so on, make judgments about the analytical solution subject to various constraints. This requires a certain degree of creativity, and lends itself to being both a science and an art.”  Often, companies fail in exploiting their business intelligence through predictive analytics from the fact that that the type of resources required are not developed or staffed leading to underwhelming results.
Big data and the IoT connected products are able to collect organize and facilitate the application of analytics to massive amounts of data. With the improvement in computing speeds, predictive analytics can take the near real-time ingestion of data and identify trends, opportunities, risks, new customer insights through product data collection, social media and product support operations (warranty, repairs, spares, etc.) to enable the business to be more intelligent on what new products to offer, where to move ahead of competition or where to identify operational efficiencies. Additionally, the article discussed predictive analytics beyond the traditional predictive modeling and strategy to include:

  • – Descriptive models – “Descriptive models quantify relationships in data in a way that is often used to classify customers or prospects into groups.”
  • – Decision models – “Decision models describe the relationship between all the elements of a decision—the known data (including results of predictive models), the decision, and the forecast results of the decision—in order to predict the results of decisions involving many variables.”

As companies invest in the infrastructure of capturing data or building connected products, they should also consider as part of their investment strategy the types of tools, resources and what intelligence will come from the data. We see that predictive analytics has a number of applications to business and operations with the flexibility to adapt to the type of predictive modeling needed to the intended business intelligence outcome.
If your company is contemplating on exploiting business intelligence through predictive analytics and are struggling with how to strategically build the value model of your data, let Capgemini help with your solution. Capgemini can bring business solutions, resources and services in areas such as: Big Data and Cloud solutions, Analytics, Architecture and Business Strategy, along with Data Scientists and Business Intelligence Services.  

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Note: This is the personal view of the author and does not reflect the views of Capgemini or its affiliates. Check out the full article here

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