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Am I Ready for Big Data?

May 17, 2015   //   by BradP   //   Big Data, Blog, Business Intelligence  //  1 Comment

The Hot Topic for the past few years in Business Intelligence has been “Big Data”. Even though this term has been discussed quite a bit, there is still some confusion about what “Big Data” really is. This post lays out my understanding of big data and how this subject has evolved since it was introduced in 2011. Hopefully after reading this you will be able to answer the question: “Am I Ready for Big Data”?

The term big data was coined by the über-management consulting firm McKinsey and Company in a research paper published in May 2011. The key takeaway from this research paper is that companies are generating much larger volumes of data than have been produced in the past. Systems such as ERP, CRM, SCM, and other three-letter acronym systems are capturing everything in a digital format. In addition to the actual transactions, these systems are generating a tremendous amount of “exhaust data” which is a by-product of other activities. Items like RFID tags, hand-held scanners, and smartphones are contributing heavily to this exhaust data.

This report focused on the sheer volume of data being captured but big data has evolved and now encompasses the variety of data that’s available. Additional data is now available in unstructured sources such as blogs and social media applications like Facebook and Twitter. Lately big data has become more about capturing what people are saying on social media.

Another “v” word associated with big data is velocity which deals with real-time (or near real-time) data warehousing. I don’t consider velocity to be unique to big data because real-time data warehousing can exist without volume and variety.

To generate business value from big data (either volume or variety), organizations need to have a mature BI program already in place. Certain steps need to be climbed before organizations can successfully implement big data sources into their Data Warehouse.

Am I Ready for Big Data?

If your BI program is in its infancy, do not attempt to implement big data – especially data of the unstructured variety. Concentrate on the data that’s already available to your organization; preferably data that’s in your control and in your databases. BI projects are an iterative part of a BI program and it’s best to start with data that’s familiar and understood by your organization.

Start by developing a BI Roadmap that will outline the steps necessary to implement a successful BI program. If your organization is new to BI, conduct a Readiness Assessment to determine if your organization is ready to embrace Business Intelligence.

MIPetersen Consulting is able to assist your organization with both of these activities. Please leave a comment or send an email using the quick contact form below.

Business Intelligence vs. Business Analytics: What’s the Difference?

Mar 15, 2015   //   by BradP   //   Blog, Business Analytics, Business Intelligence, Data Warehousing  //  1 Comment

As a student of The Data Warehousing Institute, I tend to agree with their definition of business intelligence:

“Business intelligence (BI) unites data, technology, analytics, and human knowledge to optimize business decisions and ultimately drive an enterprise’s success. BI programs usually combine an enterprise data warehouse and a BI platform or tool set to transform data into usable, actionable business information.”

To take this a step further, business intelligence in itself is not a product nor a system – it’s a program that has a defined framework. The most familiar layer within that framework, for business analysts, is the implementation layer which consists of data warehousing and information services.

Again referencing TDWI, the definitions for the elements within the implementation layer are:

Data warehousing – Systems, processes, and procedures to integrate data and prepare it to become information.

Information services – Systems, process, and procedures that turn data into information and deliver that information to the business.

The two components of information services are information delivery and business analytics. These activities generally serve the same purpose with the main differences being the “dynamic-ness” of the information that’s delivered and the time period (past vs. future orientated) being reported.

Business Analytics tend to be more interactive, more “customizable”, and more actionable.  I prefer to use Gert Laursen and Jesper Thrlund’s simple definition of Business Analytics:

“Delivering the right decision support to the right people at the right time.”

The more complex definition of Business Analytics is…

“The study of data through statistical and operations analysis, the formation of predictive models, application of optimization techniques and the communication of these results to customers, business partners and colleague executives.”

As if that’s not complex enough, Business Analytics falls into three separate categories:

  1. Descriptive Analytics: A set of technologies and processes that use data to understand and analyze business performance
  2. Predictive Analytics: The extensive use of data and mathematical techniques to uncover explanatory and predictive models of business performance representing the inherit relationship between data inputs and outputs/outcomes.
  3. Prescriptive Analytics: A set of mathematical techniques that computationally determine a set of high-value alternative actions or decisions given a complex set of objectives, requirements, and constraints, with the goal of improving business performance.

As you move from descriptive to predictive and ultimately to prescriptive, you are continuing to advance in Business Analytics and delivering solutions that are more dynamic and more future orientated.

Whether you choose to define Business Analytics using the simple or complex definition it all comes down to one thing – Business Analytics is about providing decision support to the business allowing them to take an action using their knowledge based on the information provided that has a positive impact and realizes value to the bottom line of the business.

If you are ready to improve your business performance using Business Analytics, MIPetersen Consulting is available to help. Please leave a comment or send an email using the quick contact form below.