BI vs. Big Data vs. Data Analytics By Example

Big-Data-Analytics-Final

 

 

 

 

 

 

 

Fari Payandeh

 

 

March 6, 2013

Fari Payandeh

 

I know that not everyone will agree with my definition of Business Intelligence, but my objective is to simplify things; there is enough confusion out there. Besides, who is the authority on a terminology that its traditional frame of reference is outdated and doesn’t cover the entire spectrum of the value that  intelligent-data can bring to businesses today?

 

Business Intelligence (BI) encompasses a variety of tools and methods that can help organizations make better decisions by analyzing “their” data. Therefore, Data Analytics falls under BI. Big Data, if used for the purpose of Analytics falls under BI as well.

 

Let’s say I work for the Center for Disease Control and my job is to analyze the data gathered from around the country to improve our response time during flu season. Suppose we want to know about the geographical spread of flu for the last winter (2012). We run some BI reports and it tells us that the state of New York had the most outbreaks. Knowing that information we might want to better prepare the state for the next winter. Theses types of queries examine past events, are most widely used, and fall under the Descriptive Analytics category.

 

 Now, we just purchased an interactive visualization tool and I am looking at the map of the United States depicting the concentration of flu in different states for the last winter. I click on a button to display the vaccine distribution. There it is; I visually detected a direct correlation between the intensity of flu outbreak with the late shipment of vaccines. I noticed that the shipments of vaccine for the state of New York were delayed last year. This gives me a clue to further investigate the case to determine if the correlation is causal. This type of analysis falls under Diagnostic Analytics (discovery).

 

We go to the next phase which is Predictive Analytics. PA is what most people in the industry refer to as Data Analytics. It gives us the probability of different outcomes and it is future-oriented. The US banks have been using it for things like fraud detection. The process of distilling intelligence is more complex and it requires techniques like Statistical Modeling. Back to our examples, I hire a Data Scientist to help me create a model and apply the data to the model in order to identify causal relationships and correlations as they relate to the spread of flu for the winter of 2013. Note that we are now taking about the future. I can use my visualization tool to play around with some variables such as demand, vaccine production rate, quantity…  to weight the pluses and minuses of different decisions insofar as how to prepare and tackle the potential problems in the coming months.

 

 The last phase is the Prescriptive Analytics and that is to integrate our tried-and-true predictive models into our repeatable processes to yield desired outcomes. An automated risk reduction system based on real-time data received from the sensors in a factory would be a good example of its use case.

 

  Finally, here is an example of Big Data. Suppose it’s December 2013 and it happens to be a bad year for the flu epidemic. A new strain of the virus is wreaking havoc, and a drug company has produced a vaccine that is effective in combating the virus. But, the problem is that the company can’t produce them fast enough to meet the demand. Therefore, the Government has to prioritize its shipments.  Currently the Government has to wait a considerable amount of time to gather the data from around the country, analyze it, and take action.  The process is slow and inefficient. The following includes the contributing factors. Not having fast enough computer systems capable of gathering and storing the data (velocity), not having computer systems that can accommodate the volume of the data pouring in from all of the medical centers in the country (volume), and not having computer systems that can process images, i.e, x-rays (variety).

 

 Big Data technology changed all of that. It solved the velocity-volume-variety problem. We now have computer systems that can handle “Big Data”.  The Center for Disease Control may receive the data from hospitals and doctor offices in real-time and Data Analytics Software that sits on the top of Big Data computer system could generate actionable items that can give the Government the agility it needs in times of crises.

19 thoughts on “BI vs. Big Data vs. Data Analytics By Example

  1. Reblogged this on analyticalsolution and commented:
    Big Data technology changed all of that. It solved the velocity-volume-variety problem. We now have computer systems that can handle “Big Data”. The Center for Disease Control may receive the data from hospitals and doctor offices in real-time and Data Analytics Software that sits on the top of Big Data computer system could generate actionable items that can give the Government the agility it needs in times of crises.

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  3. An excellent article to be sure, but there is another viewpoint on all this big data stuff.. Big Data is marketing term, plain and simple. . as Dr Tang said, “the present solution is to wrap up a bunch of bottle rockets (SQL, Hadoop, Cassandra, Mongo, couch etc)and call it a jet engine.. ” LOL one more term, we don’t change the Science named ASTRONOMY to “Big Stars” simply because we find more of them.. 😉 BIG DATA SOLVED http://www.youtube.com/watch?v=DeExbclijPg

    1. I couldn’t agree more. Yet, no one is able to change the reality and the reality is that no matter what we think about the terminology, Big Data has taken roots. I was expecting “smart data” or “fast data” to replace Big Data by now. Not only that didn’t happen, we are seen BI Megavendors use “Big Data” in their marketing schemes. Who knows, may be ten years from now, we will just call it data, but for now, Big Data is it. We just have to get used to it.

      1. Actually, we can change it.. 😉 and we do present “Faster” as well as “Smarter” data with ease of use a 15 year old can now do BIG data of any size.. Learning curve is an hour maybe.. 2 weeks if you want to develop an app ..

  4. Luis Alfaro

    Dear Fari.
    I actually would like to get your support to determine the differences between big data platform and big data analytics platform. I cannot really understand all about.

    Hope to hear from you, and of course an example or graphic detail is always welcome.

    Regards
    Luis

    1. Luis,

      Sorry about the late reply…

      Big Data platform refers to systems that can satisfy volume, velocity, variety of data requirements. Facebook takes in about 500 GB of data per day, and as such it makes a good example of that. big data analytics platform has the additional analytics capabilities. SAP Hana is a good example of that.

  5. Ken Mewes

    I would like to commend you on this article. It is very clear, concise, and easy to understand. Very useful explanations.

  6. Sudheer Subramanian

    Hi Fadi, how do you relate this with the below?

    •BI is reactive approach with historical data in structured data format
    •Big Data BI is the same reactive approach with unstructured data format
    •Big Analytics is the proactive or predictive approach of real time or near real time analytics of structured data.
    •Big Data Analytics is the proactive approach of real time analytics of unstructured data

  7. Pingback: BI vs. Big Data vs. Data Analytics By Example | suk2016

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