Data Intelligence, Business Analytics
Analytics, Business analytics, Predictive modelling, Advanced analytics, Big Data Analytics, Data Mining, Knowledge Discovery, Artificial Intelligence, Machine learning, Business Intelligence, OLAP, Reporting, Data warehousing, Statistics
Analytics – Analytics can simply be defined as the process of breaking a problem into simpler parts and using inferences based on data to drive decisions. Analytics is not a tool or a technology; rather it is a way of thinking and acting.
Wikipedia page on “Analytics”
Analytics has widespread applications in spheres as diverse as science, astronomy, genetics, financial services, telecom, retail, marketing, sports, gaming and health care.
Business analytics – This term refers to the application of analytics specifically in the sphere of business. It includes subsets like –
Industries which rely extensively on analytics include –
Wikipedia page on “Business analytics”
Predictive Analytics – Predictive analytics has gained popularity recently. According to Google, the popularity is largely driven by a string of business news headlines that came out in 2010 carrying this term. http://www.google.com/trends?q=predictive+analytics&ctab=0&...
The term emphasizes the predictive nature of analytics (as opposed to, say the retrospective nature of tools like OLAP). This is one of those terms that is designed by sales people and marketers to add glamour to any business. “Predictive analytics” sounds fancier than just plain “analytics”. In practise, predictive analytics is rarely used in isolation from descriptive analytics.
Wikipedia page on “Predictive analytics”
Descriptive analytics – Descriptive analytics refers to a set of techniques used to describe or explore or profile any kind of data. Any kind of reporting usually involves descriptive analytics. Data exploration and data preparation are essential ingredients for predictive modelling and these rely heavily on descriptive analytics.
Advanced analytics – Like “Predictive analytics”, “Advanced analytics” too is a marketing driven terminology. “Advanced” adds a little more punch, a little more glamour to “Analytics” and is preferred by marketers.
Big data analytics – This term has gained popularity very recently. As per IBM, the amount of data being produced in the world is increasing so fast that 90% of the data that exists today was created in the last 2 years alone. Increasingly sophisticated tools are required to deal with such vast quantities of data. Hence, the term “big data analytics”
Data Mining – Data mining is the term that is most interchangeably used with “Analytics”. Data Mining is an older term that was more popular in the nineties and the early 2000s. However, data mining began to be confused with OLAP and that led to a drive to use more descriptive terms like “Predictive analytics”.
According to Google trends, “Analytics” overtook “Data mining” in popularity at some point in 2005 and is about 5 times more popular now. Incidentally, Coimbatore is one of the only cities in the world where “Data mining” is still more popular than “Analytics”.
Knowledge Discovery – This term gained currency towards the end of 2006. It is not as well known as some of the other terms but its popularity is driven by a popular analytics website – kdnuggets. Knowledge discovery also refers to the same thing as analytics, predictive analytics, advanced analytics, big data analytics and data mining.
Artificial Intelligence –During the early stages of computing, there were a lot of comparisons between computing and human learning process and this is reflected in the terminology.
The term “Artificial intelligence” was popular in the very early stages of computing and analytics (in the 70s and 80s) but is now almost obsolete.
Machine Learning – Similar to “Artificial intelligence” this term too has lost its popularity in the recent past to terms like “Analytics” and its derivatives.
Business Intelligence (BI) – The phrase showed a lot of promise when it stormed to popularity in the late 90s. It started off as a broad phase that encompassed descriptive and predictive analytics. However, it soon got mixed up with OLAP and reporting and now its usage is largely in the context of descriptive analytics or reporting or OLAP.
OLAP – Online analytical processing refers to descriptive analytic techniques of slicing and dicing the data to understand it better and discover patterns and insights. The term is derived from another term “OLTP” – online transaction processing which comes from the data warehousing world.
Reporting – The term “Reporting” is perhaps the most unglamorous of all terms in the world of analytics. Yet it is also one of the most widely used practices within the field. All businesses use reporting to aid decision making. While it is not “Advanced analytics” or even “Predictive analytics”, effective reporting requires a lot of skill and a good understanding of the data as well as the domain.
Data warehousing – Ok, this may actually be considered more unglamorous than even “Reporting”. Data warehousing is the process of managing a database and involves extraction, transformation and loading (ETL) of data. Data warehousing precedes analytics. The data managed in a data warehouse is usually taken out and used for business analytics.
Statistics - Statistics is the study of the collection, organization, and interpretation of data. Data mining does not replace traditional statistical techniques. Rather, it is an extension of statistical
methods that is in part the result of a major change in the statistics community. The development of
most statistical techniques was, until recently, based on elegant theory and analytical methods that
worked quite well on the modest amounts of data being analyzed. The increased power of computers and their lower cost, coupled with the need to analyze enormous data sets with millions of rows, have allowed the development of new techniques based on a brute-force exploration of possible solutions.
This article was originally published on my blog http://blog.jigsawacademy.in.