Data Analytics most commonly used Words or Terminologies

Analytics is one of those fields like cricket where a lot of terms are thrown by everyone, though some terms might sound quite similar to one another but they might be different in context and on the other hand we have terms that sound quite different yet they can be used interchangeably. Hence, someone who is new to analytics is bound to get confused by the abundance of terminology that is there in data analytics field.

Following are some of the most commonly used analytics terminology, so that when you come across a popular analytics term, you understand the meaning behind it, the definition of the term and the context in which it has been used.

Analytic s: It can be defined as “The process of breaking a problem into simpler parts and using inferences based on data to drive meaningful decisions”.  Analytics is neither a tool nor a technology; rather it’s a way of thinking and acting. Analytics has a widespread application in spheres as diverse as science, astronomy, retail, finance, sports or simply put it can be used in any industry where we have sufficient data to derive meaningful inferences.

Business Analytics:  Now, based on our above definition of Analytics, Business Analytics refers to the application of analytics specifically in the sphere of Business”. This would include subsets like   marketing analytics, risk analytics, CRM analytics, loyalty analytics, operation analytics as well as HR analytics.

Predictive Analytics: This term has gained widespread 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. The term predictive analytics, emphasis the predictive nature of analytics as supposed to retrospective nature of tools like OLAP and BI. This is one of those terms devised by sales and marketers to add glamour to a business. Predictive analytics sounds fancier than plain analytics; in practice, predictive analytics is used in isolation of descriptive analytics.

Descriptive Analytics: It refers to a set of techniques used to describe or explore or profile any kind of data. Any reporting usually involves descriptive analytics, data exploration and data preparation are essential ingredients of predictive modeling and these rely heavily on descriptive analytics. So while analytics is a summation of descriptive analytics and predictive analytics, savvy sales people have decided to bifurcate the two.  So they call predictive analytics separately from descriptive analytics. While the truth is predictive analytics cannot be used in isolation of descriptive analytics.

Advanced Analytics: It’s another fancy name for Predictive analytics and is supposed to add more punch to predictive analytics. J

Big Data Analytics: This term has gained popularity in last 2 Years and it specifically refers to huge data sets that have come about nowadays which needs to be analyzed and explored. So a lot of these datasets, keeping in mind that these datasets are Terabytes of data or billions of rows of columns and millions of fields. So when you are dealing with this kind of data, the conventional tools are not enough to analyze or explore this data. In order to analyze this data, one needs specialized tools to deal with this large amount of data. Hence, due to this the term Big Data analytics came about. There is no clear cut definition as what is big data; one cannot say if a data set is above 1 GB its big data. Keeping this in mind, the definition of big data is quite flexible and is depended on the technology. So, big data refers to any data set which cannot be analyzed using the popular and conventional tools and require specialized tools for analysis.  If we go by current trend, any data that runs in terabytes is considered to be big data and this might change as the technology improves.

Data Mining: It’s a term that is most interchangeably used with   analytics. Data mining is an older term that was more popular in the 90’; s and early 2000’s. However, data mining later got confused with O-Lap and that led to the drive to use more descriptive terms as predictive analytics. If we go by Google trends data, Analytics as a term overtook data mining in popularity at some point in 2005 and currently is 5 times more popular than data mining.

Artificial Intelligence and Machine Learning:  During the early stage of computing, there was a lot of comparison between computing and human learning process and this is evident from the term itself. The term artificial intelligence was popular in the very early stages of computing that is around 70’s and 80’s and has lost its sheen since then. Machine Learning is another term similar to artificial intelligence

Business Intelligence: This term showed a lot of promise when it stormed to popularity in the late 90’s. It started off as a broad phase that encompassed both descriptive and predictive analytics. However, it soon got mixed up with O-Lap and reporting and now its usage is largely in the context of descriptive analytics, reporting or o-Lap.

OLAP: OLAP is an acronym for Online Analytical processing refers to descriptive analytics    techniques of slicing and dicing the data to understand it better and discover patterns and insights. The term is derived from another term OLTP which stands for Online Transaction processing which comes from data warehousing world.

Reporting: This is perhaps the most unglamorous term in analytics industry and yet it is an intrinsic part of an analytics industry. All businesses use reporting to aid decision making. While it’s not advanced analytics or predictive analytics, effective reporting requires a lot of skills and a good understanding of the data and the domain.

Data Warehousing: This term might beat Reporting in terms of being unglamorous. It’s essentially a process of managing a database and involves extraction, transformation and loading of data. Data warehousing precedes analytics as data that is generated by any business needs to be stored so that it can be used by analysts.

Statistics: This also one of the terms that is confused with Analytics. Statistics is the study of the collection, organization   and interpretation of data. A lot of algorithms and analytics techniques are essentially based on statistical concepts and hence there is a huge overlap between statistics and analytics. The way to differentials the two is more in terms of usage, while statistics is purely a science a theoretical subject. Analytics on the other hand is the application of this subject on problems to derive meaningful results.

Data Science: As the name suggests it’s the Science of dealing with data. So this is essentially the same thing as analytics but this is fairly new terminology that has gained popularity recently.

Data Scientist: This is closely related to data science and refers to people who practice data science.

These are essentially some of the most commonly used terminology that are used in the field of analytics and would help you in understanding these better.  Please let us know in the comments section if you have come across any new terms that are gaining mileage recently.


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