What is Analytics?

Updated: Aug 30, 2018

Abstract: - Article to address and educate on the rise of the trend and the hype around Analytics.

“It is a capital mistake to theorize before one has proper data” - Arthur Conan Doyle

Having a big business poses huge challenges to an Enterprise. The CXO’s have immense amount of decisions to make and needs to administrate. The need to optimize and quicken the decision making processes come into need and that is where Analytics comes into need.

Analytics is defined as the systematic computational analysis of data or statistics. It involves disciplines of Mathematics, Statistics, Computer Science and majorly Data Warehousing. Each of these working independently to give a result that is accurate to an extreme degree. Depending on the need, data is analyzed in various methods to trace recognizable patterns and draw conclusions. Its implications can be from huge billion dollar companies to the smallest of startups, each working for their benefit. With the recent advancements in Machine Learning and Neural Networks, the concept of Analytics has been increased multifold. In fact, a company without Analytics is blind and deaf, walking out in the World Wide Web like a Deer on a Freeway.

Uses of Analytics:

Analytics is everywhere; every single step that man has taken has a background related to analysis. From the floor you stand (analyzed for which material is best) to the food you eat (edible or not) everything has been analyzed at some point in time. However we would focus more on the digital need of Analytics.

The concept of Analytics has been a part if human life since its primitive stages, however the first time when Analytics was put into digital purposes was when Google started using its revolutionary search algorithm integrated into GoogleBot. Every search result you see on any search engine has been independently analyzed to give you the best results, and that scale goes into billions. 

So what are its uses?

1)     Internet Search results - From a search query on Google to finding friends on Google, from suggested videos on YouTube to shows you should watch on Netflix, everything is fine through with great detail for ease of access.

2)     Visual and Speech Recognition- From Siri to Cortana and Google Assistant to Alexa, all of them have had huge amounts of human hours and money spent for ease of use. Visual Recognition is slightly more complicated, from Facial Recognition to Environment Recognition, the process gets multifold complex. Although we haven't gone far in visual recognition, we still use it in scanning documents through smart phones and in Google’s breakthrough Google Lens.

3)     Insights - Vast amounts of Statistics are used to provide an Insight so as to predict the behaviour of various ventures and to back decisions. It may include both Descriptive and Predictive Insights depending upon the requirement. It involves various concepts of Market Research.

4)     Strategization - An outcome can be predicted to a fair amount of accuracy by the correct use of Analysis. Many companies specialize in such services and also provide services to other firms (Like DW Practice). This might require Data Warehousing and many other concepts to gain accurate results.

5)     Optimization of Resources - Many processes can be optimized by analyzing and eliminating resources, thus having an optimal use of resources and saving time.

6)     Prediction - This is very similar to Insights, however the process differs. This might require a Market Research Report to be implemented and also gives information about the possible problems and estimated revenue.

7)     AI and Machine Learning - Analytics is the backbone and brain in development of Artificial Intelligence, vast amount of data and scenarios are analyzed by an AI program to understand and process. These concepts can be read through in much detail at tensorflow.com, specializing in neural networks.

8)     Targeted Advertisements - At times you would have noticed that the products you were looking for are scattered in various advertisements with great deals on them. Your search history is analyzed to give you the best of what you need.

Types of Analytics

Analytics is a broad field and can be classified into various subcategories; however the three primary ones are -

  1. Descriptive Analytics

  2. Predictive Analytics

  3. Prescriptive Analytics

Descriptive Analytics:

This is the most widely used type of Analytics. As the name suggests, it involves describing, interpreting and analyzing the data. It involves basic concepts of Statistics and Inference. It forms the basis of almost every quantitative data structures.

For example, in an Enterprise with multiple products being sold, finds it difficult to keep track of the cash flow. The raw account reports cannot give you proper information about what business has given you profit and which one is going in loss. All that data is analyzed and a report us made which makes gives a systemic understanding of the cash flow. It answers the question, “What has happened?”

Predictive Analytics:

This is the most important kind of Analytics to Enterprises. Vast amount of previous Statistics are analyzed and decisions are manipulated for best results. The past Statistics and data forms a worldwide collection of Big Data (managed by Hadoop).

For example, an Enterprise wishes to launch a new product. However, they aren't sure if it will be successful or not. So previous data (either of the company or any other similar venture) is analyzed to give a foresight to products performance. This involves concepts of Market Research and plays a pivotal role in decision making.

Prescriptive Analytics:

Prescriptive Analytics is more of a successor of Predictive Analytics. It answers the question of “What to do?” From the possible options of “What can be done?” raised by Predictive Analytics. Prescriptive analytics advises on possible outcomes and results in actions that are likely to maximize key business metrics.

When Predictive Analytics is run, it gives possible outcomes, usually more than 2. Of these, using Prescriptive Analytics is run on a small group of target audience and a final report with the best possible decision is given. Almost always, this decision is considered final and beneficial. It answers the question of “What should be done?”

What is the Future of Analytics?

With such vast implications, Analytics seems to be getting the morm in the sector of applied strategies, with such impressive results in a relatively low expense has caused it to be included into various CRM’s. Analytics would definitely prove to be effective in the long run. Firms need to make sure that their practices are up to the current standards, especially those on a smaller scale to aid their quick growth.

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