Using Data Analytics for Business ask

Whenever a Business Analyst is presented with set of asks, they are required to convert those asks in requirements. This conversion cannot all together go on gut-feelings but on observations, facts and data. Data is all around : Excels, Reports, operations and more, with help of surveys, polls and interviews. Any Product conceived by business is raised by its users. In order to bring out the results of product how it is maturing with its users, data Analytics play crucial role to know the trends and patterns in its users. Data, often comes in raw form from many sources, which a business Analyst can use to his cause by understanding the patterns of exiting product, where amendments could be done or what new features could be imbibed to get the best of product.

Data typically is classifies as organized and unorganized.

a) Organized Data is quantitative and discrete in nature, usually comes in metrics. Be it the version, tables, excels etc. Organized data are the ones which is further used for trends and pattern sorting.

b) Unorganized Data is qualitative and subjective in nature, usually comes in form of surveys, polls and reports. They are further fed for processing and refining to get perspectives and decisions.

Everything we do now, is data or data-based. In one form or another, data used and analyzed to know the user’s mind. This process of inspecting, cleansing, transforming and modeling of data aimed to discover useful information, further leading to conclusions and decisions is Data Analytics. Data from the past is collected and processed to form decisions by knowing the patterns.

There are four distinct ways to implement data Analytics.

Overview of Data Analytics

1. Descriptive : This is most initial form of data analytics which are used by maximum organizations. As the name implies, Descriptive data analytics ‘describes’ data in aggression form from the statistics. It is reactive in nature as interpretation from this type of analytics is derived from past. Descriptive Analytics would provide answer to questions like “What has happened? Techniques used for descriptive analytics is applying Averages, Mean, Median, Mode, standard deviation etc. to statistical data. Data should be cleaned by removing duplicity and make it normalize as much as possible. This type of analytics is used in reports that provide hindsight regarding the company’s production, operations, sales, finance, inventory and customers. Post the descriptive data analytics, we know “What has happened”.
2. Diagnostic: Now, We might be probing “Why it happened”. For Example, if the standard deviation is more than mean, we might look for answers why is it more, what data set is taking Stddev away from averages. This all answers to “why it happened” are covered in Diagnostic data analytics. Often, the data is drilled further by applying techniques like data mining and data discovery to know the reasons for the stats. Often, analysts can apply correlation and extrapolations to derive the results. Diagnostics analytics find itself more useful for healthcare and life-crucial industry.
3. Predictive: When the current scenario and the reason behind are discovered, one can use this insight to know the foresight. Now, Business analysts can use the data to identify the patterns and trends to draw conclusions on what next should be served to users in order to get the product on its peak. Probing questions “What might happen” will be answered by applying algorithms to know the likelihood of user’s next course of action. Organizations like Amazon and other retailers use predictive analytics to identify patterns in sales based on purchase patterns of customers, forecasting customer behavior, predicting what products customers are ‘likely’ to purchase together so that they can offer personalized recommendations, predicting the amount of sales.
4. Prescriptive : Forecasting of trends are further subjected to decision modelling where questions like “what should be done to improvise the current situation” are answered. Prescriptive data analytics is proactive in nature as it would drive the future. They are done by applying the business rules, optimizations and simulating future under various assumptions. Prescriptive analytics is free from “gut-feel” and other personal bias where tangible and measurable decisions are drawn. Prescriptive analytics are complex in nature and most companies do not apply it in their daily course of action. But if applied appropriately, it would definitely lead to major measurable goals. It would often fall away from radar of business analyst but it is always ‘good to have’ skill.

Business Analyst may not master all the techniques of data analytics but understanding the numbers and identifying trends would always make long way to know the asks and convert them in requirements.