Data is something which has no meaning/ sense to its user. It’s a raw form of anything weather in structured or un-structured format, that can be processed further to make it useful/ meaningful. Every data talks to its user, we need to listen it. It is upto the skills and capability of the data user, how you massage, analyze and interrupt it. Most of the times, it is found, it tells us something, which user is not aware of. To understand data and to take it to the next level of Artificial Intelligence, we need to talk and understand what data is telling us. TALK TO DATA, IT IS WAITING FOR YOUR AUDIENCE.
Data Analytics is a systematic journey from the basics to intelligence. There is an excitement among the users/ consumers of the data to reach to the pinnacle of the analytics but they miss to put the basics in place. Let us understand how to start the journey and talk to the data.
First, you should be clear about WHAT you want and WHY you want it. Background and Objectives of the analytics should be clear and documented by the user. It is much easy to write it on a paper than to start hitting ALT key over EXCEL. Once, you have your background and objectives in place, identify the where is the data and are available in what format. Data could be structured or un-structured.
Another challenge faced by the user is getting the right and complete data on a continuous basis. Such challenges are normally resolved with the support and commitment from the Management. It is been seen that once the user get the data, there is a hurry to start the analytics, contrary user should focus on data cleaning and massaging, the process not only involves removing unwanted characters, but also requires to make sure data is reliable for analytics. Once, the data is properly cleansed, now the process of analytics starts, which shall involve use of various commands, functions, and algorithms to achieve the answer of WHY.
Standardise the basic analytics/ reporting and graduate from daily working to auto modemreporting. This forms basis for advance analytics, including Machine Learning, Predictive and Artificial Intelligence Analytics.
Machine Learning provides computers with the ability to learn – without being overtly programmed, meaning they can teach themselves to grow and change when exposed to new data. Machine learning uses analytics from historical data to detect patterns in new data and adjust programme actions accordingly. The purpose of machine learning is to discover patterns in your data and then make predictions based on often complex findings to answer business questions, detect and analyse trends and help solve problems. Machine learning is effectively a method of data analysis that works by automating the process of building data models. Machine learning examines small or large amounts of data possibly from many different sources with statistical algorithms such as clustering/ profiling; regression and classification. The objective is to discover patterns and then make predictions based on those often, complex patterns to answer business questions and solve problems.
Clustering/ Profiling – Is the task of separating a set of un-labelled objects into groups such that those in one group are more similar to each other than they are to objects in other groups. An example of a clustering problem is identifying groups of people with similar buying patterns. The input is a dataset where none of the samples is assigned to a specific group. The clustering method firstly identifies a set of groups and then associates each sample to a specific group.
Regression – Is the task of determining the numeric response of numeric or categorical variables. An example would be; given the number of past purchases what’s the probability of a purchase of a specific product. Linear Regression algorithm could be an effective tool/ formula for Predictive Analytics.
Classification – Is the task of deciding which category a new object belongs is based on a model constructed from relationships between collections of existing objects that are already labelled.
Predictive analytics is the use of statistics and modelling techniques to determine future performance based on current and historical data. Larger the historical data and with maximum possible variables of parameters with the user, better shall be predictions. There are three pillars to predictive analytics, they are the needs of the entity that is using the models, the data and the technology used to study it, and the actions and insights that come as a result. There are three types of predictive analytics techniques: predictive models, descriptive models, and decision models.
Artificial Intelligence (AI) as a standard known definition is the ability of a computer or a robot controlled by a computer to do tasks that are usually done by humans because they require human intelligence and discernment. There has been an age old statement about computers “Garbage In Garbage Out”, to achieve effective and efficient artificial intelligence from the computers, it is important to program the tools/ software with maximum possible permutations and combinations of events that it is supposed to handle and manage. It is not a one-time exercise, as it has to be updated with changing environment and requirements. This sector of analytics, is mainly driven by the users imaginations and capability to comprehend the situations both present and future along with their correct solutions. Computer shall process what is been coded in the software/ tool. There is always a risk of wrong outcome, if the situation is not correctly analysed and programmed. The risk is further increased, as the user’s increased reliability on the tool’s managing capabilities and with lower level of compensating controls and monitoring of the correctness of the results.
Summary
Analytics is driven by your imagination, it is important to keep updating the analytics that has been implemented successfully. There is a something known as Analytics Life Cycle (ALC), which means that if the user finds certain analytical results are under control or within permissible risk limits, it’s time to move-on and explore other areas with analytics. At the same time, it is also advised that user should revisit the previously analysed reports from time to time, to make sure all is well. Lastly, to have an effective and more importantly efficient system of analytics, user must think out of box and should not limit the imagination with the solution available to solve the requirements. Solutions are created based on the need, and this process shall be the centre point of future development too. Hence, it is important to keep the thinking process active, hungry for more and progressive to achieve higher heights/ improvements.
