Definition of Analytics
Analytics is elaborating, predicting, inventing and communication of different patterns of data. Analytics uses data and maths to answer unpredictable questions, unsolved queries, discovering more about customer and business, and automate decisions.
This diverse field of data science acts as a bridge which connects data and effective decision within the organisation. Analytics relies on computer programming and modelling, statistics and business models to quantify and explain unstructured data. Read more about What is the definition of Analytics?
Organisation uses data analytics to explain, predict and improve business management. Data analytics covers a wide area which includes predictive analytics, prescriptive analytics, enterprise decision management, descriptive analytics, cognitive analytics, Big Data Analytics, retail analytics, supply chain analytics, store assortment and stock-keeping unit optimisation, marketing optimisation and marketing mix modelling, web analytics, call analytics, speech analytics, sales force sizing and optimisation, price and promotion modelling, predictive science, credit risk analysis, and fraud analytics.
Earlier due to complex system, data storage and data processing speed was limited. Today, we have overcome all those complexities and we are discovering advanced technology on Deep learning and Machine learning that can easily handle a large amount of data in very less time.
As a result, data analytics like descriptive, predictive and prescriptive analytics are capable of learning and automation and thus helping us to proceed more towards an Artificial Intelligence era.
This means we are going forward from asking what will happen to how will it happen to ask our machine to automate and learn from our data, and even tell us what questions to ask.
Today, most of the business organisation consider data as a valuable asset and analytics as central to many skills and functional roles. There is a small but wide difference between analytics and analysis. An analysis is concerned with understanding the past and what will happen, and what can be its consequences whereas, Analytics primarily focusses on why it happened and what will happen next.
Data analytics is very field. It comprises the extensive use of computer skills, mathematics, statistics, use of predictive, descriptive and prescriptive analytics to gain valuable information from unstructured data and analysing them simultaneously.
This insight data are used to recommended actions and to guide decision making in business prospect. thus, analytics does not include individual analyses or analytics steps but it deals with entire philosophy and methodology.
How Analytics works:
Today, every business is an analytics business. Every process involved in the business has a room for improvement. And similarly, every employee can be an analytics user in some way.
No matter what you want to achieve using analytics in your project, you need to analyse your data. And then you need to deploy the results of your analytics to make important decisions. Analytics is mainly concerned with three categories- data, discovery and deployment. These three are the cycle of analytics. Let?s look at them one by one.
1. Data- Data is all source of information. Today, Data are big, large and complex which need analytics solutions to analyse them.
I order to analyse them you need a data management strategy. Data preparation is estimated to take up to 80 per cent of the total time spent on any analytics project. But, after the evolution of Artificial Intelligence, you can reduce this time and automate according to your demand and supply.
2. Discovery- Discovery is all about data model building and visualisation. But finding the right model is a very challenging task. It is the process of trial and error. Opting the right algorithms depends on several important factors.
These factors are the data size, business need, data points and training time. It is very difficult to predict the right model for the analysis. This works out perfectly when you combine different results and different models simultaneously.
3. Deployment- Deployment is the final and important step which helps to predict your results. Whether you are building a single model or hundreds, moving from selecting models to deploying models need strong model management.
Model management help to validate and centrally manage your model. deployment helps you to develop important grounds for procedure and rules for model deployment and monitoring. Thus, deployment help to build a model once which can be deployed to different projects through APIs.
Application of analytics:
Market has been evolving from traditional methods to highly creative data-driven process. The market organisation uses analytics to determine the outcomes of the campaign and drive important decisions for investment and customer targeting.
Geographical studies, joint analysis and other techniques combined together to conclude important decisions to understand marketing strategy.
Marketing optimisation uses both qualitative and quantitative, structured and unstructured data to calculate better revenue outcomes and relations of brands. This process involves predictive analytics, automation, marketing experimentation and communication.
This helps to maximise the profit and increase brand value in the market. Analysis techniques used in market include marketing mix modelling, pricing and sales optimisation, and segregation.
These tools and techniques help with marketing decisions (such as the budget for marketing, which area to focus and marketing mix strategies) and campaign support, in terms of targeting important potential customers.
people analytics is using data to understand how people work and change in companies changes the outcomes of the companies.
People Analytics is also called workforce analytics, HR analytics, talent analytics, people insights, talent insights, colleague insights, human capital analytics, and HRIS analytics.
The primary aim of people analytics is to understand which employee to hire, which/whom to reward or promote, which projects to be assigned and what responsibilities should be allocated in the companies.
People analytics is becoming important to understand which candidate profile is most likely to succeed and fail. People analytics looks at the work and social organisation.
Data analytics helps in portfolio analytics.
In this way, portfolio analysis help finance aim of predictive analytics is to build a strong support system that can accurately predict the next move of the business whether the decision is having a good or bad impact on the company.
The example in the financial sector is to determine which customers will take the credit and which customers are likely to make default.
Using data analytics we use different types of models to select if an application is perfect to receive credit. More specifically, analytics uses the probability that the customer will not pay the loan and therefore we can reduce the options of making a default.
Analytics uses different models to study customers with personal details and financial conditions to pay their liabilities. Different variables most commonly used by data analytics include input variables like Socio-demographic factors, bank relation and customer behaviour.
Predictive models in the banking industry is programmed in such a way that it calculates the risk scores of every individual. credit scores have been used by financial institutions to predict the behaviour and credit worthiness of each applicant.
Risk analysis is carried out in institutions like insurance companies. financial payment like online payment gateway companies uses data analysis to predict if the transaction is genuine or fraud. For this purpose companies always check your transaction history of the customer. This help to reduce the loss and fraud probability in such circumstances.
Data analytics aggregates set of business and activities include defining, create, collect, verify or predict digital data into reporting, researches, analyses, optimisation, prediction and automation.
Digital analytics include the Search Engine Optimisation, Google ranking which help to find special keywords search for marketing purposes. Even banner ads and advertisements are included in digital analytics.
Data analytics helps in security analytics to refer Information technology to collect important security-related events to understand and predict the event which has maximum risks. Security analytics includes security information and event management.