Numerous times in our lives we come across situations where we require a rather critical approach. To help gain a better answer to our problem it is always a great idea to carry out data analysis. Reviewing the statistics of a situation helps us get a clearer view. It helps us look at the situation from different point of views.
Before we continue to go into detail of data analytics lets first review what it is. Data analytics is a critical process which helps us gather, clean, inspect and model data to attain useful information concerning the respective matter. The useful information helps us support our conclusion to make the wisest decision.
Therefore, the importance of data analytics cannot be ignored. It does not matter if one has not studied data analytics or statistic before. Just because you do not have a background research carried out in this field does not make you incapable to using it in your practical life.
Major steps involved in carrying out Data analytics
Data analytics is part of a large process, but it can be divided into simpler terms to help highlight how individuals with minimal knowledge can benefit from this technique.
There are several methodologies and frameworks in Data Analytics that helps you leverage your goals. Like Cross-Industry Standard process for Data Mining (CRISP-DM) and Sample, Explore, Modify, Model, and Assess (SEMMA). Today's post is not to deep-dive into them but instead let's try to highlight some of the things that we should never neglect. Saying so, here are the points that help carry out the process in the most effective manner.
Define your objective. This is first step involved in data analytics. The study shall begin when the individual identifies its business objectives and goals that he wants to achieve. The decisions made later are dependent on this stage as it highlights how clear the vision is. Remember that the first thing you have to do before you solve a problem is to define exactly what it is.
Highlighting questions. In this step analytics identify all the questions that they need respective answers from to reach conclusion. The problem domain is considered to cater all the questions. Remember what John Tukey said:
An approximate answer to the right problem is worth a good deal more than an exact answer to an approximate problem
Ask the right questions. The precise question your organization should ask depends on your best-informed priorities. Clarity is essential.
Data collection. All the relevant data to obtain answers of the questions must be collected and evaluated. Data is collected from appropriate sources. There is no restriction as to where the data is collected. It can be collected from police accidents reports or from insurance companies. When all the respective data is collected, questionnaire is presented which mentions everything you need to reach conclusions.
Data wrangling. Where raw data is refined so that only the relevant valuable data is forward. Unnecessary information is not entertained.
Data analysis. The data refined from the earlier stage is imported into analysis tools. The analysis tools are used for various purposes. They help individuals explore the data, build relations and find respective patterns. Modeling methods and techniques are considered and implemented in one or more data sources. The data gathered is arranged and individuals turn it into something useful, so one could benefit from it. Never forgetting about evaluate the analysis not only in terms of performance metrics but also business adherence.
Drawing conclusions. Here is where individuals make predictions and draw conclusions. Enough conclusions are made, and respective prediction are proposed. The report is summarized, and individuals are finally able to propose something that would do justice to their objective. Alternatively, our goal can be to build or prototype a “data product", for instance a recommendation system, risk management application,
Another perspective about this can also be illustrated through the figure that shows next the data science process by O'Neil and Schutt (2013):
O'Neil, C. and Schutt, R. (2013). Doing Data Science. O'Reilly Media, Inc.
The importance of Data analytics and statistics
If you think data analytics does not help us reach conclusions, you are mistaken. Data analytics is a valuable tool that helps us draw conclusions, make predictions and reach significant solutions to our matters. Here are some of the numerous benefits that comes with data analytics. Look some examples:
The first major advantage of data analytics is that it helps mitigate the risk factor. It also helps identify fraud. When it comes to business usually there are numerous matters that must be considered and angles to be considered. By carrying out data analytics and reviewing the statistics one can be aware of all the internal and external threats and financial, physical and intellectual assets. Data analytics will provide extensive data concerning fraud prevention and all the risks involved.
Make use of the multiple customer identifiers such as email id and mobile numbers. This helps derive traditional and digital data that is enough to interact with the customers and understand their behavior.
With the assistance of data analytics and statistical information organizations can produce products. Products are significant and are often regarded as the largest investment a company makes. By making use of the patterns, trends and charts organizations can produce products that lead to much more innovative products with better specifications.
Another major advantage of data analytics is that is favours personalization. To build adei better and stronger connection with the respective audience, companies must make their clients feel important. Customizing products is a significant role that helps achieve that. By creating important responses that helps know better about the audience, organizations can divide the audience into groups and personalized merchandising.
Getting adequate data will help you get opportunities for interacting with customers. Understand the attitudes of your clients to deliver personalization in a positive environment.
Improving the experience of the customer
Applying data analytics in various situations will make sure you are safe from any kind of issue that may arise due to poor management. To avoid this potential chance of losing customers, brands must apply analytics and statistical data to optimize their business. Get a better control over your business by being fully aware of the ins and outs linked with your organization.
Statistical data is a significant tool that can be applied to improve field operations. Increase productivity and efficiency through this process. By carrying out data analytics continuous improvements are instigated and conclusions to problems is achieved quickly.
It is important to note that data analytics is not restricted to any respective business or field. It can favour numerous situations to reach the pinnacle of success. Data analysis is an internal function that presents valuable data concerning numbers and figures to the respective management so that the initial objective or solution to the issue could be entertained.