What is Predictive Analytics

Imagine organizations working without data, that would make a blunder.

 As the significance of data is utmost, so the data prediction must also be the same. Predictive analytics or Data prediction makes use of numerous statistical techniques comprising of from data mining, predictive modelling, machine learning. So the analysis of current and historical facts can be made in order to get the predictions about future occurrences. It involves applying statistical analysis techniques, analytical queries and automated machine learning algorithms to data sets to create predictive models.

Why Data prediction is important

• Detecting frauds – By making use of various analytic methods numerous trends in the pattern can be identified and errors can be detected. As the cyber threats are on a burgeoning day by day, predictive analytics analyzes the whole data in real time to find spots which have errors in it.
• Big data – Big data is often discussed with data prediction as in business the various types of data which need to have predictive analytics done so that businesses can make data-based decisions on the basis of predictive analytics
• Reduces risk – It checks risks such as credit claims and collections. A great example of data prediction would be a credit score as it tells about the buyer’s likelihood of purchase. A credit score number is generated by a predictive model that uses all data according to persons’ purchasing spend
• Assists in running campaigns – With the help of data prediction customer responses can be determined. Moreover, it can also be used to promote cross-sell opportunities. It also helps in retaining and attracting customers for the business
• Used in Banking sector – The finance industry has huge amounts of data, so they are making the optimum use of predictive analytics to detect frauds and risks and continue retaining their valuable customers
• Assists the oil and energy industry – Predictive analytics has lot many benefits to count upon and another great utility of it is in the oil and energy industry where it is being used to detect equipment failures and future resource needs, thus improving the overall performance
• Direct marketing – It can tell the most effective combination of various marketing channels and the products to target the right customers at the right time for the efficient running of the business
• Healthcare – Data prediction applications in healthcare guide the patient who is in the risk of developing certain health-related conditions. The healthcare sector incorporates predictive analytics to support medical decision making at the point of care
• CRM – Customer relationship management uses data analysis about customer’s history with a company to improve business relationships with prospects. It assists in achieving CRM objectives like marketing campaigns, sales service and it is applied through the customer’s life cycle
•  

Difficulties in Data Management

• Getting hold of data management is not that easy as every time new changes can be seen and according to that data has to be modified
• At each and every step, data should be carefully analyzed and evaluated as the data is stored all the time so the organizations need to monitor the type of questions in the database and change the indexes
• Gathering a lot of data will not work as sometimes people have such a huge amount of various types of data with them but they do not know how to effectively use it
• In organizations, data is stored in data warehouses and unstructured data lakes but the data scientists should know when and how to transform that data into the required model or shape
• Better regulation and compliance controls resulting in greater collaboration and revenue growth

Predictive analytics process

• Define project – Firstly you need to figure out what are the business objectives and what are the deliverable to be met. Also focus should be on defining the project and identify the data sets which will be useful overall
• Data collection – Data mining for predictive analytics arranges the data from multiple sources for the analysis providing a complete view of customer interactions
• Data analysis – It is the process of figuring and modelling the data with the aim to discover useful information at the end
• Statistical analysis – It validates the assumptions, hypothesis and test them using various statistical models and methods
• Modelling – Predictive modelling provides the facility of automatically creating predictive models about the future. Apart from that, there are also many other alternatives to opt from
• Deployment – In this stage it allows them to apply the analytical results to decide formations in order to get results and outputs by automating.
• Model monitoring – Various models are reviewed and their performances are continuously checked so that results should be effective

In conducting predictive analytics, mostly regression and machine learning techniques are used.

• Regression techniques – Regression modelling is the foundation of predictive analytics. Depending on the situation, there are many options to select from numerous models like linear regression model, probit regression, logistics regression, time series analysis and lots of others.
• Machine learning – It includes advanced statistical methods for regression and classification and has application in multifarious fields like medical, finance, etc. The machine learning techniques emulate human cognition to predict future events. In machine learning techniques some of the methods used are neural networks, support vector machines, naive Bayes, geospatial, predictive modelling and lot others.

Predictive analytics requires a high level of expertise as it is done by data scientists or data engineers who are highly skilled in it as these are guided by software developers and business analysts which help them in data visualization and generating data-driven reports.

Predictive models are used by data scientists for making correlations between different data elements. The model is further tested against the selected data to gather insights about prediction. Data sampling may also be used by some data scientists in the workflow and the model is revised and checked again too. After the results given by predictive models, data scientists can share them with business executives for finding out business opportunities.

Besides data modelling, data scientists also take the assistance of text analytics software which minimizes the text-based content, classification models which further classify the data under various categories making it easier to find and retrieve and deep neural networking which has the potential to emulate human learning.

Best Data prediction softwares

• Microsoft R Open – It is a complete open source platform which lays emphasis on statistical analysis. This software is fully compatible with various scripts and applications which can support R. It has multithread math libraries which allow common R operations to work in parallel. Moreover, it also has fixed CRAN repository making sure that every user has access to the same set of CRAN package versions. The best part of Microsoft R is that it is easy to use and free to download
• Oracle crystal ball – A spreadsheet-based application software used for predictive modelling and optimization. This software provides various advance optimization and calculation capabilities in multifarious industries. It has features like data sharing and comprises of integrated business intelligence tools which can assist you in settling up your models
• IBM SPSS predictive analytics enterprise – This software has a lot of things to offer as it comes with a bundle of advanced level features of levelling up with your various types of data. It also assists in making smarter decisions for your organizations. It also supports various programming languages so that projects can be worked upon these simultaneously. It has deployment options according to the user’s needs and has extensive machine learning library of algorithms so that you can manage your data easily anytime and anywhere
• SAS advanced analytics – It is a software offering innovative algorithms which can help users solve all types of problems. It identified the future outcome based on historical facts and aids in detecting possible frauds. It can also be used in statistical analysis, optimization and simulation. This software allows you to analyze customer data and other calculations with the assistance of various algorithms. It also supports time series exploration and automatic forecasting
• Sisense – It is a universal software suite for all types of companies and offers advanced level features of business analytics which can help your business is growing. It is designed in such a way that eases the process of data complexity and gives the output in a much faster way so that you can make smart decisions on who does not have any background in programming can also use this software easily. It has an open API framework which allows you to customize the platform. It also uses machine learning to help you find the right information all you have to do is set the right parameters. It uses core-in-chip technology that allows the data to join from multiple sources a create excellent business intelligence reports.

You will need data and that too for abundant sources and data preparation is the most time-consuming and difficult process and if predictive analysis is helping you figure out the desired outcomes of those data, then it would be a piece of cake for you to deal with any business situations as simplified and accessible data will be in your hands. As predictive analytics will work when you what you want to achieve from those data sets or in much simpler terms what do you want to understand and predict.

With the assistant of predictive analytics, you can deploy the models to work on your chosen data and then you will get your desired results but alone the software would not be sufficient as you will be needing who is highly adept at handling all these very easily. Someone who understands what is the business all about and how its problem can be solved and the significant thing the person should know how to prepare data and analyse it. There is various high level and advance data prediction software available in the market which the person can vail and make its best optimum use for growing the business.