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What is Predictive Analytics and Its Impact on the business?

Posted by Sas Chatterjee on Mar, 16, 2021 02:03

The Predictive Analytics tools and process contains several statistical techniques within its purview. Some of these statistical analysis methods include data mining techniques, predictive modeling, and machine learning techniques. The process is used by organizations to perform a comparative analysis of current and historical facts. This produces actionable insights which are used by companies in performing risk management. 

Data Analytics Tools are used for the extraction of valuable information from the raw data. This information is later used in creating predictive insights related to business patterns and in performing a complete risk assessment. In simpler words, predictive analytics solutions are used by organizations to formulate insights from the past data and predict behavior patterns of customers. But, it is important to remember that these predictive analytics models are not completely accurate. The accuracy of these insights depends on the standard of data analysis techniques and the quality of assumptions made. 

In the contemporary world, organizations have benefitted largely from predictive analytics strategies. 

The advantages of predictive analytics can be classified as follows:

  1. Competitive Advantage 

The basic benefit of using predictive analytics tools is the method of gaining insights into the changing business trends. As a result, the user organization gains a competitive advantage over other companies in the market. Customer behaviors change frequently and with this changes the business patterns and trends. This is where the predictive analysis method comes into play.

  1. Enhancing Business

Predictive Analytics patterns are most helpful in customer management. The procedure gives a detailed view of the changing customer, behaviors, and preferences. This in turn helps the company to bring about successful product campaigns that attract more customers.

  1. Satisfying customers

The customer management methods are enhanced by the predictive analytics models. As a result, companies can build lasting relations with their customers and enhance customer satisfaction methods.

  1. Using past data

Predictive Analytics processes involve the process of analyzing past data to derive meaningful insights from it. This contributes to forming smart decisions that can help the organization in the long run.

  1. Creating personalized service patterns

The use of predictive analysis models leads to gaining detailed information about the customer lifecycle. As a result, companies can develop personalized service patterns that can cater more to the individual needs of the customers.

Predictive Analytics tool: Situations of it’s the sage

In the modern business world, companies use analytics reporting tools, irrespective of their field of business. But, the concept of predictive analytics becomes clearer after understanding the use of this method in various fields. 

The fields or spheres of society where predictive analytics models are widely used can be categorised as follows:-

  1. Business

In business, customer relationship management is the most common method of using predictive analytics. Companies utilize predictive analytics tools and CRM tools to design successful campaigns, increase sales and improve customer relations. Furthermore, these analytics platforms help organizations to recognize the changing patterns of business using real-time analytics and also, future risks. This helps in improving customer service and better risk management.

  1. Child protection

Certain child protection agencies have started using predictive analytics processes to recognize potential risk cases.

  1. Clinical Analysis

The predictive analytics market has made its way into the health industry too. These methods are helping doctors to predict the diseases a certain patient is more likely to be affected with. This is contributing to proactive medical attention.

  1. Predicting Judicial Outcomes

Artificial Intelligence programs employ predictive analytics models to predict the outcomes of judicial cases.

  1. Economic Prediction

Governments employ these analytical platforms to predict economic rates in the country.

What are different kinds of Predictive Analytics Software:

Business Intelligence software is improving day by day. In recent years, business intelligence services include the machine learning feature within the range of other features. The machine learning feature is provided by predictive analytics software. This software is designed to analyze previously stored data and provide actionable insights. The companies further use these insights to make well-informed decisions and prepare for potential risks. Predictive analytics software uses existing data to make predictions. 

This software can be classified into the following categories:-

  1. Predictive Software for marketing.
  2. Analytics Software for specific industries.
  3. Analytics Platforms for Data Scientists.

In the Predictive Software for Marketing category, the following software vendors can be illustrated – 

  1. EverString:

This Predictive Analytics tools perform the functions of curating the data and mapping it simultaneously. Majorly, the software works for B2B accounts. The data in the user system is combined with the customer information by the software. In this way, an actionable database is created. In simpler words, when a user system feeds the queries in the software, it builds the customer base with the accounts and details. The customer lifecycle can then be customized before designing the campaigns.

  1. Infer

The Infer software is designed to give an assimilated view of the data sources. As a result, the user organization can recognize the lead company in the sales field. The software implies a monitoring pattern on online sources and public data. After this, Infer builds reductive models for the user based on the rules specified by the system.

  1. Radius

The Radius is designed to provide several data analytics services. But the main focus of the software is towards providing the predictive B2B service.

The Analytics Software for Specific Industries can be sub-categorized in the following manner:

  1. Halo

Halo is a predictive analytics software designed in the business intelligence platform. The tool was specifically designed to assist the business functioning in the supply chain management business. Halo is customized to use the comparative analysis method to predict future demands in the manufacturing field.

  1. BOARD

The BOARD software created a responsive interface. The tool follows a rule-based predictive model. The pre-built connectors in the tool assist the user system to pull data from any of the external data sources

  1. Statistica Decisioning Platform

Statisca is software that provides an array of analytical services. The Decisioning Platform of Statistica performs predictive analytical functions. This helps the user organization to make more well-informed decisions in business.

The Predictive Analytics tools for Data Scientists, among others contain the following categories –

  1. SAS Advanced Analytics: 

The SAS has over forty years of experience in predictive analytics. The software does commendable predictive analysis and can process large volumes of data.

  1. RapidMiner Studio: 

The software combined predictive data analytics with the customized business deployment pattern.

What are different challenges in using Predictive Analysis tools:

In recent years, Predictive Analytics has become popular among organizations from all fields. But, the analytics model is quite complex with its capabilities. This means that the implementation of the tools will be equally challenging. 

Some of the most frequent challenges faced by organizations in predictive analytics are as follows:-

  1. Lack of Expertise

The organizations intending to implement predictive analytics models generally lack the expertise to do so. The feature of predictive analysis was designed for data scientists knowing statistical modeling, R, and Python. Mostly, the teams in the organizations cannot begin predictive analytics without a data scientist.

  1. Adoption – 

The adoption rate of a software feature depends on its level of difficulty. In this case, predictive analytics solutions are extremely difficult. This is the reason why organizations tend to avoid deploying data analytics tools.

  1. Lack of empowering end-users – 

An actionable insight is of no value in a vacuum. Predictive analytics tools are designed to create predictive insights from the existing data. But, in the process, the method does not empower the end-users. This implies that the predictive analytics process provides organizations with insights but fails to empower them to take action.

  1. An extensive set of functions – 

The process of predictive analytics consists of an exhaustive list of functions that need to be performed. These functions are generally performed and handled by the data scientist. 

Some of these functions predictive analytics include the following:-

  1. Preparing data
  2. Cleaning data
  3. Recognizing important columns
  4. Recognising the co-relatives
  5. Understanding the function of various algorithms 
  6. Selecting the right algorithm for the correct problem
  7. Selecting the right properties of the algorithms
  8. Making sure the data format is correct and many others. 

Predictive Modelling versus Predictive Analytics: Understanding of the kinds of predictive modelling

In a comparative analysis, predictive analytics and predictive modelling have the following differences between them –

  1. Business procedure 

The procedure of predictive modelling includes a collection of data and transforming the data into a suitable format. Then a model is created and finally evaluates the model to find possible outcomes. But, predictive analytics involves defining the project and collecting data. Then the statistical modelling and deployment take place. Finally, the model is monitored.

  1. Procedure

Predictive modelling is an interactive procedure and runs one or more algorithms. Predictive analytics is the process of analysing historical data through statistical data mining.

  1. Kinds

The predictive modelling pattern consists of two kinds – I) parametric model and II) Non-parametric model. Predictive analytics contains three kinds – I) predictive models, II) descriptive models and III) decision models.

  1. Uses

In predictive modeling, the model is reusable and is called a regression model. In predictive analytics, the techniques used include data mining, artificial intelligence, and machine learning.

  1. Application

Predictive Modelling is used in archaeology, auto insurance and others. Predictive analytics is used in fraud detection, collection analytics and others.

  1. Types

There are three types of models in predictive modelling. They are predictive models, descriptive models and decision models. The types of techniques in predictive analytics include machine learning technique and regression technique.

Predictive modelling in Predictive Analytics tools, in general, is of four kinds. 

The type of decision models in predictive analytics are as follows –

  1. Descriptive Analytics – This is related to the data.
  2. Diagnostic Analytics – This is the next step after the descriptive data.
  3. Predictive Analytics – The process involves creating actionable insights from previous data.
  4. Prescriptive Analytics – This process provides a proposal related to the insights created earlier.

Difference between Predictive Analytics and Business Intelligence:

The terms ‘Business Intelligence’ and ‘Predictive Analytics’ are both a part of the computing services. These terms might seem to be similar but they are quite different in actuality. The differences between these two terms can be enumerated as follows:-

  • Business Intelligence involves a wide range of services and technologies. Predictive Analytics is part of the services provided through Artificial Intelligence technology.
  • Business Intelligence is used to perform a wide range of functions. Predictive Analytics is designed to perform only one function.
  • Business Intelligence performs a range of functions after acquiring data from external sources. Predictive analytics is used to perform functions on existing data.
  • Business Intelligence performs analytical functions. Predictive analytics is used to perform predictive functions.
  • Data Analysis through business Intelligence creates datasets. Data analysis feature in Predictive Analytics creates actionable insights.
  • The datasets produced through business intelligence aid in taking decisions in the present. Insights created with Predictive Analytics are used to perform successful risk management.
  • Finally, business intelligence consists of a wide spectrum of services. Predictive Analytics is a part of that wide spectrum of services or features.

How does Microsoft BI approach for Predictive Analytics:

In the Power BI system, the services are designed to provide the user organization with the benefits of predictive analytics. The use of this feature as Predictive Analytics tools can be made in creating predictive data models. This can further help the organization to make well-informed decisions and perform better risk management. The machine-learning pattern within the service makes computers perform analytics without being programmed.

What are the benefits of using Power BI over other BI solutions:

Power BI provides several analytical services. In recent years, Microsoft’s BI solution has become increasingly popular among organizations of all sizes. 

The advantages of using Power BI in comparison to other BI solutions can be categorized as follows:-

  1. Seamless integration 

The power BI system integrates seamlessly with the existing on-premise system. Furthermore, the Microsoft Azure consultants help the user organization in the process of deployment.

  1. Rich personalised dashboard

The best feature of Power BI is the ability of modelling dashboards. There are customizations available for creating reports, visuals and interactive dashboards. 

  1. Secured Report Publishing

Power BI services permit the user to publish the created reports with security. This publishing can be done within the organization or with someone outside the system. The system creates user-friendly graphic interfaces in the on-premise setting.

  1. No constraints

The system of Power BI does not pose any constraints on memory or speed. The Power BI embedded provides the cloud environment for storage.

  1. No special support needed

The Power BI services do not require any special technical support. The non-technical staff of the user organization can operate the services as well.

  1. Extraction of business intelligence

The business intelligence tools under the Power BI service helps in extracting useful insights. The raw data acquired, is put through a tedious process of analysis to generate meaningful datasets.

  1. Balanced performance

The Microsoft BI is a Predictive Analytics tools that has balance of the easy functional systems and the excelling performance abilities.

  1. Advanced-Data services

The Power BI can integrate well with several cloud environments. Further, the system can perform several advanced data services. Among these, predictive analysis is one of the standout features of the system. It performs descriptive analytics using a logistic regression method. This method uses regression trees, linear regression model, adaptive regression splines, and probit regression.

EPC Group approach for Power BI and Predictive Analysis implementation:

The EPC Group is a dedicated organization that works globally in the field of providing Power BI Consulting solutions. The consulting services of the company aimed at helping organizations in the complete process of deploying to using Power BI solutions.  Predictive analytics is a crucial part of the Microsoft BI services. This is why the consultants of the group provide discussions and training sessions to ensure the proper use of the services. The consultants work in harmony with the company employees to monitor the best use of the Power BI services.

What are different competitors for Microsoft Power BI:

The top competitors of Power BI in the market include the following –

  • EverString

This is a cloud-based analytics platform. The main function of the system is to perform predictive analysis between B2B accounts. The data platform of the software consists of automated AIs, data ingestion, machine-learning and others.

  • Infer

Infer is a predictive and analytical tool. It is widely used in the field of marketing. The software aids the user organization to locate target markets and potential future customers.

  • Radius

This software is designed for marketers who are revenue-driven. The tool offers a self-serving AI and provides real-time graph solutions. It helps organizations to resolve several marketing and business-related issues.

  •  Halo

Halo is generally considered a data analytics tool. But it increasingly performs data mining and data discovery activities. It is designed for supply chain management companies.

  • Board

Board provides advanced analytics services. These services seamlessly integrate predictive analytics solutions within daily functional patterns.

  • SAS Advanced Analytics

This software services pattern supports integrated modelling methods for predictive analytics. It helps the organization to solve complex problems, utilize data extensively and perform better.

Conclusion:

In conclusion, it can be said that predictive analytics tools and processes are complex by nature. But, the fact that this process has improved the business functioning patterns cannot be denied. Business intelligence tools in recent years have included this feature because of its popularity. The feature sure has a long list of functions that need to be performed. But, the outcome of the process facilitates the function of risk assessment and customer management in an organization. Thus, the feature, on the whole, is beneficial for organizations of all sizes.

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