Big Data Predictive Analytics Comparison & Working Simplified 101

Big Data and Predictive Analytics references are two of the most “hyped” terms in the analysis market today. Hence, the terms have been defined in many ways by different authorities. One thing is clear, notwithstanding Big Data Predictive Analytics has the potential to improve social innovation and people’s well-being.

By the time you’re done reading this tutorial article, you would have understood the nexus between Big Data Predictive Analytics, as well as how they work. You will also understand why organizations like yours need to use Big Data Predictive Analytics.

Table of Contents

• What is Big Data? 
• What is Predictive Analytics? 
• Predictive Analytics in Conjunction with Big Data: How They Work
• Big Data Predictive Analytics Processing
• Comparisons Between Big Data and Predictive Analytics
• The Rising Need for Organizations to Use Big Data Predictive Analytics
• Conclusion

What is Big Data? 

Big Data refers to high-volume, high-velocity, and high-variety information assets that necessitate cost-effective, innovative data processing to improve insight, decision-making, and process automation. This means that Big Data is a collection of unprocessed data that can be useful in research and analysis. 

In other words, Big Data is a massive amount of data that comes in various formats and from multiple sources. Typically, Big Data is real-time data that is structured, semi-structured, and unstructured.

The image below describes how Big Data can be used in decision-making.

What is Predictive Analytics?

Big Data is frequently used to discuss Predictive Analytics. Predictive Analytics isn’t a black-and-white notion or a stand-alone component of today’s database management systems. It’s, rather, a collection of data analysis tools and statistical methodologies. Thus, Big Data and business intelligence (BI) combine to bring about predictive analytics.

Predictive Analytics involves accumulating and analyzing historical data in order to predict future results.  Connecting the dots between different departments, business processes, and forms of Big Data is made possible by combining multiple datasets. 

Examples of these future results include trends (“where a particular stock is likely to move?”) and behavior traits (“what a particular customer is likely to purchase?”). To put it another way, Predictive Analytics is used to predict what will happen in a particular situation.

Many forward-thinking businesses, such as Google and Amazon, have recognized the value of Big Data Predictive Analytics in achieving a competitive advantage. These methods allow for the discovery of patterns and the improvement of optimization algorithms, among other things.

As Big Data technology evolves, businesses are turning to Predictive Analytics to help them enhance consumer engagement, streamline operations, and cut operational costs. The combination of real-time Big Data streams with Predictive Analytics— also known as “never-ending processing”— has the potential to give businesses a significant competitive advantage. Big Data Predictive Analytics is one way to use all of that data, obtain actionable new insights, and remain ahead of the competition.

Predictive Analytics in Conjunction with Big Data: How They Work? 

The heart of Predictive Analytics is that it is possible to ‘model’ most things. Underlying this idea is the notion that, between the data parameters, there is a cause and effect relationship, i.e., that as some data parameters change (cause), other data parameters will change in response (effect).

In a nutshell, the following is a step-by-step procedure for using Predictive Analytics in businesses:

• Massive amounts of historical data are gathered or compiled.
• Certain statistical procedures, such as regression models, are used to analyze the data.
• The results of these assessments are then used to make forecasts about potential future events.
• These future predictions can then be used to help with decision-making, business process improvement, waste reduction, and more.

The final step would be to review the impact of Predictive Analytics on the process under consideration.

Big Data Predictive Analytics Processing

Predictive Analytics uses Big Data to find meaningful patterns to forecast future events, and evaluate the attractiveness of different solutions. Predictive Analytics can be used to analyze any form of unknown data from the past, present, or future. Using Big Data insights, Predictive Analytics gives businesses intelligence about the future.

Comparisons Between Big Data and Predictive Analytics

The following are the top Big Data Predictive Analytics comparisons:

Big Data	Predictive Analytics
Big Data is concerned with the purification and interpretation of large amounts of data and can be applied to many business activities.	Predictive Analytics is a technique for predicting business and market events.
Big Data engines include built-in machine learning libraries, but integrating AI is still an R&D work for Data Engineers.	It deals with a platform based on mathematical calculations and probability.
The amount of data and the speed with which it is processed are enormous. It’s not recommended to use Big Data platforms for small amounts of data because their performance is exponential.	The amount of data and the speed with which it is processed are both on the medium side. In terms of models and algorithms, very large and very small data sets can contribute to inaccurate predictions and discoveries.
Big Data comes with D3.js, Tableau, infogram, and other backend technology imports for Dashboards and Visualizations.	Predictive Analytics tools have built-in reporting integrations, such as Microsoft BI tools. So there’s no need to get it from the source or a third-party seller.
The level of advancement for Big Data is high.	The level of advancement for Big Data is medium.
Big Data is a trendy topic right now. Everyone in the market wants to get into the Big Data business.	Predictive Analytics is popular, but it isn’t the same as Big Data. It is dependent on the use cases and the type of organization that is putting it in place.
It’s a tool for making data-driven decisions.	It is used in the assessment of risk and the forecasting of future results.
It’s a best practice for handling large amounts of data.	It’s a best practice for predicting the future with data.

The Rising Need for Organizations to Use Big Data Predictive Analytics

Big Data Predictive Analysis is useful for major strategic evaluations and assessments, and for making smaller, tactical decisions at the operational level. This approach is used by businesses to make decisions, solve complicated problems, identify opportunities, and much more.

In addition, these analytics are used by businesses to better understand their consumers, products, and partners, examine facts and information related to them, and identify potential dangers and/or opportunities.

Consider the following scenario: You’re preparing to give a presentation to the company’s management about the most recent data analysis project. Your data have the ability to drive new sales, produce RFP content, and arouse new marketing sales. The data are stored in the cloud, making it easy to access and interpret. You even have a dashboard with visuals showing the dataset’s incredible capabilities. You’re prepared to cause a stir.

A few minutes into the presentation, an executive asks you, “How will these data change in the future?” Before you can respond, another executive asks, “How do we know this dashboard is telling us everything?”

You pause for a moment, taken aback. Your QA team was knee-deep in testing for months, so the data you’re showing these executives is accurate. But can you say with certainty how and if this data will change? The truth is that the dashboard and dataset are only snapshots in time. Nobody can tell what will happen in the future.

But what if you were able to get close enough? More than just point-in-time reporting is required for modern brands. They must reduce future risk, boost sales and customer satisfaction, and streamline their operations, which is why companies across a wide range of industries are using Big Data Predictive Analytics to achieve this. 

Know that understanding the current applications of Big Data and Predictive Analytics, their intersection with the cloud, and their science is essential.

In all domains, the integration of Big Data Predictive Analytics has the potential to improve decision support and operations such as resource allocation and cost management systems.

Conclusion

Both Big Data and Predictive Analytics will remain for the foreseeable future. The most significant benefit of Big Data Predictive Analytics is that it provides enterprises with measurable business value. It also enables improved understanding, decision-making, and process automation. In terms of analytic focus, there is also a so-called paradigm shift. This represents a move from descriptive to Predictive Analytics.

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