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Best Predictive Analytics You Should Consider Using in 2022

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Predictive analytics software mines and analyzes historical data patterns to predict future outcomes by extracting information from data sets to determine patterns and trends. Using a range of statistical analyses and algorithms, analysts use predictive analytics products to build decision models, which business managers can use to plan for the best possible outcome. Analysts, business users, data scientists, and developers all use predictive analytics software to better understand customers, products, and partners and to identify potential risks and opportunities for a company.

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15 Listings in Predictive Analytics are Available.

1. Qlik Sense

SellerQlik Sense
HQ LocationRadnor, PA
LinkedIn Page2,844 employees on LinkedIn®
₹ MarketplaceQlik Sense Deals Page
Year Founded1993

Qlik Sense empowers people to make better data-driven decisions and take action. The solution provides augmented analytics for every business need from visualization and dashboards to natural language analytics, custom, and embedded analytics, reporting, and alerting. Our unique associative technology enhances human intuition with AI-powered insights, offering unmatched capabilities for combining data and exploring information. It indexes the associations in your data, and exposes related and unrelated values as you click, revealing hidden insights that would be missed by query-based tools.

2. SAP Analytics Cloud

SellerSAP Analytics Cloud
HQ LocationWalldorf, Germany
LinkedIn Page1,27,827 employees on LinkedIn®
₹ MarketplaceSAP Analytics Cloud Deals Page
Year Founded

With the SAP Analytics Cloud solution, you can combine analytics and plan with unique integration to SAP applications and smooth access to heterogeneous data sources. As the analytics and planning solution within SAP Business Technology Platform, SAP Analytics Cloud supports trusted insights and integrated planning processes enterprise-wide to help you make decisions without a doubt.

3. Alteryx

HQ LocationIrvine, CA
LinkedIn Page2,888 employees on LinkedIn®
₹ MarketplaceAlteryx Deals Page
Year Founded1997

Alteryx is a fun, low-code / no-code, end-to-end data analytics platform that allows anyone, anywhere, to turn extraordinary amounts of data into quick insights that help them create breakthroughs every day. Today, organizations all over the world rely on Alteryx to rapidly upskill their workforce and produce high-impact business outcomes.

4. IBM Watson Studio

SellerIBM Watson Studio
HQ LocationArmonk, NY
LinkedIn Page5,30,215 employees on LinkedIn®
₹ MarketplaceIBM Watson Studio Deals Page
Year Founded

IBM Watson Studio on IBM Cloud Pak for Data is a leading data science and machine learning solution that helps enterprises accelerate AI-powered digital transformation. It allows businesses to scale trustworthy AI and optimize decisions. Build, run, and manage AI models on any cloud through an automated end-to-end AI lifecycle–simplifying experimentation and deployment, speeding up data exploration and preparation, and improving model development and training. Govern and monitor models to mitigate drift and bias, and manage model risk. Build a ModelOps practice that synchronizes application and model pipelines to operationalize responsible, explainable AI across your enterprise.

5. RapidMiner

HQ LocationBoston, Massachusetts
LinkedIn Page98 employees on LinkedIn®
₹ MarketplaceRapidMiner Deals Page
Year Founded2007

For those driven to accelerate the pace of transformation, RapidMiner is the enterprise-ready data science platform that amplifies the collective impact of your people, expertise, and data for breakthrough competitive advantage. RapidMiner’s data science platform supports all analytics users across the full AI lifecycle. The RapidMiner Academy and Center of Excellence methodology ensure customers are successful, no matter their experience or resource levels. Since 2007, more than 1 million professionals and 40,000 organizations in over 150 countries have relied on RapidMiner to bring data science closer to their business. Explore our blog and connect with us on Twitter and LinkedIn.

6. Board

HQ LocationChiasso, Ticino
LinkedIn Page723 employees on LinkedIn®
₹ MarketplaceBoard Deals Page
Year Founded1994

Board is the Intelligent Planning Platform that offers smarter planning, actionable insights, and better outcomes for more than 2,000 companies worldwide. Board allows leading enterprises to discover crucial insights which drive business decisions and unify strategy, finance, and operations to plan smarter and achieve full control of performance across the entire organization. With Board, companies can manage and control their entire planning process from goal setting down to operational execution in one user-friendly environment.

7. JMP

HQ LocationCary, NC
LinkedIn Page15,096 employees on LinkedIn®
₹ MarketplaceJMP Deals Page
Year Founded1989

JMP, the data analysis software for Mac and Windows, combines the strength of interactive visualization with robust statistics.

Originally developed in the 1980s to capture the new value in GUI for personal computers, JMP remains dedicated to adding cutting-edge statistical methods and special analysis techniques from various industries to the software’s functionality with each release. The organization’s founder, John Sall, still serves as Chief Architect.

8. The IBM SPSS Modeler

HQ LocationArmonk, NY
LinkedIn Page530,186 Employees on LinkedIn
₹ MarketplaceThe IBM SPSS Modeler Deals Page
Year Founded1911

The IBM SPSS Modeler is a leading, visual data science and machine learning solution. It helps enterprises accelerate time to value and desired outcomes by speeding up the operational tasks for data scientists. Leading organizations worldwide rely on IBM for data discovery, predictive analytics, model management and deployment, and machine learning to monetize data assets. The IBM SPSS Modeler empowers organizations to tap data assets and modern applications with complete, out-of-box algorithms and models, suited for hybrid, multi-cloud environments with robust governance and security posture.

9. Tableau Server

SellerTableau Software
HQ LocationSeattle, WA
LinkedIn Page5,064 employees on LinkedIn®
₹ MarketplaceTableau Server Deals Page
Year Founded2003

Tableau Server is an enterprise analytics platform that is easy to deploy and scale and helps enable data-driven decision-making throughout your organization. Deploy the way that makes the most sense for your organization – on-premises or in the cloud, on Windows or Linux, while integrating with your existing security and authentication protocols. Provide governed data access while promoting sharing and collaboration with data, dashboards, and insights, all with the scalability and security requirements you require. Automate processes and workflows, manage content, define access for individual users and groups, and ensure accurate insights. Tableau Server gives you the visibility, security, and controls you need to empower your people with data.

10. Qlik AutoML

HQ LocationRadnor, PA
LinkedIn Page2,892 employees on LinkedIn®
₹ MarketplaceQlik AutoML Deals Page
Year Founded1993

Qlik AutoML (automated machine learning) brings AI-generated machine learning models and predictive analytics directly to your organization’s larger community of analytics users and teams, in a simple user experience focused on augmenting their intuition through machine intelligence. With AutoML, you can easily generate machine learning models, make predictions, and plan decisions – all within an intuitive, code-free user interface.

11. Adobe Analytics

HQ LocationSan Jose, CA
Company Websiteadobe
LinkedIn Page
₹ MarketplaceAdobe Analytics Deals Page
Year Founded2012

Adobe Analytics helps you create a holistic view of your business by turning customer interactions into actionable insights. With intuitive and interactive dashboards and reports, you can sift, sort, and share real-time information to provide insights you can use to identify problems and opportunities.

12. Pure1

SellerPure Storage
HQ LocationMountain View, CA
Company Websitepurestorage
LinkedIn Page4,872 employees on LinkedIn
₹ MarketplacePure1 Deals Page
Year Founded2009

Pure1 Meta is global intelligence built from a massive collection of storage array health and performance data. By continuously scanning call-home telemetry from Pure’s installed base, Pure1 Meta uses machine learning predictive analytics to help resolve potential issues and optimize your workloads.

13. Explorium

HQ LocationSan Mateo, California
LinkedIn Page154 employees on LinkedIn®
₹ MarketplaceExplorium  Deals Page
Year Founded2017

Explorium offers a first-of-its-kind data science platform powered by automatic data discovery and feature engineering. By automatically connecting to thousands of external data sources (premium, partner, and public) and leveraging machine learning to distill the most impactful signals, the Explorium platform empowers data scientists and business leaders to drive decision-making by eliminating the barrier to acquiring the right data and fueling superior predictive power.


SellerInformation Builders
HQ LocationNew York, NY
Company Websiteinformationbuilders
LinkedIn Page931 employees on LinkedIn®
₹ MarketplaceTIBCO WebFOCUS Deals Page
Year Founded1997

TIBCO WebFOCUS® is an enterprise business intelligence and analytics solution equipped with data management, visual discovery, predictive analytics, and powerful visualizations. Combining these capabilities and data science in one unified platform, WebFOCUS® enables you to make data-driven decisions across the enterprise and provide reports, dashboards, and customer-facing applications at scale. This same platform empowers data scientists, developers, and administrators to leverage powerful capabilities to manipulate and transform data within an unparalleled data prep and governance foundation.

15. TrendMiner

HQ LocationHasselt, Flemish Region
Company Websitetrendminer
LinkedIn Page98 employees on LinkedIn®
₹ MarketplaceTrendMiner Deals Page
Year Founded2008

Self-service Industrial Analytics Solutions for Process Manufacturing.

TrendMiner, a Software AG company and part of the IoT & Analytics division, delivers self-service data analytics to optimize process performance in industries such as chemical, petrochemical, oil & gas, pharmaceutical, metals & mining and other process manufacturing industries.

Choose Your Predictive Analytics Software:

Choosing Predictive Analytics Software can be a daunting task but it doesn’t have to be. The best thing to do is make a list of your must-haves and compare the possible tools on SaaSDekho to select the right software for your team.

Or, Schedule a Call with our SaaS Consultant and they’ll guide you in your SaaS Discovery and Buying Journey. 

Spotlight Categories in Analytics Software:

Business Intelligence | Enterprise Search | Visitor Behavior Intelligence

What is Predictive Analytics Software?

Predictive analytics software is all about making business outcomes predictable. Data scientists and data analysts are able to do this by using data mining and predictive modeling to analyze historical data. By better understanding the past, businesses can gain insights into the future. Predictive analytics is a step further than general business intelligence, which businesses use to pull actionable insights out of their data sets. Instead, users can develop machine learning algorithms and predictive models to help forecast and achieve business-critical numbers.

The reason businesses are able to hit those critical numbers and become more predictive is due to the boom of big data. Companies are able to harness their data like never before. By recording and owning more and more historical and real-time data, data scientists have larger sample sizes to work with, meaning they can be much more accurate. Additionally, companies that are investing in predictive analytics without first ensuring that their data is accurate, clean, and accessible will ultimately be wasting their time. However, those that are able to wrangle their data properly will create a significant competitive edge and hold an advantage in the market.

Key Benefits of Predictive Analytics Software

  • Accurately predict and forecast revenue numbers based on a wide range of variables
  • Understand and account for customer churn and retention
  • Predict employee churn based on historical factors for turnover
  • Make more precise, data-driven decisions in all departments based on available data
  • Determine both risks and opportunities that were otherwise hidden within company data

Why Use Predictive Analytics Software?

There are a number of applications for predictive analytics software, and reasons that businesses should adopt them, but they all boil down to understanding what has happened in the past, what could happen in the future, and what should be done to ensure positive business outcomes. These are considered descriptive analytics, predictive analytics, and prescriptive analytics.

Descriptive Analytics (understanding the past) — Descriptive analytics deals with understanding what has happened in the past and how it has influenced where a business is in the present. This means undergoing data mining on a company’s historical data. This type of analysis can be obtained by using business intelligence tools, big data analytics, or time-series data. Regardless of how it is attained, providing descriptive analytics is a key foundation of predictive analytics and creating data-driven decision-making processes. It requires thorough preparation of data and ensuring the data is organized in a manner that allows for easy descriptive analysis.

Predictive Analytics (knowing what is possible) — Predictive analytics allows users and businesses to know and anticipate potential outcomes. Building predictive models based on descriptive analysis can ensure that businesses do not make the same mistake twice. It can also provide more accurate forecasting and planning, which helps to optimize efficiency. Ultimately, this analysis makes the unknown known.

Prescriptive Analytics (so now what?) — The final step, and ultimate reason for using predictive analytics software, is to make clear actions based on the suggestions and recommendations of the predictive models. This is where machine learning and deep learning functionality come into play. Some predictive analytics solutions can provide actionable insights without human intervention. For example, it can provide a short list of sales accounts that should close quickly based on a number of variables. Becoming prescriptive take analytics a step further, and it is the ultimate reason for adopting advanced, predictive analytics.

Predictive Analytics

Who Uses Predictive Analytics Software?

To fully take advantage of predictive analytics software, businesses need to hire highly skilled data scientists with knowledge in machine learning development and predictive modeling. There is not an abundance of these skilled workers, therefore they are often paid very well. Dedicating financial resources to these positions may not be an option for every company, but those who can afford data scientists have a leg up on the competition.

While data scientists or data analysts are the employees tasked with using predictive analytics software, there are a number of industries and departments that can be impacted by utilizing predictive analytics:

Manufacturing and Supply Chain — One area that can be greatly enhanced by using predictive analysis is demand planning for manufacturing companies. By having more accurate forecasting, businesses can avoid risks, like shortages and surpluses. Additionally, companies can get predictive around quality management and production issues. By analyzing what has caused production failures in the past, companies can anticipate, and avoid, production breakdowns in the future.

Distribution is another major aspect of the supply chain that can be further optimized with predictive modeling. By better estimating where goods will need to be delivered, and the risks that may hold up distribution modes, businesses can provide a better service and more efficiently deliver their products to customers. Taking into account historical data, such as weather, traffic, and accident records, shipping can become a more precise science.

Retail — Retail is another industry that is ripe for optimization with the help of predictive analytics. Retail predictive analytics can provide businesses with insights on everything from pricing optimization to understanding how shoppers navigate brick-and-mortar stores for better in-store organization of merchandise. E-commerce businesses are able to track these factors in a much more efficient manner. All e-commerce interactions can be recorded into a database and influenced by predictive models. This is one of the main reasons that Amazon has been able to be so successful and disruptive to brick-and-mortar retailers. Every decision can be made predictive with the help of data.

Marketing and Sales — Being able to predict the actions of customers and prospects is an invaluable service for any business. Marketing teams can leverage predictive analytics software to project how marketing campaigns may perform, which segment of prospects to target with ads, and the potential conversion rates of each campaign. Understanding how these efforts impact the bottom line is critical to the success of marketing teams and translates into a much more efficient and productive sales team. At the same time, sales teams can leverage predictive modeling in such areas as lead scoring,

Determining which accounts to target first because they have a higher chance of closing. Ensuring that sales representatives are working smarter instead of harder means more revenue. A few CRM and marketing automation solutions provide some level of predictive functionality, but data scientists can separately funnel that data into dedicated predictive analytics tools to find cross-departmental correlations.

Financial Services — The banking industry has long been ripe for disruption, but financial administrations are taking advantage of predictive analytics solutions to better predict risk. Historical data can power predictive analytics software to predict fraudulent transactions and determine credit risks, among many other functions.

Kinds of Predictive Analytics Software

Predictive modeling is a complex science that takes many years of training to understand. There is a reason data scientists are in high demand. Not many people have a complete grasp of how to build predictive models. There are two main types of predictive models: classification and regression models.

Classification Models — Simply put, classification puts a piece of data into a bucket, or a class, and labels it as such. Classification models essentially label data based on what an algorithm has already learned. The ultimate goal of classification models is to accurately bucket new data points into the proper classes so that the data can become predictive and prescriptive.

Regression Models — Regression models analyze the relationship between two separate data points and help to forecast what happens when they are put side by side. Putting the technique into a baseball example, teams may perform a regression analysis on the relationship between the number of fastballs thrown and the number of home runs hit.

Decision Trees — One common type of classification model is a decision tree. These models predict a number of possible outcomes based on a variety of inputs. For example, if a sales team builds $1 million in a pipeline, they can close $100,000 in revenue, but if they build $10 million in a pipeline, they should be able to close $1 million in revenue.

Neural Networks — Neural networks, known in the AI world as artificial neural networks, are extremely complex predictive models. These models are able to predict and analyze unstructured, nonlinear relationships between data points. These solutions provide pattern recognition and can help to track anomalies. Artificial neural networks were originally created and built to mimic the synapses and neural aspects of the human brain. They are one of the contributing factors to the accelerated growth in artificial intelligence and deep learning.

Other types of predictive modeling include Bayesian analysis, memory-based reasoning, k-nearest neighbor, support vector machines, and time-series data mining, among others.

Potential Issues with Predictive Analytics Software

Lack of Skilled Employees — The main issue with adopting predictive analytics software is the need to have a skilled data scientist to interact with the data and build the models. There is a distinct skill gap in terms of finding users who both understand how to pull data and build models and the implications that the data has on the overall business. For this reason, data scientists are in very high demand and, thus, expensive.

Data Organization — Organizing data in a way that can be accessed easily is a challenge that many companies face. It is not easy in today’s world to harness big data sets that contain historical and real-time data. Companies often need to build a data warehouse or a data lake that can combine all the disparate data sources for easy access. This, again, requires highly knowledgeable employees.

Software and Services Related to Predictive Analytics Software

Predictive analytics software relates to a number of analytics and artificial intelligence software categories.

Machine Learning Software — Machine learning algorithms are a key component of building effective predictive models. Many machine learning algorithms are built to provide recommendations or suggestions, which is the end goal of predictive analytics software as well. These tools are used by developers to embed machine learning inside of applications, often to provide predictive and prescriptive analysis.

Business Intelligence Platforms — These tools are the traditional analytics solutions used to understand a company’s data. Business intelligence platforms are used by data analysts to easily visualize and understand how certain actions are impacting business-critical initiatives. Some of these platforms offer predictive features, however, their core purpose is not predictive modeling.

Big Data Analytics — Big data analytics software, like business intelligence platforms, often provides predictive modeling functionality. However, these solutions are used more to track real-time data as opposed to understanding historical data. Big data analytics software connects to Hadoop, or proprietary Hadoop distributions, to better understand structured and unstructured data. These same data sources may be important for data scientists that are tasked with building predictive models.

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