BANKING, FINANCIAL SERVICES, AND INSURANCE (BFSI)

Banks and other financial institutions gather large volumes of data every day. The BFSI vertical is undergoing large-scale transformations as banks, credit card companies, investment funds, insurance companies, and government-funded enterprises are facing continuous challenges in offering value added services for customers and launching new products. The BFSI vertical has to handle challenges such as rising costs because of numerous regulatory bodies including the US Office of the Comptroller of the Currency (OCC), Federal Reserve Board (FRB), Consumer Financial Protection Bureau (CFPB), European Central Bank, and Reserve Bank of Australia. They also need to cascade complex control requirements and handle costs effectively, across the supply and services networks. Organizations in the banking, financial services, and insurance vertical, are running the risk of default in payment or repayment, and volatility across the trading market. Risk Analytics, financial analytics, and customer analytics are some of the major predictive analytics solution that are utilized by companies operating in the BFSI vertical.

Additionally, banking and financial services organizations have to face frauds at different levels which vary from purchases made by stolen credit card, money laundering, first-party fraud, and insurance claims. Predictive analytics also complements an organization’s current transaction monitoring systems, by tracking frauds before the occurrence.

COMPETITIVE LEADERSHIP MAPPING TERMINOLOGY

The vendors of predictive analytics software in BFSI are placed into 4 categories based on their performance in each criterion: “visionary leaders,” “innovators,” “dynamic differentiators,” and “emerging companies.” The top 23 vendors evaluated in the data quality tools market include Agilone, Alteryx, Inc, Angoss Software Corporation, Dataiku, Domino Data Lab, Exago, Inc., Fair Isaac Corporation (Fico), Good Data, Greenwave Systems, Inc, IBM Corporation, Information Builders, Inc., Knime Ag, Kognitio, Microsoft Corporation, NTT Data Corporation, Oracle Corporation, Qliktech, Inc., Rapidminer, Inc, Sap Se, Sas Institute Inc, Sisense, Tableau Software Inc, Teradata Corporation and Tibco Software Inc

Uses Cases of Predictive Analytics Software in BFSI

  • Customer experience management / Customer churn: Predictive Analytics is used to analyze repository of past customer queries and feedback. The insights are used to prevent customer issues in future by improving customer related processes.
  • Fraud analytics: Based on past customer records (such as transactional data, credit history, etc.) probable fraudulent cases are predicted. Banking institutions can use such insights for offering loans, ensuring loan paybacks and prevent financial crimes.
  • Personalized marketing / increasing sales: Using customer specific insights (such as buying patterns, purchase history, preferences, spending capabilities, etc.), BFSI organizations can target their messaging around their offerings for increasing sales.


Case Studies of Predictive Analytics Software in BFSI

IBM

Case Study: Improving customer satisfaction issues in real time for Bank of Ireland

Bank of Ireland’s Retail Strategy Marketing was focusing on improving customer incentive program by analyzing huge data associated with consumer sentiment records. It aimed to increase operational efficiency of data collection processes as well. Bank of Ireland leveraged capabilities of IBM SPSS for improved data collation and analysis along with associated automation processes.

Business Outcome:

  • Production cycle time decreased by approximately 66% and analysts could be utilized for high value activities
  • Bank developed capabilities to parse the verbatim of customer collected in each cycle. It can utilize the collected customer feedback in more efficient manner for quantitative analysis.
  • TAT for Ad-hoc requests of internal customers is reduced to hours rather than days.

SAS

Case Study: RSA Canada implemented SAS solutions to increase the accuracy of pricing models

The information technology department of RSA Canada combines the data coming from different systems into a single view. Company wanted to implement a solution to charge different rates for different risk characteristics, and standardize the effect on the total premium in the pricing model. The company feds the data into the company’s SAS server so the actuarial department can build the premium rating models.

Business Outcome:

  • SAS solution is used in all aspects of rating model construction
  • The solution was deployed for building predictive models, manipulating these models for business implementation and monitoring the implementation results. 
  • Improved decile analysis
  • SAS gives RSA Canada the ability to easily add new risk factors

Oracle

Case Study: Religare Health Insurance enhanced business control, resolved bank statements at a faster rate, and ensures GST compliance with Oracle ERP Cloud

Religare Health Insurance (RHI), a specialized player in providing health insurance products, including travel and personal accident. The company aims to deliver solutions that benefit customers, distributors, employees, and shareholders. RHI needed an integrated platform to improve process efficiency and comply with the tax regulations. RHI configured Oracle ERP Cloud based on the needs of its health insurance business. RHI also created project documentation and conducted pilot testing and user interface prototyping.

Business Outcome:

  • Modernized ERP platform with new technology and practices help RHI gain visibility to efficiently manage the entire financial and supply chain processes
  • RHI meet compliance requirements
  • Faster and accurate payment transmission
  • Reduced manual intervention and automated the entire risk-free business process

Pega

Case Study:  PNC Financial Services utilizing personalized real time marketing for improved customer experience 

PNC Financial Services aimed to offer rich customer experience, raise its revenue with personalized offers and eliminate the time consuming manual processes hampering their decision-making. Hence, a comprehensive solution was required to coordinate all customer real-time interactions across all channels. The bank utilized Pega’s business process management and real-time decision making capabilities for creating a centralized hub for managing all customer treatments across all channels.

 Business Outcome:

  • PNC Financial Services witnessed immediate revenue uplift.
  • It was ranked as the no 1 for customer experience amongst northeast banks; while nationally it ranked 2nd in banking customer experience
  • It also helped to ensure consistent automated processes across the enterprise

 

Palantir Technologies, Inc.

Case Study:  A global bank making smart, data-driven decisions for preventing foreclosure of troubled home assets

A global bank managing a multibillion dollar home lending portfolio of delinquent mortgages required to prevent foreclosures, modifications, and cancellations of troubled home assets.  Hence, an efficient solution was required to manage several systems of incomplete records and analyze huge data sources to understand the home lending portfolios for further avoiding losses during the mortgage crisis. The bank employed Palantir’s enterprise-wide analytics and visualization tools to build advanced automated model, and market forecasts for making smart-data driven decisions.

Business Outcome:

  • Hundreds of millions of dollars saved by reducing borrower’s debt, henceforth preventing unavoidable foreclosures
  • Up-to date and accurate loan level pricing
  • Increased efficiency of bank’s short sales processes
  • The bank fulfilled national economic recovery objectives, motivated local communities, and successfully tackled drivers of financial crisis

 

Vertafore

Case Study:  Improving book roll process and carrier relationship for Reliable Insurance Agency  

Reliable Insurance Agency providing insurance and financial expertise to the inhabitants of Duluth/ Superior area of Minnesota needed to automate the time consuming, inefficient manual book rolling for improving carrier relationships and communication. The agency leveraged the advantages of Vertafore’s Book Roll Analytics to automate the entire book roll process and successfully managed to save significant amount of time.

Business Outcome:

  • Eliminated the necessity for key-staff to conduct the book roll through fully automated book roll process
  • Improved carrier relationship and communication
 

Splunk

Case Study:  Länsförsäkringar Bank utilizing machine data to enhance customer experience

Länsförsäkringar Bank, a Swedish Bank aimed at collaborating its machine data into a unified platform needed an exquisite solution to gain deeper insight into its operations.  The bank utilized Splunk’s Enterprise platform, and Splunk DB Connect to collect and analyze data generated via its online services and mobile applications for improving customer support, analyzing customer behavior and developing new services.

Business Outcome:

  • Bank achieved faster resolution of service issues
  • Successfully enhanced customer experience
  • Gained deeper insights into customer’s demands, and subsequently developing new services

 

 

Birst

Case Study: Clear sales forecast across a growing selection of products and expanding sales force

A Financial Data Services Company maintains information on more than 220 million companies worldwide and licenses this information for use in credit decisions, business-to-business marketing and supply chain management.

With a sales org of over 1,000 reps, the Company did not have a clear outlook on its sales forecast. Combined with hundreds of thousands of customers, an ever-growing selection of products, and an expanding sales force, the executive team had difficulty aligning and agreeing on priorities. Objectives were to:

  • Understand sales metrics across a complex sales organization
  • Incorporate scenario and predictive analysis for better fidelity
  • Run a data-driven sales organization

 

Technical Challenges

  • Hundreds of reports on sales, product performance, orders, deals, products, etc.
  • Reports for 1000+ sales people
  • Replace BOBJ and Cloud9 reporting solutions

 

Why Birst

  • Birst automated predictive capabilities: pipeline snapshots, matching with conforming dimensions (orders), and slowly changing dimensions (territory changes)
  • Robust integration with Salesforce
  • Automatic aggregation of multiple data sources to provide a full view of sales – from historical to future

Business Outcome:

  • Successfully delivering sales analytics to 1000+ sales reps
  • Improved sales performance by targeting deals & products with the highest chance to close

 

 

SAP SE

Case Study: HDFC ERGO adopted SAP Ariba Sourcing to optimize savings and efficient supplier negotiations

HDFC Ergo wanted to keep growing by having greater process efficiency particularly in sourcing. The company turned its attention to automation and single integrated sourcing platform to deliver a fairer and clearer process for all suppliers. The main objectives were to improve sourcing visibility, validation, and cost savings, migrate to a centralized sourcing program to add value to the company by securing the best cost through online sourcing and enhance the transparency of agreements made with suppliers and negotiated terms.

Business Outcome:

  • Greater credibility and fairness for suppliers through consistent and clear online negotiations
  • Improved cost optimization and supplier management
  • More-efficient documentation and record keeping
  • A strategic savings of around 15% to 20%

 

 

Microsoft Corporation

Case Study:  Westpac New Zealand upgraded its on-premise CRM environment using Microsoft Dynamics 365 to build a customer-centric banking strategy

Westpac New Zealand wanted to build more valuable relationships with its banking customers by upgrading its aging on-premises CRM environment. The company aims to forge new ways to do business, make more meaningful connections with customers, and compete effectively. Westpac find it hard to differentiate on products and services, so Westpac set out to compete by building more valuable customer relationships. The bank struggled with manual processes, duplicated effort, and missed opportunities for more productive customer engagements. The company turned its attention to cloud based Microsoft Dynamics 365 to follow a more consistent approach to customer onboarding, follow-up, and personalization without having to enter data multiple times into multiple systems.

Business Outcome:

  • Dynamics 365, hosted in Microsoft Azure went live for the commercial banking division and will be available throughout the bank by 2019
  • Real time customer data help in creating more meaningful customer connections
  • Reduce operational cost and effort, and generate new revenue opportunities
  • Westpac can easily track the customers moved across channels within the bank, from online or retail banking or credit services to wealth management, investment, or commercial banking

 

 

BRIDGEi2i

Case Study:  Improving customer experience 

Client, a large lending Non-Banking Financial Company (NBFC) wanted to build a framework to scale the experience given to high net worth individuals to the entire customer universe. The client collaborated with BRIDGEi2i to build a hierarchy of machine learning models to predict next best product in next 12 months and next best product for 3-5 years. Build segmentation to predict the lines for customers basis inference from bureau. Solution has been implemented real-time on the customer facing client’s application and for the sales force to aid good customer service BRIDGEi2i proprietary personalization platform was used to execute the project. The platform was hosted in Azure (client environment) and was integrated with data base and the front-end User Interface (UI) layer.

Business Outcome:

  • Improved the marketing efficiency by 25% Year Over Year (YoY)
  • Helped in decreasing the “Not interested” action by customer thereby decreasing “Do Not Contact” proportion and indirectly boosting Net Promoter Score.

PRADS INC.

Case Study: To optimize investigation process for identifying additional premium opportunities TBA

A worker compensation insurance company wanted to optimize its investigation process for identifying additional premium collection opportunities. The current investigation process in the company for identifying additional premium opportunities was not efficient and did not meet its Return on Investment (RoI).  The insurance provider collaborated with PrADS to address the problem. PrADS developed an action plan after the assessment and review of the current investigation process in the company, evaluation of data points used, identification of improvement areas to gain efficiency, validation of existing models, building a new analytics model using customer’s and Dun & Bradstreet (D&B) business data, and defining useful data variables for future procurement. PrADS helped the insurance provider by developing a predictive model to estimate the probability of a customer paying additional premium after combining customer data with D&B data to estimate the expected yield and recommend the optimum cut off point.

Business Outcome:

The PrADS solution enabled the insurance company in identifying the customers with high probability of paying additional premium and optimizing its audit process with the following benefits resulting in significant RoI increase:

  • Cost of investigation reduced by USD 3.5 million
  • USD 12 million collected as additional premium

 

MicroStrategy Inc.

Case Study: Improving customer experience with real-time customer insights

The Commonwealth Bank of Australia, an Australian multinational bank, provides a variety of financial services including retail, business and institutional banking, insurance, funds management, investment and broking services. The bank serves the customers spread across Australia, New Zealand, US and UK. The bank is aiming to provide enhanced experience to its retail customers with better recommendations about its products and services. The banks has developed a mobile app to take advantage of big data and deliver superior results to customers. This app has made possible to analyze the transactional data and find insights about merchants, customer profitability, cash flow, and key markets among others. The bank has added analytics capabilities from MicroStrategy to gain accurate and real-time insights about its customers. 

Business Outcome:

  • The mobile app developed by Commonwealth Bank enabled the small and medium-sized enterprise customers to not only monitor their performance in real-time and identify purchasing and demographic trends.
  • The mobile app also helped the bank to build loyalty and trust with across its clients, allowing bank to improve customer satisfaction.

 

Guidewire Software

Case Study: Improvised management reporting and decision support systems with predictive analytics capabilities 

Promutuel Insurance, leading property and casualty insurance provider in Canada, offers variety of insurance products. The company offers personalized quality service to more than 6,30,000 insureds and employs 1,910 people. The company deployed predictive analytics solutions from Guidewire Software to create analytics-based agency prospecting tool to appoint agents in high potential areas to reach untapped markets.  

Business Outcome:

  • The addition of predictive analytics capabilities to get real-time insights into its management reporting and decision support systems.

 

Qlik Technologies

Case Study: Monitoring corporate financial risk with Qlik Technologies solutions

Leading Italian bank Mediocredito’s specialized in consulting, advanced finance and leasing and merchant banking catering its services to small and medium sized banks. The bank was facing challenges to provide targeted corporate analysis and consultation as well as monitor the financial risk. The bank was also needling solution to view and interpret data in simple and easy-to-understand manner. The bank connected with Qlik Technologies to implement predictive analytics solutions to provide common dashboard to its employees and manage financial risk across platforms from rate risk, treasury risk, ALM risk and operating risk. 

Business Outcome:

  • The bank improvised its abilities to analyze and monitor financial risks across ALM, rate risks, treasury risk, and operating risk via one system X-Match.
  • The bank improvised processing speed and output.

 

Hexaware Technologies

Case Study: Leading Banking and Insurance company using BI solution to improve the operational efficiency

The leading BFSI organization based in Europe needed to deal with various challenges, including increasing ownership cost, report proliferation with self-service BI, stringent data policies, and high pressure on ETL batch window due to increasing data volume. Hence, a comprehensive solution was required to resolve these issues. The bank utilized Hexaware’s Solution Accelerato, BIMA capabilities for reducing the overall cost.

Business Outcome:

  • Reduced cost of licensing
  • Improved productivity of IT team
  • Improved performance and storage of data warehouse

 

OpenText Corporation

Case Study: Digitization of business process of DHFL Pramerica Life Insurance

DHFL Pramerica Life Insurance is the India based insurance company needed to digitize the administrative activities, provide quick access to the varied insurance policies and maintain the high customer service across the industry. The insurance company turned to OpenText Corporation and deployed the OpenText AppWorks, OpenText Content Suite, OpenText Managed Services, and OpenText Professional Services. These products helped the insurance company digitize end-to-end business process.    

Business Outcome:

  • Digitized business process
  • Lower processing cost by 20% to 30%
  • Minimum transaction time

 

VERISK ANALYTICS

Case Study: To meet the EDI compliance challenges

Liberty Mutual needed to comply with Electronic Data Interchange (EDI) by keeping update on advanced compliances and rules of EDI and make changes accordingly. Moreover, the insurance company did not have any internal tool to analyze and measure the compliance internally. Thus, Liberty Mutual used wcAnalyzer Compliance Cube to track the compliances issues and minimize the cost and time consumed during the process.          

Business Outcome:

  • Tracking the compliance issues
  • Linking the analytics to injury report side in reduced time
  • Real time access to the EDI data.

 

BRIDGEi2i

Case Study: To meet the EDI compliance challenges

A large lending Non-Banking Financial Company (NBFC) wanted to build framework to scale the experience given to high net worth individuals to the entire customer universe. The client collaborated with BRIDGEi2i to build the hierarchy of machine learning models to predict next best product in next 12 months, next best product for 3-5 years, and building segmentation to predict the lines for customers basis interference from bureau.    Solution has been implemented real-time on the customer facing client’s application and for the sales-force to aid good customer service.

Business Outcome:

  • Improved the marketing efficiency by 25% YoY
  • Helped in decreasing the Not Interested action by customers

Frequently Asked Questions

  • How will the Predictive Analytics Market perform in near future?
    The predictive analytics market size is expected to grow from USD 4.6 billion in 2017 to USD 12.4 billion by 2022, at a Compound Annual Growth Rate (CAGR) of 22.17% during the forecast period. Proliferation of internet and the availability of various means for accessing the internet have led to a massive increase in the data volumes being generated. This will help in the advancement and expansion of high-speed internet services.
  • What are the opportunities in the predictive analytics market?
    With the rise in touchpoint and the need for collecting data to understand consumer behavior, every touch by a consumer has become an important data point that can be processed to reveal user behavior. With the exponential rise in individual and organizational data, businesses are now deploying teams of data scientists and analysts to process the collected data. Another factor accelerating adoption is the revenue generating potential of predictive analytics. This is compelling firms to invest in predictive analytics.
  • What is the competitive landscape in the market?
    The predictive analytics ecosystem comprises vendors, such as Alteryx, Inc. (US), AgilOne (US), Angoss Software Corporation (Canada), Domino Data Lab (US), Dataiku (US), Exago, Inc. (US), Fair Isaac Corporation (FICO) (US), GoodData Corporation (US), International Business Machines (IBM) Corporation (US), Information Builders (US), Kognitio Ltd. (UK), KNIME.com AG (Switzerland), MicroStrategy, Inc. (US), Microsoft Corporation (US), NTT DATA Corporation (Japan), Oracle Corporation (US), Predixion Software (US), RapidMiner (US), QlikTech International (US), Sisense, Inc. (US), SAP SE (Germany), SAS Institute, Inc. (US), Tableau Software, Inc. (US), TIBCO Software, Inc. (US), and Teradata Corporation (US). The exponential growth in data volume is due to the expansion of businesses worldwide, which is driving the rise in data volumes and sources. The accumulation of big data in a single location has rapidly developed the evaluation capabilities of data science experts in every organization. Additionally, companies prefer to provide stand-alone solutions rather than combined solutions. This is eventually resulting in a rise in the number of big data analytics startups, which are driving noteworthy innovations.
  • What are the regulations that will impact the market?
    Predictive analytics leads to ad hoc analysis, which assists companies to have all workable solutions for their business specific questions and forecast past, present, and possible future predictive scenarios. In the current competitive business scenarios, companies need more than accurate predictive statements and reports from its predictive analytics. Companies now need more forward-looking, predictive insights that can help them shape impactful business strategy and improve the day-to-day decision-making in real time.
  • How are mergers and acquisitions evolving the market?
    July 2017, SAP collaborated with energy and services company Centrica to help their customers in managing assets and energy consumption on insights available through the IoT. February 2017, Oracle announced the expansion of its IoT portfolio with the introduction of 4 new cloud solutions to assist businesses to fully utilize the advantages of the digital supply chain. By applying advanced predictive analytics to devise signals, IoT applications can help in automating business processes and operations across the supply chain to enhance customer experience. March 2016, the company has extended their strategic partnership to offer combined capabilities of cloud analytics and big data to their users. This will help users to automate and simplify the decisions while attaining greater business insights for smarter business decisions.
  • What are the dynamics of the market?
    Proliferation of internet and the availability of various means for accessing the internet have led to a massive increase in the data volumes being generated. This will help in the advancement and expansion of high-speed internet services. Globalization and economic growth are also playing major roles in driving greater data generation worldwide. Also, the rise in connected and integrated technologies has provided a platform to predictive analytics software vendors for leveraging this development and the unprecedented growth of the internet. Additionally, the eCommerce sector has modified the traditional shopping behavior of customers. Dedicated email campaigns, online/social media advertising, and cognitive analyzing of customers are the key enablers driving sales and increasing customers’ loyalty. With connected devices coming to the forefront, retailers are focusing on real-time analysis of customers’ shopping behavior and market basket analysis for analyzing consumers’ perception, which can be used for building tailor-made offers to increase customer retention. Similarly, with the rise in the global IoT analytics demand in the retail sector, the market is expected to have unprecedented growth opportunities for predictive analytics.

Predictive Analytics Software in BFSI

Comparing 168 vendors in Predictive Analytics Software across 193 criteria.
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SPSS Modeler reduces the complexities involved in the transformation of data by providing easy-to-use models. SPSS Modeler is extensively used across various languages to analyze data from multiple databases. It majorly helps in analyzing data to predict customer churn rates and data sets. The application can be used across various industry verticles.
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Rapid Miner Studio enables users to create complex predictive models by using a drag and drop visual interface. It has a library of 1500+ machine learning algorithms and functions that can be used to build models specific to any situation. It also offers templates for common cases such as prediction of customer churn, fraud detection, predictive maintenance, etc. It provides proactive recommendations at each step for guidance. Rapidminer helps increase productivity across teams.
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SAP Predictive Analytics software enables users to create, deploy and maintain various predictive models. These on-premise tools can help users anticipate future behavior and outcomes and better guide the decision-making ability to help grow the business. SAP Predictive Analytics Cloud works alongside the BI and planning tools to visualize, plan and predict context. The tool uses in-memory technology and machine learning to uncover relevant predictive insights in real-time.
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ORACLE Analytics Cloud’s Database Platform allows the use of seamless predictive analytics software within the platform, giving it an edge over other vendors. ORACLE Analytics Cloud helps mine various data types, eradicate movement of data, and deliver actionable insights. Application developers deploy this analytics model along with SQL and R functions. ORACLE Analytics Cloud helps predict the behavior of customers, the gap between the demand and supply, and make better marketing strategies.
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Angoss uses data and predictive modeling to present insights that help users make better decisions faster. It uses advanced statistical algorithms for the prediction of outcomes. These outcomes are generated across all stages of model cycles. It helps improving predictive analytics for organizations looking to monetize their data.
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SAS Advanced Analytics provides users with better response time and faster insights provided by its in-memory analytics. SAS Advanced Predictive Analytics software helps organize data in a structured manner, making it easy to understand and present. It enables the user to analyze past, present, and future models using quality-tested algorithms. Automation of large-scale forecasts is also possible without the need for high levels of technical knowledge.
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Use the full potential of information to unleash the capability of the human resources of an organization.
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Information Builders WebFocus RStat is a cost-effective, robust, intuitive, and accurate predictive analytics software. WebFocus can help organizations by extracting meaningful insights from data of any kind. It creates interactive dashboards to consolidate information which increases the chances of actionable insights to be used in the everyday conduct of data-driven businesses.
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FICO Decision Management Suite is an integrated environment for development that is compatible with web as well as mobile applications. It is a platform that handles real-time streaming of data including its visualization, indexing, search, and pre-processing, based on rules that are defined in advance. The company's USP is the ability to provide models based on precise customer requirement to reduce time and cost.
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Alteryx provides information science that can viably and productively tap into a code-free and code-accommodating easy-to-use application. The predictive analytics software requires no coding, however, it is coding friendly for those interested. It has a fantastic interface without code for both analytics modelling and advanced modelling with code. It enables easy deployment and management of analytic models, flexibility, agility, and high speed. It supports visualization tools and all data sources. Alteryx helps find, manage, and understand all sort of analytic information of an organization at a high speed, thereby making better decisions and increasing productivity.
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Rapid assembling of predictive data that changes crude information into a business affecting service. This product has advantages for all types of users: analytics leaders, data scientists, IT professionals, and business analysts. It helps analytics leaders in terms of managing productivity, collaboration, coordination, and measuring team growth. Data scientists benefit in terms of automation, modelling, flexibility, and reproducibility. IT professionals gain advantages pertaining to scalability, code & integration, operationalization, and data governance; while business analysts obtain data access, preparation, exploration, and automated ML benefits.
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GoodData is a cloud-based platform with high SLA availability and maintenance. It allows for easy incorporation of already existing data warehouses. It allows the platform to be integrated into web or mobile applications. It is one of the most dominant cloud data warehouse that meets most versatile analytics platform requirements.
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Extensive set of cloud services that enables associations to address business problems, construct, oversee, and convey applications on a massive, worldwide system utilizing various tools and frameworks. Microsoft Azure ML Studio can be used to prepare and manage the data they need for machine learning. It can improve productivity through its powerful capabilities that can integrate the current model cycle with that of the app lifecycle.
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Natural language search and AI-powered bits of knowledge discovery make creating bits of knowledge a characteristic, instinctive, and intuitive experience. Spotfire has strong built-in predictive analytical methods that are smart, yet easy to use. Its intelligent data wrangling helps you clean and modify data, and auto-records it so you can edit it later as well. It is flexible and can scale secured documents as well.
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AXON Predict permits OEMs and enterprises to catch, screen, and break down information. AXON Predict is a product suitable for OEMs as well as large enterprises since it collates, scrutinizes, and analyzes data across networks and provide valuable visual analytical insights to users. This gives users real-time analytics to work with in order to enhance innovation and reduce costs.
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NTT Data offers effective solutions that supplement the decision-making process in an organization. This is possible across multiple business platforms and across different development and deployment capabilities. With the help of a comprehensive analytics and business insight methodology, NTT Analytics Solutions can change a client organization into an information-driven pioneer.
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Teradata predictive analytics software provides “Flip the Switch” analytics which allows on-the-fly switching of best campaign users from reverse modeling to forward prediction. Teradata Analytics for Enterprise Applications eradicates the complexity of enterprise application integration, delivers real-time access to integrated data from ERP and other enterprise applications, as well as provides transparency and visibility into business and customer insights.
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KNIME is an open source software and it helps create data science applications and services. Being open, intuitive, and able to integrate new developments, this platform makes reusable components accessible to all the users and helps understand data science workflows. The software provides actual data analysis as well as a number of processes and has productivity funtions to help operations.
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Sisense makes the analytics process easy for users right from the preparation of data to the creation of insights. Sisense is an intelligence software known for its agility and easy implementation. It can be used by varied companies. This platform offers a range of business analytics features. It is designed to make complex data preparation and visualizations simple to make better business decisions and intelligent strategies.
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Domino delivers predictive models using ML and AI techniques capturing all the dependencies of experiments. It is perfect for models across cloud databases as well as distributed systems. Powering model-driven organizations to rapidly create and convey models that drive business impact.
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Cloud-based Predictive Intelligence is used to generate insights into the behavior of customers and provides recommendations based on these insights to enhance revenue generation. Delivers reliable and customized experiences over each interaction point through a flexible, adaptable, and versatile stage that addresses enterprise needs.
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Cutting edge projects delivered within deadlines. This software is majorly used by marketing and sales teams. It combines machine learning and artificial intelligence and empowering both, sellers and buyers. It is used to create and implement marketing campaigns and is compatible with most platforms. This software also helps users discover new sources of income.
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Kognitio empowers data analysts and business clients to run a large number of complex questions simultaneously, providing a competitive edge to large-volume business data activities. Kognitio is an immensely flexible platform that has the strong feature of viewing images and executing complex manipulations on images using standard SQL queries. It can be deployed on both, an existing framework clusters or on a complete standalone one, on-premises or on cloud.
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