Data Mining Techniques and How Businesses Implement Them

Aashish Yadav | March 31, 2022 | 21 views

Data Mining Techniques and How

Businesses have more data than ever in contemporary times because of rapidly evolving technology. Data is one of the most valuable resources available to any business or entrepreneur. Businesses today are overflowing with data from a wide range of sources, including websites, mobile devices, social media, and other digital channels, but they don’t know where to start.


It doesn't matter if you have massive data; what matters is what you do with it. This is where data mining comes into play. Turning unstructured data into insights helps companies accomplish their goals and improvement strategy for the future. These days, data mining techniques are widely used by organizations from different industries.

“With data collection, ‘the sooner the better’ is always the best answer.”

- Marissa Mayer, Ex-CEO of Yahoo!

Let’s explore some of the top data mining techniques used by businesses.


What are the Must-Have Data Mining Techniques?

Data mining is a successful process that uses a variety of data mining approaches. The problem is determining which data mining techniques are most appropriate for your situation and business. Even though many data mining techniques are often required to uncover insights hidden inside massive datasets, it's wise to use more than one.


Data Cleaning

When it comes to data mining, data cleaning is a must-have approach. For raw data to be used for various sorts of analytical techniques, it must first be cleaned, formatted, and analyzed. This data mining technique is used in data modeling, transformation, aggregation, and migration processes.


Clustering Analysis

Clustering analysis is the recognition and clustering of related data from an big data collection. Clustering analysis can assist an organization in evaluating the differences and similarities in data from the perspective of a company. This can help them develop customer personas, similar segment leads, and others.


Association Rule Discovery

This is a technique for discovering interesting connections and interdependencies among variables in big datasets. This data mining technique can help decipher hidden data patterns that would otherwise go unnoticed. Association rule discovery a term that's frequently used in machine learning.


Classification Analysis

The technique of extracting information about the data is called classification analysis. It is the most complicated data mining technique. Data classification involves splitting data into categories that have similarities in their context. As a result, classification analysis is useful in combination with clustering analysis. The structure or recognition of the data is known as classification.


Data Visualization

Data visualization uses real-time graphs and charts to provide users with extra insights into their data and help them better grasp performance targets. Data visualization is a popular data mining technique because it can get data from any source, such as file uploads, database queries, and application connections.


Top Industries Using Data Mining

Data mining in business can help you manage risk by detecting fraud, errors, and discrepancies that can result in revenue loss and reputation damage. Data mining is used in various industries to gain a deeper understanding of their customers and businesses. Many brands in various industries perfectly portray how data mining is used in business.


Finance and Banking

Using data mining approaches, financial organizations gather information about loans and credit reports. Financial institutions can evaluate if a lender has a good or bad credit score by using an analysis model based on historical financial data. Banks can also keep a watch on suspicious or fraudulent transactions with the help of data mining techniques.


E-commerce

E-commerce platforms are among the most well-known examples of data mining and business analytics. Many e-commerce websites utilize data mining and business intelligence to provide cross-sells and up-sells. Amazon is, of course, one of the most popular users of data mining and business intelligence.


Retail

Customers are segmented into 'recency, frequency, and monetary' (RFM) categories by retailers, focusing on marketing to those segments. A consumer who spends little but frequently and recently will be treated differently from one who spends a lot just once, which was some time ago. Loyalty, up-sell, and cross-sell offers may be made to the frequent buyer, while the big spender may provide a win-back deal.


Top 3 Companies Leveraging Data Mining Techniques

Businesses use data mining to boost revenue, save expenses, locate consumers, improve customer experience, listen to what others have to say, and conduct competitive intelligence. These are just a few ways of using data mining techniques.
Here are the top three examples of data mining in business.


Amazon

Amazon is gathering competitive intelligence and pricing data from its competitors. Consumers who use the Amazon Price Check Mobile App to scan items in-store, capture an image of the product, or do a text search to find the best price will get a $5 discount. The application also encourages customers to report the in-store price.


Netflix

House of Cards – the American thriller series was an ideal entertainment experience for creating data models and discovering what makes a show or movie successful among viewers based on the insights data gathered. They went all out for the license, winning a bidding battle with rival businesses and instantly scheduling two seasons before presenting a single episode. It was a super hit, and the best thing is that they had a pretty good idea of what it would be.


Walmart

The current search engine of Walmart contains semantic data. Polaris, an in-house platform, uses text analysis, machine learning, and even synonym mining to provide relevant search results. Walmart says that integrating semantic search has led to a 10% to 15% increase in the number of people who buy things online.


Conclusion

Data mining's ultimate significance for data-driven growth and progress cannot be overstated. Using the right data mining technique will give you unparalleled insight into your massive data. Data mining will only get better as technology improves, which will allow for more in-depth analysis.


FAQ


What are the 3 types of data mining?

Pictorial data mining, text mining, social media mining, online mining, and audio and video mining are only a few examples of data mining.


What are the 6 processes of data mining?

Data mining is both an analytical process and a collection of algorithms and models. The CRISP-DM process model has been broken down, just like the CIA Intelligence Process. These parts are business understanding, data understanding, data preparation, modeling, evaluation, and deployment.


What are the most commonly used data mining processes?

Clustering, data cleansing, association, data warehousing, machine learning, data visualization, classification, neural networks, and prediction are just a few of the important data mining techniques to consider when starting out in the industry.

Spotlight

HTC Global Services

HTC Global Services (HTC) is a leading global provider of Information Technology (IT) and Business Process Services (BPS), headquartered in Troy, Michigan, USA. Established in 1990, HTC is an Inc. 500 Hall of Fame company and one of the fastest growing Asian American companies in the USA. Our client base spans over 2000 organizations across the globe. HTC acquired CareTech Solutions in December 2014 and Ciber, Inc. (Currently Ciber Global LLC) in June 2017. These acquisitions enable us to expand our operational capabilities in Healthcare IT and Technology Transformation services.

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Predictive Analytics: Implementation in Business Processes

Article | March 31, 2022

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Predicting customer requirements accurately is a huge opportunity for businesses. Companies can use AI and predictive analytics models to figure out what customers will do based on data instead of guesswork. Pricing Optimization Predictive business analytics can help companies improve pricing optimization quickly and affordably. A business can use predictive analytics to figure out how to make a product more affordable in the future by looking at past data, industry trends, competitive prices, and other data sources. Each customer provides a unique value to the products. To add to the complexity, a consumer's value of a product may vary depending on the purchase circumstances and environment. Simplicity in pricing misses opportunities and can result in a significant drop in revenue. Product information, consumer segmentation, and purchase circumstances are all enhanced by predictive analytics. Businesses can use this data to uncover trends and patterns to help them price more profitably. Predicting Growth and Market Trends Businesses can use predictive market analysis to decipher existing and future market trends. With this data, businesses can develop a plan to maximize opportunities, expand market share, and sustain disruption and new competition. Companies can use it to detect unmet customer demand and fill any gaps. Consumption sentiment is revealed through social media data. A product that does not match customer demand creates a market opportunity for a new product or service. Predictive market analysis can uncover customer perceptions of a product or service and unmet consumer demands. Predictive business analytics helps businesses better understand their customers, meet their needs, and find new ways to earn revenue and grow. 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Article | April 29, 2021

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The Art of Developing a Successful Business Intelligence Strategy

Article | February 16, 2022

Business intelligence, or the art of using data to discover insights, has become a crucial part of developing business strategy for leading organizations, including government agencies, Fortune 500 companies, and educational institutions. Businesses that want their operations to be more informed and backed by data must use business intelligence. This is done with the help of a BI strategy. A BI strategy is a blueprint that helps you decide how to use data in your company. You need a business intelligence strategy, as merely choosing a BI technology is not enough to leverage the benefits of business intelligence. Many organizations struggle with implementing business intelligence solutions because of a lack of a proper BI strategy. The Downside of Not Developing a BI Strategy A business intelligence strategy will help you to address all your needs and problems related to data, develop a cohesive system, and maintain it. 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Later, if they don’t use that feature because they don’t require it, paying more for that feature will be a complete waste of money. Wastage of Time A company without a business intelligence strategy has to begin all over again with its business intelligence software search. Failure to adopt business intelligence tools can also be frustrating for the employees because of the inconvenience caused. This emphasizes that having a defined business intelligence plan is always better. Steps to Build a Business Intelligence Strategy The business intelligence strategy should align with the overall business goals resulting in an exponential growth. So, let’s start with the five steps to developing a successful business intelligence strategy. Determine Business Intelligence Strategic Objectives The first step towards developing a BI strategy is identifying and highlighting the strategic objectives. 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Embedded Business Intelligence- A Guide to an Upgraded BI

Article | April 12, 2022

Businesses are becoming more data-driven, and the potential to use data and analytics to differentiate market leaders is becoming increasingly important. Customers are demanding actionable insights into the apps, products, and services they use daily, and businesses of all sizes are trying to meet these demands. Product managers understand they must provide their consumers with concrete insights derived from processed data. However, creating these features from scratch can sometimes be a difficult task. The answer is simple: add an analytics platform into your core product, like integrated business intelligence. Embedding an analytics system may also help a company get more value out of the data it has already spent time acquiring, keeping, and analyzing. Embedded business intelligence is among the most important use cases in the broader data analytics sector, as companies leverage the technology to build extranet apps and give analytics as part of a larger business application. Those looking to integrate analytics tools into their existing business operations must prioritize their requirements in order of importance. Why Should Businesses Choose Embedded Business Intelligence? Embedded business intelligence (Embedded BI) is the future of BI, because it makes it easy for your employees to use dashboards and make data-based decisions as they go about their work. Let's look at some of the reasons why you should opt for embedded business intelligence. Insightful Decision-Making Embedding BI allows you to leverage insights, making data more accessible irrespective of technical skills. Embedded analytics tools provide you with quick access to data that can help you make better business decisions. If “glitches” show up on the radar, strategic decision-makers can raise the alarm, assess the threat, develop remedies, and come up with solutions, and change the business course. Create an Effortless Workflow According to MarTech Today and Blissfully, "businesses with fewer than 50 employees have approximately 40 applications in total." The truth is that current employee operations are complicated and scattered across several platforms. BI platforms aren't a silver bullet for this challenge. Embedded BI, on the other hand, can be beneficial. Embedded BI eliminates the need for your sales executive to make choices and streamlines their workflow. It seamlessly integrates the data into this team's existing tool process with minimal disruption. Reduce your Reliance on Developers Businesses that depend entirely on their overburdened developers to implement an analytics solution will invariably create a data bottleneck. Embedded BI tools reduce this barrier and encourages everyone who works with embedded data to be more flexible and iterative. With the help of embedded business intelligence, you can check and analyze business data and adjust visuals on the go by utilizing dynamic data visualization. Drill-down, filtering, and search are interaction options available on these embedded BI tools, allowing to freely explore reports and dashboards and extract crucial business insights. Should You Build In-House Embedded BI or Buy a Third-Party? When it comes to deploying an embedded BI tool, you have two options. Organizations can either develop their products in-house or buy them from a third party. Building an embedded BI platform from scratch might take a long time and may be costly like most businesses with software as their key competence, general companies should first explore commercially available embedded BI solutions. Also, purchasing embedded BI allows businesses to focus on their core competencies while leveraging the tools to deliver embedded BI features to users faster. Top Embedded Business Intelligence Tools for C-Suite (Include cases) Many embedded BI tools are available in the market but choosing the most appropriate tool from among them is a major task. So, to end your search for the perfect embedded BI tool, you can check out the list below. We have also included case studies of these embedded business intelligence applications for you to make a better decision. Sisense BI Helps Crunchbase Get Access to the Right Data across the Organization In the business world, Crunchbase is the most important database, and they needed a powerful platform to get all their data together, so they went with Sisense BI. Crunchbase was able to take its analytics to the next level using Sisense for Cloud Data Teams, which allowed them to access their data, from their marketing stack to Salesforce platforms to website impression data, to create a holistic view of their business and customers. It's also good for Crunchbase's marketing team because the interface of Sisense is easy to use. This makes it easy for business users to understand data on their own and use it for decision making. Microsoft Power BI Helps Heathrow Airport in Making Travels Less Stressful Heathrow Airport serves as the U.K.'s international gateway. Heathrow Airport serves 80 million passengers each day, and the airport is utilizing Microsoft Power BI and Microsoft Azure to make travel less stressful for travelers. With the help of Power BI, Heathrow Airport gets real-time operational data for its employees. It enables to assist passengers in navigating the airport despite bad weather, canceled flights, and other delays. For example, a disturbance in the jet stream caused a delay of 20 flights, resulting in 6,000 more passengers arriving at the airport at 6:00 p.m. Previously, employees at immigration, customs, luggage handling, and food services would not be aware of the unexpected passengers until they arrived, forcing them to make do with what they had. But now, all these employees are notified one to two hours prior so that they can arrange extra workers, buses, food, and other resources to assist with the inflow. Qlik Sense Helps Tesla Users Get Information About Tesla SuperCharge Stations Tesla customers use a Qlik Sense application to track the locations of Tesla supercharger stations and obtain information about them. The software uses real-world road network computations and overlap predictions based on Tesla vehicles' typical battery range. This app needs to work with Qlik GeoAnalytics because it displays supercharging stations on a map. Charger status is also displayed on the dashboard. You can make choices based on where you are on the dashboard, and the program will respond based on the associations between data sets. Closing Lines Embedded business intelligence has significant potential for small firms and enterprise powerhouses alike. Embedded analytics outperforms previous solutions in extracting the most value from your data and enabling today's crucial business choices. However, long-term use of embedded analytics will require a significant amount of work on the part of the C-suite. The C-suite will have a positive influence and assure continued analytics success by applying predictive analytics, integrating machine learning, and encouraging a data-driven culture. FAQ Is there a limit to embedding analytics into existing applications? Embedded BI products have less limitations than independent tools and are mostly more capable. Machine learning, NLP, and artificial intelligence (AI) are included in the current, more modern generation of embedded systems, although these abilities are generally not included in standalone solutions. What should purchasers keep in mind while selecting a vendor? Users who have only used a typical BI or data analytics tool should be wary of colorful charts and data visualizations. Buyers must think about the long term, particularly when it comes to product maintenance, making changes across instances, and offering a simple yet tailored experience to the end-user. Are embedded business intelligence solutions easy to set up? The beauty of embedded analytics and BI solutions is quick and simple to deploy. You can either add them to an existing system or design a new one based on your requirements.

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KNIME | June 10, 2022

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BIG DATA MANAGEMENT`

MOSTLY AI Opens up Its Synthetic Data Platform to Revolutionize Software Testing for Mid-market Companies

MOSTLY AI | July 06, 2022

MOSTLY AI, who pioneered the creation of AI-generated synthetic data, has today launched new editions of its platform, for mid-market businesses wanting to speed up test data generation through automation, and better support agile processes. By experimenting with the free edition of the platform, test engineers, QA leads, and test automation experts can see for themselves how the pioneering platform easily and automatically synthesizes complex data structures. Boosting efficiency is coupled with the benefit of generating high quality test data for QA - a critical need for businesses that are required to deliver customer experiences that are increasingly personal and relevant. “Scaled synthetic datasets generated through our platform offer absolute protection of customer data, with zero risk of re-identification and therefore full compliance with data privacy laws such as GDPR. What’s more is that the datasets preserve granular behavioral insights embedded in the production data “This is of course valuable for innovative companies focused on accelerating the agile delivery of robust software applications that enhance customer experience.” Dr. Tobias Hann, CEO at MOSTLY AI For tests, such as load and performance testing, MOSTLY AI’s platform completely removes the need to use production data or manually created dummy data, which is what the majority of testers are still using now. Apart from clear privacy issues that come with doing it this way, it’s a massive time thief with a huge chunk of the average tester’s time being spent waiting for test data, looking for it, or creating it manually. “Our research over the past months confirms this risky habit of testers using production or dummy data,” says Hann, adding, “and coupled with the fact that 20% of test data will be synthetically generated by 2025, it’s the right time for us to bring AI-generated synthetic data to the mid-market and be instrumental in reaching the synthetic-data tipping point we know is on the horizon.” AI-generated synthetic data is not mock data or fake data. It’s not generated manually - as it was ten years ago - but by a powerful AI engine that is capable of learning all the qualities of the dataset on which it is trained. Using the MOSTLY AI platform, testers don’t need to manually configure business rules anymore, plus it enables them to create as little data or as much data as they need - generating small, manageable and referentially intact subsets of data to speed up cycles and reduce storage sizes or upscale small datasets to massive sizes for stress testing applications. “Mid-market companies have an advantage over larger corporations - they can adopt and roll out new tech quickly without the red tape that often makes this a drawn-out process. Adopting AI-generated synthetic data for testing is a win-win situation – for testers who get to work smarter and faster, and for businesses wanting to innovate and deliver the best in customer experience,” concludes Hann.

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BIG DATA MANAGEMENT

Katipult Launches Enterprise-Grade Data Integration Capabilities to its DealFlow Platform

Katipult | July 06, 2022

Katipult Technology Corp. , a leading Fintech provider of software for powering the exchange of capital in equity and debt markets, announced today that its private placements platform, DealFlow, has been upgraded with the addition of a new enterprise-grade data integration module – DealFlow: DataHub. This module enables users to securely link their backend systems with the DealFlow platform, allowing them to directly populate subscription documents with the latest information from their systems of record. "We're very excited to announce the launch of the DealFlow: DataHub module. Our experience working with investment banks and broker dealers showed us that being able to seamlessly interface with their legacy systems of record is critical for helping them accelerate the pace of digital transformation. DealFlow:DataHub further amplifies the efficiency-boosting capabilities of DealFlow by removing yet another manual step in the private placements process. Not only is scalability improved, but there are also positive knock-on effects on compliance as data integrity and continuity are preserved." Gord Breese, Katipult CEO DealFlow:'s DataHub extracts large volumes of data from the commonly used systems of record in the industry, such as ISM or Dataphile. The data is then streamlined and used to populate the intelligent digital subscription documents that are core to the DealFlow platform. With the addition of DealFlow: DataHub, customers will no longer need to manually input or update the data that will populate the subscription documents. Further, DataHub will also enable single sign-on to the DealFlow platform, allowing users to sign on with their standard enterprise credentials. Katipult's goal with DealFlow is to help institutions unlock the full potential of private placements by streamlining as many processes as possible. DealFlow: DataHub represents yet another step forward in that direction. About Katipult Katipult is a provider of industry leading and award-winning software infrastructure for powering the exchange of capital in equity and debt markets. Our cloud-based platform and solutions digitize investment workflow by eliminating transaction redundancy, strengthening compliance, delighting investors, and accelerating deal flow. Katipult provides unparalleled adaptability for regulatory compliance, asset structure, business model, and localization requirements.

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DATA SCIENCE

KNIME Accelerates Data Science Democratization Through Snowflake Collaboration

KNIME | June 10, 2022

KNIME, the open source data science company, today announced a strategic partnership with Snowflake, the Data Cloud company, to democratize access to data analytics across all roles and departments. Understanding data is critical for creating business value. With the global data analytics market worth more than $200 billion, it’s necessary for as many people as possible across roles, departments and industries to have access to analytics in their daily jobs for overall better productivity. “Many of our customers rely on Snowflake to power virtually any data workload at scale, while utilizing KNIME to gain value from that data.” Paul Treichler, VP of global partnerships at KNIME Tarik Dwiek, Snowflake’s head of technology partnerships, added, “In partnership with KNIME, we look to enrich the Snowflake ecosystem with tools that can enable an even greater share of enterprises and both technical and non-technical users of data.” The joint offering means that users can access and manipulate data in Snowflake with a low-/no-code platform at no cost. KNIME Analytics Platform is a fully featured analytics workflow “designer” that can be used in conjunction with Snowflake’s Data Cloud to perform a broad range of analytics tasks from data prep to data science. Users can leverage the drag-and-drop interface to prepare and explore data, rapidly build analytical models, create data apps, and present results in BI tools such as Tableau or Power BI. KNIME is flexible and extensible, giving data experts the freedom to work in their preferred environment. Users can build sophisticated analytic models in its low-code/no-code environment or script custom algorithms in a language of their choice with built-in integrations with R, Python, Java and more. KNIME has a vibrant open source community of users who share their knowledge and expertise in specialized forums. Technical and non-technical teams can make use of this community to leverage pre-built components and workflows to accelerate their time to value and also upskill themselves through comprehensive free training and learning content available from KNIME. Upskilling non-technical teams to use data science and analytics leaves technical teams with greater bandwidth and freedom to concentrate on more complex tasks. Across industries, enterprises can also take advantage of KNIME’s commercial offering. KNIME Server offers a suite of features for automation, governance, production deployment and MLOps. Snowflake working in concert with KNIME Server enables organizations to move beyond pilot projects and build enterprise-scale data solutions that are compliant and accessible across the organization. Lastly, KNIME extends the deployment flexibility of Snowflake to the analytics layer, allowing enterprises to utilize the right resources for a given workload or scenario. “We are excited about the partnership between Snowflake and KNIME," said Ryan Bosshart, CEO of phData, the Snowflake 2021 RSI Partner of the Year and KNIME Elite Partner. “We've been building with both Snowflake and KNIME because we believe in platforms and technology that make it easier for people to build data products, in both business and technical roles. I’m excited to see what new use cases are possible with this combination.” About KNIME KNIME helps individuals and organizations make sense of data. KNIME software bridges the worlds of dashboards and advanced analytics through an intuitive interface, appropriate for anybody working with data. It empowers more business experts to be self-sufficient and more data experts to push the business to the bleeding edge of modern data science, integrating the latest AI and machine learning techniques. KNIME is distinct in its open approach, which ensures easy adoption and future-proof access to new technologies.

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BIG DATA MANAGEMENT`

MOSTLY AI Opens up Its Synthetic Data Platform to Revolutionize Software Testing for Mid-market Companies

MOSTLY AI | July 06, 2022

MOSTLY AI, who pioneered the creation of AI-generated synthetic data, has today launched new editions of its platform, for mid-market businesses wanting to speed up test data generation through automation, and better support agile processes. By experimenting with the free edition of the platform, test engineers, QA leads, and test automation experts can see for themselves how the pioneering platform easily and automatically synthesizes complex data structures. Boosting efficiency is coupled with the benefit of generating high quality test data for QA - a critical need for businesses that are required to deliver customer experiences that are increasingly personal and relevant. “Scaled synthetic datasets generated through our platform offer absolute protection of customer data, with zero risk of re-identification and therefore full compliance with data privacy laws such as GDPR. What’s more is that the datasets preserve granular behavioral insights embedded in the production data “This is of course valuable for innovative companies focused on accelerating the agile delivery of robust software applications that enhance customer experience.” Dr. Tobias Hann, CEO at MOSTLY AI For tests, such as load and performance testing, MOSTLY AI’s platform completely removes the need to use production data or manually created dummy data, which is what the majority of testers are still using now. Apart from clear privacy issues that come with doing it this way, it’s a massive time thief with a huge chunk of the average tester’s time being spent waiting for test data, looking for it, or creating it manually. “Our research over the past months confirms this risky habit of testers using production or dummy data,” says Hann, adding, “and coupled with the fact that 20% of test data will be synthetically generated by 2025, it’s the right time for us to bring AI-generated synthetic data to the mid-market and be instrumental in reaching the synthetic-data tipping point we know is on the horizon.” AI-generated synthetic data is not mock data or fake data. It’s not generated manually - as it was ten years ago - but by a powerful AI engine that is capable of learning all the qualities of the dataset on which it is trained. Using the MOSTLY AI platform, testers don’t need to manually configure business rules anymore, plus it enables them to create as little data or as much data as they need - generating small, manageable and referentially intact subsets of data to speed up cycles and reduce storage sizes or upscale small datasets to massive sizes for stress testing applications. “Mid-market companies have an advantage over larger corporations - they can adopt and roll out new tech quickly without the red tape that often makes this a drawn-out process. Adopting AI-generated synthetic data for testing is a win-win situation – for testers who get to work smarter and faster, and for businesses wanting to innovate and deliver the best in customer experience,” concludes Hann.

Read More

BIG DATA MANAGEMENT

Katipult Launches Enterprise-Grade Data Integration Capabilities to its DealFlow Platform

Katipult | July 06, 2022

Katipult Technology Corp. , a leading Fintech provider of software for powering the exchange of capital in equity and debt markets, announced today that its private placements platform, DealFlow, has been upgraded with the addition of a new enterprise-grade data integration module – DealFlow: DataHub. This module enables users to securely link their backend systems with the DealFlow platform, allowing them to directly populate subscription documents with the latest information from their systems of record. "We're very excited to announce the launch of the DealFlow: DataHub module. Our experience working with investment banks and broker dealers showed us that being able to seamlessly interface with their legacy systems of record is critical for helping them accelerate the pace of digital transformation. DealFlow:DataHub further amplifies the efficiency-boosting capabilities of DealFlow by removing yet another manual step in the private placements process. Not only is scalability improved, but there are also positive knock-on effects on compliance as data integrity and continuity are preserved." Gord Breese, Katipult CEO DealFlow:'s DataHub extracts large volumes of data from the commonly used systems of record in the industry, such as ISM or Dataphile. The data is then streamlined and used to populate the intelligent digital subscription documents that are core to the DealFlow platform. With the addition of DealFlow: DataHub, customers will no longer need to manually input or update the data that will populate the subscription documents. Further, DataHub will also enable single sign-on to the DealFlow platform, allowing users to sign on with their standard enterprise credentials. Katipult's goal with DealFlow is to help institutions unlock the full potential of private placements by streamlining as many processes as possible. DealFlow: DataHub represents yet another step forward in that direction. About Katipult Katipult is a provider of industry leading and award-winning software infrastructure for powering the exchange of capital in equity and debt markets. Our cloud-based platform and solutions digitize investment workflow by eliminating transaction redundancy, strengthening compliance, delighting investors, and accelerating deal flow. Katipult provides unparalleled adaptability for regulatory compliance, asset structure, business model, and localization requirements.

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