Predictive Analytics: Implementation in Business Processes

Aashish Yadav | April 28, 2022 | 37 views

Predictive Analytics: Implementation in Business Processes
Knowledge is power in business, and knowing what will happen in the future is a superpower. When data analytics, statistical algorithms, AI, and machine learning are combined, this superpower, also known as predictive analytics, becomes a skill that can significantly influence on a company's choices and outcomes.

Predictive analytics is the use of modern analytical tools. For example, machine learning concludes about the future based on historical data. Businesses can consider application of predictive analytics tools and models to forecast trends and generate accurate future predictions by leveraging historical and current data. Let’s look at the top three reasons why predictive analytics is important for your business.

Why is Predictive Analytics Important for Businesses?

Businesses are looking at predictive analytics to help them solve challenges and discover new opportunities. Here are some of the most common benefits of predictive business analytics and an understanding of how is predictive analytics used in business.

Fraud Detection

In general, various analyzing techniques are merged to analyze data to enhance the accuracy of pattern recognition and discover criminal behavior, thereby reducing the incidence of frequent fraud. With behavioral analytics, you can look at any suspicious behavior and activities that happen on a network in real-time to look for fraud, zero-day breaches, and underlying threats.

Enhancing Business Campaigns

The predictive analytics process can help you optimize marketing campaigns and promotional events. Predictive designs helps businesses attract, retain, and increase valuable customers by determining their purchase responses and promoting cross-sell opportunities.

Minimizing Potential Risk

The predictive analytics process helps businesses decide on appropriate steps to avoid or reduce losses. Predictive analytics is revolutionizing risk management by alerting businesses about future developments. For example, credit scores, which financial institutions use to predict defaulters depending on a user's purchasing behavior.

How Does Predictive Analytics Help the C-Suite?

The C-suite is the final decision maker, so they are the ones who must use predictive analytics the most for insightful decision-making. Let’s look at ways in which predictive analytics can help C-level executives.


Predict Customer Behavior

Predictive analytics utilizes data to forecast future customer behavior. Customer intent becomes the primary aspect rather than historical transactional data, allowing for hyper-personalized marketing and communications. For example, researchers at China's Renmin University used predictive analytics and machine learning to figure out that data on consumer interests and jobs can predict customer preferences and purchase intent for cars.

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.


Example: Reu La La Uses Predictive Analytics to Increase its Revenue by 10%

You often hear about giant enterprises like Amazon, Airbnb, Microsoft, Google, and others utilizing predictive analytics to extend their reach, boost sales, and more. Today let’s look at Reu La La and how they used predictive analytics to enhance their revenue.

Rue La La, a boutique retailer, often needs to predict sales and fix pricing for products being sold for the first time in its online store with no existing sales data. They observed that many products were either sold out within the first few hours of release or did not sell, which lead to revenue loss.

Rue La La took action by creating a set of quantitative qualities for its items and predicting future demand by utilizing historical sales data. They used statistical and computing technologies, such as regression analysis and machine learning, to create a demand forecast and pricing optimization model. In partnership with the Massachusetts Institute of Technology, they created an automated price decision assistance tool. Revenue increased from 10% to 13% across all departments because they used the pricing tool's proposed optimal rates.


Conclusion

“As data piles up, we have ourselves a genuine gold rush. But data isn’t the gold. I repeat, data in its raw form is boring crud. The gold is what’s discovered therein.”

Eric Siegel

You can consider the predictions that predictive analytics makes as gold, but, using predictive analytics is like a crystal ball that shows the future. You can look into the future, prevent issues in your company from escalating, and recognize profitable possibilities.

If you haven't started leveraging predictive analytics, start by experimenting with it on a modest scale and gradually build up as you acquire expertise and observe positive outcomes.


FAQ


How can Predictive Analytics Improve Performance Measurement?

Predictive analytics improves performance measurements by expanding an organization's understanding of the important performance drivers. It also helps with the weighting of different performance metrics based on how important they are.
 

What Are the Four Steps in Predictive Analytics?

In simple terms, predictive analytics involves four steps: creating a baseline prediction, assessing it, adding assumptions, and building a consensus demand plan. To do so, we must first choose a modeling technique, create a test design, then construct the model, evaluate the mode, and achieve alignment.
 

What Are the Three Different Types of Predictive Analytics?

Businesses utilize three forms of analytics to drive their decision-making:

Descriptive analytics — tells something that has already happened;
Predictive analytics — shows what can happen;
Prescriptive analytics — tells what should happen in the future

 

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Qbase is a worldwide provider of innovative decision support technologies and information technology professional services. Focused on supporting the mission-critical operations of the federal government, civilian agencies and commercial entities in the defense, law enforcement, national security & intelligence, healthcare, and energy sectors, Qbase delivers global life-cycle IT services and world-class data technology and products that solve our customer’s most complicated business challenges.

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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. 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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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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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BIG DATA MANAGEMENT

SingleStore and Intel Collaborate to Deliver Next-Generation Real-Time Data Technology

SingleStore | July 07, 2022

SingleStore, the cloud-native database built for speed, scale and powering real-time data-intensive applications, announced it has been accepted as a Gold Status Partner in Intel’s Disruptor Initiative. The program helps companies push the limits of innovation through technical enablement and multi-channel go-to-market activities. As a result, SingleStore customers will benefit by realizing enhanced database performance and hardware-enhanced security to address real-time data challenges while lowering total cost of ownership (TCO). "SingleStore is excited to join Intel’s prestigious Disruptor Initiative and to work closely with their engineers to optimize the performance of SingleStoreDB on current and future Intel architectures. “This collaboration helps our customers reach new levels of data intensity with real-time analytical and transactional workloads.” Oliver Schabenberger, chief innovation officer at SingleStore In running internal benchmarks fueled by 3rd Gen Intel Xeon Scalable processors with built-in AI accelerators, SingleStore has seen a performance improvement of up to 30%*. In addition, Intel and SingleStore are both members of the Bytecode Alliance, a nonprofit organization dedicated to creating a secure network software foundation, building on standards such as WebAssembly (Wasm) and WebAssembly System Interface. SingleStore recently brought Wasm technology to the database market and has collaborated with Intel to bring this unique optimized technology to market. Arijit Bandyopadhyay, chief technology officer for enterprise analytics and AI and head of strategy for the enterprise and cloud, DCAI group at Intel Corporation, said: “As the demand for all types of data and high intensity data applications increase, encapsulating complex queries with high ingest speed, high concurrency and low latency requirements, we couldn't be more excited about collaborating with SingleStore to deliver the next generation of real-time data technology on AI – to enhance digital customer experience, improve operations and security, plus generate new revenue streams.” The effort with Intel is the latest that SingleStore has recently forged with other industry-leading technology companies. Earlier this year, SingleStore partnered with IBM and SAS to deliver ultra-fast insights to accelerate insights for data-intensive applications and reduce TCO. SingleStore growth continues to accelerate due to its unique ability to address real-time applications, strategic partnerships, and investments from leaders like IBM, HPE, Dell Technologies and now Intel. Users and media see the value of SingleStore, too. SingleStore has been recognized with several industry awards, including San Francisco Business Times Fastest-Growing Private Companies in the Bay Area and the Deloitte Fast 500 awards. SingleStore also was recently recognized when it won in four top-rated categories from verified user review site TrustRadius in May. Christoph Malassa, managing consultant and head of analytics and intelligence solutions at Siemens, said this about SingleStore: “With SingleStore, we no longer look at the database as a limiting factor in our business.” About SingleStore The world’s leading brands rely on data — to make the right business decisions, to deliver exceptional customer experiences and to stay ahead of the competition. This reliance on data brings with it a need for simplicity, speed and scale. SingleStore delivers the world’s fastest distributed SQL database for real-time applications, SingleStoreDB. By combining transactional and analytical workloads, SingleStore eliminates performance bottlenecks and unnecessary data movement to support constantly growing, demanding workloads. Digital giants like Hulu, Uber and Comcast, and many more of the world’s leading SaaS providers choose SingleStore to unleash the power of their data — supercharging exceptional, real-time data experiences for their customers.

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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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