Predictive Analytics in Finance: Understanding What 2022 Holds

Shaivi Chapalgaonkar | August 9, 2021 | 89 views

The financial industry has been going through digital transformation for years. Digital technologies have helped to automate manual and tedious tasks like processing and reporting of historical data to forecasting and financial predictive analytics.

The financial services industry owes its success to data. Data is constantly evolving in the form of market trends, client investment, customer service, campaigns. Data gives a boost to banking strategies. As reported by Accenture in a recent survey, 78 percent of banks have made the shift to using data for operations; however, only seven percent of them have extended to using predictive analytics in finance.

Predictive analytics in finance has had a slow but steady start. It is an area of growing interest for banks and other institutions as new newer technologies launch in the market. To complete your company’s digital transformation, data analytics in finance will make a difference in that process.

To be successful, organizations must have the ability to adapt to changes.

Having predictive analytics on your side, your organization can deal with ever-changing circumstances with less to no difficulty.

Understanding Predictive Analytics: What is it?

Predictive analytics is a process of interpreting data to measure any possible future outcomes. It is carried out with the help of statistical modeling, historical data sets, and machine learning. The collected historical data is fed into an algorithm that recognizes patterns and forecast trends and possible future behavior from days to years in advance.

Analyzing historical data and predicting the future has been an old practice in the finance sector. Banks and financial institutions have been evaluating past events or historical data for a long time now.

Making precise forecasts in trends and analyzing data becomes easier due to predictive analytics.
There is a wider scope to predictive efforts with more speed and accuracy and apply them throughout strategic and tactical business practice areas. 

Predictive Analytics in the Financial Sector: What are the Benefits?

Many organizations are ready to accept the positive applications of predictive analytics but remain skeptical about the return on investment.

It is worth understanding the potential of predictive analytics to any business big or small. It doesn’t matter if you are not in the banking sector to benefit from taking a peek into the future of financial performance.

Any finance and accounting department can take advantage of advanced predictive analytics for the following reasons:

Precise Monitoring

The technology keeps a regular track of the consistency between expectations and reality to warn you about possible gaps.

Risk Alleviating

Analytics accurately helps you identify any possible threats to your business and warns you.

Enhanced User Experience

Predictive analytics guides you to recognize the strengths of your business and lets you know how to maximize customer satisfaction.

Analyzed Decision Making

You can understand your customers better with predictive analytics. With this information, you can correctly match your customers with the product in a better way.

Importance of Predictive Analytics

Most successful banking and financial institutions depend on predictive analytics because it simplifies and integrates data to increase profits for companies. Predictive analytics can improve different finance processes.

But the importance of analytics goes beyond just banking services and actually goes into a better quality of customer service. Better customer service is only possible because of the advanced technology that shares customer feedback and preferences throughout the organization, in turn giving relevant information to every employee to make necessary product enhancements.

To understand the importance of predictive analytics, below are some of its use cases:

Customer first

Predictive analytics in financial institutions and banking give you a complete profile of your customer base. It is impossible to contact every customer and interview them about their likes, needs and wants. This is where big data analytics in finance comes into play. It gives you the whole information about your customers regardless of the services they subscribe.

Customers usually don’t have the same needs throughout their lives. As they grow older and have families, their financial needs change accordingly. For instance, a young person considering getting married will always try and save monetarily to buy a house, life insurance, college funds, whereas an older couple will save that money for their retirement.

Apart from enabling different financial services, predictive analytics empowers you to serve individual customers with ease. Let’s take an example. When a customer applies for a loan, predictive financial services can help you analyze if the customer can repay the loan.

Predictive analytics also helps offer alternative services like secured loans to customers who may not qualify for the originally applied services.

Online Banking Made Better

Consumer interest fluctuates in spikes. Predictive analytics informs managers enough in advance so they can set up online infrastructures in those areas. Predictive analytics has made it easier to identify a possible customer base. For example, it can provide metrics to the marketing teams. In turn, the marketing teams can target the customers with ads for probable mortgage loans or business loans in hopes of converting them into their customers.

Data analytics in finance also helps in preventing and detecting fraud and abuse. Although detecting fraud doesn’t necessarily fall under predictive analytics, it can inform the IT department about potential scammers and which online services must be protected.

Foreseeing Market Variations

Predictive analytics can predict market variations and changes. By combining internal and external data, your organization can predict revenue growth in particular market sectors.

For nascent or growing companies, predicting market changes is an important ability. Profitable companies should also be reviewed through predictive analytics to generate demand projections owing to the uncertainties caused by the Covid-19 pandemic. Your return on investment can grow or reduce even with the minutest changes to the growth plans that would seriously impact investor confidence in the future.

Predictive analytics also help to establish which marketing campaigns are working and which strategies need to change.

Predictive Analytics and the Future: What Next?

Technological improvements have allowed predictive analytics in finance to improve and change constantly. Any organization can use customized data solutions to meet your customers’ needs and reach new ones efficiently. Your organization can use predictive analytics to move your business and products ahead and understand how the market will thrive, giving you the much needed heads up you would need to change your strategies and tactics. 

Frequently Asked Questions

Is predictive analytics is the future of finance?

Predictive analytics is called the ‘future of financial software,’ which means it can provide accurate planning and cost-effectiveness.

How can analytics be used in finance?

Analytics helps in predicting revenue, improve supply chains, identify trouble spots, understand where the company is bleeding money, and fraud detection.

How do predictive analytics benefit financial institutions?

Predictive analytics can help financial institutions and customers detect fraud, financial management, predicting markets, improving products, better user experience, etc.

Spotlight

Wolters Kluwer ELM Solutions

The global market leader for transformative legal software. We empower corporate legal and insurance departments as well as law firms succeed in delivering higher organizational value. Through rich and relevant technologies, services, solutions, and learning opportunities, clients become more productive and efficient through specialized technology solutions, find answers to complex problems through the use of deep domain-specific analytics, and cross performance barriers to breakthrough to possible.

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BIG DATA MANAGEMENT

Predictive Analytics: Implementation in Business Processes

Article | July 6, 2022

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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Embrace Corporate Performance Management to Enhance Your Business

Article | June 13, 2022

When it comes to improving business performance, quite a bit of jargon gets thrown around. Corporate performance management (CPM), for example, is often used to refer to business performance management and enterprise performance management, but these terms don't always refer to the same thing. CPM improves a company's capability. It helps the company enhance three fundamental values: performance monitoring, information delivery, and performance effectiveness. These values assist in understanding, improving, and managing the business. Within an integrated ecosystem, a corporate performance management system coordinates the performance of managers, employees, customers, and suppliers. Information access and strategic planning are the foundations of corporate performance management. 3 Reasons Why You Need Corporate Performance Management (CPM) In the era of exploding business intelligence, businesses need to embrace process automation. CPM may profoundly impact your team's productivity, coherence, insight, and more. CPM functions are critical to the C-suite and the long-term success of an organization. As a result, several businesses have developed departments solely dedicated to strategy and performance management. Let's look at the top reasons why you should use CPM for your business. Addressing Challenges in Financial Data Compiling your financial data takes time. To see and organize your financial data easily and quickly, you can use CPM software to connect with your ERP system. This application will also make the finance team's job simpler. It will be easier to understand and manage the projected estimates and how important they are. Real-time Feedback Smart dashboards in business performance management or CPM software provide every quantifiable statistic that a management team will need to use in its decision-making. Even though there are so many different types of data, it can be a good thing to read and use it as changes happen in real-time in the company. Streamlined Reporting Most businesses have several individuals involved in performance management, right from C-level executives to back-office administrators. Although not everyone is actively participating in the performance management process, many users need access to and analysis of reports. CPM technology for a business focuses on a single source of information or data. That is why it provides greater control over it. It also gives more control and security over the results that come out of the process. Who Uses CPM? Earlier, CPM was primarily used by businesses with more than 1,000 workers. However, due to the affordability and simplicity of next-generation CPM solutions, dynamic and ambitious organizations from the startup phase to the enterprise level are now utilizing them. This is one of the prime reasons for the rapid increase in the CPM software market. Companies that sense an opportunity to grow, large businesses that operate globally, organizations that merge with others, and businesses that strive to improve company performance are the most likely to use CPM. Overcoming the Corporate Performance Management Challenges When a business imparts great performance management throughout the workplace, critical expectations and desired outcomes must be set. Also, this does not always go as planned. As a result, CPM presents significant challenges that need immediate attention, as stated below. Strategic Alignment This involves ensuring that all organizational processes and essential components, such as finances, project and program management, risk management, etc., align with the primary goal. Smart Automation A poorly implemented CPM will result in complete failure. To make sure that information can be easily integrated, processed, and reported to meet specific standards, a company should build an ICT infrastructure that is easy to use, complete, and appropriate. Synchronization of Objectives Businesses should not depend only on current tactics while neglecting to develop their own. Instead, they should focus on getting their main objectives out in the open so that CPM and all stakeholders are on the same page. Things to Consider While Choosing a CPM Platform Before investing in corporate performance management software, understand your team’s requirements. What manual tasks do they currently execute? What tools will the team require to keep pace with the growth of the company? Here we have mentioned the top three things to consider while choosing a CPM platform for business. Usability You want your employees to be passionate about the platform and its potential; choose an option that will significantly enhance their day-to-day functioning. 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There are graphical scorecards and dashboards for displaying corporate information in the CPM software. Forecasting, budgeting, and planning are some of the features that come with the software. What Are the Primary Corporate Performance Metrics? CPM is an aspect of business intelligence (BI) that includes monitoring and controlling a company's performance based on key performance indicators (KPIs) such as revenue, ROI, overhead, and operational expenses. What Is the Difference Between CPM and EPM? CPM concentrates on delivering a company-wide performance management solution, especially for the organization's finance department. EPM focuses on the overall performance of the organization, going beyond the finance departments to sales, marketing, supply chain, and other areas.

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The Art of Developing a Successful Business Intelligence Strategy

Article | June 9, 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. You might encounter various problems if you don’t have a BI strategy. Here are some of the pitfalls of not having a business intelligence strategy, which you can simply avoid by developing one. Reduced Possibility of Successful BI Implementation According to Gartner, business intelligence adaptation is only around 30% in most industries. If an organization wants to avoid being one of those who haven’t implemented BI, a pre-planned BI strategy is the key to successful adoption. A company that lacks knowledge about the system they want to implement is at a higher risk of failing. Risk of Overspending A company that does not have a BI strategy is likely to overspend compared to those with a business intelligence plan. This is because there are no strict guidelines to follow. A company without a business intelligence strategy will agree to whatever a vendor tells them about their company or industry requirements. 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. Next, the business intelligence team must evaluate the unique business objectives, align them with relevant data and resources, and recognize processes that empower the company. Conduct Cost and Benefits Analysis Let’s assume you have ten possible BI software options but which one will help you deliver the larger business objectives. How will you prioritize and choose your platform? In such a case, conducting a cost and benefits analysis is always helpful. The steps for conducting the analysis are as follows: Set a framework for your analysis Add the cost of implementation Consider the margin impact per product Check if the cost and benefit projection is favorable for you Analyse the need for additional resources or re-alignments to be made with existing resources Choose a Business Intelligence Platform Business intelligence software can do a lot, but it is not the entire BI strategy. Now, that you have identified the strategic objectives and have conducted a cost and benefit analysis, you can consider the following components while choosing a BI platform: Data access and the viewing of useful content Data interactivity within a visual interface The ability to go deeper into data on your own and find new insights Promote new insights into a governed environment Collaborate on data analysis and visualized analytics Build a Strong Team You should never forget that only a strong team with a data and analytics mindset can ensure a successful business intelligence implementation. They must be tech-savvy to handle complex IT issues and should be familiar with convoluted statistics and mathematics. They should also have a creative approach to problem-solving. Create a Business Intelligence Roadmap The BI team needs to develop a roadmap for the implementing a business intelligence strategy. You can consider the following things to create a BI roadmap: Keep track of deliverables and dependencies Keep a watch on the future and make adjustments to your strategy as required Be proactive instead of reactive Case Study: Customer Satisfaction Boosted by Business Intelligence Expedia is the parent company of Hotwire and TripAdvisor, all the three are leading tourism companies. Expedia was facing challenges related to customer satisfaction, which is extremely crucial to the company's mission, strategy, and success. The online experience should resemble a pleasant journey, but the company had no access to the customer's voice. To tackle this issue, the organization had to manually aggregate heaps of data with insufficient time for analysis. The customer satisfaction team was able to examine consumer data using business intelligence and correlate results to ten objectives that were directly tied to corporate priorities. KPI owners create, monitor, and analyze data to spot trends or patterns. As a result, the customer service team can now monitor how well it is performing against KPIs and take corrective action as needed. In addition, the data can be used by other departments. Conclusion If done correctly, a strong business intelligence strategy can bring irresistible power to your company. You can prevent yourself from overspending, save time and gain a competitive advantage by having an approach based on BI strategy while selecting BI software. FAQ What is a business intelligence roadmap? Business intelligence managers and their teams utilize business intelligence roadmaps to visualize all aspects of BI, including analytics, adoption, data, and training. Plan how to use business intelligence to increase efficiency and performance across your organization. What is the business intelligence lifecycle? Business intelligence lifecycle management is a way to design, build, and manage BI that includes business customers. It focuses on making data models, database objects, data integration mappings, and front-end semantic layers right away from input from business users. How does Netflix utilize business intelligence? Netflix utilizes traditional business intelligence tools and big modern data technologies. As a result, it creates algorithms that predict what consumers are most likely to watch. It also makes extensive use of open-source software in this regard.

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BUSINESS INTELLIGENCE

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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Spotlight

Wolters Kluwer ELM Solutions

The global market leader for transformative legal software. We empower corporate legal and insurance departments as well as law firms succeed in delivering higher organizational value. Through rich and relevant technologies, services, solutions, and learning opportunities, clients become more productive and efficient through specialized technology solutions, find answers to complex problems through the use of deep domain-specific analytics, and cross performance barriers to breakthrough to possible.

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J.D. Power Acquires We Predict Data and Predictive Analytics Business

J.D. Power | June 06, 2022

J.D. Power, a global leader in data analytics and customer intelligence, today announced that it has acquired the data and predictive analytics business of We Predict, the UK-based provider of global automobile service and warranty analytics. We Predict’s software, which is used by auto manufacturers and suppliers to project future component failures and future warranty claims and costs, will be leveraged by J.D. Power to enhance its vehicle quality and dependability analytics, expand repair cost forecasting and provide critical valuation data. “Robust data and powerful analytics that help manufacturers, suppliers and consumers better predict future repair costs are a key link in the auto industry value chain that will only become more important as fleets of new electric vehicles start rolling off the assembly line,” said Dave Habiger, president and CEO of J.D. Power. “By augmenting our existing offerings with We Predict’s forecasting software, we will be able to deliver a more complete, detailed view of repair-related costs to better anticipate financial risk exposures.” “As the automobile industry enters a phase of massive transformation in which electric vehicles and ever-more complex technologies are rapidly becoming the norm, warranty claims and repair costs are a critical variable for manufacturers and suppliers to incorporate into their forecasting. “By incorporating We Predict’s comprehensive data and powerful analytics into our vehicle quality, dependability and valuation platforms, we will be able to create the industry’s most robust and accurate view of future warranty claims and repair costs.” Doug Betts, president of the global automotive division at J.D. Power We Predict software uses machine learning and predictive analytics to develop detailed projections of future warranty claims and repair costs for the global automobile industry. Drawing on a database of billions of service records, We Predict can accurately forecast true vehicle ownership costs, residual values, repair and warranty claims costs and more. “J.D. Power invented the idea of using data and analytics to evaluate vehicle quality and dependability, so the opportunity to become a part of that team and bring our software and operational data into the offering is enormously exciting to all of us at We Predict,” said James Davies, We Predict CEO. “The industry and consumers need accurate repair cost forecasting now more than ever and we look forward to being the leader in delivering those solutions.” Davies will become vice president of repair analytics and data at J.D. Power. We Predict will become part of the global automotive division at J.D. Power. About J.D. Power J.D. Power is a global leader in consumer insights, advisory services and data and analytics. A pioneer in the use of big data, artificial intelligence (AI) and algorithmic modeling capabilities to understand consumer behavior, J.D. Power has been delivering incisive industry intelligence on customer interactions with brands and products for more than 50 years. The world's leading businesses across major industries rely on J.D. Power to guide their customer-facing strategies. About We Predict Formed in 2009, We Predict uses machine learning and unique predictive methodologies to assist global blue-chip customers in anticipating and accelerating decisions on product, on market, as well as on financial performance. Our top-notch data scientists, mathematicians, computer scientists and industry experts work together with our clients to explore and gain new insights into where your business is headed, creating the opportunity to course-correct with confidence. Using our service, clients gain insights into huge amounts of data at the touch of a button so they can take action—fast. Some guess, we know.

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Huron to Acquire Healthcare Predictive Analytics Company Perception Health

Huron | December 23, 2021

Global professional services firm Huron today announced it has entered into an agreement to acquire Perception Health Inc., a healthcare predictive analytics company focused on bringing data sources together to illuminate opportunities for improved clinical and business decision-making. Huron’s deep healthcare expertise, technology and analytics capabilities combined with Perception Health’s analytics, predictive models and data platform will strengthen the firm’s ability to help providers uncover patterns of care to lower costs, improve patient outcomes and deliver a better healthcare experience. “The healthcare industry is under immense pressure to deliver high-quality, individualized care. This acquisition allows Huron to offer providers, payors and research institutions data insights across the care continuum to make better decisions and proactively impact patient care and clinical outcomes.” James H. Roth, chief executive officer of Huron Since its founding in 2014, Perception Health has been providing the healthcare industry predictive data insights and intelligence to illuminate opportunities for their clients to gain a competitive advantage. Perception Health’s robust intelligence platform of solutions enables providers, payors and research institutions to analyze network integrity, identify early disease risk factors and optimize patient care. All Perception Health employees will join Huron, including Gregg Loughman, chief executive officer, and Tod Fetherling, co-founder and chief data scientist. “We are thrilled to join a values-led and people-focused organization that shares our vision for transforming healthcare,” said Gregg Loughman, chief executive officer of Perception Health. “Huron and Perception Health are strategically aligned and committed to helping our clients harness the power of curated data and analytics to make smarter decisions that profoundly impact patient outcomes, experience and cost of care.” Perception Health will be included in Huron’s Healthcare operating segment. Terms of the acquisition, which is expected to close in December, were not disclosed. ABOUT HURON Huron is a global consultancy that collaborates with clients to drive strategic growth, ignite innovation and navigate constant change. Through a combination of strategy, expertise and creativity, we help clients accelerate operational, digital and cultural transformation, enabling the change they need to own their future. By embracing diverse perspectives, encouraging new ideas and challenging the status quo, we create sustainable results for the organizations we serve.

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DATA ARCHITECTURE

dotData and Tableau Partner to Accelerate Augmented and Predictive Analytics for the Business Intelligence Community

dotData | September 15, 2021

dotData, a leader in full-cycle enterprise AI automation solutions, today announced a partnership with Tableau, the world's leading analytics platform, to enable Tableau users to leverage the power of dotData's AI Automation Capabilities. As a result of this partnership, Tableau users will be able to build customized predictive analytics solutions faster and more easily. By combining Tableau's data preparation and visualization capabilities with dotData's augmented insights discovery and predictive modeling capabilities, Tableau users can perform full-cycle predictive analysis from raw data through data preparation and insight discovery through AI-based predictions and actionable dashboards. This partnership empowers a new class of citizen data scientists through our low code and no-code platforms and allows users to discover deeper, more diverse, and more predictive insights. We are very excited about this partnership with Tableau, one of the world's most renowned analytics platforms. This partnership accelerates our vision to democratize augmented and predictive analysis for enterprise through AI automation. Ryohei Fujimaki, Ph.D., founder and CEO of dotData dotData automates the full-cycle AI/ML development process, including data and feature engineering, the most manual and time-consuming step in AI and ML development. dotData's proprietary AI technology automatically discovers hidden and multi-modal insights from relational, transactional, temporal, geo-locational, and text data. Business intelligence and analytics teams can leverage dotData's no-code AI/ML automation solution to make their reporting and dashboards more predictive and actionable. It offers a streamlined integration of automated feature discovery and automated machine learning (AutoML) and allows BI teams to develop full-cycle ML models from raw business data, without wiring code. About dotData dotData pioneered AI-Powered Feature Engineering to accelerate and automate the process of building AI/ML models, to drive higher business value for the enterprise. dotData's automated data science platform accelerates ROI and lowers the total cost of model development by automating the entire data science process that is at the heart of AI/ML. dotData ingests raw business data and uses an AI-based engine to automatically discover meaningful patterns and build ML-ready feature tables from relational, transactional, temporal, geo-locational, and text data. dotData's scalable, flexible platform enables data scientists to discover and evaluate outstanding AI features; and empowers business intelligence professionals to addAI/ML models to their BI stacks and predictive analytics applications quickly and easily. Fortune 500 organizations around the world use dotData to accelerate their ML and AI development to drive higher business value. dotData has been recognized as a leader by Forrester in the 2019 New Wave for AutoML platforms. dotData has also been recognized as the "best machine learning platform" for 2019 by the AI breakthrough awards; was named a CRN "emerging vendor to watch" in the big data space in 2019 and featured on CRN's 2020 and 2021 Big Data 100 list; and was named to CB Insights' Top 100 AI Startups in 2020.

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BIG DATA MANAGEMENT

J.D. Power Acquires We Predict Data and Predictive Analytics Business

J.D. Power | June 06, 2022

J.D. Power, a global leader in data analytics and customer intelligence, today announced that it has acquired the data and predictive analytics business of We Predict, the UK-based provider of global automobile service and warranty analytics. We Predict’s software, which is used by auto manufacturers and suppliers to project future component failures and future warranty claims and costs, will be leveraged by J.D. Power to enhance its vehicle quality and dependability analytics, expand repair cost forecasting and provide critical valuation data. “Robust data and powerful analytics that help manufacturers, suppliers and consumers better predict future repair costs are a key link in the auto industry value chain that will only become more important as fleets of new electric vehicles start rolling off the assembly line,” said Dave Habiger, president and CEO of J.D. Power. “By augmenting our existing offerings with We Predict’s forecasting software, we will be able to deliver a more complete, detailed view of repair-related costs to better anticipate financial risk exposures.” “As the automobile industry enters a phase of massive transformation in which electric vehicles and ever-more complex technologies are rapidly becoming the norm, warranty claims and repair costs are a critical variable for manufacturers and suppliers to incorporate into their forecasting. “By incorporating We Predict’s comprehensive data and powerful analytics into our vehicle quality, dependability and valuation platforms, we will be able to create the industry’s most robust and accurate view of future warranty claims and repair costs.” Doug Betts, president of the global automotive division at J.D. Power We Predict software uses machine learning and predictive analytics to develop detailed projections of future warranty claims and repair costs for the global automobile industry. Drawing on a database of billions of service records, We Predict can accurately forecast true vehicle ownership costs, residual values, repair and warranty claims costs and more. “J.D. Power invented the idea of using data and analytics to evaluate vehicle quality and dependability, so the opportunity to become a part of that team and bring our software and operational data into the offering is enormously exciting to all of us at We Predict,” said James Davies, We Predict CEO. “The industry and consumers need accurate repair cost forecasting now more than ever and we look forward to being the leader in delivering those solutions.” Davies will become vice president of repair analytics and data at J.D. Power. We Predict will become part of the global automotive division at J.D. Power. About J.D. Power J.D. Power is a global leader in consumer insights, advisory services and data and analytics. A pioneer in the use of big data, artificial intelligence (AI) and algorithmic modeling capabilities to understand consumer behavior, J.D. Power has been delivering incisive industry intelligence on customer interactions with brands and products for more than 50 years. The world's leading businesses across major industries rely on J.D. Power to guide their customer-facing strategies. About We Predict Formed in 2009, We Predict uses machine learning and unique predictive methodologies to assist global blue-chip customers in anticipating and accelerating decisions on product, on market, as well as on financial performance. Our top-notch data scientists, mathematicians, computer scientists and industry experts work together with our clients to explore and gain new insights into where your business is headed, creating the opportunity to course-correct with confidence. Using our service, clients gain insights into huge amounts of data at the touch of a button so they can take action—fast. Some guess, we know.

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BIG DATA MANAGEMENT

Huron to Acquire Healthcare Predictive Analytics Company Perception Health

Huron | December 23, 2021

Global professional services firm Huron today announced it has entered into an agreement to acquire Perception Health Inc., a healthcare predictive analytics company focused on bringing data sources together to illuminate opportunities for improved clinical and business decision-making. Huron’s deep healthcare expertise, technology and analytics capabilities combined with Perception Health’s analytics, predictive models and data platform will strengthen the firm’s ability to help providers uncover patterns of care to lower costs, improve patient outcomes and deliver a better healthcare experience. “The healthcare industry is under immense pressure to deliver high-quality, individualized care. This acquisition allows Huron to offer providers, payors and research institutions data insights across the care continuum to make better decisions and proactively impact patient care and clinical outcomes.” James H. Roth, chief executive officer of Huron Since its founding in 2014, Perception Health has been providing the healthcare industry predictive data insights and intelligence to illuminate opportunities for their clients to gain a competitive advantage. Perception Health’s robust intelligence platform of solutions enables providers, payors and research institutions to analyze network integrity, identify early disease risk factors and optimize patient care. All Perception Health employees will join Huron, including Gregg Loughman, chief executive officer, and Tod Fetherling, co-founder and chief data scientist. “We are thrilled to join a values-led and people-focused organization that shares our vision for transforming healthcare,” said Gregg Loughman, chief executive officer of Perception Health. “Huron and Perception Health are strategically aligned and committed to helping our clients harness the power of curated data and analytics to make smarter decisions that profoundly impact patient outcomes, experience and cost of care.” Perception Health will be included in Huron’s Healthcare operating segment. Terms of the acquisition, which is expected to close in December, were not disclosed. ABOUT HURON Huron is a global consultancy that collaborates with clients to drive strategic growth, ignite innovation and navigate constant change. Through a combination of strategy, expertise and creativity, we help clients accelerate operational, digital and cultural transformation, enabling the change they need to own their future. By embracing diverse perspectives, encouraging new ideas and challenging the status quo, we create sustainable results for the organizations we serve.

Read More

DATA ARCHITECTURE

dotData and Tableau Partner to Accelerate Augmented and Predictive Analytics for the Business Intelligence Community

dotData | September 15, 2021

dotData, a leader in full-cycle enterprise AI automation solutions, today announced a partnership with Tableau, the world's leading analytics platform, to enable Tableau users to leverage the power of dotData's AI Automation Capabilities. As a result of this partnership, Tableau users will be able to build customized predictive analytics solutions faster and more easily. By combining Tableau's data preparation and visualization capabilities with dotData's augmented insights discovery and predictive modeling capabilities, Tableau users can perform full-cycle predictive analysis from raw data through data preparation and insight discovery through AI-based predictions and actionable dashboards. This partnership empowers a new class of citizen data scientists through our low code and no-code platforms and allows users to discover deeper, more diverse, and more predictive insights. We are very excited about this partnership with Tableau, one of the world's most renowned analytics platforms. This partnership accelerates our vision to democratize augmented and predictive analysis for enterprise through AI automation. Ryohei Fujimaki, Ph.D., founder and CEO of dotData dotData automates the full-cycle AI/ML development process, including data and feature engineering, the most manual and time-consuming step in AI and ML development. dotData's proprietary AI technology automatically discovers hidden and multi-modal insights from relational, transactional, temporal, geo-locational, and text data. Business intelligence and analytics teams can leverage dotData's no-code AI/ML automation solution to make their reporting and dashboards more predictive and actionable. It offers a streamlined integration of automated feature discovery and automated machine learning (AutoML) and allows BI teams to develop full-cycle ML models from raw business data, without wiring code. About dotData dotData pioneered AI-Powered Feature Engineering to accelerate and automate the process of building AI/ML models, to drive higher business value for the enterprise. dotData's automated data science platform accelerates ROI and lowers the total cost of model development by automating the entire data science process that is at the heart of AI/ML. dotData ingests raw business data and uses an AI-based engine to automatically discover meaningful patterns and build ML-ready feature tables from relational, transactional, temporal, geo-locational, and text data. dotData's scalable, flexible platform enables data scientists to discover and evaluate outstanding AI features; and empowers business intelligence professionals to addAI/ML models to their BI stacks and predictive analytics applications quickly and easily. Fortune 500 organizations around the world use dotData to accelerate their ML and AI development to drive higher business value. dotData has been recognized as a leader by Forrester in the 2019 New Wave for AutoML platforms. dotData has also been recognized as the "best machine learning platform" for 2019 by the AI breakthrough awards; was named a CRN "emerging vendor to watch" in the big data space in 2019 and featured on CRN's 2020 and 2021 Big Data 100 list; and was named to CB Insights' Top 100 AI Startups in 2020.

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