The Future of Big Data: What to Expect in 2022?

DUMMYOXYLABS IO | January 29, 2022 | 13 views

Data management professionals getting the central stage in business, focus on ethics increasing, pressure on Big Tech affecting the landscape of the public web data industry. Thesewill be among the most prominent trends in the big data industry in 2022, according to a public data gathering solutions provider, Oxylabs. Their experts predict what to expect in the year ahead.

Growing Markets for External Data

Tomas Montvilas, Chief Commercial Officer at Oxylabs, says that more industries will discover the benefits of using external data in the upcoming year. He lists a few:

“The market of SaaS products that use external data to provide insights for their clients will grow further in 2022. The successful IPOs of companies like Semrush, Similarweb, Zoominfo and others are driving further investments in the field, and we are likely to see more stars emerging,” - Tomas says.

Another important area he sees for the web scraping industry’s growth is cybersecurity. Cyber threats are becoming more advanced and require new measures of defense. This is where web monitoring and scraping technologies come in. “Constant monitoring of both the public and dark web can help identify malicious sites and programs early. It can also help catch data leaks sooner by finding data sets when they go for sale on the dark web and recognize the actions of hacker groups. Meanwhile, proxies can help with email security by allowing you to scan emails from different IP addresses," he explains.

Data Management Role in Business Further Increasing

With the recent explosion in digitizing everything, data management and analytics have become pivotal in business. Data departments have been experiencing exponential growth during the past few years and the growth will continue well into 2022.

Gediminas Rickevicius, Vice President of Global Partnerships at Oxylabs, notes that the increasing importance of data departments can be easily illustrated by budgeting trends. According to several recent surveys Oxylabs conducted in the UK's finance and ecommerce industries, most data departments are expecting to increase their budgets (51% ecommerce, 43% financial services). Another trend Gediminas predicts for data departments will be the increasing outsourcing of automated public web data gathering tools. There will be several reasons for this. First of all, it is obvious that as companies become dependent on external data, manual data gathering processes are simply not sufficient. Another important factor is the current job market landscape.

“With “the great resignation” and lack of human resources being the dominant topics of 2021, it became even harder to find in-house professionals that could dedicate all their time to maintaining and adjusting web scraping infrastructure. Outsourcing this task allows optimizing resources and focusing on data analysis rather than acquisition.” - says Gediminas.


Pressure for Big Tech Could Affect Web Data Industry
Recent years have been marked by the growing pressure on Big Tech from governments around the world. 2022 will be no different; there will likely be a push for new regulations, especially around personal data and its acquisition and aggregation. According to Denas Grybauskas, Head of Legal at Oxylabs, the data gathering industry should not turn a blind eye to these processes. In light of government pressure, some big tech companies might already be in the process of restricting access to public web data, which could affect many businesses.

“Some companies are preparing for the old death tactic of pointing fingers. At least in accordance with the leaked emails, Meta (Facebook) is planning to do in terms of personal data leaks and data scraping companies - to shift the attention from leaks by stating that personal data got out in the wild not due to Facebook’s mistakes, but those of scrapers”, - Denas says.


Moving Towards Industry Self-regulation
When it comes to the strategic development of the data gathering industry, ethics and legal implications will remain the hot topics in 2022, pushing the industry to continue raising the standards. Ethical proxy acquisition and strong KYC practices will dominate the conversation, predicts Julius Cerniauskas, CEO of Oxylabs. He explains that, as with most new technologies, web scraping is developing faster than the regulations that could safeguard it from potential misuse cases. Therefore, the industry itself has to take the lead in developing self-regulation guidelines and standards for the proper use of technology.

“For several reasons, the issue is set to become more mainstream in 2022. First of all, as the largest industry players are setting the tone, smaller players are likely to follow. Secondly, brands that use proxy services are putting more emphasis on the nature of proxies too, as potential misuse could damage their reputation as well,” - says Julius.

Authors:  Julius Cerniauskas, CEO, Oxylabs, Tomas Montvilas, Chief Commercial Officer, Oxylabs, Gediminas Rickevičius, VP of Global Partnerships, Oxylabs, Denas Grybauskas, Head of Legal, Oxylabs

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DATA SCIENCE

Predictive Analytics: Implementation in Business Processes

Article | March 15, 2021

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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DATA SCIENCE

Embrace Corporate Performance Management to Enhance Your Business

Article | March 31, 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. Involve your team in the process of choosing a platform so they can give their opinion on how easy it is to use. A user-friendly and accessible CPM system will lead to successful training, deployment, and an instant ROI. Integrations with Existing Tools Make sure your new CPM system can integrate with your existing systems. You may want to import data from your ERP system, BI tools, and spreadsheets in real-time to save time and effort by copying and pasting data between applications. Manual data re-entry takes a lot of time and puts data at risk of being missed or entered incorrectly. This integration is crucial if your business utilizes a data warehouse to integrate data from multiple cloud tools. To do more analysis, you can also export data from the CPM platform into models and spreadsheets and presentations and word documents. Value to the Organization The cheapest option is not always the best one for your business. 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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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DATA SCIENCE

The Art of Developing a Successful Business Intelligence Strategy

Article | March 31, 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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ForMotiv Joins Guidewire Insurtech Vanguards Program to Bring Behavioral Data Science to Guidewire Carriers

ForMotiv | June 13, 2022

ForMotiv, a leading behavioral data science and intent scoring platform, announced that the company has joined Guidewire’s Insurtech Vanguards program, an initiative led by property and casualty (P&C) cloud platform provider Guidewire (NYSE: GWRE), to help insurers learn about the newest insurtechs and how to best leverage them. “We’re excited about this new partnership with Guidewire to continue to expand our reach in insurance,” said Bill Conners, CEO of ForMotiv. “We have worked hard for four-plus years establishing a footprint with leading carriers – and are excited to be a part of Guidewire’s Insurtech Vanguards program.” Insurtech Vanguards is a community of select startups and technology providers that are bringing novel solutions to the P&C industry. As part of the program, Guidewire provides strategic guidance to and advocates for the participating insurtechs, while connecting them with Guidewire’s P&C customers. “ForMotiv’s digital behavioral intelligence solution leverages machine learning to produce data analytics and scoring, enabling insurers to quickly view users’ clickstreams on their apps, which can accelerate underwriting and claims analytics. “We are thrilled to welcome ForMotiv and its innovative technology to our program so our mutual customers can raise the bar in leveraging their user data.” Laura Drabik, chief evangelist, Guidewire With its industry-leading behavioral data capture and intent scoring solution, ForMotiv works with carriers to help them analyze and monitor customer and agent digital behavior while accurately predicting user intent in real-time. ForMotiv provides robust behavioral reporting and analytics as well as a granular behavioral dataset leverageable across multiple departments. Armed with instant intent scoring and deterministic behavioral signaling, carriers can confidently predict buying intent, identify risk and nondisclosure, and expand their accelerated underwriting offerings to genuine users while dynamically intervening on applications requiring further qualification. ForMotiv’s real-time predictive behavioral analytics enable next-generation dynamic experiences, or SmartApps, that adapt to individual users based on their behavior. Its suite of products ranges from robust data capture and behavioral analytics to signaling and intent scoring. Carriers can leverage ForMotiv’s expansive behavioral dataset for both offline and real-time use cases. About ForMotiv ForMotiv is the only Behavioral Science Platform on the market that enables leading insurance companies to accurately and instantly predict user intent, in real-time. Our solution helps carriers improve digital customer & agent experiences, increase conversions, reduce risk & fraud, and more by analyzing users' digital body language (consisting of thousands of behavioral micro-expressions i.e. keystrokes, mouse movements, hesitation, corrections, copy/paste, and 150+ additional user engagement signals) while users engage with digital applications and claims forms to identify genuine, confused, risky or other behavior. Armed with real-time intent intelligence, ForMotiv carrier customers create next-generation dynamic experiences that adapt to individual users based on their intent. ForMotiv works with Marketing, Risk, Fraud, Data Science, Underwriting, Digital Strategy, and Claims teams.

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BIG DATA MANAGEMENT

IBM Aims to Capture Growing Market Opportunity for Data Observability with Databand.ai Acquisition

IBM | July 07, 2022

IBM today announced it has acquired Databand.ai, a leading provider of data observability software that helps organizations fix issues with their data, including errors, pipeline failures and poor quality — before it impacts their bottom-line. Today's news further strengthens IBM's software portfolio across data, AI and automation to address the full spectrum of observability and helps businesses ensure that trustworthy data is being put into the right hands of the right users at the right time. Databand.ai is IBM's fifth acquisition in 2022 as the company continues to bolster its hybrid cloud and AI skills and capabilities. IBM has acquired more than 25 companies since Arvind Krishna became CEO in April 2020. As the volume of data continues to grow at an unprecedented pace, organizations are struggling to manage the health and quality of their data sets, which is necessary to make better business decisions and gain a competitive advantage. A rapidly growing market opportunity, data observability is quickly emerging as a key solution for helping data teams and engineers better understand the health of data in their system and automatically identify, troubleshoot and resolve issues, like anomalies, breaking data changes or pipeline failures, in near real-time. According to Gartner, every year poor data quality costs organizations an average $12.9 million. To help mitigate this challenge, the data observability market is poised for strong growth.1 Data observability takes traditional data operations to the next level by using historical trends to compute statistics about data workloads and data pipelines directly at the source, determining if they are working, and pinpointing where any problems may exist. When combined with a full stack observability strategy, it can help IT teams quickly surface and resolve issues from infrastructure and applications to data and machine learning systems. Databand.ai's open and extendable approach allows data engineering teams to easily integrate and gain observability into their data infrastructure. This acquisition will unlock more resources for Databand.ai to expand its observability capabilities for broader integrations across more of the open source and commercial solutions that power the modern data stack. Enterprises will also have full flexibility in how to run Databand.ai, whether as-a-Service (SaaS) or a self-hosted software subscription. The acquisition of Databand.ai builds on IBM's research and development investments as well as strategic acquisitions in AI and automation. By using Databand.ai with IBM Observability by Instana APM and IBM Watson Studio, IBM is well-positioned to address the full spectrum of observability across IT operations. For example, Databand.ai capabilities can alert data teams and engineers when the data they are using to fuel an analytics system is incomplete or missing. In common cases where data originates from an enterprise application, Instana can then help users quickly explain exactly where the missing data originated from and why an application service is failing. Together, Databand.ai and IBM Instana provide a more complete and explainable view of the entire application infrastructure and data platform system, which can help organizations prevent lost revenue and reputation. "Our clients are data-driven enterprises who rely on high-quality, trustworthy data to power their mission-critical processes. When they don't have access to the data they need in any given moment, their business can grind to a halt. "With the addition of Databand.ai, IBM offers the most comprehensive set of observability capabilities for IT across applications, data and machine learning, and is continuing to provide our clients and partners with the technology they need to deliver trustworthy data and AI at scale." Daniel Hernandez, General Manager for Data and AI, IBM Data observability solutions are also a key part of an organization's broader data strategy and architecture. The acquisition of Databand.ai further extends IBM's existing data fabric solution by helping ensure that the most accurate and trustworthy data is being put into the right hands at the right time – no matter where it resides. "You can't protect what you can't see, and when the data platform is ineffective, everyone is impacted –including customers," said Josh Benamram, Co-Founder and CEO, Databand.ai. "That's why global brands such as FanDuel, Agoda and Trax Retail already rely on Databand.ai to remove bad data surprises by detecting and resolving them before they create costly business impacts. Joining IBM will help us scale our software and significantly accelerate our ability to meet the evolving needs of enterprise clients." Headquartered in Tel Aviv, Israel, Databand.ai employees will join IBM Data and AI, further building on IBM's growing portfolio of Data and AI products, including its IBM Watson capabilities and IBM Cloud Pak for Data. Financial details of the deal were not disclosed. The acquisition closed on June 27, 2022. About Databand.ai Databand.ai is a product-driven technology company that provides a proactive data observability platform, which empowers data engineering teams to deliver reliable and trustworthy data. Databand.ai removes bad data surprises such as data incompleteness, anomalies, and breaking data changes by detecting and resolving issues before they create costly business impacts. Databand.ai's proactive approach ties into all stages of your data pipelines, beginning with your source data, through ingestion, transformation, and data access. Databand.ai serves organizations throughout the globe, including some of the world's largest companies in entertainment, technology, and communications. Our focus is on enabling customers to extract the maximum value from their strategic data investments. Databand.ai is backed by leading VCs Accel, Blumberg Capital, Lerer Hippeau, Differential Ventures, Ubiquity Ventures, Bessemer Venture Partners, Hyperwise, and F2. About IBM IBM is a leading global hybrid cloud and AI, and business services provider, helping clients in more than 175 countries capitalize on insights from their data, streamline business processes, reduce costs and gain the competitive edge in their industries. Nearly 3,800 government and corporate entities in critical infrastructure areas such as financial services, telecommunications and healthcare rely on IBM's hybrid cloud platform and Red Hat OpenShift to affect their digital transformations quickly, efficiently, and securely. IBM's breakthrough innovations in AI, quantum computing, industry-specific cloud solutions and business services deliver open and flexible options to our clients. All of this is backed by IBM's legendary commitment to trust, transparency, responsibility, inclusivity, and service.

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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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DATA SCIENCE

ForMotiv Joins Guidewire Insurtech Vanguards Program to Bring Behavioral Data Science to Guidewire Carriers

ForMotiv | June 13, 2022

ForMotiv, a leading behavioral data science and intent scoring platform, announced that the company has joined Guidewire’s Insurtech Vanguards program, an initiative led by property and casualty (P&C) cloud platform provider Guidewire (NYSE: GWRE), to help insurers learn about the newest insurtechs and how to best leverage them. “We’re excited about this new partnership with Guidewire to continue to expand our reach in insurance,” said Bill Conners, CEO of ForMotiv. “We have worked hard for four-plus years establishing a footprint with leading carriers – and are excited to be a part of Guidewire’s Insurtech Vanguards program.” Insurtech Vanguards is a community of select startups and technology providers that are bringing novel solutions to the P&C industry. As part of the program, Guidewire provides strategic guidance to and advocates for the participating insurtechs, while connecting them with Guidewire’s P&C customers. “ForMotiv’s digital behavioral intelligence solution leverages machine learning to produce data analytics and scoring, enabling insurers to quickly view users’ clickstreams on their apps, which can accelerate underwriting and claims analytics. “We are thrilled to welcome ForMotiv and its innovative technology to our program so our mutual customers can raise the bar in leveraging their user data.” Laura Drabik, chief evangelist, Guidewire With its industry-leading behavioral data capture and intent scoring solution, ForMotiv works with carriers to help them analyze and monitor customer and agent digital behavior while accurately predicting user intent in real-time. ForMotiv provides robust behavioral reporting and analytics as well as a granular behavioral dataset leverageable across multiple departments. Armed with instant intent scoring and deterministic behavioral signaling, carriers can confidently predict buying intent, identify risk and nondisclosure, and expand their accelerated underwriting offerings to genuine users while dynamically intervening on applications requiring further qualification. ForMotiv’s real-time predictive behavioral analytics enable next-generation dynamic experiences, or SmartApps, that adapt to individual users based on their behavior. Its suite of products ranges from robust data capture and behavioral analytics to signaling and intent scoring. Carriers can leverage ForMotiv’s expansive behavioral dataset for both offline and real-time use cases. About ForMotiv ForMotiv is the only Behavioral Science Platform on the market that enables leading insurance companies to accurately and instantly predict user intent, in real-time. Our solution helps carriers improve digital customer & agent experiences, increase conversions, reduce risk & fraud, and more by analyzing users' digital body language (consisting of thousands of behavioral micro-expressions i.e. keystrokes, mouse movements, hesitation, corrections, copy/paste, and 150+ additional user engagement signals) while users engage with digital applications and claims forms to identify genuine, confused, risky or other behavior. Armed with real-time intent intelligence, ForMotiv carrier customers create next-generation dynamic experiences that adapt to individual users based on their intent. ForMotiv works with Marketing, Risk, Fraud, Data Science, Underwriting, Digital Strategy, and Claims teams.

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BIG DATA MANAGEMENT

IBM Aims to Capture Growing Market Opportunity for Data Observability with Databand.ai Acquisition

IBM | July 07, 2022

IBM today announced it has acquired Databand.ai, a leading provider of data observability software that helps organizations fix issues with their data, including errors, pipeline failures and poor quality — before it impacts their bottom-line. Today's news further strengthens IBM's software portfolio across data, AI and automation to address the full spectrum of observability and helps businesses ensure that trustworthy data is being put into the right hands of the right users at the right time. Databand.ai is IBM's fifth acquisition in 2022 as the company continues to bolster its hybrid cloud and AI skills and capabilities. IBM has acquired more than 25 companies since Arvind Krishna became CEO in April 2020. As the volume of data continues to grow at an unprecedented pace, organizations are struggling to manage the health and quality of their data sets, which is necessary to make better business decisions and gain a competitive advantage. A rapidly growing market opportunity, data observability is quickly emerging as a key solution for helping data teams and engineers better understand the health of data in their system and automatically identify, troubleshoot and resolve issues, like anomalies, breaking data changes or pipeline failures, in near real-time. According to Gartner, every year poor data quality costs organizations an average $12.9 million. To help mitigate this challenge, the data observability market is poised for strong growth.1 Data observability takes traditional data operations to the next level by using historical trends to compute statistics about data workloads and data pipelines directly at the source, determining if they are working, and pinpointing where any problems may exist. When combined with a full stack observability strategy, it can help IT teams quickly surface and resolve issues from infrastructure and applications to data and machine learning systems. Databand.ai's open and extendable approach allows data engineering teams to easily integrate and gain observability into their data infrastructure. This acquisition will unlock more resources for Databand.ai to expand its observability capabilities for broader integrations across more of the open source and commercial solutions that power the modern data stack. Enterprises will also have full flexibility in how to run Databand.ai, whether as-a-Service (SaaS) or a self-hosted software subscription. The acquisition of Databand.ai builds on IBM's research and development investments as well as strategic acquisitions in AI and automation. By using Databand.ai with IBM Observability by Instana APM and IBM Watson Studio, IBM is well-positioned to address the full spectrum of observability across IT operations. For example, Databand.ai capabilities can alert data teams and engineers when the data they are using to fuel an analytics system is incomplete or missing. In common cases where data originates from an enterprise application, Instana can then help users quickly explain exactly where the missing data originated from and why an application service is failing. Together, Databand.ai and IBM Instana provide a more complete and explainable view of the entire application infrastructure and data platform system, which can help organizations prevent lost revenue and reputation. "Our clients are data-driven enterprises who rely on high-quality, trustworthy data to power their mission-critical processes. When they don't have access to the data they need in any given moment, their business can grind to a halt. "With the addition of Databand.ai, IBM offers the most comprehensive set of observability capabilities for IT across applications, data and machine learning, and is continuing to provide our clients and partners with the technology they need to deliver trustworthy data and AI at scale." Daniel Hernandez, General Manager for Data and AI, IBM Data observability solutions are also a key part of an organization's broader data strategy and architecture. The acquisition of Databand.ai further extends IBM's existing data fabric solution by helping ensure that the most accurate and trustworthy data is being put into the right hands at the right time – no matter where it resides. "You can't protect what you can't see, and when the data platform is ineffective, everyone is impacted –including customers," said Josh Benamram, Co-Founder and CEO, Databand.ai. "That's why global brands such as FanDuel, Agoda and Trax Retail already rely on Databand.ai to remove bad data surprises by detecting and resolving them before they create costly business impacts. Joining IBM will help us scale our software and significantly accelerate our ability to meet the evolving needs of enterprise clients." Headquartered in Tel Aviv, Israel, Databand.ai employees will join IBM Data and AI, further building on IBM's growing portfolio of Data and AI products, including its IBM Watson capabilities and IBM Cloud Pak for Data. Financial details of the deal were not disclosed. The acquisition closed on June 27, 2022. About Databand.ai Databand.ai is a product-driven technology company that provides a proactive data observability platform, which empowers data engineering teams to deliver reliable and trustworthy data. Databand.ai removes bad data surprises such as data incompleteness, anomalies, and breaking data changes by detecting and resolving issues before they create costly business impacts. Databand.ai's proactive approach ties into all stages of your data pipelines, beginning with your source data, through ingestion, transformation, and data access. Databand.ai serves organizations throughout the globe, including some of the world's largest companies in entertainment, technology, and communications. Our focus is on enabling customers to extract the maximum value from their strategic data investments. Databand.ai is backed by leading VCs Accel, Blumberg Capital, Lerer Hippeau, Differential Ventures, Ubiquity Ventures, Bessemer Venture Partners, Hyperwise, and F2. About IBM IBM is a leading global hybrid cloud and AI, and business services provider, helping clients in more than 175 countries capitalize on insights from their data, streamline business processes, reduce costs and gain the competitive edge in their industries. Nearly 3,800 government and corporate entities in critical infrastructure areas such as financial services, telecommunications and healthcare rely on IBM's hybrid cloud platform and Red Hat OpenShift to affect their digital transformations quickly, efficiently, and securely. IBM's breakthrough innovations in AI, quantum computing, industry-specific cloud solutions and business services deliver open and flexible options to our clients. All of this is backed by IBM's legendary commitment to trust, transparency, responsibility, inclusivity, and service.

Read More

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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