InetSoft Product Information: Visual Analytics Tools

Looking for visual analytics tools? InetSoft offers free and commercial options. The commercial app is a web-based BI platform that can mashup disparate data sources. The free one is also web-based and you can simply upload your data file or spreadsheet and begin visualizing it. Read articles below about InetSoft's BI and analytics solution.

Good IT Analytics Tool - With a good IT analytics tool, you can drill down. You can do all sorts of things, but the important thing is, #1, looking at it from the business perspective, in terms of really how you are delivering that service, because that’s why you exist in a sense. And then, what’s the history of doing it, and how you are going to do that in the future, and then, it becomes a vehicle for conversation between the business and IT and the data warehousing people to understand where you are going and to understand your options. We can do it this way or that way, and here’s the cost involvement. We can look at all of those details, and we can figure out what’s the best way to move forward. Think about how this would impact the relationship between IT and businesses. Getting back to changes the culture between them because in the beginning, that’s “Oh, my God, this is broken, we will fix it as soon as we can.” This tends to be not a great relationship with the business side because the business side tends to look at IT, “The system is down, or it’s going down for a day.” Whereas when you arm them with this type of information, they become proactive, and now, they are having a business conversation with the other side of the company saying, “Look, here is what we are doing, here is how we are serving you, which is great, but here is what we need to plan for this.” That’s a completely different culture and conversation...

 

Healthcare Analytics in the Cloud - Now Jim, there are also sensitive data and privacy issues that impact healthcare analytics in the cloud. There are regulations and potential audits involved. How do you manage to protect the data even as you have to go through a lot of these cleansing and joining steps across different formats types and even sources of data? Jim: So there's actually lots of encryption involved at various places and along the way in a pipeline, and so we do keep the data in our archives in an encrypted fashion. When we move data along from one part of the pipeline to another we keep control of the environment by having really good controls on each of the stages. This is where Vertica actually helps us out quite a bit because we have the ability to nicely go in and assign roles to go there and put in some protections, and that was one of the things that we were looking for in a data store which is to have some ability to have controls over the data. So as it moves along the pipeline we keep these controls in place, and as things move along everywhere where we have an attack surface. We have to keep the data either protected by network access or by encryption, and the infrastructure that we build has to deal with that...

Healthcare Analytics and Data Science - So Jim what's been the common thread from cyber security to healthcare in terms of analytics and data science? What are the technical and other hurdles that are common between them that allowed you to make that leap? Jim: So the ability to detect various kinds of events in your data streams, and I'm going to use that term somewhat loosely, detecting events and data streams and then combining them together into patterns is something that happens in cyber security as well as fraud, waste and abuse. So whether you do that by looking at a series of bad claims or a series of services that shouldn't have occurred in sequence is a simple idea. We can't pull a tooth from a baby. That's a great example of a very simple idea, that sequence of events shouldn't be happening in healthcare, and you can see in a series of network activity the same kinds of events in principle would occur as a series of patterns and as you get good at detecting those kinds of patterns you can apply the same skill and learning about how to deal with the 3Vs of big data, the variety and the velocity and whatnot to really harness this in very high speed fashion. Abhishek: Right, now not that the cyber security issues aren't important, but are the stakes higher for healthcare, or is the market larger? What's at stake when you point your technology and expertise at the healthcare sector...

Healthcare Data Science Platform - Okay, so we have a sense of your healthcare data science platform. The challenges, let's talk a little bit about how you now take this and apply it to the problem that your healthcare sector clients have. What are you doing in terms of being able to create recommendations to make analysis available, and the speed, how does that factor. So I guess I'm trying to get to what are the requirements that people have that exploit the technology that you put in place when it comes at being actionable. Jim: Sure, so let's start from the user's perspective, and then I'll work a little bit backwards more towards the technology. From a user perspective, people involved in, any an intelligence environment where you're trying to figure out whether some behavior was good or bad. They're just inundated with lots of little questions, and the longer that those little questions take to answer the harder their job is to get it done. So what we provide by leveraging the column store technology in Vertica is the ability to rapidly cycle through data and do measurements in an interactive fashion and the column stores provide very, very fast answers, and so we can let people filter data down and get new metric calculations on the fly, and this allows them to essentially self serve by getting answers to questions. So we allow the users to filter data, and as they filter their data they get new measurements, and new metrics coming back on a very, very rapid fashion. That allows them to answer questions very, very fast, and they don't lose the context...

Healthcare Machine Learning Analytics - We're here to learn how a leading healthcare analytics solution provider and OEM partner of InetSoft's delivers actionable investigative intelligence for healthcare fraud detection using machine learning analytics. As an analytics industry professional and a social media producer I speak with a lot of consumers of technology to uncover the business value from innovative uses of the latest IT systems and processes, and among the most exciting and interesting intersections of commerce and technology today is the way that machine learning analytics identifies and quantifies risk from massive and previously inaccessible data volumes. These machine learning case studies have expanded far and wide to include many vertical industries. Healthcare is the focus of today's discussion, with trillions of dollars involved per year in the United States alone, it is no less than imperative to bring improved efficiency, productivity, quality and security to the vast healthcare ecosystem of payers, providers, patients and consumers. We're going to learn today how this company uses advanced machine learning analytics platforms and methods to identify risk across complex healthcare activities. The payoff is delivery of faster, easier and more actionable findings to among other things advance governance and oversight to often dispersed and unwieldy and even hard to track transactions. To hear how this company addresses massive data volume challenges, to identify risk in healthcare networks and deliver answers instantly to generate more revenue, save wasted costs, and improve patient outcomes, we're pleased to be welcoming to our webcast their CTO. Welcome, Jim...

 

Home Sales Industry Uses Analytics to Improve Performance - The real estate industry has gone through numerous changes in the past decade. Just like in other industries, new technology has opened new doors, and people are able to do their jobs on a more professional level. Using data intelligence, predictive analytics, and improved machine learning, the real estate industry is flourishing and making fast improvements. But, how exactly is the real estate industry using analytics to improve their management system? The truth is, they were able to implement analytics into different sectors of their business processes to make them faster, more efficient, and better. Let's break it down together and see how analytics is helping the real estate industry improve its management system...

Hotel Analytics with InetSoft - In the dynamic world of hospitality, where every detail counts, InetSoft's hotel performance dashboard emerges as a beacon of insight and clarity. Crafted with the precision and expertise characteristic of InetSoft's innovative solutions, this dashboard is a confluence of intuitive design and powerful analytics, tailored specifically for the hospitality industry....

Hotels Use Big Data Analytics to Improve Their Performance - Big data as a concept has earned tremendous popularity over the last few years, but most entrepreneurs still believe that it is strictly reserved for international corporations and high-end IT projects. The truth is exactly the opposite since data analytics is already capable of helping businesses of all sizes thrive and grow long-term. The hospitality industry is not an exception to this rule as top-performing hotels already utilize the power of big data to boost performance and maximize the profit. You are probably wondering: How is that possible...

How AI In Analytics Is Improving Scientific Reporting - More than 2.5 quintillion bytes of data are produced every minute. 250,000 chemical lab reports are generated every day. How many hours are wasted in this data analysis including turning data into easily digestible reports? Scientists use forty percent of their working time preparing reports rather than trying to come up with discoveries. Artificial intelligence can make a significant impact in this area. Up to 90% of reports in laboratories will be produced through AI by 2027. Even now, it is possible to perform 60% to 70% of these using technology. This includes data analysis, table generation, or graphing. For instance, the chemical analysis that once would require a couple of days or even weeks can be done in minutes using machine learning algorithms. Integrating technology, which involves the application of artificial intelligence, Smodin's AI chemistry solver, data analytics software such as InetSoft or Tableau, plus virtual labs such as Labster, help data be processed and analyzed more effectively. 90% off information developed for scientific papers will be produced with AI by 2027, increasing research productivity...

How Analytical Dashboards Help Situation Awareness - Analytical dashboards are powerful tools that help organizations gain a comprehensive understanding of their performance, operations, and market conditions. They provide a single source of truth for critical information, and allow decision makers to quickly and easily identify trends, patterns, and opportunities for improvement. One of the most important benefits of analytical dashboards is their ability to enhance situation awareness. Situation awareness refers to the perception of the environment, identification of patterns and trends, and projection of future events. It is a critical aspect of decision making, as it allows individuals and organizations to anticipate and respond to changes in real-time. In today's fast-paced and complex business environment, having an accurate understanding of the situation is essential for success. Analytical dashboards provide a visual representation of data, making it easier to understand complex information and identify trends and patterns. This is especially useful for large organizations, where data is often siloed and difficult to access. By bringing all relevant data together in one place, analytical dashboards provide a holistic view of operations, enabling decision makers to identify trends and patterns that would otherwise be overlooked...

How Analytics Software Facilitates the Discovery Process - Could you talk about how the analytics software facilitates the discovery process? You say using visual analysis software, you can facilitate that process, and I understand that, for example, you will connect a data set to an application that you purchase, and you bring in certain columns or certain fields of course and then basically can you run a preview then apply different algorithms and kind of get different visualizations? Do you look for the spikes in the trend lines, or the red areas or the green areas of a heat map? Flaherty: I think the way things used to be done with someone with tremendous experience and education in this area is he would start by doing all that work himself. I think as the visual analysis tools have gotten better, we can either bring in different types of people with less experience or make that expert much more productive. And one of the ways that we can do that is to not start off with a whole bunch of manual data transformations that could take the next two months putting together. Let the analytics software automate the data transformations. A lot of the visualization tools now have the capability to do some level of automated data transformations to get that started. So you know with the typical example would be there are always dates in a file. You can't use a date to predict anything, but you can use a time interval. So a lot of the software will automatically do that conversion...

How BI & Data Analytics Can Boost Efficiency in The Construction Sector - As an essential and ever-expanding sector, the construction industry commands collaboration, communication, and razor-sharp operational vision. The problem is that with so many sites and personnel to manage at any one time, many construction organizations suffer from a level of fragmentation that leads to financial and logistical inefficiency. To ensure that a building project is safe and successful, adhering to a colossal amount of regulations and red tape is mandatory....

How to Create a Cluster Analysis Dashboard - This document will explain how to create a cluster analysis dashboard such as the one below using dashboard creation software from InetSoft. A working version of this dashboard can be found on the InetSoft Gallery. This dashboard is composed primarily of simple charts that display cluster data that has been produced by machine learning techniques. (Note that this document does not cover the use of machine learning software, but focuses on the creation of the dashboard from processed data.) A cluster analysis dashboard is a data visualization tool used to analyze and present the results of cluster analysis. Cluster analysis is a statistical technique that groups similar data points or objects into clusters based on their characteristics or similarities. It is commonly used in various fields such as data mining, pattern recognition, customer segmentation, and market research...

How Do Auditors Use Analytic Dashboards? - Analytic dashboards are powerful tools that auditors can use to monitor, analyze and visualize large amounts of financial data. Here are some ways auditors can use analytic dashboards: Data Visualization: Analytic dashboards can help auditors visualize financial data in an easily digestible manner. By using graphs, charts, and tables, auditors can quickly identify trends and patterns in financial data. This can help auditors identify potential areas of risk, such as unexpected spikes or dips in revenue or expenses. Trend Analysis: Analytic dashboards can help auditors analyze trends in financial data over time. For example, auditors can compare financial data year-over-year or quarter-over-quarter to identify changes or anomalies in financial performance. Performance Monitoring: Analytic dashboards can help auditors monitor the financial performance of individual business units or departments within an organization. By tracking key performance indicators (KPIs) such as revenue, profit margins, and expenses, auditors can identify areas of the business that are underperforming or overperforming...

How Do Department Store Buyers Use Predictive Analytics? - Department store buyers rely on predictive analytics to make informed decisions regarding merchandise selection, pricing, inventory management, and marketing strategies. Predictive analytics involves analyzing historical data, current trends, and various influencing factors to forecast future outcomes. Here's how department store buyers typically leverage predictive analytics: Demand Forecasting: Predictive analytics helps buyers anticipate consumer demand for different products. By analyzing past sales data, seasonal trends, demographic information, and economic indicators, buyers can forecast which products will be popular in the upcoming seasons. This allows them to make informed decisions about which items to stock up on and which ones to reduce or eliminate from inventory. Inventory Management: Effective inventory management is crucial for department stores to avoid stockouts and overstock situations. Predictive analytics can help buyers optimize inventory levels by predicting when and how much of each product to order. By considering factors like lead times, supplier reliability, and sales forecasts, buyers can ensure that they have the right amount of inventory on hand to meet customer demand without tying up excess capital in unsold merchandise...

How Do Grocery Chains Use Customer Purchase Analytics? - Grocery chains extensively leverage customer purchase analytics to enhance their operations, optimize inventory management, and improve overall customer satisfaction. Here's a detailed exploration of how grocery chains utilize customer purchase analytics: Inventory Management: Demand Forecasting: By analyzing historical purchase data, grocery chains can predict future demand for specific products. This enables them to optimize inventory levels, reduce stockouts, and minimize overstock situations. Seasonal Variations: Analytics help identify seasonal trends and variations in customer preferences. Grocery chains can adjust their inventory accordingly, ensuring they have the right products in stock during peak seasons. Assortment Planning: Product Assortment: Understanding which products are frequently purchased together allows grocery chains to optimize product placement and create effective product bundles. This, in turn, can increase sales and customer satisfaction. New Product Introductions: Analytics help assess the success of new product launches by tracking initial sales and customer response. This information guides decisions on whether to expand or modify the product assortment. Promotion Effectiveness: Promotion Optimization: Grocery chains use analytics to evaluate the impact of promotions on sales. This includes understanding which promotions are most effective, what discounts resonate with customers, and how promotions influence overall purchasing behavior. Customer Segmentation: Analyzing customer data helps in creating targeted promotions for specific customer segments. This personalization can significantly improve the effectiveness of promotional campaigns...

How Does an Analyst at a Clinical Lab Use an Ad Hoc Report Analysis Solution? - As an analyst at a clinical lab, leveraging an ad hoc report analysis solution can provide you with the flexibility and agility to extract valuable insights from your data. Here's a step-by-step guide on how a clinical lab analyst can use an ad hoc report analysis solution: Define Objectives and Questions: Clearly define the objectives of your analysis and the specific questions you aim to answer. This will guide your selection of data elements and parameters. Access the Ad Hoc Reporting Tool: Log in to the ad hoc reporting tool provided by your clinical lab's information system. This tool should allow users to create custom reports based on their specific requirements. Select Data Sources: Identify and select the relevant data sources within the system. This may include patient records, test results, sample information, and other relevant datasets. Choose Data Fields: Pick the specific data fields and variables that are pertinent to your analysis. Depending on your objectives, this could involve patient demographics, test types, time periods, and any other relevant parameters...

How Does a News & Media Analytics Lead Use Business Analysis Tools? - A News & Media Analytics Lead plays a crucial role in leveraging business analysis tools to extract actionable insights from data within the news and media industry. Here's a detailed exploration of how such a professional may use business analysis tools: Audience Engagement Analysis: Tool Usage: Utilizes analytics tools to analyze user engagement metrics, such as click-through rates, time spent on articles, and social media interactions. Objective: Understands audience behavior to optimize content placement, improve user experience, and tailor content to meet audience preferences. Content Performance Monitoring: Tool Usage: Monitors content performance through analytics tools that track page views, shares, and comments. Objective: Identifies high-performing content, enabling data-driven decision-making for content creation, curation, and promotion strategies. Social Media Analytics: Tool Usage: Leverages social media analytics tools to track the reach, engagement, and sentiment of news content on various platforms. Objective: Understands social media trends, gauges public sentiment, and adjusts content strategy to maximize social media impact...

How Does a Physicians' Office Manager Use Business Analysis Tools? - A Physician's Office Manager plays a crucial role in ensuring the smooth operation of a medical practice. They can leverage various business analysis tools to enhance efficiency, improve patient care, and drive financial success. Here's a detailed exploration of how a Physician's Office Manager can use business analysis tools: Revenue Cycle Analysis: Purpose: This tool helps in tracking the financial performance of the practice by analyzing the revenue generated through patient visits, services, and procedures. Utilization: The Office Manager can use revenue cycle analysis to identify trends in billing, collections, and reimbursements. This can help in optimizing billing processes, reducing claim denials, and improving cash flow. Key Performance Indicators (KPIs): Purpose: KPIs are essential metrics that measure various aspects of the practice's performance, such as patient satisfaction, appointment scheduling, no-show rates, and provider productivity...

How Does Upper Management Use Analytical Tools? - Upper management utilizes analytical tools in various ways to support decision-making and gain valuable insights into their organization's performance. Here are some common use cases for analytical tools at the upper management level: Strategic Planning: Analytical tools provide upper management with data-driven insights to support strategic planning and decision-making processes. They can analyze historical data, market trends, and internal performance metrics to identify growth opportunities, assess risks, and develop long-term strategies. Performance Monitoring: Analytical tools help upper management monitor and track key performance indicators (KPIs) and metrics across different departments and business units. They can quickly assess performance against targets, identify areas of improvement or underperformance, and take necessary actions to address issues. Financial Analysis: Analytical tools enable upper management to perform in-depth financial analysis. They can analyze financial statements, cash flow, profitability, and cost structures to understand the financial health of the organization, identify trends, and make informed decisions related to budgeting, investments, and resource allocation. Operational Efficiency: Analytical tools allow upper management to evaluate and optimize operational efficiency. They can analyze process flows, identify bottlenecks, and make data-driven decisions to streamline operations, reduce costs, and improve productivity...

How Energy Companies Use Analytics - Learning how to generate fire followed by agriculture, lastly industrial revolution & domination by fossil fuels are some instances that enabled the early men to apprehend the notion of energy. Starting from that era to the contemporary one, we have tapped into the resources of both renewable & non-renewable energy sources like solar, wind, oil, hydraulic, gas. As a matter of fact our energy systems in the current times are more flexible, progressive and digital. Given these important facts, analytics which is the accumulation of statistics or data is enabling organizations to make better decisions at the correct time. For instance, analytics can greatly enhance drilling & exploration. It can provide greater insights into supply & logistics chain, etc. For sales forecasting, risk assessment and customer targeting, analytics proves to be highly useful. When you are an online merchant or vendor, you must come in contact with a reputed WordPress development company that will help you in benefitting from predictive analytics...

How Energy Industry Analytics Software Is Used - Analytics in the energy industry refers to all the processes that are involved in gathering electrical data. This also consists of the use of software for assisting the energy suppliers in the analysis, supervision, and optimization of energy-related KPIs. Some of the common energy KPIs are production distribution, production costs, consumption, etc. An energy dashboard refers to a reporting tool used to monitor the KPIs of the energy in real-time while using an interactive interface. It is beneficial for energy suppliers as it helps them cover the market demands that are always changing, and also analyzes and optimizes the cost of production, which will lead to an increase in overall profitability. While different types of energy powers the industry, our everyday lives, and businesses. There is energy analytics software that can harness the nuances that come with the data that the different energy sector produces and then use it more beneficially. This allows for the use of an energy dashboard, which we can take advantage of improve the margin for-profit and also manipulate large scale industry trends. Data analytics is essential to the energy industry as it gives an insight into the different dimensions of the energy sector. It is a commodity and should, therefore, be treated in the same way...

How Export Companies Use Analytics - In International trade, there are several trade blocks and International blocks present. These trade blocks often have specific rules and regulations that can change the statistics and the overall profits and losses of exports. To avoid these specific tools that can be termed analytics, keep track of the regular up-downs of international trade. How Do you Analyze Export Data? Trade Statistics Trade statistics is all about understanding the pattern of trade, trade policies of a particular country, and specific trade blocks in general. It is keeping track of various policies and programs introduced by the Trade blocks all over the world. It requires specific tools to analyze the strategic position of export conditions of the world. Individual companies can use these to diversify trade and adjust the sail towards them. Tax and tariffs in the market and market requirements are essential points to focus on. Market requirements tilt a country's production towards those goods, and tariffs help the market requirements stabilize. Using different market analysis maps, it is possible to understand these trends beforehand for a particular firm...

How the Food Industry Uses Analytics - The food industry is a highly competitive one with over a million restaurants in the United States alone. Because of this there is a huge emphasis on innovation and adaptation. The biggest adaptation has to be turning to food eCommerce, especially as countries go in and out of quarantine. Since word of mouth doesn't exactly work as well online, that's where data science and analytics come to the rescue. The average customer has certain needs that need to be met in order to have a positive experience in a restaurant or when ordering online. Things like on-time delivery and guaranteeing fresh produce is ethically harvested are important management goals. Here we will explore some of the ways Big Data and the uses of analytics can help food industry managers excel...

How Important Is It to Your Organization to Have Analytics? - How important is it to your organization to have each of the following analytics and metrics related analyst capabilities. The choices were search for existing data, analytics and metrics. One of the reasons your projects might fail is if the data is not getting prepped and delivered quickly or properly. Two-thirds of the organizations surveyed spend more time prepping the data than actually analyzing it. If your customers have not tackled the data preparation process, it's going to reflect poorly on your BI solution because people are going to just say, I don’t have the information I need, because they are spending too much time in the data preparation process. Does that also include the activities of exporting data from databases and bringing into the spreadsheet because that’s where the person is going to analyze it? Waiting for data is one of them, believe it or not. Preparing the data for analysis, reviewing it for consistency and then the others are what they are actually doing once they are analyzing. But add up those pieces, waiting for data, preparing it, reviewing it for quality and consistency, that’s 69% of the organizations do most of their time...

How to Apply Predictive Analytics in Business - Today’s topic is How to Apply Predictive Analytics in Business. These economic times require changes in how businesses manage, in particular, the use of critical information to optimize decision making and other business processes. But getting this information is not simple. It can’t be found in historical reports but instead requires a forward looking analytics that can help gain insight on potential outcomes from existing operations. Then how do you build predictive analytics into your management efforts and what are the issues you need to be aware of this? This podcast is designed to provide prospective on you use predictive analytics actively and as an ingredient to your success. We’re talking today about analytics, but we’re talking in the context of an entire new environment of competitive and global business pressures. How do those two relate? Well the current economic environment is not a pretty one and second of all, the pace of business is increasing quite dramatically. So as we look at those two facts, one of the things that is pretty obvious is that if we’re going to make improvements in this type of economic environment we’re going to have to have information, and we’re going to have to make decisions on a very timely basis. Both of these realizations correlate together and make it a pretty complex environment for organizations...

How to Implement Business Analytics - We are here today to talk about how to implement business analytics. You know through the course of my career here at InetSoft I have been around many analytics projects and certainly have developed some of my own opinions as to why it matters but at the end of the day my opinion probably doesn’t matter that much to you. What I think matters more are the opinions and more importantly the experiences of your peers who are using analytics effectively, in some cases, to drive double digit growth in major performance metrics like profitability and cash flow. Hopefully, today the data that I show will give you some ideas as how you might build a stronger analytical environment within in your own organizations and navigate the waters of Big Data as the title suggests and produce some real results at the end of the day. So here I’ve put together a fairly straight forward agenda for the presentation today. The world of business analytics is not surprisingly somewhat in flux. I want to start by talking through a couple of the high level data points that help show the current state of analytics. We will really highlight the value of analytics from the data side to the front end delivery of insights. And finally, I want to close things up by talking through a couple of recommendations and takeaways that will hopefully help you inform your own journey or voyage of analytics as it were...

 

How Loan Originators Use Data Analytics - Fintech companies and NBFCs are leveraging cutting-edge predictive data science to revolutionize the lending process. With automated risk analysis and lightning-fast loan disbursements, these innovative organizations can now make informed decisions in record time, resulting in reduced risks for all involved. With modern lending software and apps, borrowers can quickly apply for loans from anywhere. All it takes is a few taps on their phone. The algorithm powering this service uses predictive data analytics to determine if applicants qualify according to pre-set criteria. Once approved, funds are credited almost instantly, so you'll never miss out when times call for swift access to cash. Let's take a look at how predictive analytics is assisting loan firms in making better decisions...

How Machine Learning Is Changing Business Analytics - Thank you all for joining us today for a discussion about how machine learning is changing business analytics. We have four major points that we want to discuss today. The first one being the importance of data science and data scientist and bringing machine learning into organizations. The second one being we've all heard of the V's of big data. and we know that one is velocity, and we know that there's a lot of streaming data out there now. This is going to be a big part of organizational strategies moving forward. Point three, how an organization can keep creativity with machine learning. We have all of these different tools to choose from today, all of this different data, but we deal with regulation, we deal with documentation, we deal with productionizing code. How do we keep infusing creativity into the machine learning workflow within an organization? Then, we've also heard a lot about the citizen data scientist recently and just in general more and more people in organizations wanting to get involved with analytics and machine learning, so that's point four. Okay, so we're going to start our discussion here. Is any of this really new? Is machine learning new? Is data science new? To me this is resounding no. In fact, machine learning has been studied at least since the 1950s, maybe before. Data science you could say goes back to John Tukey's 1962 Future of Data Analysis Paper. There's a great recent paper by David Donoho out of Stanford that talks about 50 years of history of data science, and I urge you to read that. We have a link to that at the end. We're seeing machine learning in organizations now. This isn't coming out of the blue. This has a long history, and so we wanted to spend a little bit of time here. One good thing to do at first is of course to define machine learning, and that's really tricky. I think for better or for worse, in a certain sense machine learning has taken on sort of a pop culture, meaning it's just the rebranding of analytics or data mining...

How Maritime Shipping Companies Use Analytics - The world depends on maritime shipping now more than ever. The maritime industry is perpetually devising means of bettering their services without hiking costs. The opportunity to invest in innovations like big data to help them achieve optimum performance is great. Meanwhile, the demand for these innovations and solutions increases rapidly among end-users, including commercial shippers. Maritime shipping companies use analytics or big data in more ways than one, including fraud detection. For instance, when users buy any cryptocurrency on Paybis.com to pay for goods, this solution helps the industry spot potentially fraudulent transactions. 5 Ways Maritime Shipping Companies Use Analytics Every industry is always looking for the best ways to improve their working conditions and enhance the services they offer. Maritime shipping companies are no exception to this as they continue to find ways to improve their services. The maritime industry uses analytics to make better decisions, spot problems early on, address them and improve general performance. Here are five ways maritime shipping industries use analytics...

How Much Data To Start Predictive Analytics - Yes. The next question I have, and this is an old chestnut that we're constantly asked when talking to organizations throughout all industries. When do you know you have enough data to start your predictive analytics and what type of data should you be looking at? Again, Natalie, would you like to comment on that? Natalie: Yes, so I think it's difficult to answer that in terms of when do you know you have enough data. There's never a time when you should limit the analysis that you do because of data. You can always do something with whatever data you have. There's always a starting point, and there's always something that you can do. I think that like for me personally data is obviously what drives us here and the use of it, and one of the first things I would always do with organizations is assess their level of data. What do you have? What could we do? How could we align with your strategic goals. Yeah, so there's no point with which you should limit yourself. In fact we always start with them with small amounts of data. Jessica: Tony any comments there...

How Predictive Analytics Can Help Transform the Insurance Industry - Well, good morning everybody my name is Natalie Chan. I'm delighted to be here today to talk to you about how predictive analytics can help transform the insurance industry. My background is in mathematical modeling and statistics, I have over 15 years experience of working in areas where these skills are applied in a practical setting. I'm extremely passionate about using data to try to support evidence based decision making, and I've been working with organizations across a wide variety of sectors insurance, retail, banking and telcos to basically build and execute analytical strategies to ensure the success implementation of data driven and analytics. I'm here today to talk to you about how predictive analytics can improve your customer's experience, increasing customer satisfaction and reduce cost. You might wonder why I want to focus on the customer experience. Well most of the analytics employed in the insurance industry is focused on identifying or reducing fraud, estimating and managing risk, and on improving customer retention. However, reports from the insurance industry consistency highlight that the quality of customer experience remains the biggest factor driving customers to remain loyal or to switch to another insurance provider. We should focus on how to improve the quality of the customer experience rather than focusing solely on fraud...

How the Railroad Industry Use Analytics to Improve - Big data is everywhere and it has begun to have a huge impact on the railroad industry. A great example of this is by using an emergency dashboard and report to improve the working standards. There are various applications of analytics in the railroad industry that improve operational results and service availability. What are the common techniques and tactics this has been done? Here is how the railroad industry is using analytics to improve the processes. Shifting towards a modern railroad system Every industry is racing towards reaching a completely modernized system that allows heightened automation and collaboration between technology and humans. Various solutions have been developed by OEM equipment manufacturers outfitted on freight cars. Some of this equipment includes sensors and other hardware installed to monitor a variety of parameters. That has made the jobs of railroad personnel much more streamlined with increased productivity and frees up time to focus on other sensitive matters...

How Sales Managers Use Data Analytics to Consistently Hit Sales Targets - In today's day and age where data drives everything, it's no surprise to hear that data analytics play a critical role in sales performance management. No matter the type of business you run, getting a deep understanding of your prospects' behavior and product statistics can ensure that you are consistently able to hit your targets. You just need to know how to segment, break down, and assess raw data to use it to your advantage. Doing so may seem tricky at first. But with targeted courses for analytics and the usage of the right tools, you can enhance your sales through the power of factual datasets. To see how you can use data analytics to scale your business, here's how modern sales managers utilize this skill to consistently achieve their goals...

How to Use Webinar Analytics Dashboards - Webinars have become an indispensable tool for businesses, educators, and marketers aiming to reach broad or niche audiences with impactful content. Whether your webinar's purpose is to educate, sell, or build relationships, tracking and analyzing its performance is crucial for continuous improvement and measurable success. Webinar analytics dashboards provide the insights necessary to evaluate effectiveness and make informed adjustments. This article explores how to use webinar analytics dashboards, key performance indicators (KPIs) and metrics to track, their significance, and strategies to improve them. A webinar analytics dashboard is a centralized platform that consolidates data about your webinar's performance. These dashboards are often part of webinar hosting platforms like Zoom, GoToWebinar, and Demio or standalone analytics tools. They visualize data through graphs, tables, and summaries, allowing you to quickly interpret results...

HR Team Analytics Platform - Welcome everyone to today's webinar. I am Melissa Powell, and I'll be the moderator. Abhi and Michelle, would you like to introduce yourselves? Abhi: Sure. Hi, I'm Abhi Gupta, and I'm here with Michelle Ahn. And we work on the Intelligence and Insights Team here at InetSoft, under the Information Services Group specifically. Our team is responsible for building and deploying internal reports that provide business insights for the whole firm. And I specifically build websites for the firm to view these reports. One of the biggest projects we've been working on recently is transitioning the firm to a more modernized way of reporting. Michelle: Hi, my name is Michelle. I'm a developer on the Intelligence and Insights Team as well with Abhi. And as Abhi mentioned, we work with different internal teams. I specifically work with the HR team for their reporting meets. And like Abhi, I've been part of this big project where we were transitioning to a more modernized reporting system. Melissa: Alright, well, this is Melissa, again. I have a couple of questions about that. So Michelle, you mentioned you know, you do work with a number of internal teams and you have some stories to share with us today about something you've worked on with the HR organization. Could you tell us a bit about the analytics users in the HR organization at InetSoft...

InetSoft vs Analyzer Comparison - The InetSoft promise of easy, agile, and robust business intelligence is now backed up by a professional analysis. To create its comparison of InetSoft Style Intelligence and Analyzer, analyst firm G2 Crowd compiled reviews and ratings done by independent users of the two BI vendors, comparing the BI tools in the areas of reporting and building reports, self-service, advanced analytics, and the strength of the overall platform...

InetSoft vs ClicData Comparison - So how does InetSoft's Style Intelligence weigh against ClicData? From the latest G2 crowd ratings, it is pretty impressive. InetSoft has received rave reviews from a majority of its users and beats ClicData in various categories across the board. Style Intelligence has been lauded for its robustness and described as a complete and powerful go-to tool for data analysis...

InetSoft's Media Analytics Dashboard -Using the above media analytics dashboard as an example, publishers can easily find out the top 20 authors by reviews through sorting. Based on this information, publishers can print and inventory more books that come from the most popular authors. By applying the genre filter, publishers can easily identify which kind of books are worth investments such as advertising, helping publishers improve their ROI...> InetSoft's Style Intelligence vs Mode Analytics -A comparison between InetSoft's Style Intelligence and Mode Analytics was done based on peer reviews in 6 broad categories - Ratings, Reports, Self Service, Advanced Analytics, Building Reports and Platform. Each category was further divided under various parameters and measured in detail and the Style Intelligence was the clear winner, beating Mode Analytics on 30 out of 31 metrics...

InetSoft's Style Intelligence vs Dundas BI -How does InetSoft's Style Intelligence compare to Dundas when the two business intelligence solutions are compared? The data from verified user reviews on analyst firm website G2 crowd rates InetSoft Style Intelligence with 4.5 /5 stars over that of Dundas BI which has 4.4 / 5 stars...> InetSoft's Style Intelligence vs Mode Analytics -A comparison between InetSoft's Style Intelligence and Mode Analytics was done based on peer reviews in 6 broad categories - Ratings, Reports, Self Service, Advanced Analytics, Building Reports and Platform. Each category was further divided under various parameters and measured in detail and the Style Intelligence was the clear winner, beating Mode Analytics on 30 out of 31 metrics...

InetSoft's Style Intelligence vs Geckoboard - The InetSoft promise of easy, agile, and robust business intelligence is now backed up by a professional analysis. To create its comparison of InetSoft Style Intelligence and Geckoboard, analyst firm G2 Crowd compiled reviews and ratings done by independent users of the two BI vendors, comparing the BI tools in the areas of reporting and building reports, self-service, advanced analytics, and the strength of the overall platform...

InetSoft's Style Intelligence vs SAP Lumira - Choosing the right solution for business intelligence and automated reporting can be daunting. InetSoft's Style Intelligence and SAP Lumira are two solutions which give robust visual analytics software for organizations to improve their operations...

InetSoft's Style Intelligence vs ThoughtSpot - Selecting Business Intelligence (BI) solutions for any organization is hard, risky, and inherently biased. It is made easy by the G2 Crowd review platform with its real-time, transparent and unbiased user reviews. This helps an organization to objectively assess what is best by leveraging the wisdom of the crowd, limiting the risk, and finding out what works. The reviews are validated by G2 Crowd thereby helping organizations make better buying decisions...

InetSoft vs Izenda Comparison -InetSoft has been providing unrivaled business intelligence solutions since 1996. InetSoft's Style Intelligence is an agile business intelligence tool that users from all around the world recognize as one of the best visualization dashboard software tools on the market today. Recently, users on G2 Crowd roundly endorsed the superiority of Style Intelligence over competing BI tool Izenda. From the user level to the administrative, InetSoft's Style Intelligence outranked Izenda in a majority of categories...

InetSoft's Style Intelligence vs Oracle Cloud Comparison - How does InetSoft's Style Intelligence compare to Oracle Cloud when the two business intelligence solutions are compared? The data from verified user reviews on analyst firm website G2 crowd rates InetSoft Style Intelligence with 4/5 stars over that of Oracle Cloud BI which has 3.8 / 5 stars...

Innovations in Discovery and Analysis Tools - What are some innovations in discovery and analysis tools? There’s a range of new charts types. We have 15. I think the important point here is that there are charts that are more report-like: data sheets, counts, text filters, and basically rows and columns. We can create a good visual display in each chart type, but you also need to take some steps in advance. If it’s customers that you are analyzing, you need customer names. If it’s products, then product names because people need that to get context. We tend to balance this more standard type of chart with more visual charts, bar charts, and pie charts. We also have a set of richer charts you’ll see in a few minutes, heat maps, paretos, and time tables which are more in that analysis category. In fact when we can do a demonstration right now of a project that uses the time table. Let me just click this. I’m opening a web visualization project, and we’re looking at event logs from a factory. This is a manufacturing operations dashboard. It’s one day of event logs. There are 880,203 of them. They are listed out here. This chart groups them by command...

Insurance Claims Analytic Views - Now with this insurance claims data, in the context of healthcare provider and physician profiles, there's a bunch of different analytic views that we're able to show you right off the bat. Not only can we provide you detailed analytics on diagnoses and procedures and financial breakouts, but what we can also do is bring you insight and intelligence related to those prescription drug claims that might be processed and incorporated into a physician's profile. Each of these data discovery tools gives you not only the ability to get a lot of great detail on an individual provider, but also gives you the opportunity to do some powerful searching, whether that might be a particular ICD code, whether that be by a CPT code, or maybe that's a drug class or an NDC code. Really as a result what we've seen is that being able to integrate all of this information into each of their profiles gives you the sense of understanding of what's actually taking place in market today. But also having the option to go ahead and understand how trends have been impacted over time by having access to that historical information is a big plus. With this data in context of all of the other healthcare industry intelligence that you track, now I'd like to go ahead and walk you through three different use cases in which we really have seen some of our customers and clients really gain some extreme value out of by having access to the commercial claims intelligence. The first use case that we're going to go ahead and explore is really around mapping influence and being able to understand how you can think about targeting physicians...

Insurance Coverage Analysis Dashboard - The Insurance Coverage Analysis Dashboard below is an example of InetSoft's interactive BI software. Targeted towards organizations of a many scopes, InetSoft's dashboard solution is ideal for users looking for simplicity, power, and performance. A pioneer since 1996, InetSoft prides itself on combining data mashup, dashboards, and reporting solutions to help businesses enhance business performance in a range of ways. The chart below clearly demonstrates the simple nature of the dashboard. Still, there's a rich level of information and data that users can absorb from the available information chart by using the dragging sliders to filter information for effective and efficient analysis. By being able to narrow the results of different statistics, users can get a quicker and more accurate glance at the data being used. Effective business practices only enhance performance and InetSoft's feature-packed solution does just that for organizations...

Insurance Analytics Solution Case Study - This InetSoft client requested to remain confidential. The client sought to create an end-to-end insurance BI solution which would provide insurance companies with access to and analysis of real-time data, so that insurance companies could discover trends and insights across their entire customer base. The client wanted to build their solution from insurance industry standard data models (Acord & OMG) that include line-of-business details such as worker compensation. Incorporating these data models into the solution would allow insurance companies to do consolidated operational reporting and analytics...

Integrate, Report, and Analyze Data Any Way You Want - Well let’s create a globally consistent way to integrate, report and analyze data and then just open up lots of local franchises throughout the company in either the sales, service, marketing, HR, finance or some of the different lines of business units. And then by bringing it to the local market it’s going to be more responsive. It’s going to have more domain expertise, but there is still a sort of global consistency. So that might be more right for a more centralized self-service BI approach. Now I have seen other folks, and a lot of manufacturers come to mind who tend to be very decentralized, base their BI strategy upon lines of business units. They wanted to be even more decentralized, and they almost had what I would almost view as a bottoms-up approach where essentially the different departments get to do whatever they want. They can integrate, report and analyze data any way they want, and they had full autonomy and full control. And the job of the centralized team is really just to kind of watch what the decentralized teams are doing, and when they are doing something really useful, identify that and promote that, and disseminate that out to the other departments and say hey, this team over here is doing something really cool, let’s make this more widely available to other aspects of our enterprise...

Interact More with the Data in Terms of Analytics - Interact more with the data in terms of analytics. As more and more users in the organization start thinking about building predictive models and trying to work with the data and look at different levels of the data, particularly, if they're looking at a dashboard and want to dive in and understand some of the metrics that they're taking to look at. In early stages of embedded data analytics, that really wasn't possible. That was something where you had to go back to IT and try to develop something that they can work with. Now we're starting to see more of that kind of ad hoc querying capability moving into the embedded reporting systems which is definitely a good trend. Think again about the nontechnical user being able to self-serve their reporting, how they can receive analytic insights. How is that done? How do they do it if they're not actually building predictive models or working with analytics themselves? How can they receive those insights so that they're in context? As I mentioned before, that they have to be understandable so they're actionable. If they're built off of a predictive model what does this mean for their particular area of interest, their responsibility, their business process? The ability to perform self-service analytics is through that query and search capabilities, so forth. That's obviously very broad idea of what it is all about...

Key Ingredient for a Successful Business Analyst - The second key ingredient for a successful business analyst is having good business skills. One of the things I hear an awful lot from the business analyst community out there is the challenge that they have to sit at the conference table with senior executive leaders and quantify strategies about the next fiscal year. Where are we going? What are we doing? What sort of insights do the senior BAs have? How can we guide the future? I think the profession still needs to grow, I think the acknowledgement still needs to be there, but I also think one of the missing pieces is what I would call ownership accountability. We need to get in synch with the phases our executive management team is using, return on investment and the creation of efficiencies. You should be able to talk about strategic plans and be able to articulate it. Be able to present that information which is relevant and important to a senior executive audience. I am not suggesting that we don’t do this already, but I’m suggesting that we need to really fine tune this ability. Things like critical thinking and problem solving, change management, things like integrity, things like setting goals and objectives, these are all the soft skills that are important...

Key Performance Indicators Analysis Tool - Are you looking for the best key performance indicators analysis tools? Since 1996 InetSoft has been making BI software that is easy to deploy and easy to use. Build self-service oriented dashboards and visual analyses quickly. InetSoft's data mashup engine solves the data access and transformation challenges that other tools cannot. View a demo and download one of our applications for free...

Kickstarter Analytics Dashboards - From art to technology, crowdfunding projects are making a lasting impact on society, culture, and the economy. Kickstarter, the largest crowdfunding platform in the United States, has helped fund 221k creative projects successfully, generated $13.5 billion in economic impact, boosting overall employment. In the future we can expect the crowdfunding industry to grow even more. Yet, despite all the impressive numbers and potential, there remains common threats in recent years, from the Covid pandemic to financial recession...

Liberating Analysts and Managing Output - Now, obviously on the BI side what we try to do is to get ahead of these spreadmarts by encapsulating as many of them as possible into various data marts and data warehouses. And this is a never ending process and one that we need to take on and never get defeated in pursuing, but it is kind of like playing whack a-mole because it seems as soon as we consolidate one spreadmart into a data mart or data warehouse, two or three pop up to take its place. Nonetheless through a very persistent and consistent creation of new subject areas within a warehouse and proper application of BI governance, we can get ahead of the spreadmart dilemma that afflicts many of us. But, since we are talking about analytics and business analysts in particular, we have to ask why do these analysts create these spreadmarts? So I have got here the true deepest confessions of a real honest to goodness business analyst. And she’ll remain anonymous for the time being. She said, “I have been one of those so called “spreadsheet jockeys,” and I have an affinity for Excel, but out of necessity. Often the BI team generates a report that gives me 90 percent of what I need...

Life Sciences Companies Use Analytics - In healthcare today, we are actually taken on a very similar challenge, though not centralized in one physical place. We are now collecting massive amounts of information. Information that people did not have access to before. The following questions arise: How do you use all of this information? How do you find it? How do you ensure that you can read and interpret it, and how can you use it to make new kinds of decisions? Those are the questions that fall into the domain of analytics. The keys to health industry innovation will not be found in simply trying to aggregate and report retrospective views of data collective. Nor will they be found in systems that reflect an old way of delivering care...

Logistics Analytics - There are multiple functionalities on the InetSoft logistics dashboard which visualize data in diverse styles of charts, aggregates and filters. By using map charts to illustrate the global product shipment trends and setting a flyover treemap chart on the map chart, logistics managers would be able to find out which countries have the highest shipments and click the country on the map to further explore the top cities that have most logistics demand in that country...

Machine Learning Analytics Company - As a machine learning analytics company, we tend to focus on automation, time savings, and something that we are going to talk about in more detail later, graphical interfaces that can bring people who know more about the business closer to the data. Proprietary solutions also tend to focus on the deployment of models. We were talking about one sort of workflow which when the data scientist develops the model and then hands it off to a programmer to deploy the model. Well, we see InetSoft and other proprietary solutions working towards a one click sort of deploy button, and what a time savings that can be for an organization. What we want to look for and things that we've done is how do we feed the creativity of data scientists, and we think that allowing for bi-directional integration with open source products is one way. I can be in InetSoft, and make calls out to Open Source. Then we're talking about things like using the open standard of a PMML. I think it's really silly, and I do see less and less of this, thank God, people debating is R better than Python better than InetSoft? These are not productive discussions, I don't think. I mean I think it's good to know which tool to use for what. I certainly think that's good. I teach a data mining class and where I expose this to my students. Know which tool saves you the most time at what point of the process. I think that's more important...

Machine Learning and Predictive Analytics Create Data to Analyze - So we talk about machine learning and analytics, predictive analytics, the platform itself actually becomes a data creation platform, and that also adds to the different varieties of data. So it actually ends up compounding the drive to work towards the variety of the platform itself. The opportunities that it creates for using the data that comes in will undoubtedly be unlocked. It will keep growing. The varieties are never going to go in other direction. It's all is going to be increasing, and that's a great opportunity. It means different data types, script data and non SQL data will continue to increase a little. Coming back to how you actually get that data coming the other direction with SQL becoming more and more important, but in the underlying data, the variety is only going to increase. Abhishek: Larry, any thoughts you may want to add in here...

 

Machine Learning Utilized For Predictive Analytics - Now let's move on to machine learning which is a subset of artificial intelligence. It provides computers with the ability to learn without being explicitly programmed and utilize predictive analytics to forecast outcomes and also to assess the probability of future predictive events. Machine learning has the ability to identify risk and to identify opportunities for businesses by using cognitive computing techniques, and it supports much greater efficiency. It can understand and can respond to human sentiments and emotions as well. Machine learning has gotten beyond the capabilities of predictive analytics and beyond the capabilities of big data analytics. It also surpasses in some ways human capabilities by thinking independently and making its own evaluations and its own conclusions. If we look to the machine learning development, we see that it's accelerating very quickly, and we will discuss now the cause of this rapid advancement. One such cause is the recent explosion of big data. Data is everywhere, and it is expanding from a number of sources. You can think about the sources for text, for images, for digitized documents and for internet devices. Everything is connected right now...

Making a Dashboard Solution Plug into Predictive Analytics - Now let’s talk in terms of making a dashboard solution plug into predictive analytics. There is something called PMML. That’s industry standard language, it’s called Predictive Modeling Markup. And PMML is a way that you can exchange predictive models to run against standard databases. So a BI solution should support PMML. Analytic solutions like SAS and SPSS can output PMML structures, and you just need to have a way to import them. There are a couple of issues there but delivery formats that are appropriate for organizations, so dashboards, alerts, mobile capabilities, time limits, we talked about. There are lots of problems with spreadsheets. One of the things we see that distinguishes the innovative firms is they are delivering information in a much more timely fashion. You can see a huge difference between the innovative firms and the tactical ones. Does timely mean day prior information...

Main Features of Financial Analysis Software - Financial analysis is the process of analyzing finances to identify and fix issues. To do this, you need good financial analysis software with the right features. Luckily, the market has several financial analysis software you can invest in for your business. What Is Financial Analysis Software? Financial analysis software is a tool that helps you analyze your business data and make decisions based on that data. This software isn't only for people doing accounting or finance work. Even if you're not an accountant, you can use it to understand your business better and make higher-quality decisions. Financial analysis software can be used as a standalone tool or integrated into an accounting or financial management system. In either case, it has many features that make it useful in helping you perform financial analysis tasks...

Making Data Usable For Broad Analytics - Yeah, there is a question that one of the attendees asked, is Alteryx a data shaper, and the answer is absolutely. In addition to you mentioned Trifacta and Paxata. Those are two technologies that that were kind of born with Hadoop, and that's where you were seeing the largest variety of data. In that variety, you have to find a way of making the data really usable for broad analytics use cases. It depends on the shape of the data, whether it's a nested files or something else. And so you saw technology as Trifacta and Paxata that are really born around leveraging the Hadoop platform to do that data shaping and processing right on there. Now it has expanded to other technologies so it's not just dependent on Hadoop, but Alteryx has just gone the other way where they started with being able to shape data and prepare data off of a number of different sources whether they are now actually leveraging the processing of Spark or Hadoop to be able to do some of the transformations in memory...

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