Big Data Analytics Vendors: InetSoft Technology

Researching big data analytics vendors? InetSoft is a pioneer BI provider, and its platform employs a hybrid in-memory and disk caching architecture to power a full suite of visualization applications. Below are relevant articles, and evaluation resources can be found here.

Biotech Industry Big Data Analytics - The power of data in contemporary business and science is immense. No industry can really make progress without the analysis of massive information libraries that give us fresh insights and enable new breakthroughs. Biotech is not an exception here as it relies heavily on big data analytics. But how does data science influence the biotech industry? What are the most common use cases of big data in biotechnology? If you are interested in seeing the answers, keep reading to learn more about this amazing topic...

Bitly Analytics Dashboard Software - InetSoft's business intelligence dashboarding software connects to Bitly's link management platform, for interactive data visualizatons and better app visualizations. In addition, you get better KPI dashboarding with a Web-based drag-and-drop design tool that lets you chart any data including custom conversion types...

 

Build Flexibility Into Data Mining Programs - I think the first thing to think about when you think about flexibility is what is your business response time? If you are talking about a business response time where the amount of time that you can react with something is a week or so, then your flexibility has to be tied to that kind of environment. There is also the possibility that you might talking about online customer service requests which demands showing quick results. Your CSR’s or your end users or actual customers are interacting with your predictive models through instantaneous predictions in a Web site. In that case the models actually can change in a real-time basis without having to bring your infrastructure down. So you would want to have a system in place where you can deploy new models seamlessly in the backend that impact your customers or your CSRs or whatever interact with those models in real time. And those systems are fairly convenient to put together with today’s technology so that you can actually update your response rate immediately. So if you see changes in the market, and you have done the data analysis to see how those changes in the market can impact your models and impact the patterns that the models are using, you can actually apply those in real-time while people are interactively using your systems...

Building a Business Case for Master Data Management - Today we are talking about ‘Building a Business Case for Master Data Management.’ Our customers have told us that this is not always an easy process since it requires calculating the cost of bad data to justify the pricing of potentially multiyear master data management project. And if you haven't attempted to calculate these costs, don’t worry you are not alone. One research study recently surveyed over 500 businesses to determine the state of master data management maturity. Almost 50% of those surveyed indicated they have not even attempted to calculate the cost of bad data. And over 6% estimated that their costs are reaching well beyond $11 million. And we have heard that implementing master data management isn’t always easy either. Experts tell us you can't just throw money or software at a master data problem. Organizations also need to budget for data governance, data stewardship and other process improvements. So today’s Webinar is intended to sort this all out and give some tips and advice about building a business case for master data management...

Business Analytics and Big Data Exploration - Today we’re talking about business analytics and the interesting questions that lead to big data exploration. But first, what exactly is business analytics and how is it different from business intelligence? I define it as just the systematic use of data and quantitative analysis to make decisions. business intelligence was mostly about reporting whether it is a standard report run every month or ad hoc reports or queries. Business analytics is more about prediction, optimization, and more sophisticated uses of mathematics and statistics. Put another way, business intelligence is about gaining the hindsight and insight into what’s happening in my business and what do I do about it. Examples of business analytics include looking into the future to understand my customer interaction, and figuring out how can I improve it. Anotehr example is understanding the profitability of a product line and how will I continue to extend that. A third example is understanding risk in the market and how I respond to reputational risk or any other components that might jeopardize my success. What’s going on at the frontier of business analytics? It’s a very popular topic today. At every client we talk to, from the boardroom down to the front lines, everyone wants to understand how they should apply business analytics to all the information we have gathered from all the different sources over the yearsin a way that it really makes a difference in their business. So they’re looking at that information asset frontier and trying to apply business analytics to everyday decision making and get better results...

Business Case for Data Mashup - InetSoft offers a unique capability in its BI platform for enabling end-users to combine disparate data sources that are not already mapped within a data warehouse schema. While traditional information management philosophy requires IT to be the gatekeeper of a "single truth" of enterprise data definitions, it is misguided to believe that this can ever be fully accomplished. Moreover, by limiting end-user abilities IT creates obstacles to delivering the fullest benefits of BI and burdens itself with unnecessary change requests, work backlogs, and administration overhead. This article explains further what data mashup means in the BI space and makes the business case from both the business-side and the IT-side for enabling this level of self-service: What is data mashup? The fallacy of the single-truth Why end-users need data mashup Why IT needs data mashup Business case results of enabling data mashup...

Business Data Visualization Tool - Visualization is the subsection of analysis that turns data into visible insight. Traditional data visualization tools used to be aimed at highly trained professionals. They used complicated inputs and statistical models to enable their users to discover low-level patterns. InetSoft’s visualization application, the viewsheet, is a business intelligence tool that brings the power of visualization to both business executives and mainstream users alike. Because viewsheets allow users to interact with complex data using familiar objects such as charts, sliders, and check-boxes, they have a very fast learning curve. They allow seamless integration of data warehouses and other BI data stores with operational data sources for a complete view of an organization...

Business Intelligence Developer Tools - InetSoft's Style Studio gives developers the ability to build perfectly formatted reports and and define new data sources with sophisticated, easy-to-use tools. Style Studio is used within a desktop-based, integrated environment..

Buzz Around Big Data - There is no doubt about it. There is a lot of buzz around Big Data. The definition of Big Data is not exactly clear. It can mean different things to different people. One definition is that Big Data is essentially the tools and technologies that make managing or getting value from data at extreme scale, affordable or economical. And that seems like a very simple definition. It seems like a very simple definition, but really I think that it's the key of the way we are telling clients they need to think about Big Data. Extreme scale makes sense except the term extreme scale keeps changing. Big Data is not two terabytes or two petabytes. There is no demarcation. It's whatever is not affordable for you today but can be affordable with new techniques and technologies.It's the frontier of data management. It’s the techniques and technologies. There is no single magic technology box where you dump your data into it and turn the crank and out come valuable insights...

 

Can a Data Lake Be Based on Hadoop? - And the last question, it's a good one, so even for the data lake, is there a single framework? Can a data lake be based on Hadoop? Or is that not even a good approach? And I think that the answer here is again there is no one-size-fits-all, and companies do different things. We see Hadoop being used incredibly often as the data lake, and that is probably the default option, but that Netflix scenario Larry talked about earlier Amazon S3 is really the data like, and that is the whole data store. Amazon announced a product called Athena which will enable querying against the data in S3. So now it's opening up the data store for more interactive connectivity, but companies are taking a multi-tiered approach, especially in the cloud where there is a simple storage bucket like an Amazon S3 or Azure Blob or Azure data lake with Microsoft. That's even a layer below Hadoop, and so there isn't a one-size-fits-all which is easy...

Can Hadoop be the end all for big data analysis in the future? - So that’s the presentation. We’ve got about six minutes left over to do some questions. I think we have a number of them here. Oh, I just want to mention some recommended research here that says how to deliver self-service BI notes. The first one list is probably the main one I would take a look at. Alright, as far as questions, just give me a second to look through these quickly. Can Hadoop be the end all for big data analysis in the future? That’s one of those crystal ball kind of questions. There is no question I think right now that Hadoop is -- I mean Big Data itself as a term and a lot of people equate Hadoop as Big Data is the #1 search term by far in BI. So there is a huge amount of interest here. When I look at vendors that do data discovery on Hadoop based data, I don’t see doing it comparably to BI vendors like us. They are dependent upon Hadoop becoming more widely deployed. I think right now, a lot of people are using Hadoop for cheap storage if you have got petabytes of data. If you need cheap storage, you are going to use Hadoop for that...

Catering Data Visualization for Your Users - Eric Kavanagh: Well, let’s bring in our resident expert of the week on data visualization and dashboards. Wayne Eckerson, of the BI Leadership Forum, welcome back to DM Radio...

Challenges with Big Data Analytic Tools - There’s some great advice there on listening to what other people are doing and how they are approaching it. What were the commonalities in challenges of big data analytic tools that organizations are facing? Part of this pragmatism comes with kind of its alter ego, which is caution. What they saw as the primary obstacle for organizations at each level of adoption is the need to create this compelling business case that clearly connects the technology that they want to be using with the business outcomes and the business challenges that they are intending to solve. Once they get beyond the business case, the obstacles vary in somewhat natural ways. In the early stages they are very focused on understanding how to use Big Data to solve their business challenges that they have. Once they start to have those ideas, the challenge becomes getting the management focus and support for moving from more traditional analytics into this Big Data market. Again that’s part of the need for the strategy in the business case. Once organizations begin piloting Big Data, they saw a skills gap begin to emerge, and it’s not just the Big Data scientist skills gap that we hear a lot about in the press, but it’s also a technology skills gap...

Chemical Manufacturing Industry Uses Big Data - The chemical manufacturing industry leverages big data in various ways to improve processes, enhance safety, and drive innovation. Here's an in-depth exploration of how they utilize big data: Process Optimization Chemical manufacturers use big data analytics to monitor and optimize the chemical production process. This involves analyzing real-time data from sensors and IoT devices to ensure that the production process operates at maximum efficiency. Data-driven insights can help in adjusting variables like temperature, pressure, and chemical composition to achieve desired outcomes. Quality Control and Assurance Big data technologies allow for the continuous monitoring of product quality. Sensors and automated systems collect data throughout the production process, enabling manufacturers to detect deviations from quality standards in real-time. Statistical process control (SPC) techniques can be applied to ensure consistent product quality. Predictive Maintenance Chemical plants have a multitude of complex equipment. Big data analytics, combined with IoT sensors, are used to monitor the condition of machinery. By analyzing this data, manufacturers can predict when maintenance is needed, reducing unplanned downtime and costly repairs...

Cloud-native Microservice Architecture - Cloud-native microservice architecture is an approach to designing, building, and deploying applications that leverages cloud computing principles and is based on the microservices architectural style. This combination enables applications to be highly scalable, resilient, and portable, making them well-suited for dynamic, distributed environments such as public, private, and hybrid clouds. Here's a breakdown of its key components and features: 1. Cloud-Native Principles Cloud-native refers to applications specifically designed to take advantage of cloud computing's scalability, elasticity, and resilience. Characteristics include: Containerization: Applications are packaged into lightweight, standalone containers (e.g., using Docker) that are portable across environments. Dynamic orchestration: Platforms like Kubernetes automate the deployment, scaling, and management of these containers. DevOps practices: Continuous Integration and Continuous Delivery (CI/CD) pipelines enable fast and reliable software updates. Resilience: Applications are designed to handle failures gracefully, often using patterns like circuit breakers or retries. 2. Microservices Architectural Style Microservices architecture breaks applications into small, independent, and loosely coupled services that perform specific business functions. Each microservice...

Combining Data Analysis - This is what we mean by Self-Service BI: It's the ability to work with this data that’s much larger than where you can traditionally work with In-Memory right on our laptop. You can also directly connect to your databases because in some cases, you may have invested in very fast databases like a teradata or vertica database and you want be able to leverage that fast infrastructure. So you can connect directly in InetSoft too and you can actually switch back and forth between your data grid cache and your live data source connection. And in both cases, you can create this kind of ad-hoc explorations of your data just as we are doing here. Another thing that’s very important when you are working with Big Data is the ability to mash two data sources together. In BI we have been somewhat siloed in saying okay we have got some data over here in this database and some data over here in other databases around Excel Spreadsheets. And we can do this analysis and that analysis but would never combine the two. And that’s really limiting because when you think about having sales data and operational data, there are all kinds of questions that you may wanted to answer by mashing up those two data sources...

Complete List of Data Connectors - Next question is where can we find a complete list of the new data connectors for Style Intelligence? Katie: In terms of a new list of the data connectors, I don't believe that in the actual feature listings that we have on the website, like we mentioned that we have new connectors for the different web APIs as just like a generic note, what you would want to do is you would want to either actually look to install on your local development environment actually work on the upgrade with 2020, because you would be able to just explore that data source page directly and get to know that list. However, you can see each of those data sources documented within our documentation. That is a place that you can always look to review any available data sources that are in that listing right there...

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