Information About Data Analysis Programs

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Performance Management and Analytics Vision - I recently read a research study where they asked a number of questions about where organizations are in realizing this whole performance management and analytics vision. And where are they at in evolving their finance infrastructure. They asked the question, does your organization and your senior management have a clearly defined strategy, and if so, how consistently and how well are you executing against that strategy? Nineteen percent of organizations said they didn’t have a clearly defined strategy. The other 80 percent said, yes we do think our management has a well defined and clearly defined strategy. Unfortunately of that 80 percent, only 14 percent said that they thought their organizations consistently executed against it. So that’s a bit troubling that over 60 percent of the organizations said that they did have a strategy, but they weren’t consistently executing against it. One of the reasons we see people struggle with that is not for lack of interest and not for lack of intent, but a lot gets lost in translation because of all the challenges that we’ve been talking about and because the strategy is being able to monitor and act and respond, because of the information infrastructure of complexity and challenges...

Pharmaceutical Analytics Dashboard Dashboards and analytics play a crucial role in the field of pharmaceutical drug testing trials, providing valuable insights and facilitating informed decision-making. InetSoft's pharmaceutical testing dashboard presents complex pharma testing data in a visually appealing and comprehensible format...

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Popular OLAP Server Products - There are several popular OLAP servers available in the market. Here are some of the most widely used OLAP server names: Microsoft SQL Server Analysis Services (SSAS): This is a multidimensional OLAP server from Microsoft that provides data mining and business intelligence capabilities. Oracle OLAP: This is a multidimensional OLAP server from Oracle that provides advanced analytics and business intelligence capabilities. IBM Cognos TM1: This is an OLAP server from IBM that provides in-memory multidimensional analysis, planning, and forecasting capabilities. SAP BW/4HANA: This is an OLAP server from SAP that provides multidimensional analysis, reporting, and planning capabilities. Pentaho Mondrian: This is an open-source OLAP server from Pentaho that provides multidimensional analysis, reporting, and visualization capabilities...

Powerful Analytical Tool for What If Scenarios - It’s a really powerful analytical tool that you can then use for things like what if scenarios, for those advanced calculations, and for that data manipulation that you need. Any user can come in and create a data worksheet, and then another user can create a dashboard or report based on that data worksheet as long as they have permission to view that data worksheet. The data worksheet can be used over and over and over again just like a data model can be used over and over and over again. Reusable components are really at the core of our product where you can create a couple of models, a couple of worksheets, and they can fit all of your needs for twice or three times as many dashboards and reports. So, this is just a brief overview of data worksheets. Do you have any questions on the concept of the data mashup tool where you’re joining a data source together. I’m just going to do a brief overview and show how you can have someone creating the data worksheet and someone creating the dashboard off of those. They don’t have to be the same person. Do you have questions on the functionality, on the end user interface for creating dashboard...

Predictive Analytics Software - In today's business environment, uncertainty has become the norm. The best course of action cannot be found in historical reports; simply reacting to past and current conditions is not sufficient to run a business. Organizations need to make use of predictive analytics to gain insight on potential future outcomes from already existing operations...

Preparing to Take on a Data Analytics Problem - When preparing to take on a data analytics problem, start by asking, what is it that you are really trying to do and how do you really think you are going to answer the real problem at hand, rather than kind of a naïve interpretation of the problem. And for example, when I say I want to forecast inventory levels, anybody who does physical forecasting understands that a forecast is by definition incorrect. So I am going to tell you that the level is going to be three or five or seven with some range, is that going to be helpful to you? Is the question they really want to know, are the inventory levels sufficient or not sufficient. And if you can change things to a classification problem, then you can actually take action on the results. If they think about the results that they are going to get, how they are going to use that result, then they will have much higher success rate in understanding what predictive analytics can do for you...

Projects Are Growing Out of the Hadoop Ecosystem - So these types of projects are growing out of the Hadoop ecosystem. Our partners like Cloudera and Hortonworks are really driving a lot of the work behind these changes. Ultimately they are adding what we will call enterprise grade capabilities that you would expect from your traditional relational database platforms like Oracle or IBM and an SQL Server. So these capabilities are moving into the forefront of emerging Big Data technologies, and they are really eliminating another one of those barriers to true enterprise adoption and that the states moving really, really quickly. Holly, is there anything you are seeing around this that you think would be interesting for the audience to hear? Holly: So I am encouraged by the effort the data platform vendors, the Hadoop vendors and the Big Data vendors are putting into this because I can say that I have worked with a lot of customers helping them with governance and security specifically for their Big Data platforms...

Publishing Analytics Dashboards - With the development of digital information over the past years, publishers have been enduring rapidly evolving challenges and opportunities in the digital space. Publishers have to find ways to adapt quickly to the market in order to turn informational challenges into revenue and opportunity. From bestsellers to reviews/ratings, from book genres to year published, publishers are dealing with more and more data streams and considerations in order to stay competitive...

QuickBooks Dashboard Reporting - InetSoft's partner Bison Analytics has created a solution for QuickBooks customers that brings with it the power of Style Intelligence. Bison is a leading specialist in the extraction and delivery of accurate QuickBooks data for analysis and business intelligence. Powered by InetSoft, the Bison System is a specialized, hosted BI tool that lets small and mid-sized companies take charge of their data in the same fashion as Fortune 500 companies...

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Railroad Industry Analytics Usage - 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...

Real Estate 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...

Recruit Business Analysts and Develop Competencies - Late last year I worked with one of those larger organizations where we developed a program to recruit business analysts, to develop competencies, and to train them. I helped them to build marketing material to present to their customers about the value of business analysis as a practice, as an add-on service to the services that they were already offering in this company. Their competitors are doing just the same thing, and the reason that it’s happening is that these outsource vendors are seeing some of the turmoil that’s going on inside of organizations where requirements and project management and quality assurance are these big mixing bowls of activity, and stuff is coming out, but we are never really certain what the quality of that stuff is that is coming out. And so inevitably what happens is we’re turning to our vendors and asking them to develop software for us when we are challenged with identifying the requirements to help them develop software. So what’s happening is service level agreements that are put into place with these outsource vendors are being blown out of the budgetary water, so to speak...

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Reducing eCommerce Fraud with Better Analytics - Predictive analytics and big data are two ways eCommerce sellers can identify fraud, which is becoming a pressing issue. According to the Global Fraud Report 2018, almost three-quarters of online retailers (72 percent) agree that eCommerce fraud is a growing concern; moreover, 63 percent of them "have experienced the same or more fraud losses in the past 12 months." Before the arrival of data analytics tools, eCommerce sellers utilize a sample of customer data for fraud analysis. This means spending a lot of time and money to investigate the entire sample because the analysis would have to be manual. Now that big data analytics systems are available, retailers can analyze all data for fraud much quicker. One way to battle eCommerce fraud with data analytics is to use predictive analytics. Here are the steps involved in this process: Prepare the database of online orders from your store. This means defining a timeframe for orders Define the types of transactions to include in the database. Since eCommerce fraud happens only with credit cards, exclude orders where creating a chargeback is impossible Identify the patterns of fraudulent orders to differentiate between the good and the bad transactions Model the data to teach the algorithm to define suspicious orders based on the patterns. This is where data intelligence professionals and/or tools perform approaches like deep learning algorithms Implement the model Add new fraud patterns to the algorithm. As a result, it would be possible to reduce the amount of fraudulent orders by rejecting them and prohibiting fraudsters from making purchases...

Reducing Latency for Interactive Analysis -Yes, this is Holly here, so I'm really excited to see this trend and all the focus on the Big Data tools. It's very interesting to see in the industry, the specific analytic requirements for encrypted data. A lot of the vendors in the BI industry are focused very heavily on reducing latency for interactive analysis and queries, all through the whole platform and making Big Data approachable as well as fast. So, you will see a lot more today in the other trends that speak to this major trend and focus on analytics of the Big Data. So that's three, and then our customers are very excited with this and are participating in this. It's very exciting to me to see this happening. Abhishek: Great, all right onto trend number two. Big Data no longer is just Hadoop. Purpose-built tools for Hadoop have become obsolete. Holly, how about you kick us off with some thoughts on this one...

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Reporting Jump-Off for Analytics - We can sum up the differences between reporting and analytics as reporting offers structured navigation to users through the data, in other words predefined drill paths, whereas analytics is much more ad hoc. Reporting is push-based. We push this information sandbox to users. Whereas with analytics, the users go get the data on their own. And a major difference is that reports are aligned around KPIs and metrics. Someone has decided in advanced what metrics are of importance for this group of users to look at. And all the data is aligned around those in form of predefined drill paths and things like that...

Retail Store Analytics - With InetSoft's dashboard, retailers have all the KPIs they need to track, gathered in one efficient overview. Utilizing Style Scope's features in InetSoft's retail dashboard, retailers are able to visualize and report all important retail KPIs in one central interface and turn this collected data into actionable insights, which makes it easy to add more metrics, adjust according to retailers' needs, and ensure their analytics are up-to-date. Style Scope offers extremely flexible tables and chart types that can embed sophisticated calculations so that users can easily engage in creating new dashboards and performing ad hoc chart editing for their business analysis....

Role of Analytics Is to Extract Value - The topic of mobile and the whole issue of ubiquitous knowledge anytime, anyplace, anywhere, the way in which information is consumed is important. The issue of the emergence of new distribution channels, and the tradeoff often giving away location for example, in return for value, are other challenges. It's interesting that for different demographics there are different viewpoints in terms of what information they are prepared to share on their mobile device in return for, for example, a cheaper cup of coffee. Analytics in all its different forms, I will talk about that in more detail later, but fundamentally the role of analytics is to extract value from the data and provide impact at the point of need. Then finally, there is the issue of fintech, financial technology, most specifically the issue of or the subject of insurance tech. Insurance tech is one of the fastest growing areas in the insurance market which by definition is disruptive, involving new cutting-edge technology and perhaps bringing some element of risk to the insurance operation. All these things are occurring really and are affecting the way that we do business. But the insurance executives of today need to be able to look around the corner and think about the new mega trends, the new mega trends in insurance...

Role of Data Analytics in Building a High-Performance Workforce - Nowadays, organizations face the ongoing challenge of optimizing their workforce to achieve peak performance. The key to unlocking the full potential of any organization lies in leveraging employee data at scale and using powerful data analytics tools to make informed decisions about talent acquisition, development, and retention. Data analytics in recruiting is the application of data-driven tools and insights to enhance the efficacy and efficiency of the recruitment process. HR professionals may improve applicant sourcing, make well-informed decisions, and find top talent more quickly by using data. Here are some examples of how data analytics is changing recruitment: Sourcing and talent acquisition: With data analytics, recruiters can identify the most effective sourcing channels. This way, they can focus their efforts on the channels that yield the best results according to historical data. Candidate screening: Candidate data and employee APIs allow recruiters to identify patterns and features that are common among successful employees. This data-driven approach encourages more objective decisions during the selection process. This also helps to reduce bias and improve the quality of hires...

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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...

Savings From Good Customer Analytics - So what is the size of the opportunity for savings from good customer analytics? For let’s say a moderate-sized health insurance company, you can be talking $6 billion to $10 billion in medical cost that they are paying out. So the ability to apply analytics to better understand how they can help improve the wellness of their population, to help understand how they can then control those costs, not control the health care treatment, but control the costs of those treatments overtime, you are talking about a massive opportunity on the medical cost side. Then administratively again, if you work the percentages, you are talking anywhere from a billion to $3 or $4 billion in administrative cost. So applying those analytics to automate some of those internal processes, to eliminate paper, to move to imaging, to move to workflow is an opportunity then to drive cost out of the internal support system, which then overall reduces the overall cost structure for the healthcare system. When you look at competing in the marketplace today, obviously competition is getting a lot greater. More and more companies need to separate themselves, and they are using analytics to do it. How are companies in the health care industry focusing with regards to customer analytics and operational excellence to differentiate themselves...

Student Major Analysis Example - The Student Major Analysis Example is an interactive dashboard detailing the majors of college students in a 16 year time span utilizing InetSoft's cutting-edge business intelligence software. Focusing on numerous majors from different years, this dashboard clearly portrays the statistics with a visually pleasing chart along with a detailed data sheet showing the information with text. Furthermore, along with other data sets, the specific chart below allows users to filter by different majors. This helps improve the analyzation process when digging for specific information. More importantly, InetSoft's solution is easy to use and navigate so that organization members can all have access to the tools and capabilities as well. With InetSoft's point-and-click environment, users can efficiently and effectively build powerful analytical tools like this one to monitor and easily visualize statistical data. Not only does this enhance everyday business procedures, but also helps record any significant data that may be beneficial for future use...

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Steps to Generate Analytics Reports - 1. Create a Prototype Report A prototype is a skeleton report that can be used as the basis for creating new reports using an Ad Hoc Wizard or using the Ad Hoc editing tools. (See Self-Service Paginated Reporting). Once a report is registered as a prototype, it will be available when a user creates a new report in the Portal. When you design a prototype report in Style Studio, you should set a 'Max Number of Pages' limit to prevent a user from accidentally designing an excessively large report. See the section on limiting the maximum number of pages in the DesktopApp documentation for more details. You should design a prototype template in such a way that it is suitable for modification by an end user. Typically, this means you design the report's basic layout, and include a single component (crosstab, table, chart) which the user can later modify using the Ad Hoc Wizard. You should assign a meaningful ID to this editable component so that the user can easily select it from the Wizard. (See the section on element ID and alias in the DesktopApp documentation for more details...

Successful Data Mining Projects - That’s a really good point in terms of preparing and massaging the data for a successful data mining project. That time factor could be, for example, if we don’t see any purchases from Joe Smith over a period of 18 months, we probably lost that customer, right? Flaherty: Exactly, in that time window, and I think that’s where the come difference between the data scientist and the decision maker is that time window is a marketing and sales business question. It’s not a science question, right? For example, the example you gave, you might be able to find out if somebody is going to leave over 18 month window. There are a lot of drivers that come directly from the business and have to be applied to the predictive analytics problem and the data transformation problem which allow you to answer these questions. But they are not directly from, they are not necessarily things that fall out of the data. They’re business drivers. They had to be taken into account when you perform predictive analytics. And there is kind of a balance where you have to make sure that the business drivers don’t interfere with the ability to do any predictive analytics...

Superstore Analytics -The dashboard provides a variety of filters to drill down and up for different granularity. The users can also filter the visualizations in the dashboard by region, state, category of products, sub-categories, and customer segments. It is also possible to filter the dashboard with 3 sliders which can determine the total amount of sales, and the amount of profit and one slider to choose the upper bound and lower bound for the order date. The dashboard is dynamic, and the filter will be applied to all the visualizations available in the dashboard...

Supply Chain Analytics - What are some of the key challenges in planning for manufacturing companies? One of the key challenges manufacturing companies is how to make the planning process much better, using much better data to do much better forecasting, particularly in the area of supply chain management. This is an area that is complex because supply chain management needs to bring together a lot of different processes. And if you want to bring together processes, you need first to bring together the right information. Every business unit has to deal with the same information and with the same analytics that they can use for doing the planning. This is I think one of the key challenges which is still found in many companies. Why is analytics a key driver for success? Analytics closes the loop between process management and the results of the processes and the data that you get out of the processes. So if you collect all this data, you are doing the analytics, and you are doing the planning based on the analytics in best case in real time, for example, and you use it also for planning...