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Visual Analytics Software - Are you looking for a method to explore data in a simpler, more effective way than traditional static reports? Visual analytics software provides critical insight into solving problems and answering questions in data using interactive graphics...
What Are the 5V's of Data Analytics? - The process of reviewing and analysing data in order to extract insights and make educated decisions is referred to as data analytics. It entails collecting, processing, and analysing data from multiple sources, including as databases, spreadsheets, and internet platforms, using a variety of methodologies and tools. The 5V's of Data analytics are: Velocity Volume Variety Value Veracity. Volume refers to the amount of data present in the database. The value of the data is determined by its size. When you have an enormous amount, it is considered big data. It is also relative to the computing power available. But generally, Data analytics is founded upon the presence of a large volume of data without which it is impossible to create advanced models for machine learning or AI. The tech world is progressing toward AI which requires processing, learning, and understanding huge volumes of data. Companies trying to beat their competitors must have such data to develop and use advanced analytics. The speed with which data is being accumulated and accessed refers to the Velocity. In this tech era, you can find huge amounts of data flowing in and out every day. This continuous flow of data must be quick so that it is available for businesses to use to their advantage at the right time. The market situation is highly competitive which demands creating timely strategies. This can only be possible with the help of big data...
What Are All the Types of Production Analysts? - There are different types of production analysts, depending on the nature of the production process and the specific industry. Here are some examples: Manufacturing Production Analysts: They are responsible for monitoring and analyzing production processes in manufacturing facilities. They collect data on productivity, quality, efficiency, and safety, and use statistical methods to identify opportunities for improvement. Supply Chain Production Analysts: They focus on the supply chain and logistics aspects of production. They track inventory levels, analyze demand patterns, and optimize production schedules to ensure timely delivery of goods. Operations Production Analysts: They work in a variety of industries and are responsible for analyzing production operations. They may monitor plant performance, equipment utilization, and workforce efficiency to improve productivity and reduce costs...
What Are the Different Types of Data Analysis? - Descriptive Analysis Descriptive analysis is the process of using statistical techniques to describe or summarize a set of data. It is popular for its ability to generate accessible insights from otherwise uninterpreted data with simple discrete numerical answers. They are frequency, mean, median, mode, percentiles, and quartiles. 2. Diagnostic Analysis Diagnostic analysis is a form of advanced analytics that examines data or content to answer the question "why." It is performed by using human-driven techniques such as drill-down, data discovery,and data mining. It also includes making calculations using statistical software or functions for such things as correlations and trend lines. 3. Exploratory Analysis Exploratory analysis is the critical process of performing initial investigations into data to discover patterns, groupings, correlations, spot anomalies, identify outliers, develop hypotheses and test assumptions. It is entirely a human-driven visual approach...
What Are the Operations of OLAP? - OLAP (Online Analytical Processing) is a multidimensional analysis and reporting technology that enables businesses to quickly analyze and explore their data. OLAP operations can be classified into two categories: Slice and Dice and Roll-up and Drill-down. Slice and Dice: "Slice and Dice" operations allow users to analyze data from different perspectives. They involve selecting a subset of data from a multidimensional dataset based on one or more criteria. The two types of "Slice and Dice" operations are: Slice: This operation involves selecting a single dimension from the OLAP cube to slice the data along that dimension. For example, a user can slice the data by selecting only the data for a particular region or time period. Dice: This operation involves selecting multiple dimensions from the OLAP cube to slice the data along those dimensions. For example, a user can dice the data by selecting only the data for a particular region and time period...
What-If Analysis - InetSoft's what-if analysis feature assists analysts in quantifying uncertainty in causal relationships and optimizing resource allocation while guiding decisions. InetSoft's Style Intelligence is the comprehensive real-time analytical reporting and dashboard software solution used at thousands of enterprises worldwide. View the example below to learn more about the Style Intelligence solution...
What Is Augmented Analytics? - Augmented analytics is a kind of data analytics that automates and improves the analytical process by using machine learning and artificial intelligence (AI) technologies. Data scientists must manually compile, analyze, and interpret data in conventional analytics, which may be a laborious and difficult procedure. But firms may automate data preparation and analysis using augmented analytics, giving business users access to insights and the ability to make wise choices in real-time. In this post, we'll examine what augmented analytics are, why they're important, and how they may help businesses in a variety of sectors. Defining Augmented Analytics Augmented analytics is a sophisticated kind of data analytics that automates and improves the analytical process using machine learning and AI techniques...
What Is the Difference Between a Sales Operations Analyst and an Operations Analyst? - A sales operations analyst and an operations analyst are two distinct roles that serve different functions within an organization. A sales operations analyst is primarily responsible for analyzing and optimizing the sales operations of a company. They use data and analytics to identify trends, patterns, and areas for improvement in the sales process. They monitor key performance indicators (KPIs) such as revenue, pipeline, win rates, and customer acquisition costs to improve the effectiveness and efficiency of the sales team. On the other hand, an operations analyst is responsible for analyzing and optimizing the operations of a company as a whole. They focus on improving the efficiency and effectiveness of various business processes across departments. They analyze data to identify bottlenecks, inefficiencies, and areas for improvement, and make recommendations for process optimization, cost reduction, and productivity improvements...
What Key Performance Indicators Do Hospital Operations Analysts Use? - Hospital operations personnel must report out on areas for improvement or correction for upper management. These professionals use key performance indicators (KPIs) and analytics to assess and track the performance of their hospitals in order to make educated choices. In this article, we will cover the key performance indicators (KPIs) and analytics that are used by hospital operations experts. Patient satisfaction is one of the most important KPIs for hospital operations experts. Because they gauge how successfully hospitals are serving patients' needs and expectations, patient satisfaction measures are crucial. Patient satisfaction may be measured in a number of ways, including via surveys, feedback forms, and other means. To assess the level of care and support offered by their hospitals, hospital operations professionals utilize these criteria...
What Kind of Analytics Do Actuaries Perform? - Actuaries create risk categories and design models to minimize the damage when undesirable events do occur. Actuaries also help make strategic decisions and communicate solutions for complex financial issues. Actuaries can work in various types of companies, such as insurance, consulting, government, hospitals, banks, and investment firms. They typically specialize in one field of insurance, such as health, life, property, casualty, or pension. Actuaries also perform actuarial analysis, which is a form of asset-to-liability analysis that ensures they have the funds to pay the required liabilities.Some of the analytics initiatives that actuaries are involved in are marketing and distribution, pricing and underwriting, claims and fraud, and customer retention and loyalty. Actuaries use data from various sources and apply advanced statistical techniques to provide insights and recommendations for these initiatives...
What KPIs and Analytics Do Airline Operations Professionals Use? - Even the smallest inefficiency or delay may have serious repercussions in the highly competitive and sophisticated aviation business. As a result, experts in airline operations are always searching for methods to streamline their processes and raise their performance. Professionals in airline operations employ key performance indicators (KPIs) and analytics as crucial tools to accomplish these objectives. We will examine the KPIs and analytics used by airline operations specialists to manage their operations in this post. One of the most important KPIs for airlines is OTP. It calculates the proportion of flights that reach their destination on schedule. OTP is a critical component of customer satisfaction and is used by airlines as a gauge of their performance and dependability. An airline that continuously has a high OTP is more likely to draw in new passengers and keep up a good reputation...
What KPIs and Analytics Do Data Operations Professionals Use? - The efficient, precise, and secure processing of data is the responsibility of data operations specialists. They oversee data analysis, storage, warehousing, and pipelines. Data operations experts employ key performance indicators (KPIs) and analytics to assess the effectiveness of their operations and make wise choices. We will examine the most popular KPIs and analytics utilized by data operations experts in this post. Data consistency, reliability, and error-freeness are gauged by data correctness. Inaccurate data may result in inaccurate conclusions and judgments, which can have serious repercussions for a company. Data correctness may be measured by data operations experts using KPIs like data error rates, data completeness, and data consistency...
What KPIs and Analytics Do FinOps Analysts Use? - The importance of the FinOps (Financial Operations) professional has grown as firms continue to reap the advantages of cloud computing. These people are in charge of overseeing the financial elements of cloud operations, such as budgeting, cost allocation, and cost optimization. Key performance indicators (KPIs) and analytics are used by FinOps professionals to measure and evaluate their organization's cloud expenditures. We will examine some of the KPIs and metrics that FinOps professionals utilize most often in this post. Making ensuring that their organization's cloud expenditure is optimized to get the most value for the money invested is one of the main duties of FinOps professionals. They depend on KPIs and analytics to analyze expenditure patterns and pinpoint places where expenses may be cut in order to do this...
What KPIs and Analytics Do Project Analysts Use? - Understanding key performance indicators (KPIs) and analytics as a project analyst is crucial to making sure a project is successful. Project analysts may use these indicators to monitor progress, spot possible problems, and reach data-driven conclusions. We'll look at some of the most popular KPIs and analytics in project management in this post. The effort required to get the intended result is referred to as the project's scope. Project analysts may gauge a project's advancement in relation to its initial scope with the use of project scope KPIs. The most typical scope KPIs are as follows: Scope Creep: Any unauthorized additions or modifications to the project scope are referred to as scope creep. Monitoring changes in the project's scope over time will allow project analysts to assess scope creep...
What KPIs and Analytics Do Vendor Analysts Use? - Vendor analysts are experts who focus on assessing the goods and services provided by vendors in a certain market. Their objective is to provide companies information about these suppliers' performance so they can make wise investments in the goods and services they need. Vendor analysts employ key performance indicators (KPIs) and analytics as crucial tools to do this. We will examine the KPIs and analytics used by vendor analysts to evaluate the performance of vendors in this post. Competitive Analytics: Analyzing competitor data to understand their advantages and disadvantages, positioning in the market, and pricing tactics is known as competitive analytics. Competitive analytics are used by vendor analysts to assess how well vendors are doing in comparison to their rivals and to spot possibilities to achieve a competitive edge...
What KPIs and Analytics Do Security Intelligence Analysts Analysts Use? - Security concerns have grown significantly for companies of all sizes. Nowadays, businesses spend money on effective security measures to safeguard their systems, networks, and data. They must have a thorough awareness of their security posture and dangers in order to do this. Security intelligence analysts can help in this situation. These experts are in charge of collecting, analyzing, and interpreting data to spot possible risks and weaknesses. They achieve this by using a variety of statistics and key performance indicators (KPIs). In this post, we'll examine in more detail the KPIs and analytics that security intelligence analysts use to safeguard the information assets of their organizations. The accuracy of their threat intelligence is one of the major KPIs for security intelligence analysts. This entails calculating the proportion of notifications that are reliable and suitable for action. High levels of precision show that the team is successfully identifying and addressing serious dangers. On the other side, low accuracy levels can mean that the team is spending time and money looking for false positives...
What KPIs Do Sales Operations Analysts Use? - Key performance indicators (KPIs), trends, and sales data are measured and analyzed by sales operations analysts. They are essential in fostering corporate development by offering perceptions and suggestions that boost sales results. We will examine the KPIs and analytics used by sales operations analysts to assist companies in achieving their sales objectives in this post. Any firm that depends on sales must have sales operations. It serves as the foundation of the sales department and is in charge of making sure all operations linked to sales function smoothly. Sales operations include a wide range of tasks, including controlling the sales funnel, predicting sales, controlling territories and quotas, as well as collecting data and offering insights into sales performance. Sales data must be gathered and examined by sales operations analysts in order to spot patterns and areas for development. They collaborate closely with sales executives to create plans and programs that boost sales. Their work enables companies to make choices that optimize profits and revenue...
What KPIs Do Network Analysts Use? - The best possible performance of an organization's network infrastructure must be guaranteed by network analysts. For network performance analysis, monitoring, and optimization, they use a variety of tools and methods. The use of key performance indicators (KPIs) and analytics to assess the network's performance and pinpoint opportunities for development is one of the most crucial facets of this position. The list of KPIs and analytics that network analyzers utilize will be covered in this article. The proportion of time a network is operational and accessible to users is known as network availability. This KPI is used by network analysts to verify that the network is up and operating as often as possible, reducing downtime and interruptions to business activities. The amount of time it takes for data to move between two points on a network is known as network latency. This KPI is used by network analysts to gauge network speed and pinpoint locations where latency is impacting performance. Slow response times and a poor user experience may be caused by high network latency...
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What Makes a Predictive Analytics Enterprise - Essentially it is this cross-functional use of data directed from a strategic perspective to inform decision making across the organization that makes you a truly predictive analytics enterprise. What do we mean by that? Well, for example, marketing should interact with risk and understand the profiles of potentially high risk customers. You don't necessarily want to spend money enticing high risk customers to take your product. If you have analytics embedded across the organization at an enterprise level, you can ensure that you are attracting the right customer, and you're spending your money in the right way. Your company probably has a very wide range of individual customers with very different needs, and a significant proportion of insurance today is provided through brokers. Very often the first time a customer makes contact directly with the insurance provider is when they're in the position to make a claim. That makes it very difficult for an insurance provider to build a relationship with their customer. The goal for the insurance provider is essentially to ensure that the experience of the customer within the claim process is a satisfactory one and that it's tailored to the individual needs of that customer. I'd like to show you how predictive analytics can help you know your customer at that individual level...
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What We Do with the Analytics Which Makes a Difference - Moving on, what we recognize is that analytics is not a destination. It's really what we do with the analytics which makes a difference. This slide indicates really that we can use analytics to ensure that the insurance company is meeting the strategy intended set out at the beginning, and if that strategy is being missed or diverted to make the right connection, analytics can help us in terms of the creation of new tactics to deliver on a strategic imperative, for example, or for the creation of best practices to modify our behaviors. The analytics solution can fit in many part of the organization through distribution, through to sales, supply chain, operational management, marketing, and claims. We will be talking specifically about a couple of those areas a little later. At the end of the day we're not doing this for the fun of it. It's around using analytics to change your behaviors and change our practices for an insurance company to obtain competitive advantage and to create a differentiated service and product, and ultimately to obtain profitable growth. We are nowadays in what's described as the fourth age of analytics, and it might be helpful for you as individuals just to think about where you sit in this particular hierarchy of analytics. The foundational analytics is very much the description level, the use of BI tools for reporting to identify what has happened and what is currently happening. The second layer of analytics is in the area of prediction which is our focus today which helps us anticipate what may happen and perhaps raise alerts or forecast. The third age is what we call prescriptive where we align rule based technology into our predictive models to help us automate our decision...
Where Predictive Analytics Is Being Used - Where within the organization do we find predictive analytics being used? Predictive analytics historically has been in two areas. One of the most common uses has been within the marketing organization looking at the likelihood of individuals’ potential purchasing behavior of products and services. What are the influences and impressions of advertising? We’ve also seen predictive analytics used quite a bit for determining customer behavior. How do we optimize our interactions with our customers knowing that they’re going to react in certain ways based on their demographic profiles or their previous purchasing habits. We’re also seeing now how predictive analytics can be used across the manufacturing supply chain. We’re looking at things like mean time between failure and other particular processes that are well defined. Potential results will be known based on how things are operated. Besides marketing, customer facing issues and the supply chain, we also do see that many organizations are starting to apply predictive analytics into areas such as sales and finance and also looking at now the potential use of them in regards to working with suppliers and where materials are coming from to actually manufacturer products. Since there’s a lot of information that’s outside of our organization, we can start using predictive analytics as guideposts to everything from potentially prices of how companies stock is trading down to the commodity pricing of materials that are used for manufacturing products as well...
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Why are BI and data analytics important in the construction industry? -Without a clear-cut vision or definitive ways to measure or benchmark progress, many construction projects fall behind key deadlines while overshooting their budgets. The key reason why using analytical BI solutions and dashboard software in the construction industry is so important comes down to the sheer scale or scope of the task at hand. If you work with BI-boosting dashboard software, your construction company will...
Why Big Data Analytics Is So Important In Government - Today we’re going to talk about why big data analytics is so important in government globally, and especially in this economy. Commercial industry has been using data. The FedEx’s, the Wal-Mart’s, companies are really taking advantage of using their data, viewing it as an asset, making better decisions, faster decisions, and responding to shifts in the marketplace. Certainly in today’s environment the government is very interested in trying to do those same things. Budgets are shrinking. There are shortfalls in revenue. They just have to get smarter and better at what their doing. The Obama administration is out there promoting transparency, promoting visibility into what the government does, how they spend their money, the decisions they are making, and passing to on to every single citizen. So what they are doing today to put that in front, increase that visibility, make better decisions around policy, understand the direction of the country, itself, is very critical. In terms of where in the government the move towards better analytics is taking place, it’s in the financial departments of the various agencies. They are leading the way in terms of increasing that visibility of spending levels. Also, with healthcare reform, there is interest in knowing what is happening with costs and the aging population...
Why Do Companies Need Analytics? - Let’s start with a simple question of why? Why is it that companies need analytics? What is it that’s driving the urgency? The answer to that question is really closely tied to some of the main trends that we talked about before in terms of data users and time. So at the top of the list once we get once again we see data. Companies feel that all too often their most critical decisions are based on data that they have little faith in, either because it’s inaccurate, it’s dirty, it’s incomplete or sometimes just too fragmented or siloed within different departments. As we see with the third pressure on the chart here, the pressure also becomes evident from that business user perspective, that tactical or operational level. Companies struggle with getting the visibility they need into their key processes and then understanding what it all means. They look at analytics to help really sift through that operational data to produce insights that can help improve those processes. And lastly, we again see the urgency for analytics. A common reason why companies implement analytics, well it’s because people are clamoring for it. And not always technical people, line of business decision makers across many areas of the company again are raising their hands and asking for that better and deeper analytical capability...
Why 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...
With Predictive Analytics It's Individualized Decision Making - In that particular case while the customer's claim is not being fast tracked, they are happy because they have an understanding of the length of time it will take to process their claims so they're not operating in the dark. Without predictive analytics, essentially it's simple rules based decision making, but it's more of a one size fits all manner of application.
Whereas, with predictive analytics it's individualized decision making, and it's tailored to the behavior of the customer and other related data. The main benefit I guess in this particular instance of applied predictive analytics was the time to resolution. The claim is being resolved in a much shorter time, and the number of contacts required between the customer and the insurance provider has been reduced which leads to an increase in customer satisfaction and lower cost. I would also like to take a moment to remind you of the ability to use unstructured data to inform decision making. There was a lot of pretext information out there from interactions during phone calls that may be posted in social media. We have essentially seen and conducted analyses of the type of data, and we can see how it can be used to understand what is it these customers are actually saying.
Also applying an appropriate framework to the study of these type of data ensures that you have an objective and repeatable and automated analytics process. Don't forget that the unstructured data is very valuable also...