About InetSoft's Data Integrator Solution

Looking for a data integrator? InetSoft offers a solution in its BI software that includes a flexible powerful data mashup engine.

Virtualizing Access to All Data Sources - This is the anti-silo approach. Clearly, this needs to be a unified and extensible set of applications that build on common reusable components. So often you’re driving towards real time or near real time data access which is essentially a cross platform silo agnostic infrastructure for multi-latency integration, batch, near real time, and real time that can all be accommodated as different delivery models within an end-to-end fabric. Then of course the semantic abstraction layer and registry are key of course to rolling up unified access, unified administration to all these disparate data repositories and so forth throughout your infrastructure. So if you build a semantic abstraction layer and registry, think of it in a broader sense which means it needs to be unified infrastructure where you maintain all the important artifacts which is metadata, which is schema definitions, which is business rules, predictive models, statistical models, a full range of report definitions, a full range of artifacts that are absolutely essential for maximum reuse across disparate BI efforts...

 

Virtualization Platforms Make Mashups Possible - Data mashup also has a second connotation in that it is more federated real time or right time as opposed to being persistent. There is value in persistent data which is typically found in enterprise data warehouses, marts, master data management systems, etc, but they live side by side in a sense with these virtualized data repositories. Again virtualization platforms make it possible to mash up all of your source systems including some of your transactional systems together into a data services layer. So as you will see today’s best of breed data virtualization platforms really are not just a data federation platform or an EII platform as in the past. They provide a broader range of virtualization across more structured and unstructured data and provide the data services capability. Now how does that relate to the rest of the layers? Obviously you have got infrastructure. You will have business processes or SOA types of middleware and message buses. You will have transaction systems, transactional services, applications, logic etc and analytical systems as well...

Visual Data Analysis - Visual data analysis is becoming one of the biggest trends for businesses across all industries, but what is it exactly and how can it benefit your business? Visual data analysis is the expansion of analytical reasoning to incorporate interactive visuals and dashboards.

Visual Data Mining - In modern day business, visual data mining is a technique that is increasingly providing a competitive advantage to those who want to harvest insights from their data to increase efficiency, spot trends, and get a better ROI on business efforts. This technique allows users to dynamically view the impact of different factors on data, helping companies with future decision making. InetSoft’s award-winning visual data mining software takes data mining to a whole new level, combining data mining with high-performance visuals, interactive dashboards, and intuitive reporting. Using these features, business users can simultaneously extract information from multiple sources using data mashup technology...

Visualization Big Data - When dealing with big data, it can be difficult to find a software that allows you to present effective visualizations in order to gain greater insight into the information. However, with InetSoft's big data visualization software, users can create pixel-perfect interactive dashboards and reports with just a few simple clicks.

Visualization and Data Mashups - In May 2010, InetSoft's Product Manager Byron Igoe participated in Information Management’s DM Radio Webcast, “The Last Mile: Data Visualization in a Mashed-Up.” The following is a transcript of that Webcast, which was hosted by Eric Kavanagh and included BI consultants William Laurent and Malcolm Chisholm. The topic for today is really interesting stuff. We’re talking about mashups. We’re going to find out what a mashup is all about. We had a little chat before the show, and there was some bantering going back and forth about a Business Week article back in 2007, perhaps, that said this is the year of the mashup, and well, it really wasn’t. But it’s not the first time the press has been a bit ahead of the game. But we’re going to find out from several experts what is going on in the field of data visualization and how mashups can really help you get a strategic view of enterprise data. So several great guests on today’s show, goodness gracious. We have several great guests. We have a couple expert guest hosts who are going to help us out today: William Laurent of William Laurent Advisors and Malcolm Chisholm of Ask Get, are in our New York studio, and then we’re going to hear from Byron Igoe of InetSoft. So we’re going to hear the consultant perspective and the vendor perspective, and of course, we’ll have our exciting roundtable discussion...

Visualization and Memory-Based Data Discovery Tool - The only other mega BI vendor I want to single out there is probably SAP with MIRO. So they are trying to build out a visualization and memory-based data discovery tool called Lumira. You will probably see it at SAPPHIRE which is next week, SAP’s major conference. You will probably see Lumira focused on very heavily. Probably the biggest only concern here many people already complain that there are so many tools in the Business Objects and SAP portfolio, and there’s been some shifting focus on which tools to use in which situations. So we haven’t seen a lot of SAP Lumira adoption yet because I think the entire SAP and Business Objects installed base has been looking at Webi and Crystal and a lot of the BW tools around design studio and analysis, et cetera. So we haven’t really seen as much adoption yet of Lumira in the market. But anyway, the moral of the story is data discovery alive and well and becoming really the dominant segment of the BI space right now and this is how people are integrating, reporting, and analyzing their data...

 

Visualize Coronavirus Infection and Test Data - Interact with this data visualization of Coronavirus infection and test data provided for free by InetSoft, a leader in data intelligence...

Visualizing Data on Google Map Charts - This page explains how you can use Google Maps in an InetSoft dashboard. By using a Google Map in your chart, you can lend a degree of realism to your geographical data, and provide important geographical context such as mountains, rivers, highways, and landmarks. In order to incorporate a Google Map in a dashboard, you must have an appropriate Google Maps Platform account. Obtain the API key from your Google Maps Platform account, and enter it in the InetSoft Enterprise Manager on the Web Map tab. Enable Use Web Map By Default to automatically use Google Maps when creating any Map Chart. Press Apply to save the settings. If you do not already have a data source with geographical data configured, begin by importing a data set into a Data Worksheet. The sample data we use here is from World Population Review which contains the top 200 US cities by population and has the structure shown below. The data set contains a lot of information about the different cities, but for this example we will just use the "pop2023" field as the measure. Create a new Dashboard based on the Data Worksheet (or other data source) you have created. To create a Google Map Chart using geographical regions such as states or cities or zip codes, add a new Chart to the Dashboard. Do not make the Chart size too large, because Google Maps have a 640px size limit imposed by Google. If the Chart component is too large, you will see a warning when you try to plot data on the map, and you will need to resize it...

What Are Join Types? - This article will explore the different types of joins that can be used for accessing database tables, and will explain how to implement these joins in the InetSoft application. In database theory, a join is a method of combining data from multiple tables while enforcing a constraint on the data. The nature of the constraint determines the type of join. The following sections explore the various join types and join constraints. The join of two database tables, A and B, can be thought of theoretically as comprising two steps: Form the Cartesian product (or Cross Join) of table A and table B, keeping all columns from both tables. The cross join is the combination of every row in Table A with every row in Table B, which results in a new table that has size length(A)*length(B). Remove every row that does not meet the specified join constraint. Note that from an implementation perspective, performing the above steps (in particular, forming the cross join) is not computationally efficient, and database software uses more efficient algorithms to do this. However, the steps above are useful for conveying the theoretical basis of joins, and for facilitating explanation of join types...

What Are the Types of Multidimensional Data? - Data with several dimensions or properties is referred to as multidimensional data. To comprehend complicated systems and events better, it is often utilized in data analysis, machine learning, and data visualization. We shall talk about numerous multidimensional data types and their uses in various domains in this post. Multidimensional Data Types and Categories Space Data Data with a geographic or geographical component is referred to as spatial data. It may include details on the position, size, and dimensions of objects, as well as the separation and direction between them. Satellite photography, topography maps, and GPS coordinates are a few examples of spatial data...

What Big Data Reporting Tools Are There? - What Big Data reporting tools are there? If we have all this data, and we need to do analytics on it. What reporting tools can we use to get the stuff back out? Can we use SSRS which is SQL Server Reporting Services? The short answer to that one is yes. The key to all of these, and I want to put a big asterisks on this, because these is the approach we’re going to use today. I really think it’s kind of a stop gap measure, and it’s going to change, but I mention thing called Hive that in effect creates a SQL abstraction layer over Hadoop. And what Microsoft has done is to create an ODBC driver for Hive, so in effect any ODBC client can talk to Hadoop via this ODBC driver, and the Hive layer over Hadoop, that includes reporting services. That includes knowledge integration services. It includes only the new tabular mode of analysis services. The original multidimensional mode of analysis services actually wouldn’t really work with ODBC. It’ll only it only work with only the Big Data sources. I didn’t even know that until I tried to get it to work against Hadoop, and I discovered that. And powerview, which is Microsoft’s new analysis and data visualization product, that is part of SQL Server, and it runs inside of Share Point 2010...

What Is Data Architecture as a Service? - Data governance might be hard to execute in a decentralized company. During the Enterprise Data World conference, two primary methodologies were presented: top-down and peer-based. Peer-based efforts, on the other hand, were shown to be more successful in decentralized companies, while top-down techniques were mostly beneficial in centralized organizations with a specific emphasis. Data Architecture as a Service (DAaaS) The authors suggest using a technique called Data Architecture as a Service to handle the issue of data governance in a decentralized organization (DAaaS). The hype around Software as a Service (SaaS) and Platform as a Service (PaaS) is combined with peer-based data architecture ideas in DAaaS. (PaaS). The authors aim to make it appear as if they are providing their customers with a valuable service while carrying out data architecture chores that are typically carried out via governance...

What is Data Friction? - Any impediments or inefficiencies that prevent a company's data from flowing freely are referred to as data friction. Technical problems like mismatched systems, poor data quality, or ineffective data management techniques like manual data input, a lack of automation, or insufficient data storage and retrieval techniques are just a few examples of the many various ways that these barriers might appear. Organizational impediments, such as siloed data, when data is divided into many departments or systems and is challenging to access and exchange, may also cause data friction. Making strategic choices based on the whole picture might be difficult without a full perspective of the organization's data. Businesses may have severe effects from data friction, including lost opportunities, wasted time and resources, increased risk of data breaches or compliance violations, and poor decision-making as a result of erroneous or incomplete data...

What Is Data Mesh Architecture? - Big data is expanding at a never-before-seen pace, and with it come the problems of data silos and data governance. A new method of data architecture is required since conventional methods often fail to address these issues. Enter data mesh architecture, a cutting-edge method of data architecture created to deal with the problems of data silos and data governance. The definition, guiding principles, advantages, and implementation of data mesh architecture will all be covered in this article. A new method of data architecture called data mesh architecture places an emphasis on decentralizing data ownership and management. Data ownership and administration are centralized in conventional methods to data architecture, which means that a single team is in charge of gathering, storing, and managing data. This often results in data silos, where several teams or departments within an organization each have their own data, making it difficult to access and utilize the data efficiently. Data mesh design, on the other hand, encourages decentralized data ownership and management by enabling individual teams to oversee their own data domains...

What Data Governance Success Looks Like - When we talk about what data governance success looks like, what it looks like is making sure that the policies that come down from this data governance process are actually executed in a regular and sustainable way. And that can look like anything from making data quality more robust, to making sure that metadata is formalized, to enacting data privacy and security logging within the data itself. So data stewardship is really the act of aligning the policies from data governance with the execution of data management, and tracking all of that data across its supply chain with the company. What does success look like. Success looks like a closed loop between the policy making and the constant evolution of the data quality and deployment over time. What is the role of data stewardship in data governance? Data stewards for better or for worse have really become roving line backers in a lot of companies, where, you know, anything having to do with data, the data steward shows up, but we find there are two qualifiers for a successful data steward. And one of them is that he or she is somebody who understands data at either the subject area level, or even the element level enough to track it across its supply chain or its lineage in a company. A data steward really understands the systems of origin, where the data’s created, where it comes from, how it’s touched across the organization by different systems and different users, and what its life cycle looks like in a company...

What Is Agile Development? - Agile development is a method of software development that emphasizes flexibility, collaboration, and customer satisfaction. The Agile methodology is based on the Agile Manifesto, a set of guiding values and principles for Agile software development. The Agile Manifesto states that individuals and interactions are more important than processes and tools, that working software is more important than comprehensive documentation, and that customer collaboration is more important than contract negotiation. One of the key principles of Agile development is the use of short development cycles, called sprints, which typically last between one and four weeks. During each sprint, a team works to deliver a potentially shippable product increment. This approach allows for rapid delivery of working software, which can then be tested and refined based on customer feedback. Another important aspect of Agile development is the use of cross-functional teams. In an Agile team, all members are responsible for the success of the project, and each team member brings their own unique skills and expertise to the table. This helps to ensure that all necessary skills are represented on the team, and that the team can work together effectively to deliver a high-quality product...

What Is a Cloud Flexible Solution? - A business intelligence solution that delivers the benefit of cloud computing and software-as-a-service while giving you the maximum level of control. - A solution that is designed and optimized for cloud computing and software-as-a-service where software and data are increasingly distributed between cloud-based and in-house applications. - A solution that allows highly flexible options for embedding and rebranding regardless of InetSoft-hosting, self-hosting, hybrid-cloud, or on-premise deployment. - A solution that is expressly designed to be embedded, whether inside an enterprise portal or another solution provider's cloud-based solution...

What is a Data Mashup Tool - So what is a data mashup tool? First of all, what you see in front, it looks like an Excel spreadsheet, but it has nothing to do with Excel, it's just a grid. How do I access my data? I select my query node, and I see a list of all my data sources. A data source could be a relational database like Oracle, SQL Server, or DB2. It could be a flat file. It could be a Web service. It could be an XML file, or even be another API. So we are very flexible in terms of the kind of data we can access. We can access almost any kind of data. I choose my data source. Now how would you expose the data to your developers, to your file users? You have two options, option one, you can create one or more predefined queries, you can just drag and drop fields for the basis of the visualization. Now the problem with query is that it's a fixed result set, so it's hard to envision every possible used case, every possible requirement upfront. So you may end up creating a large bunch of queries, and you may still have this back and forth process between your business team and your IT team, so instead of building all these queries, you can replace them with single, more flexible data model, or as you said the partition. The data model is not a query, it's a mapping, it's a logical layer, it's a business layer...

What Is Data Mining? - What is data mining? Data mining means search for hidden information. The locating of previously unknown patterns and relationships within data, using a business intelligence application, is called data mining. For example, it could be the locating of customers with a common interest in a retail establishments' database. Through a variety of techniques, data mining identifies nuggets of information in bodies of data. Data mining extracts information in such a way that it can be used in areas such as decision support, prediction, forecast and estimation. The data is often voluminous but of low value and with little direct usefulness in its raw form. It is the hidden information in the data that has value. In data mining, success comes from combining your knowledge of the data, with advanced active analysis techniques in which the computer identifies the underlying relationships and features in the data. The process of data mining generates models from historical data that are later used for prediction, pattern detection and more. The technique for building these models is called machine learning or modeling...

What Is a Data Mapping Tool? - You must have heard the phrase "form follows function." Most people know that the first step in software engineering is to create a user interface (UI) for your application. The UI consists of controls, panels, gadgets, etc. For people to seamlessly use your work product, you should make it easy to understand where they can find various widgets and controls. A data mapping tool can automate this process by enabling you to quickly design a user interface that follows the data architecture. Without an intelligent framework such as a data mapping tool, designing a flexible and responsive UI can be tricky and time-consuming. Read on to learn more about data mapping tools. Data mapping is a process that enables you to transform an unstructured data set into a structured one. This data transformation may be required based on the type of query you want to perform on it. For example, you might want to create a report or generate data for a specific business process. Mapping is an integral part of any database development project because it can help improve the performance and usability of your application. Mapping allows you to use your existing database tables to create reports and dashboards that are impossible with traditional database queries...

What Is Data Product Management? - Data product management is a lot like making the perfect pizza. You want it to be delicious, but you also want to be efficient with your ingredients. This can be tricky when dealing with large data sets and tons of customer data. However, if you use these best practices from data product management, you will be able to enjoy a tasty pie in no time. What Is Data Product Management? Data product management is the process of creating, managing, and optimizing data products. Data products are a combination of data and analytics that can be used to make business decisions. A data product may be a report, presentation, or dashboard. Various departments in an organization create data products, including sales, marketing, HR, finance, and operations. Data product managers are responsible for coordinating these teams to create and manage their data products. They must have strong technical skills in both programming languages, such as Python or R and SQL, and be able to write well-crafted reports. They should also be able to work closely with other departments to ensure that the information is accurate and relevant for their users...

What is a Database Field and What Are the Types? - A database is a set of data arranged in a particular way so that a computer program can use the necessary parts from it. Every database has several fields, records, and files. A database field refers to a set of values arranged in a table and has the same data type. A field is also known as a column or attribute. It is not necessary for the values included in a field to be in the form of text alone, as this is not a requirement. Some databases have the capability of having fields that contain images, files, and other types of media, while others have the capability of having data that links to other files that can be accessed by clicking on the field data. example of database fields in a table More Dashboard Examples In every database system, you can find three modes of fields. They are: Required Calculated Optional...

What is a Database Schema? - A database schema is the table structure of a database, independent of the data it contains. Database theory offers a mathematical description of database schemas, but from a practical perspective a schema specifies the table names, number of columns in each table, column names, and data types. The schema fully specifies the scope of data that can be read or written to the database, but does not include any data. The schema also specifies the certain columns are special 'key' columns for purposes of relating data. For example, the SALES_EMPLOYEES table below has a primary key column called EMPLOYEE_ID. This is the unique employee identifier. When this column appears within other tables, such as ORDERS, it is called a foreign key. A foreign key is simply a primary key from a different table...

What Is the Difference Between a Measure and a Metric? - Metrics and measures are frequently used interchangeably. They are frequently mistaken with one another and presented as the same item. It is easy to mix the two since, in some ways, a metric is a kind of measure, although a more useful and informative measure. While a measure is a basic number - for instance, how many kilometers you have drivenâ€"a metric contextualizes that measure - how many kilometers you have traveled per hour. This added information increases the usefulness of the same statistic by several orders of magnitude, particularly when looking at commercial KPIs. Conversions per thousand impressions are an illustration of a vital metric for an internet business. Understanding you have twenty conversions is a restricted measure in and of itself. A really positive KPI is knowing that those twenty conversions came from a hundred impressions. It is less beneficial if they came from a thousand impressions - context is crucial...

What Is the Difference Between Web Based and Cloud Based? - Web-based and cloud-based are two terms that are often used interchangeably, but they refer to different types of technology. Web-based refers to any application or software that is accessed through a web browser over the internet. These applications are hosted on a web server and can be accessed from any device with internet access and a web browser. Examples of web-based applications include Google Docs, Salesforce, and Trello. Cloud-based, on the other hand, refers to software and services that are hosted on remote servers, often referred to as the "cloud." These services can also be accessed over the internet, but they are not limited to web browsers. Cloud-based services can include software as a service (SaaS), infrastructure as a service (IaaS), and platform as a service (PaaS). Examples of cloud-based services include Amazon Web Services, Microsoft Azure, and Google Cloud Platform. One key difference between web-based and cloud-based is that web-based applications are typically designed for specific tasks, such as word processing or project management. Cloud-based services, on the other hand, provide the infrastructure and tools for organizations to build and run their own applications...

What Is Digital Business Observability? - The capacity of a company to acquire insight into the performance of its digital operations in real-time is referred to as "digital business observability." This implies that companies may continually monitor their digital systems, apps, and networks to find faults, solve difficulties, and find chances for improvement. Organizations may use observability to make data-driven choices that foster innovation, enhance the customer experience, and boost revenue. This article will explore the idea of "digital business observability," why it's important, and how businesses may use it to improve their online operations. Monitoring and analyzing the behavior of digital systems in order to find problems that affect performance, user experience, and business consequences is known as "digital business observability." This entails monitoring the behavior of software, networks, physical systems, and other digital assets to spot possible bottlenecks, flaws, and optimization possibilities. The objective of observability is to provide insight into intricate digital systems, which is essential for resolving problems, seeing trends, and coming to informed conclusions...

What Is Executional Agility? - Now let’s talk about executional agility. Nowadays you have to be able to deliver a new service in weeks, not months, because within two weeks all your competitors are going to offer the same thing. When you’re taking a look at marketing campaigns, you need a better understanding of customer buying behavior. Does it take you 5 or 6 months to get those insights disseminated, or can you quickly get those into your organization? That is the flexibility and agility that you need to build in. You have to start taking a very strong focus around processes and understanding of your customers. Through a focus around process, you can find what drives innovation and thereby continuously stay ahead of the competition. You need to drive agility. Agility is more than a term. You can quantify the benefits of this focus around agility, and you can see the return on your investment. It’s as much around driving the revenue growth as it is profitability. There is also a benefit in terms of resource utilization. A higher return on capital, as well as a more consistent return, are goals. In this case you are serving to lower valuation volatility. The result is your business can better anticipate changes as well as can better respond to those changes going forward. You have to make sure that as you focus on agility, you focus on the integrity of the process. Consistently manage those customer expectations. Build in operational dexterity...

What is Governed Data Integration? - Data integration is a complex process that involves combining data from different sources into a single, unified view. This process is essential for many organizations to gain insights and make informed decisions. However, the process of data integration can also be quite challenging, especially when it comes to ensuring that the data being integrated is accurate, consistent, and secure. In recent years, there has been a growing emphasis on the concept of "governed" data integration. This refers to the use of specific policies and procedures to ensure that data integration is done in a controlled and systematic way. The goal of governed data integration is to ensure that the resulting data is accurate, consistent, and secure, while also complying with legal and regulatory requirements. While governed data integration may seem straightforward, it can actually be quite complex. There are many factors to consider, including data quality, security, and privacy. In addition, there may be legal or regulatory requirements that must be taken into account...

What Is Graph Data Science? - The study of intricate interconnections and interactions between data pieces is the focus of the quickly expanding area known as "graph data science." To make sense of massive, interconnected datasets, it makes use of graph databases, graph algorithms, and machine learning techniques. This article will examine what Graph Data Science is, how it functions, and the many sectors in which it is used. A branch of data science called data science focuses on the analysis of data presented as graphs. A graph is a type of mathematical structure made up of a set of nodes (also known as vertices) and a set of connecting edges. Each edge depicts a connection or relationship between two nodes.Numerous types of data, such as social networks, transportation networks, biological networks, and others, can be represented using graphs. To glean insights from such data, graph algorithms and machine learning methods are used in graph data science...

Which KPIs Are Most Likely To Be a Vanity Metric? - There's a saying that your vanity metrics should be "all things to all people." This is true, but only up to a point. Sure, it's great to have analytics that everyone can use and are "user-friendly," but you also need to ensure they're metrics that can impact the business. Suppose you can't figure out which KPIs are most likely vanity metrics. In that case, it's time to stop doing them and start focusing on things like revenues and net new clients. These two will get your business more sales leads and clients better than any other existing metric. What Are Vanity Metrics? Vanity metrics are a type of metric that are not directly related to the core business. For instance, some companies track the number of likes on their Facebook page or the number of followers on Twitter. These metrics may be important to the company, but they don't really help them grow their business. The term "vanity metric" was coined by marketing expert Seth Godin in his book Linchpin: Are You Indispensable? The term is used because it sounds like something that's important only to you and not your customers...

Which Technologies Combine to Make Data a Critical Organizational Asset? - What has made customer data so crucial to modern businesses is a combination of two technologies. The first technology is content customization, the ability to adapt content and advertising to the individual customer. This is easiest to appreciate in the context of digital delivery of videos and other media, where content and ads are tailored to the individual viewer. But the development of content customization has penetrated less conspicuous areas also, such as the coupons delivered to customers at supermarket check-out, political fundraising emails, music playlists, financial service offers, cell phone plans, and so on...

Who Uses Data Reporting Tools? - Data reporting tools are used by a wide variety of individuals and organizations across industries. Some common users of data reporting tools include: Business analysts: They use data reporting tools to analyze business data and make informed decisions. Marketers: They use data reporting tools to analyze marketing campaigns and measure their effectiveness. Sales professionals: They use data reporting tools to track sales performance and identify areas for improvement. Financial analysts: They use data reporting tools to analyze financial data and make investment recommendations. Executives: They use data reporting tools to monitor organizational performance and make strategic decisions. Researchers: They use data reporting tools to analyze data collected during research studies. Data scientists: They use data reporting tools to explore, analyze and visualize large amounts of data to extract insights. Healthcare professionals: They use data reporting tools to track patient health data and improve patient outcomes. Government agencies: They use data reporting tools to track and report on key metrics related to their programs and services. Non-profit organizations: They use data reporting tools to track donations, monitor program effectiveness and report to donors and funders...

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 Effective Data Management Matters -Today, the global analytics market is worth $274 billion-an astonishing figure to say the least. With 44 zettabytes of data (and counting) currently active in the digital universe, 43% of top IT decision-makers are concerned that their current infrastructure won't be able to meet future data demands. Without an effective data management solution, your business will drown in data. While you will have access to information linked to every conceivable business department or function, wading through the data to extract actionable insights that will push your business forward will sap your time and drain your budget...

Why You Need Data Discovery Software - Welcome to our webinar today. I’m looking for to talking about this topic today because I think we’re at the cutting edge of a lot of what’s going on in the BI landscape today. Let me shift to my agenda here to show what I am talking about today. So what I want to do is set the scene a little bit with some trends and objectives and just talk about what's going in the BI landscape and what I see as business drivers for change in BI. And then we’ll talk a little bit more about data discovery and unified information access, some definitions and descriptions so you can see where we’re going with that. There was a report on data discovery and UIA recently so I will enumerate some of the issues that came up in that. So I think you know this current observation. Probably no one would dispute this, but I think one of the big things going on in BI and analytics right now is the idea that we’re moving from a time when the focus has been on getting the data in and storing the data. Certainly all those issues are very important, and we could devote many, many webinars to those issues, but really I think most organizations are focused on getting information into the workplace, putting it to work, so that is not just sitting some place...

Why Your Business Needs Machine Learning - Below is an article by Sara Carta, an experienced tech expert who writes on her site Enlightened Digital, to share her passion with others around the web. After 15 years in the industry, her goal is to bring information on all technology to the masses. Her philosophy is to create each article so that anyone can understand the content, whether a consumer or a tech expert. Check out her site at Enlightened-Digital.com. Machine learning, or ML, is an important advancement for businesses. Computers can now do some of the time consuming work that humans used to do. Those humans can now focus on other more complicated or high level work. It's a relatively new technology, but it's making an impact for all the right reasons. The main benefit of machine learning is that it can gather insights while collecting data and analyze it in depth. It can discover things in the data that people cannot. Which means it's beneficial from a time saving standpoint as well as the quality of insights it can derive. There's more to it than just analyzing data all day, the benefits are numerous and the possibilities seemingly endless...

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