What Is The Best Business Intelligence Software

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Analytics Beyond Spreadsheets If the term "self-service business intelligence (BI) tools" makes you think you'll be using spreadsheets for your data analysis and graphing needs, then you're not alone. While Microsoft Excel and other spreadsheets have existed now for many years, spreadsheets aren't always the right tools for many BI tasks. Creating charts in Excel is often a frustrating hit-or-miss proposition because you don't always know what data you are trying to show at first.

You also don't always begin with the right kind of data and oftentimes you don't even know how to interact with the spreadsheet to show your results in the best possible circumstances. Spreadsheets also fall down when the data isn't well-structured or can't be sorted out in neat rows and columns. And, if you have millions of rows or very sparse matrices, then the data in a spreadsheet can be painful to enter and it can be hard to visualize your data.

Spreadsheets also have issues if you are trying to create a report that spans multiple data tables or that mixes in Structured Query Language (SQL)-based databases, or when multiple users try to maintain and collaborate on the same spreadsheet. A spreadsheet containing up-to-the-minute data can also be a problem, particularly if you have exported graphics that need to be refreshed when the data changes.

Finally, spreadsheets aren't good for data exploration; trying to spot trends, outlying data points, or counterintuitive results is difficult when what you are looking for is often hidden in a long row of numbers. While spreadsheets and self-service BI tools both make use of tables of numbers, they are really acting in different arenas with different purposes. A spreadsheet is first and foremost a way to store and display calculations.

While some spreadsheets can create very sophisticated mathematical models, at their core it is all about the math more than the model itself. What Is Business Intelligence? Business intelligence (BI) is an umbrella term meant to cover all of the activities necessary for a company to turn raw information into actionable knowledge. In other words, it's a company's efforts to understand what it knows and what it doesn't know of its own existence and operations.

The ultimate goal is being able to increase profits and sharpen its competitive edge. Framed that way, BI as a concept has been around as long as business. But that concept has evolved from early basics [like Accounts Payable (AP) and Accounts Receivable reports and customer contact and contract information] to much more sophisticated and nuanced information. This information ranges across everything from customer behaviors to IT infrastructure monitoring to even long-term fixed asset performance.

Separately tracking such metrics is something most businesses can do regardless of the tools employed. Combining them, especially disparate results from metrics normally not associated with one another, into understandable and actionable information, well, that's the art of BI. The future of BI is already shaping up to simultaneously broaden the scope and variety of data used and to sharpen the micro-focus to ever finer, more granular levels.

BI software has been instrumental in this steady progression towards more in-depth knowledge about the business, competitors, customers, industry, market, and suppliers, to name just a few possible metric targets. But as businesses grow and their information stores balloon, the capturing, storing, and organizing of information becomes too large and complex to be entirely handled by mere humans. Early efforts to do these tasks via software, such as customer relationship management (CRM) and enterprise resource planning (ERP), led to the formation of "data silos" wherein data was trapped and useful only within the confines of certain operations or software buckets.

This was the case unless IT took on the task of integrating various silos, typically through painstaking and highly manual processes. While BI software still covers a variety of software applications used to analyze raw data, today it usually refers to analytics for data mining, analytical processing, querying, reporting, and especially visualizing. The main difference between today's BI software and Big Data analytics is mostly scale.

BI software handles data sizes typical for most organizations, from small to large. Big Data analytics and apps handle data analysis for very large data sets, such as silos measured in petabytes (PBs). Self-Service BI and Data Democratization The BI tools that were popular half a decade or more ago required specialists, not just to use but also to interpret the resulting data and conclusions. That led to an often inconvenient and fallible filter between the people who really needed to get and understand the business—the company decision makers—and those who were gathering, processing, and interpreting that data—usually data analysts and database administrators.

Because being a data specialist is a demanding job, many of these folks were less well-versed in the actual workings of the business whose data they were analyzing. That led to a focus on data the company didn't need, a misinterpretation of results, and often a series of "standard" reporting that analysts would run on a scheduled basis instead of more ad hoc intelligence gathering and interpretation, which can be highly valuable in fast-moving situations.

This problem has led to a growing new trend among new BI tools coming onto the market today: that of self-service BI and data democratization. The goal for much of today's BI software is to be available and usable by anyone in the organization. Instead of requesting reports or queries through the IT or database departments, executives and decision makers can create their own queries, reports, and data visualizations through self-service models, and connect to disparate data both within and outside the organization through prebuilt connectors.

IT maintains overall control over who has access to which tools and data through these connectors and their management tool arsenal, but IT no longer acts as a bottleneck to every query and report request. As a result, users can take advantage of this distributed BI model. Key tools and critical data have moved from a centralized and difficult-to-access architecture to a decentralized model that merely requires access credentials and familiarity with new BI software.

This results in a whole new kind of analysis becoming available to the organization, namely, that of experienced, front-line business people who not only know what data they need but how they need to use it. The emerging crop of BI tools all work hard at developing front-end tools that are more intuitive and easier to use than those of older generations—with varying degrees of success. However, that means a key criteria in any BI tool purchasing decision will be to evaluate who in the organization should access such tools and whether the tool is appropriately designed for that audience.

Most BI vendors indicate they're looking for their tool suites to become as ubiquitous and easy to use for business users as typical business collaboration tools or productivity suites, such as Microsoft Office. None have gotten quite that far yet in my estimation, but some are closer than others. To that end, they tend to focus on three core types of analytics: descriptive (what did happen), prescriptive (what should happen now), and predictive (what will happen later).

What Is Data Visualization? In the context of BI software, data visualization is a fast and effective method of transferring information from a machine to a human brain. The idea is to place digital information into a visual context so that the analytic output can be quickly ingested by humans, often at a glance. If this sounds like those pie and bar charts you've seen in Microsoft Excel, then you're right.

Those are early examples of data visualizations. But today's visualization forms are rapidly evolving from those traditional pie charts to the stylized, the artistic, and even the interactive. An interactive visualization comes with layered "drill downs," which means the viewer can interact with the visual to reach more granular information on one or more aspects incorporated in the bigger picture.

For example, new values can be added that will change the visualization on the fly, or the visualization is actually built on rapidly changing data that can turn a static visual into an animation or a dashboard. The best visualizations do not seek artistic awards but instead are designed with function in mind, usually the quick and intuitive transfer of information. In other words, the best visualizations are simple but powerful in clearly and directly delivering a message.

High-end visuals may look impressive at first glance but, if your audience needs help to understand what's being conveyed, then they've ultimately failed. Most BI software, including those reviewed here, comes with visualization capabilities. However, some products offer more options than others so, if advanced visuals are key to your BI process, then you'll want to closely examine these tools. There are also third-party and even free data visualization tools that can be used on top of your BI software for even more options.

Products and Testing In this review roundup, I tested each product from the perspective of a business analyst. But I also kept in mind the viewpoint of users who might have no familiarity with data processing or analytics. I loaded and used the same data sets and posed the same queries, evaluating results and the processes involved. My aim was to evaluate cloud versions alone, as I often do analysis on the fly or at least on a variety of machines, as do legions of other analysts.

But, in some cases, it was necessary to evaluate a desktop version as well or instead of the cloud version. One example of this is Tableau Desktop, a favorite tool of Microsoft Excel users who simply have an affinity for the desktop tool (and who just move to the cloud long enough to share and collaborate). I ended up testing the Microsoft Power BI desktop version, too, on a Microsoft representative's recommendation because, as the rep said, "the more robust data prep tools are there.

" Besides, said the rep, "most users prefer the desktop tool over a web tool anyway." Again, I don't doubt Microsoft's claim but that does seem weird to me. I've heard it said that desktop tools are preferred when the data is local as the process feels faster and easier. But seriously, how much data is truly local anymore? I suspect this odd desktop tool preference is a bit more personal than fact-based, but to each his own.

Then there's Google Analytics, a pure cloud player. The tool is designed to analyze website and mobile app data so it's a different critter in the BI app zoo. That being the case, I had to deviate from using my test data set and queries, and instead test it in its natural habitat of website data. Nonetheless, it's the processes that are evaluated in this review, not the data. While I didn't test any of these tools from a data scientist's role, I did mention advanced capabilities when I found them, simply to let buyers know they exist.

IBM Watson Analytics is one tool with the ability to extend to highly advanced features and was also one of the easiest to use upfront. IBM Watson Analytics is well-suited for business analysts and for widespread data democratization because it requires little, if any, knowledge of data science. Instead, it works well by using natural language and keywords to form queries, a characteristic that can make it valuable to practically anyone.

It's highly intuitive, very powerful, and easy to learn. Microsoft Power BI is a strong second as it, too, is powerful while also familiar, certainly to any of the millions of Microsoft business users. However, there are several other powerful and intuitive apps in this lineup from which to choose; they all have their own pros and cons. We'll be adding even more in the coming months. One thing to watch out for during your evaluations of these products is that many don't yet handle streaming data.

For many users, that won't be a problem in the immediate future. However, for those involved with analyzing business processes as they happen, such as website performance metrics or customer behavior patterns, streaming data can be invaluable. Also, the Internet of Things (IoT) will drive this issue in the near future and make streaming data and streaming analytics a must-have feature. Many of these tools will have to up their game accordingly so, unless you want to jump ship in a year or two, it's best to think ahead when considering BI and the IoT.

BI and Big Data Another area in which self-service BI is taking off is in analyzing Big Data. This is a newer development in the database space but it's driving tremendous growth and innovation. The name is an apt descriptor because Big Data generally refers to huge data sets that are simply too big to be managed or queried with traditional data science tools. What's created these behemoth data collections is the explosion of data-generating, tracking, monitoring, transaction, and social media tools (to name a few) that have become so popular over the last several years.

Not only do these tools generate loads of new data, they also often generate a new kind of data, namely "unstructured" data. Broadly speaking, this is simply data that hasn't been organized in a predefined way. Unlike more traditional, structured data, this kind of data is heavy on text (even free-form text) while also containing more easily defined data, such as dates or credit card numbers. Examples of apps that generate this kind of data include the customer behavior-tracking tools you use to see what your customers are doing on your e-commerce website, the piles of log and event files generated from some smart devices (such as alarms and smart sensors), and broad-swath social media tracking tools.

Organizations deploying these tools are being challenged not only by a sudden deluge of unstructured data that quickly strains storage resources [think beyond terabytes (TB) into the PB and even exabyte (EB) range] but, even more importantly, they're finding it difficult to query this new information at all. Traditional data warehouse tools generally weren't designed to either manage or query unstructured data.

New data storage innovations such as data lakes are emerging to solve for this need, but organizations still relying exclusively on traditional tools while deploying front-line apps that generate unstructured data often find themselves sitting on mountains of data they don't know how to leverage. Enter Big Data analysis standards. The golden standard here is Hadoop, which is an open-source software framework that Apache specifically designed to query large data sets stored in a distributed fashion (meaning, in your data center, the cloud, or both).

Not only does Hadoop let you query Big Data, it lets you simultaneously query both unstructured as well as traditional structured data. In other words, if you want to query all of your business data for maximum insight, then Hadoop is what you need. You can download and implement Hadoop itself to perform your queries, but it's typically easier and more effective to use commercial querying tools that employ Hadoop as the foundation of more intuitive and full-featured analysis packages.

Notably, most of the tools reviewed here, including Chartio, IBM Watson Analytics, Microsoft Power BI, and Tableau Desktop, all support this. However, each requires varying levels of configuration or even add-on tools to do so—with IBM, Microsoft, and Tableau offering exceptionally deep capabilities. However, both IBM and Microsoft will still expect customers to utilize additional tools around aspects such as data governance to ensure optimal performance.

Finding the Right BI Tool Given the issues spreadsheets can have when used as ad hoc BI tools and how firmly ingrained they are in our psyches, finding the right BI tool isn't a simple process. Unlike spreadsheets, BI tools have major differences when it comes to how they consume data inputs and outputs and manipulate their tables. Some tools are better at exploration than analysis, and some require a fairly steep learning curve to really make use of their features.

Finally, to make matters worse, there are dozens if not hundreds of such tools on the market today, with many vendors willing to claim the self-serve BI label even if it doesn't quite fit. Getting the overall workflow down with these tools will take some study and discussion with the people you'll be designating as users. Tableau Desktop and Microsoft Power BI, for example, will start users out with the desktop version to build visualizations and link up to various data sources.

Once you have this together, you can start sharing those results online or across your organization's network. With others, such as Chartio or Google Analytics, you start in the cloud and stay there. Given the wide price range of these products, you should segment your analytics needs before you make any buying decision. If you want to start out slowly and inexpensively, then the best route is to try something that offers significant functionality for free, such as Microsoft Power BI.

Such tools are very affordable and make it easy to get started. Plus, they tend to have large ecosystems of add-ons and partners that can be a cost-effective replacement for doing BI inside a spreadsheet. Tableau Desktop still has the largest collection of charts and visualizations and the biggest partner network, though both IBM Watson Analytics and Microsoft Power BI are catching up fast. IBM Watson Analytics scored the highest, and Microsoft Power BI and Tableau Desktop scored the next highest in our roundup.

However, all three products received our Editors' Choice award. Tableau Desktop may have a big price tag depending on which version you choose but, as previously mentioned, it has an exceptionally large and growing collection of visualizations plus a manageable learning curve if you're willing to devote some effort to it. Microsoft Power BI and Tableau Desktop also have large and growing collections of data connectors, and both Microsoft and Tableau have their own sizable communities of users that are vocal about their wants and needs.

This can carry a lot of weight with the vendors' development teams so it's a good idea to spend some time looking through those community forums to get an idea where these companies are headed. Featured Self-Service Business Intelligence Reviews: Zoho Reports Review MSRP: $50.00Bottom Line: Zoho Reports is a hidden business intelligence gem. It's well designed, nicely priced and does a great job of brining enterprise-grade analytics to businesses of any size.

 Read Review IBM Watson Analytics Review MSRP: $360.00Bottom Line: Excellent business intelligence tool that combines a powerful analytics engine with natural language querying that's currently unrivaled in the industry. A great choice both for data scienti...  Read Review Microsoft Power BI Review MSRP: $0.00Bottom Line: Microsoft has done an excellent job building a powerful, highly intuitive business intelligence tool with standout data visualization capabilities and excellent integration with the rest of .

..  Read Review Tableau Desktop Review MSRP: $42.00Bottom Line: Tableau Desktop is one of the most mature players in the self-service business intelligence space. That means lots of power and flexibility, but it also means a not insignificant learning cu...  Read Review Sisense Review MSRP:Bottom Line: Sisense is a powerful, agile tool that's elegantly designed and can be a real boon to experienced business users, though it falls a little short for analytics newcomers or less technical use.

..  Read Review Domo Review MSRP: $2000.00Bottom Line: Domo is a powerful business analytics tool capable of complex queries and able to plug into multiple data sources, even streaming data, right out of the box. For organizations with data scie...  Read Review Google Analytics Review MSRP: $0.00Bottom Line: Google Analytics is not only free to use, it's also an extremely powerful website and mobile app analytics tool.

It integrates with many Google services, including search and Google G Suite,...  Read Review SAP Analytics Cloud Review MSRP: $21.00Bottom Line: For business users who are already familiar with SAP's app product line and its SAP HANA database engine, the SAP Analytics Cloud is a no-brainer. But for those accustomed to other tools or ...  Read Review Chartio Review MSRP: $2000.00Bottom Line: Chartio is a solid tool for those familiar with SQL querying.

Its powerful processing engine handles complex queries with aplomb, but it represents a steep learning curve for users unfamilia...  Read Review Looker Review MSRP: $3000.00Bottom Line: Looker is a great self-service business intelligence (BI) tool that can help unify SQL and Big Data management across your enterprise.  Read Review Qlik Sense Enterprise Server Review MSRP: $1500.00Bottom Line: Qlik Sense Enterprise Server is a self-service business intelligence (BI) tool that delivers the best collection of user access roles among the BI tools we tested, and also demonstrates a pr.

..  Read Review Information Builders WebFocus Review MSRP: $30000.00Bottom Line: The company's Focus query language is showing its age but Information Builders' self-service business intelligence (BI) tool WebFocus nevertheless has some powerful analysis features.  Read Review Tibco Spotfire Review MSRP: $650.00Bottom Line: While Tibco is still making the transition from a desktop to a cloud software vendor, its self-service business intelligence (BI) tool Tibco Spotfire is a great way to start visualizing your.

..  Read Review Clearify QQube Review MSRP: $425.00Bottom Line: Clearify QQube is the best self-service business intelligence (BI) tool for in-depth analysis of your Intuit QuickBooks files, though you'll need to look elsewhere for broader BI tasks.  Read Review

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Today the IT sphere is almost the most quickly creating field and also the variety of unique software package solutions proliferates with each and every moment. There are many organizations generating several sorts of application for a variety of operational programs.



Lower price computer software sellers get software at wholesale premiums and pass to the positive aspects of minimal price ranges to final individuals. They have engineering share agreements with different firms which include Microsoft, Apple, Adobe, Macromedia, Nikon, HP, Palm, Quark, Autodesk, Inspiration, Norton, McAfee, Know-how Adventure, and Riverdeep. They also buy application from firms that have substantial overstocks or are closing their business. Other order venues include things like auctions and closeouts that provide application at discounted charges.

Business Intelligence (BI) comprises the strategies and technologies used by enterprises for the data analysis of business information.[1] BI technologies provide historical, current and predictive views of business operations. Common functions of business intelligence technologies include reporting, online analytical processing, analytics, data mining, process mining, complex event processing, business performance management, benchmarking, text mining, predictive analytics and prescriptive analytics.

BI technologies can handle large amounts of structured and sometimes unstructured data to help identify, develop and otherwise create new strategic business opportunities. They aim to allow for the easy interpretation of these big data. Identifying new opportunities and implementing an effective strategy based on insights can provide businesses with a competitive market advantage and long-term stability.

[2] Business intelligence can be used by enterprises to support a wide range of business decisions - ranging from operational to strategic. Basic operating decisions include product positioning or pricing. Strategic business decisions involve priorities, goals and directions at the broadest level. In all cases, BI is most effective when it combines data derived from the market in which a company operates (external data) with data from company sources internal to the business such as financial and operations data (internal data).

When combined, external and internal data can provide a complete picture which, in effect, creates an "intelligence" that cannot be derived from any singular set of data.[3] Amongst myriad uses, business intelligence tools empower organizations to gain insight into new markets, to assess demand and suitability of products and services for different market segments and to gauge the impact of marketing efforts.

[4] Often BI applications use data gathered from a data warehouse (DW) or from a data mart, and the concepts of BI and DW combine as "BI/DW"[5] or as "BIDW". A data warehouse contains a copy of analytical data that facilitate decision support. Components Business intelligence is made up of an increasing number of components including: Multidimensional aggregation and allocation Denormalization, tagging and standardization Realtime reporting with analytical alert A method of interfacing with unstructured data sources Group consolidation, budgeting and rolling forecasts Statistical inference and probabilistic simulation Key performance indicators optimization Version control and process management Open item management History The earliest known use of the term "Business Intelligence" is in Richard Millar Devens’ in the ‘Cyclopædia of Commercial and Business Anecdotes’ from 1865.

Devens used the term to describe how the banker, Sir Henry Furnese, gained profit by receiving and acting upon information about his environment, prior to his competitors. “Throughout Holland, Flanders, France, and Germany, he maintained a complete and perfect train of business intelligence. The news of the many battles fought was thus received first by him, and the fall of Namur added to his profits, owing to his early receipt of the news.

” (Devens, (1865), p. 210). The ability to collect and react accordingly based on the information retrieved, an ability that Furnese excelled in, is today still at the very heart of BI.[6] In a 1958 article, IBM researcher Hans Peter Luhn used the term business intelligence. He employed the Webster's dictionary definition of intelligence: "the ability to apprehend the interrelationships of presented facts in such a way as to guide action towards a desired goal.

"[7] Business intelligence as it is understood today is said to have evolved from the decision support systems (DSS) that began in the 1960s and developed throughout the mid-1980s. DSS originated in the computer-aided models created to assist with decision making and planning. From DSS, data warehouses, Executive Information Systems, OLAP and business intelligence came into focus beginning in the late 80s.

In 1989, Howard Dresner (later a Gartner analyst) proposed "business intelligence" as an umbrella term to describe "concepts and methods to improve business decision making by using fact-based support systems."[8] It was not until the late 1990s that this usage was widespread.[9] Critics see BI as evolved from mere business reporting together with the advent of increasingly powerful and easy-to-use data analysis tools.

In this respect it has also been criticized as a marketing buzzword in the context of the "big data" surge.[10] Data discovery Data discovery is a buzzword in BI for creating and using interactive reports and exploring data from multiple sources. The market research firm Gartner promoted it in 2012.[11] Data discovery is a user-driven process of searching for patterns or specific items in a data set.

Data discovery applications use visual tools such as geographical maps, pivot tables, and heat maps to make the process of finding patterns or specific items rapid and intuitive. Statistical and data mining techniques can be employed to accomplish these goals. Data discovery is a type of business intelligence in that they both provide the end-user with an application that visualizes data using dashboards, static and parameterized reports, and pivot tables.

Visualization of data in traditional BI incorporated standard charting, key performance indicators, and limited graphical representation and interactivity. BI is undergoing transformation in capabilities it offers, with a focus on end-user data analysis and discovery, access to larger volumes of data and an ability to create high fidelity presentations of information. Data warehousing To distinguish between the concepts of business intelligence and data warehouses, Forrester Research defines business intelligence in one of two ways: Using a broad definition: "Business Intelligence is a set of methodologies, processes, architectures, and technologies that transform raw data into meaningful and useful information used to enable more effective strategic, tactical, and operational insights and decision-making.

"[12] Under this definition, business intelligence also includes technologies such as data integration, data quality, data warehousing, master-data management, text- and content-analytics, and many others that the market sometimes lumps into the "Information Management" segment. Therefore, Forrester refers to data preparation and data usage as two separate but closely linked segments of the business-intelligence architectural stack.

Forrester defines the narrower business-intelligence market as, "...referring to just the top layers of the BI architectural stack such as reporting, analytics and dashboards."[13] Comparison with competitive intelligence Though the term business intelligence is sometimes a synonym for competitive intelligence (because they both support decision making), BI uses technologies, processes, and applications to analyze mostly internal, structured data and business processes while competitive intelligence gathers, analyzes and disseminates information with a topical focus on company competitors.

If understood broadly, business intelligence can include the subset of competitive intelligence.[14] Comparison with business analytics Business intelligence and business analytics are sometimes used interchangeably, but there are alternate definitions.[15] One definition contrasts the two, stating that the term business intelligence refers to collecting business data to find information primarily through asking questions, reporting, and online analytical processes.

Business analytics, on the other hand, uses statistical and quantitative tools for explanatory and predictive modelling.[16] In an alternate definition, Thomas Davenport, professor of information technology and management at Babson College argues that business intelligence should be divided into querying, reporting, Online analytical processing (OLAP), an "alerts" tool, and business analytics. In this definition, business analytics is the subset of BI focusing on statistics, prediction, and optimization, rather than the reporting functionality.

[17] Applications in an enterprise Business intelligence can be applied to the following business purposes, in order to drive business value. Measurement – program that creates a hierarchy of performance metrics (see also Metrics Reference Model) and benchmarking that informs business leaders about progress towards business goals (business process management). Analytics – program that builds quantitative processes for a business to arrive at optimal decisions and to perform business knowledge discovery.

Frequently involves: data mining, process mining, statistical analysis, predictive analytics, predictive modeling, business process modeling, data lineage, complex event processing and prescriptive analytics. Reporting/enterprise reporting – program that builds infrastructure for strategic reporting to serve the strategic management of a business, not operational reporting. Frequently involves data visualization, executive information system and OLAP.

Collaboration/collaboration platform – program that gets different areas (both inside and outside the business) to work together through data sharing and electronic data interchange. Knowledge management – program to make the company data-driven through strategies and practices to identify, create, represent, distribute, and enable adoption of insights and experiences that are true business knowledge.

Knowledge management leads to learning management and regulatory compliance. In addition to the above, business intelligence can provide a pro-active approach, such as alert functionality that immediately notifies the end-user if certain conditions are met. For example, if some business metric exceeds a pre-defined threshold, the metric will be highlighted in standard reports, and the business analyst may be alerted via e-mail or another monitoring service.

This end-to-end process requires data governance, which should be handled by the expert. Prioritization of projects It can be difficult to provide a positive business case for business intelligence initiatives, and often the projects must be prioritized through strategic initiatives. BI projects can attain higher prioritization within the organization if managers consider the following: As described by Kimball, the BI manager must determine the tangible benefits such as eliminated cost of producing legacy reports.

[18] Data access for the entire organization must be enforced.[19] In this way even a small benefit, such as a few minutes saved, makes a difference when multiplied by the number of employees in the entire organization. As described by Ross, Weil & Roberson for Enterprise Architecture,[20] managers should also consider letting the BI project be driven by other business initiatives with excellent business cases.

To support this approach, the organization must have enterprise architects who can identify suitable business projects. Using a structured and quantitative methodology to create defensible prioritization in line with the actual needs of the organization, such as a weighted decision matrix.[21] Success factors of implementation According to Kimball et al., there are three critical areas that organizations should assess before getting ready to do a BI project:[22] The level of commitment and sponsorship of the project from senior management.

The level of business need for creating a BI implementation. The amount and quality of business data available. Business sponsorship The commitment and sponsorship of senior management is according to Kimball et al., the most important criteria for assessment.[23] This is because having strong management backing helps overcome shortcomings elsewhere in the project. However, as Kimball et al. state: “even the most elegantly designed DW/BI system cannot overcome a lack of business [management] sponsorship”.

[24] It is important that personnel who participate in the project have a vision and an idea of the benefits and drawbacks of implementing a BI system. The best business sponsor should have organizational clout and should be well connected within the organization. It is ideal that the business sponsor is demanding but also able to be realistic and supportive if the implementation runs into delays or drawbacks.

The management sponsor also needs to be able to assume accountability and to take responsibility for failures and setbacks on the project. Support from multiple members of the management ensures the project does not fail if one person leaves the steering group. However, having many managers work together on the project can also mean that there are several different interests that attempt to pull the project in different directions, such as if different departments want to put more emphasis on their usage.

This issue can be countered by an early and specific analysis of the business areas that benefit the most from the implementation. All stakeholders in the project should participate in this analysis in order for them to feel invested in the project and to find common ground. Another management problem that may be encountered before the start of an implementation is an overly aggressive business sponsor.

Problems of scope creep occur when the sponsor requests data sets that were not specified in the original planning phase. Business needs Because of the close relationship with senior management, another critical thing that must be assessed before the project begins is whether or not there is a business need and whether there is a clear business benefit by doing the implementation.[25] The needs and benefits of the implementation are sometimes driven by competition and the need to gain an advantage in the market.

Another reason for a business-driven approach to implementation of BI is the acquisition of other organizations that enlarge the original organization it can sometimes be beneficial to implement DW or BI in order to create more oversight. Companies that implement BI are often large, multinational organizations with diverse subsidiaries.[26] They may go through the implementation of a Business Intelligence Competency Center (BICC).

A well-designed BI solution provides a consolidated view of key business data not available anywhere else in the organization, giving management visibility and control over measures that otherwise would not exist. Amount and quality of available data Without proper data, or with too little quality data, any BI implementation fails; it does not matter how good the management sponsorship or business-driven motivation is.

Before implementation it is a good idea to do data profiling. This analysis identifies the “content, consistency and structure [..]”[25] of the data. This should be done as early as possible in the process and if the analysis shows that data is lacking, put the project on hold temporarily while the IT department figures out how to properly collect data. When planning for business data and business intelligence requirements, it is always advisable to consider specific scenarios that apply to a particular organization, and then select the business intelligence features best suited for the scenario.

Often, scenarios revolve around distinct business processes, each built on one or more data sources. These sources are used by features that present that data as information to knowledge workers, who subsequently act on that information. The business needs of the organization for each business process adopted correspond to the essential steps of business intelligence. These essential steps of business intelligence include but are not limited to: Go through business data sources in order to collect needed data Convert business data to information and present appropriately Query and analyze data Act on the collected data The quality aspect in business intelligence should cover all the process from the source data to the final reporting.

At each step, the quality gates are different: Source Data: Data Standardization: make data comparable (same unit, same pattern...) Master Data Management: unique referential Operational Data Store (ODS): Data Cleansing: detect & correct inaccurate data Data Profiling: check inappropriate value, null/empty Data warehouse: Completeness: check that all expected data are loaded Referential integrity: unique and existing referential over all sources Consistency between sources: check consolidated data vs sources Reporting: Uniqueness of indicators: only one share dictionary of indicators Formula accuracy: local reporting formula should be avoided or checked User aspect Some considerations must be made in order to successfully integrate the usage of business intelligence systems in a company.

Ultimately the BI system must be accepted and utilized by the users in order for it to add value to the organization.[27][28] If the usability of the system is poor, the users may become frustrated and spend a considerable amount of time figuring out how to use the system or may not be able to be productive. If the system does not add value to the users´ mission, they simply don't use it.[28] To increase user acceptance of a BI system, it can be advisable to consult business users at an early stage of the DW/BI lifecycle, for example at the requirements gathering phase.

[27] This can provide an insight into the business process and what the users need from the BI system. There are several methods for gathering this information, such as questionnaires and interview sessions. When gathering the requirements from the business users, the local IT department should also be consulted in order to determine to which degree it is possible to fulfill the business's needs based on the available data.

[27] Taking a user-centered approach throughout the design and development stage may further increase the chance of rapid user adoption of the BI system.[28] Besides focusing on the user experience offered by the BI applications, it may also possibly motivate the users to utilize the system by adding an element of competition. Kimball[27] suggests implementing a function on the Business Intelligence portal website where reports on system usage can be found.

By doing so, managers can see how well their departments are doing and compare themselves to others and this may spur them to encourage their staff to utilize the BI system even more. In a 2007 article, H. J. Watson gives an example of how the competitive element can act as an incentive.[29] Watson describes how a large call centre implemented performance dashboards for all call agents, with monthly incentive bonuses tied to performance metrics.

Also, agents could compare their performance to other team members. The implementation of this type of performance measurement and competition significantly improved agent performance. BI chances of success can be improved by involving senior management to help make BI a part of the organizational culture, and by providing the users with necessary tools, training, and support.[29] Training encourages more people to use the BI application.

[27] Providing user support is necessary to maintain the BI system and resolve user problems.[28] User support can be incorporated in many ways, for example by creating a website. The website should contain great content and tools for finding the necessary information. Furthermore, helpdesk support can be used. The help desk can be manned by power users or the DW/BI project team.[27] BI Portals A Business Intelligence portal (BI portal) is the primary access interface for Data Warehouse (DW) and Business Intelligence (BI) applications.

The BI portal is the user's first impression of the DW/BI system. It is typically a browser application, from which the user has access to all the individual services of the DW/BI system, reports and other analytical functionality. The BI portal must be implemented in such a way that it is easy for the users of the DW/BI application to call on the functionality of the application.[30] The BI portal's main functionality is to provide a navigation system of the DW/BI application.

This means that the portal has to be implemented in a way that the user has access to all the functions of the DW/BI application. The most common way to design the portal is to custom fit it to the business processes of the organization for which the DW/BI application is designed, in that way the portal can best fit the needs and requirements of its users.[31] The BI portal needs to be easy to use and understand, and if possible have a look and feel similar to other applications or web content of the organization the DW/BI application is designed for (consistency).

The following is a list of desirable features for web portals in general and BI portals in particular: Usable User should easily find what they need in the BI tool. Content Rich The portal is not just a report printing tool, it should contain more functionality such as advice, help, support information and documentation. Clean The portal should be designed so it is easily understandable and not over-complex as to confuse the users Current The portal should be updated regularly.

Interactive The portal should be implemented in a way that makes it easy for the user to use its functionality and encourage them to use the portal. Scalability and customization give the user the means to fit the portal to each user. Value Oriented It is important that the user has the feeling that the DW/BI application is a valuable resource that is worth working on. Marketplace There are a number of business intelligence vendors, often categorized into the remaining independent "pure-play" vendors and consolidated "megavendors" that have entered the market through a recent trend[32] of acquisitions in the BI industry.

[33] The business intelligence market is gradually growing. In 2012 business intelligence services brought in $13.1 billion in revenue.[34] Some companies adopting BI software decide to pick and choose from different product offerings (best-of-breed) rather than purchase one comprehensive integrated solution (full-service).[35] Industry-specific Specific considerations for business intelligence systems have to be taken in some sectors such as governmental banking regulations or healthcare.

[36] The information collected by banking institutions and analyzed with BI software must be protected from some groups or individuals, while being fully available to other groups or individuals. Therefore, BI solutions must be sensitive to those needs and be flexible enough to adapt to new regulations and changes to existing law. Semi-structured or unstructured data Businesses create a huge amount of valuable information in the form of e-mails, memos, notes from call-centers, news, user groups, chats, reports, web-pages, presentations, image-files, video-files, and marketing material and news.

According to Merrill Lynch, more than 85% of all business information exists in these forms. These information types are called either semi-structured or unstructured data. However, organizations often only use these documents once.[37] The managements of semi-structured data is recognized as a major unsolved problem in the information technology industry.[38] According to projections from Gartner (2003), white collar workers spend anywhere from 30 to 40 percent of their time searching, finding and assessing unstructured data.

BI uses both structured and unstructured data, but the former is easy to search, and the latter contains a large quantity of the information needed for analysis and decision making.[38][39] Because of the difficulty of properly searching, finding and assessing unstructured or semi-structured data, organizations may not draw upon these vast reservoirs of information, which could influence a particular decision, task or project.

This can ultimately lead to poorly informed decision making.[37] Therefore, when designing a business intelligence/DW-solution, the specific problems associated with semi-structured and unstructured data must be accommodated for as well as those for the structured data.[39] Unstructured data vs. semi-structured data Unstructured and semi-structured data have different meanings depending on their context.

In the context of relational database systems, unstructured data cannot be stored in predictably ordered columns and rows. One type of unstructured data is typically stored in a BLOB (binary large object), a catch-all data type available in most relational database management systems. Unstructured data may also refer to irregularly or randomly repeated column patterns that vary from row to row within each file or document.

Many of these data types, however, like e-mails, word processing text files, PPTs, image-files, and video-files conform to a standard that offers the possibility of metadata. Metadata can include information such as author and time of creation, and this can be stored in a relational database. Therefore, it may be more accurate to talk about this as semi-structured documents or data,[38] but no specific consensus seems to have been reached.

Unstructured data can also simply be the knowledge that business users have about future business trends. Business forecasting naturally aligns with the BI system because business users think of their business in aggregate terms. Capturing the business knowledge that may only exist in the minds of business users provides some of the most important data points for a complete BI solution. Problems with semi-structured or unstructured data There are several challenges to developing BI with semi-structured data.

According to Inmon & Nesavich,[40] some of those are: Physically accessing unstructured textual data – unstructured data is stored in a huge variety of formats. Terminology – Among researchers and analysts, there is a need to develop a standardized terminology. Volume of data – As stated earlier, up to 85% of all data exists as semi-structured data. Couple that with the need for word-to-word and semantic analysis.

Searchability of unstructured textual data – A simple search on some data, e.g. apple, results in links where there is a reference to that precise search term. (Inmon & Nesavich, 2008)[40] gives an example: “a search is made on the term felony. In a simple search, the term felony is used, and everywhere there is a reference to felony, a hit to an unstructured document is made. But a simple search is crude.

It does not find references to crime, arson, murder, embezzlement, vehicular homicide, and such, even though these crimes are types of felonies.” The use of metadata To solve problems with searchability and assessment of data, it is necessary to know something about the content. This can be done by adding context through the use of metadata.[37] Many systems already capture some metadata (e.g. filename, author, size, etc.

), but more useful would be metadata about the actual content – e.g. summaries, topics, people or companies mentioned. Two technologies designed for generating metadata about content are automatic categorization and information extraction. 2009 predictions A 2009 paper predicted[41] these developments in the business intelligence market: Because of lack of information, processes, and tools, through 2012, more than 35 percent of the top 5,000 global companies regularly fail to make insightful decisions about significant changes in their business and markets.

By 2012, business units will control at least 40 percent of the total budget for business intelligence. By 2012, one-third of analytic applications applied to business processes will be delivered through coarse-grained application mashups. A 2009 Information Management special report predicted the top BI trends: "green computing, social networking services, data visualization, mobile BI, predictive analytics, composite applications, cloud computing and multitouch".

[42] Research undertaken in 2014 indicated that employees are more likely to have access to, and more likely to engage with, cloud-based BI tools than traditional tools.[43] Other business intelligence trends include the following: Third party SOA-BI products increasingly address ETL issues of volume and throughput. Companies embrace in-memory processing, 64-bit processing, and pre-packaged analytic BI applications.

Operational applications have callable BI components, with improvements in response time, scaling, and concurrency. Near or real time BI analytics is a baseline expectation. Open source BI software replaces vendor offerings. Other lines of research include the combined study of business intelligence and uncertain data.[44][45] In this context, the data used is not assumed to be precise, accurate and complete.

Instead, data is considered uncertain and therefore this uncertainty is propagated to the results produced by BI. According to a study by the Aberdeen Group, there has been increasing interest in Software-as-a-Service (SaaS) business intelligence over the past years, with twice as many organizations using this deployment approach as one year ago – 15% in 2009 compared to 7% in 2008.[46] An article by InfoWorld’s Chris Kanaracus points out similar growth data from research firm IDC, which predicts the SaaS BI market will grow 22 percent each year through 2013 thanks to increased product sophistication, strained IT budgets, and other factors.

[47] An analysis of top 100 Business Intelligence and Analytics scores and ranks the firms based on several open variables[48] See also Accounting intelligence Analytic applications Artificial intelligence marketing Business Intelligence 2.0 Business process discovery Business process management Business activity monitoring Business service management Comparison of OLAP Servers Customer dynamics Data Presentation Architecture Data visualization Decision engineering Enterprise planning systems Infonomics Document intelligence Integrated business planning Location intelligence Media intelligence Meteorological intelligence Mobile business intelligence Multiway Data Analysis Operational intelligence Business Information Systems Business intelligence tools Process mining Real-time business intelligence Runtime intelligence Sales intelligence Test and learn References ^ Dedić N.

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ISBN 978-0-470-39240-9.) ^ Coker, Frank (2014). Pulse: Understanding the Vital Signs of Your Business. Ambient Light Publishing. pp. 41–42. ISBN 978-0-9893086-0-1. ^ Chugh, R & Grandhi, S 2013, ‘Why Business Intelligence? Significance of Business Intelligence tools and integrating BI governance with corporate governance’, International Journal of E-Entrepreneurship and Innovation, vol.

4, no.2, pp. 1-14. https://www.researchgate.net/publication/273861123_Why_Business_Intelligence_Significance_of_Business_Intelligence_Tools_and_Integrating_BI_Governance_with_Corporate_Governance ^ Golden, Bernard (2013). Amazon Web Services For Dummies. For dummies. John Wiley & Sons. p. 234. ISBN 9781118652268. Retrieved 2014-07-06. [...] traditional business intelligence or data warehousing tools (the terms are used so interchangeably that they're often referred to as BI/DW) are extremely expensive [.

..] ^ Miller Devens, Richard. Cyclopaedia of Commercial and Business Anecdotes; Comprising Interesting Reminiscences and Facts, Remarkable Traits and Humors of Merchants, Traders, Bankers Etc. in All Ages and Countries. D. Appleton and company. p. 210. Retrieved 15 February 2014. ^ H P Luhn (1958). "A Business Intelligence System" (PDF). IBM Journal. 2 (4): 314. doi:10.1147/rd.24.0314. Archived from the original (PDF) on 2008-09-13.

^ D. J. Power (10 March 2007). "A Brief History of Decision Support Systems, version 4.0". DSSResources.COM. Retrieved 10 July 2008. ^ Power, D. J. "A Brief History of Decision Support Systems". Retrieved 1 November 2010. ^ "Decoding big data buzzwords". cio.com. 2015. BI refers to the approaches, tools, mechanisms that organizations can use to keep a finger on the pulse of their businesses. Also referred by unsexy versions -- “dashboarding”, “MIS” or “reporting.

” ^ Kern, Justin (2013-06-07). "Data Discovery, SaaS Lead BI Market Review". Information Management. Retrieved 2017-07-06. ^ Evelson, Boris (21 November 2008). "Topic Overview: Business Intelligence". ^ Evelson, Boris (29 April 2010). "Want to know what Forrester's lead data analysts are thinking about BI and the data domain?". ^ Kobielus, James (30 April 2010). "What's Not BI? Oh, Don't Get Me Started.

...Oops Too Late...Here Goes..." “Business” intelligence is a non-domain-specific catchall for all the types of analytic data that can be delivered to users in reports, dashboards, and the like. When you specify the subject domain for this intelligence, then you can refer to “competitive intelligence,” “market intelligence,” “social intelligence,” “financial intelligence,” “HR intelligence,” “supply chain intelligence,” and the like.

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Gartner. Retrieved 11 May 2017. ^ Imhoff, Claudia (4 April 2006). "Three Trends in Business Intelligence Technology". ^ Mettler T, Vimarlund V (2009). "Understanding business intelligence in the context of healthcare". Health Informatics Journal. 15 (3): 254–264. doi:10.1177/1460458209337446. ^ a b c Rao, R. (2003). "From unstructured data to actionable intelligence" (PDF). IT Professional. 5 (6): 29.

doi:10.1109/MITP.2003.1254966. ^ a b c Blumberg, R. & S. Atre (2003). "The Problem with Unstructured Data" (PDF). DM Review: 42–46. Archived from the original (PDF) on 25 January 2011. ^ a b Negash, S (2004). "Business Intelligence" (PDF). Communications of the Association of Information Systems. 13: 177–195. ^ a b Inmon, B. & A. Nesavich, "Unstructured Textual Data in the Organization" from "Managing Unstructured data in the organization", Prentice Hall 2008, pp.

1–13 ^ Gartner Reveals Five Business Intelligence Predictions for 2009 and Beyond. gartner.com. 15 January 2009 ^ Campbell, Don (23 June 2009). "10 Red Hot BI Trends". Information Management. ^ Lock, Michael (27 March 2014). "Cloud Analytics in 2014: Infusing the Workforce with Insight". ^ Rodriguez, Carlos; Daniel, Florian; Casati, Fabio; Cappiello, Cinzia (2010). "Toward Uncertain Business Intelligence: The Case of Key Indicators".

IEEE Internet Computing. 14 (4): 32. doi:10.1109/MIC.2010.59. ^ Rodriguez, C.; Daniel, F.; Casati, F. & Cappiello, C. (2009), Computing Uncertain Key Indicators from Uncertain Data (PDF), pp. 106–120 ^ Julian, Taylor (10 January 2010). "Business intelligence implementation according to customer's needs". APRO Software. Retrieved 16 May 2016. ^ SaaS BI growth will soar in 2010 | Cloud Computing.

InfoWorld (2010-02-01). Retrieved 17 January 2012. ^ "Top 100 analytics companies ranked and scored by Mattermark - Business Intelligence - Dashboards - Big Data". Bibliography Ralph Kimball et al. "The Data warehouse Lifecycle Toolkit" (2nd ed.) Wiley ISBN 0-470-47957-4 Peter Rausch, Alaa Sheta, Aladdin Ayesh : Business Intelligence and Performance Management: Theory, Systems, and Industrial Applications, Springer Verlag U.

K., 2013, ISBN 978-1-4471-4865-4. External links "The Key Role Hadoop Plays in Business Intelligence and Data Warehousing" - St. Joseph's University Chaudhuri, Surajit; Dayal, Umeshwar; Narasayya, Vivek (August 2011). "An Overview Of Business Intelligence Technology". Communications of the ACM. 54 (8): 88–98. doi:10.1145/1978542.1978562. Retrieved 26 October 2011. v t e Data warehouse Creating the data warehouse Concepts Database Dimension Dimensional modeling Fact OLAP Star schema Aggregate Variants Anchor Modeling Column-oriented DBMS Data vault modeling HOLAP MOLAP ROLAP Operational data store Elements Data dictionary/Metadata Data mart Sixth normal form Surrogate key Fact Fact table Early-arriving fact Measure Dimension Dimension table Degenerate Slowly changing Filling Extract-Transform-Load (ETL) Extract Transform Load Using the data warehouse Concepts Business intelligence Dashboard Data mining Decision support system (DSS) OLAP cube Data warehouse automation Languages Data Mining Extensions (DMX) MultiDimensional eXpressions (MDX) XML for Analysis (XMLA) Tools Business intelligence software Reporting software Spreadsheet Related People Bill Inmon Ralph Kimball Products Comparison of OLAP Servers Data warehousing products and their producers Retrieved from "https://en.

wikipedia.org/w/index.php?title=Business_intelligence&oldid=813611968"

Felicia Watson

Many software vendors offer original equipment manufacturer (OEM) versions at really low prices, and you will find that lots of active people who use computers purchase them.