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Business intelligence is a data analysis process aimed at boosting business performance by helping corporate executives and other end users make more informed decisions.

Business intelligence (BI) is a technology-driven process for analyzing data and presenting actionable information to help corporate executives, business managers and other end users make more informed business decisions.

BI encompasses a variety of tools, applications and methodologies that enable organizations to collect data from internal systems and external sources, prepare it for analysis, develop and run queries against the data, and create reports, dashboards and data visualizations to make the analytical results available to corporate decision makers as well as operational workers.

1. Project Sponsorship and Governance 2. Project Management 3. Development Team (Core Team) 4. Extended Project Team

1. Project Sponsorship and Governance IT and the business should form a BI steering committee to sponsor and govern design, development, deployment, and ongoing support. It needs both the CIO and a business executive, such as CFO, COO, or a senior VP of marketing/sales to commit budget, time, and resources. The business sponsor needs the project to succeed. The CIO is committed to what is being built and how.

2. Project Management Project management includes managing daily tasks, reporting status, and communicating to the extended project team, steering committee, and affected business users. The project management team needs extensive business knowledge, BI expertise, DW architecture background, and people management, project management, and communications skills. The project management team includes three functions or members: Project development manager - Responsible for deliverables, managing team resources, monitoring tasks, reporting status, and communications. Requires a hands-on IT manager with a background in iterative development. Must understand the changes caused by this approach and the impact on the business, project resources, schedule and the trade-offs. Business advisor - Works within the sponsoring business organization. Responsible for the deliverables of the business resources on the project's extended team. Serves as the business advocate on the project team and the project advocate within the business community. Often, the business advocate is a project co-manager who defers to the IT project manager the daily IT tasks but oversees the budget and business deliverables. BI/DW project advisor - Has enough expertise with architectures and technologies to guides the project team on their use. Ensures that architecture, data models, databases, ETL code, and BI tools are all being used effectively and conform to best practices and standards.

Dezvoltarea afacerilor aduce cu sine întrebări din ce în ce mai complexe: care este cel mai profitabil produs pe care îl vând? Care este evoluţia marjei mele de profit pe produsul X de-a lungul ultimilor ani? Cum pot fi reduse eficient cheltuielile operaţionale fără a afecta performanţa afacerii? Cum pot fi reduse pierderile pe lanţul de aprovizionare sau pe circuitul de producţie? Care este volumul de afaceri pe care îl fac cu furnizorul Y şi ce discount aş putea solicita în perioada următoare? Care este cel mai performant vânzător al meu? etc…

Sigur, răspunsuri la întrebări complexe pot fi obţinute şi fără aportul soluţiilor de „business intelligence” insă de cele mai multe ori obţinerea rezultatelor dorite presupune cel puţin: personal specializat (analişti), inevitabila eroare umană şi mai ales timp. Timp petrecut cu activităţi repetitive de extragere a datelor, de formatare a acestora, de definire a diverselor formule şi calcule necesare etc. Mai mult decât atât, datele odată extrase din sistemul de gestiune şi „împrăştiate” la diverse nivele din organizaţie (sub forma de fişiere text, Excel sau DBF) îşi pierd proprietăţi precum integritate, acurateţe, confidenţialitate şi, poate cea mai importantă, credibilitate.

Alternativa? Un sistem ce poate gestiona în mod automat transformarea datelor în informaţie şi accesul la aceasta în funcţie de rolul utilizatorului în cadrul organizaţiei, în mod securizat, cu posibilitatea interacţiunii facile cu datele şi generarea de rapoarte şi analize complexe din câteva click-uri de mouse. Şi acestea sunt numai câteva din facilităţile oferite de furnizorii soluţiilor de „business intelligence”.

Pentaho, for instance, is one of this year’s rookies. It had been knocking on the door for a year or two and received an honorable mention last year. It needed to increase its overall sales figures to be cited by Gartner, a requirement it satisfied this year.

Free reporting software Now enhanced for mobile intelligence Perfect solution for departments Scalable as your needs expand For Windows, Unix, Linux, Solaris, HP-UX, and AIX operating systems and any data source, including Hadoop, SAP BW, Microsoft Analysis Services, Essbase, and IBM TM1.

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Schema on write This is the traditional approach for Business Intelligence. A model, often dimensional, is built as part of the design process. This model is an abstraction of the complexity of the underlying systems, put in business terms. The purpose of the model is to allow the business users to interrogate the data in a way they understand.

The model is instantiated through physical database tables and the date is loaded through an ETL (extract, transform and load) process that takes data from one or more source systems and transforms it to fit the model, then loads it into the model.

The key thing is that the model is determined before the data is finally written and the users are very much guided or driven by the model in how they query the data and what results they can get from the system. The designer must anticipate the queries and requests in advance of the user asking the questions.

Mistake 1.  Not putting a strong interdisciplinary team together. It is impossible to put together an analytics platform without understanding the needs of the customers who will use it.  Sounds simple, right?  Who wouldn’t do that?  You’d be surprised how many analytics projects are wrapped up by IT because “they think” they know the customer needs.  Not assembling the right team is clearly the biggest mistake companies make.  Many times what is on your mind (and if you’re an IT person willing to admit it) is that you are considering converting all those favorite company reports.  Your goal should not be that.  Your goal is to create a system—human engineered with customers, financial people, IT folks, analysts, and others—that give people new and exciting ways to look at information.  It should give you new insights. New competitive information.  If you don’t get the right team put together, you’ll find someone longing for the good old days and their old dusty reports.  Or worse yet, still finding ways to generate those old dusty reports. Mistake 2.  Not having the right talent to design, build, run and update your analytics system.  It is undeniable that there is now high demand for business analytics specialists.  There are not a lot of them out there that really know what to do unless they’ve been burned a few times and have survived and then built successful BA systems.  This is reflected by the fact you see so many analytics vendors offer, or often recommend, third-party consulting and training to help the organization develop their business analytic skills.  Work hard to build a three-way partnership between the vendor, your own team, and an implementation partner.  If you develop those relationships, risk of failure goes way down.

Mistake 3.  Putting the wrong kind of analyst or designer on the project. This is somewhat related to Mistake 2 but with some subtle differences.  People have different skillsets so you need to make sure the person you’re considering to put on the project is the right “kind.”  For example, when you put the design together you need both drill-down and summary models.  Both have different types of users.  Does this person know how to do both?  Or, for example, inexperience in an analyst might lead to them believing vendor claims and not be able to verify them as to functionality or time to implement. Mistake 4.  Not understanding how clean the data is you are getting and the time frame to get it clean.  Profile your data to understand the quality of your source data.  This will allow you to adjust your system accordingly to compensate for some of those issues or more importantly push data fixes to your source systems.  Ensure high quality data or your risk upsetting your customers.  If you don’t have a good understanding of the quality of your data, you could easily find yourself way behind schedule even though the actual analytics and business intelligence framework you are building is coming along fine. Mistake 5.  Picking the wrong tools.  How often do organizations buy software tools that just sit on the shelve?  This often comes from management rushing into a quick decision based on a few demos they have seen.  Picking the right analytics tools requires an in-depth understanding of your requirements as well as the strengths and weaknesses of the tools you are evaluating.  The best way to achieve this understanding is by getting an unbiased implementation partner to build a proof of concept with a subset of your own data and prove out the functionality of the tools you are considering. Bottom Line.  Think things through carefully. Make sure you put the right team together.  Have a data cleansing plan.  If the hype sounds too good to be true—have someone prove it to you.

Companiile locale încep să resimtă acum nevoia utilizării unor soluţii software de business intelligence (BI) cu ajutorul cărora să interpreteze şi să analizeze datele colectate, precum şi să facă prognoze, astfel încât să dispună de informaţiile necesare pentru luarea deciziilor de management.

„Până de curând, acum doi ani, discutam de partea de raportare, analiza evenimentelor post-factum. În general analizezi un eveniment după ce acesta s-a întâmplat. În momentul de faţă se simte nevoia de un element de business intelligence, care adaugă elementele de predicţie. Spre exemplu, în cazul în care creşte temperatura afară, se pune problema impactului asupra logisticii, relaţiei cu furnizorii etc“

Integration BI infrastructure: All tools in the platform use the same security, metadata, administration, portal integration, object model and query engine, and should share the same look and feel. Metadata management: Tools should leverage the same metadata, and the tools should provide a robust way to search, capture, store, reuse and publish metadata objects, such as dimensions, hierarchies, measures, performance metrics and report layout objects. Development tools: The platform should provide a set of programmatic and visual tools, coupled with a software developer's kit for creating analytic applications, integrating them into a business process, and/or embedding them in another application. Collaboration: Enables users to share and discuss information and analytic content, and/or to manage hierarchies and metrics via discussion threads, chat and annotations.

Information Delivery Reporting: Provides the ability to create formatted and interactive reports, with or without parameters, with highly scalable distribution and scheduling capabilities. Dashboards: Includes the ability to publish Web-based or mobile reports with intuitive interactive displays that indicate the state of a performance metric compared with a goal or target value. Increasingly, dashboards are used to disseminate real-time data from operational applications, or in conjunction with a complex-event processing engine. Ad hoc query: Enables users to ask their own questions of the data, without relying on IT to create a report. In particular, the tools must have a robust semantic layer to enable users to navigate available data sources. Microsoft Office integration: Sometimes, Microsoft Office (particularly Excel) acts as the reporting or analytics client. In these cases, it is vital that the tool provides integration with Microsoft Office, including support for document and presentation formats, formulas, data "refreshes" and pivot tables. Advanced integration includes cell locking and write-back. Search-based BI: Applies a search index to structured and unstructured data sources and maps them into a classification structure of dimensions and measures that users can easily navigate and explore using a search interface. Mobile BI: Enables organizations to deliver analytic content to mobile devices in a publishing and/or interactive mode, and takes advantage of the mobile client's location awareness.

Analysis Online analytical processing (OLAP): Enables users to analyze data with fast query and calculation performance, enabling a style of analysis known as "slicing and dicing." Users are able to navigate multidimensional drill paths. They also have the ability to write back values to a proprietary database for planning and "what if" modeling purposes. This capability could span a variety of data architectures (such as relational or multidimensional) and storage architectures (such as disk-based or in-memory). Interactive visualization: Gives users the ability to display numerous aspects of the data more efficiently by using interactive pictures and charts, instead of rows and columns. Predictive modeling and data mining: Enables organizations to classify categorical variables, and to estimate continuous variables using mathematical algorithms. Scorecards: These take the metrics displayed in a dashboard a step further by applying them to a strategy map that aligns key performance indicators (KPIs) with a strategic objective. Prescriptive modeling, simulation and optimization: Supports decision making by enabling organizations to select the correct value of a variable based on a set of constraints for deterministic processes, and by modeling outcomes for stochastic processes.

But just as those forms of collaboration out-paced phone calls (of which I am still keen on), inter-company envelope exchanges, and hand-written letters, collaboration in applications will seek to become the new social collaboration successor

By the end of 2013, 72% of business executives will have tablets as their main means of consuming an Organization’s KPI’s and other analytics, scorecarding, etc

Although Roambi, and Microstrategy seem to be doing all of the right things with Mobile BI, there are several major BI Vendors that are not or there is simply room for improvement.  Also on the topic of Mobile BI, one of the barriers of 2012 was the concern for securing Mobile BI within an organization.  There has been a rise of solutions but no true standardization.  Oracle has produced their Mobile Security Tool Kit for Oracle BI and we will dive into that very soon as well (have Mac OS, will travel).

We hope this year to compare Oracle Endeca along with QlikView, and Spotfire to break apart the vendor specific functionality and give rise to insights on how to form successful solutions to the overall problem of self-service analysis of Big Data and traditional Data Warehouse data.

a lot of articles calling data scientists and statisticians the jobs of the future

there are more immediate needs that, when addressed, will have a much greater business impact.

Right now we have huge opportunities to make the data more accessible, more “joinable” and more consumable. Leaders don’t want more data – they want more information they can use to run their businesses.

First observation: Dice. I’m trying to be cautiously optimistic, but it seems as Business Intelligence market in general is picking up.

One of the most challenging aspects of performance tuning is knowing where to begin. To maximize Oracle® Business Intelligence Enterprise Edition performance, you need to monitor, analyze, and tune all the Fusion Middleware / BI components. This guide describes the tools that you can use to monitor performance and the techniques for optimizing the performance of Oracle® Business Intelligence Enterprise Edition components.

Oracle Business Intelligence Foundation Suite 11 Essentials Exam (1Z0-591)

You cannot accept problems as handed to you in the business environment. Never allow yourself to be the analyst to whom problems are “thrown over the fence.” Engage with the people whose challenges you’re tackling to make sure you’re solving the right problem. Learn the business’s processes and the data that’s generated and saved. Learn how folks are handling the problem now, and what metrics they use (or ignore) to gauge success.

Solve the correct, yet often misrepresented, problem. This is something no mathematical model will ever say to you. No mathematical model can ever say, “Hey, good job formulating this optimization model, but I think you should take a step back and change your business a little instead.” And that leads me to my next point: Learn how to communicate.

In today’s business environment, it is often unacceptable to be skilled at only one thing. Data scientists are expected to be polyglots who understand math, code, and the plain-speak (or sports analogy-ridden speak . . . ugh) of business. And the only way to get good at speaking to other folks, just like the only way to get good at math, is through practice.