Skip to main content

Home/ BI-TAGS/ Group items tagged skills

Rss Feed Group items tagged

cezarovidiu

What Skills Does an Oracle BI Developer Need in 2011? - 0 views

  • OBIEE 11g skills, both in terms of new functionality (mapping, analyses, KPIs and Scorecards etc) and new infrastructure (WebLogic, EM, OPSS etc) A smattering of Essbase skills, focused mainly on the integration with OBIEE and Essbase (and the many workarounds and gotchas) Good ODI skills, both in terms of the basics, but also being able to write knowledge modules, integrate with OBIEE, deployment and migration Solid database skills – OBIEE gave the illusion through aggregates etc that database tuning was redundant, but time has shown it’s by far the biggest success factor in a project – get the database design and optimisation wrong, and your project is toast. You need to know partitioning, materialized views, index types, and increasingly, you need to get yourself on an Exadata project as customers are buying the technology but you can’t teach it to yourself at home BI Apps skills, but watch out for everything changing when BI Apps 11g comes out, and be prepared to learn the Fusion Apps and JDeveloper if you want to stay in the game Looking to the future, keep an eye on technologies such as in-memory (TimesTen), mid-tier caching (Coherence), plus technologies such as Business Activity Monitoring (BAM), “big data” (Hadoop, large data sets, NoSQL), complex event processing and maybe products such as Qlikview, just in case Oracle buys them, or at least to know what the competition are up to, or more importantly pitching to your boss
  • The other thing to bear in mind of course, if you’re an Oracle BI developer, is that you need to have great business, communication and data modeling skills.
cezarovidiu

8 Principles That Can Make You an Analytics Rock Star -- TDWI -The Data Warehousing Ins... - 0 views

  • Great design, high-quality code, strong business sponsorship, accurate requirements, good project management, and thorough testing are some of the obvious requirements for successful analytics systems.
  • As a professional in the field, you must be able to do these things well because they form the foundation of a good analytics implementation.
  • Successful analytics professionals should follow a set of guiding principles.
  • ...11 more annotations...
  • Principle #1: Let your passion bloom
  • If you do not love data analytics, it will be hard to become an analytics rock star. No significant accomplishments are achieved without passion. For many people, passion does not come naturally; it must be developed. Cultivate passion by setting goals and achieving them. Realize that the best opportunity in your life is the one in front of you right now. Focus on it, grow it, and develop your passion for it! That excitement will become obvious to those around you.
  • Principle #2: Never stop learning
  • Dig down deeper about the business details of your company. What, exactly, does your company do? What are some of its challenges and opportunities? How would the company benefit from valuable and transformative information you can deliver? Take the time necessary to learn the skills that are valuable for your business and your career. Keep up-to-date with the latest technologies and available analytics tools -- learn and understand their capabilities, functions, and differences.
  • Deepen your knowledge with the tools that you are currently working on by picking new techniques and methodologies that make you a better professional in the field.
  • Principle #3: Improve your presentation skills and become an ambassador for analytics
  • persuasiveness and effectiveness
  • Improve your presentation and speaking skills, even if it is on your own time. Excellent and no-cost presentation training resources are readily available on the internet (for example, at http://www.mindtools.com/page8.html. Practice writing and giving presentations to friends and colleagues that will give you honest feedback. Once you have practiced the basic skills, you need to enhance your skills by improving your
  • You must be able to explain, justify, and "sell" your ideas to colleagues as well as business management. Organizational change does not happen overnight or as a result of one presentation. You need to be persistent and skillful in taking your ideas all the way up the leadership chain.
  • Principle #4: Be the "go-to guy" for tough analytics questions
  • Tough analytics problems typically don't have an obvious answer -- that's why they're tough! Take the initiative by digging deep into those problems without being asked. Throw out all the assumptions made so far and follow logical trial and error methodology. First, develop a thesis about possible contributors to the problem at hand. Second, run the analytics to prove the thesis. Learn from that outcome and start over, if needed, until a significant answer is found. You are now well on your way to rock star status.
cezarovidiu

Filling a Critical Role in Business Today: The Data Translator - Microsoft Business Int... - 0 views

  • 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.
  • ...5 more annotations...
  • Every company has hundreds of millions of records about their sales, expenses, employees and so on, with dozens of insights yet to be discovered through simple comparison or triangulation of relevant data.
  • Why don’t we focus on this? I think because it’s very difficult to do – being successful in this “data translator” role requires a unique set of skills and knowledge, the combination of which I call the BASE skillset: Business understanding Ability to synthetize and simplify Storytelling skills Expertise in data visualization
  • Business Understanding This one seems obvious, but it doesn’t mean simply understanding the financials of a business. Rather, it means truly knowing the operational details, the incentives, the install base, market growth, penetration, the competition, etc. An analyst can’t just know the technical aspect of a report or the math behind the numbers, but what is truly driving a pattern in terms of product quality, competition, incentives and/or offerings. The best analysts are able to mathematically isolate the key levers of a trend and then suggest actions to react to or take advantage of those trends. Ability to Synthetize and Simplify This is, in my opinion, the most underrated and underappreciated skill. Combing through thousands of data points and netting out 3-4 key issues in under 10 minutes, and then communicating these to a group of execs with very different analytical skills, is truly difficult. The key is to make it simple but not simplistic, which means you still capture the complexity even as you get to the few core insights. It requires a very thorough effort to gather all the relevant information before categorizing, prioritizing and deciding if it is significant. After a while, you become an expert and can sniff things out quickly. At the same time, there is the danger of missing anomalies when you jump to conclusions based only on a summary look.
  • Storytelling Skills There are stages that should be followed when explaining complex ideas, something data translators are frequently expected to do. The best storytellers start by giving context and trying to couple the current discussion to something the audience already knows, ensuring the story is well structured and connected. We have to move from a “buffet style” business review with thousands of numbers packed in tables to a layered approach that will guide the audience to focus first on the most relevant messages, diving deeper only when necessary. Minto Pyramid Principles, which are built around a process for organizing thought and communication, are helpful in making sure you really focus on what is important and relevant, versus being obsessed in telling every fact. Expertise in Data Visualization I am glad to finally see so much focus on Information Visualization and I believe this is correlated to the explosion of data. Traditional methods of organizing data do not facilitate an intuitive understanding of key information points or trends. For instance, the two examples below contain data on car sales across the U.S. The first, an alphabetized list, is much less intuitive than the second, which shows those sales on a map in Power View. With Power View, right away you can identify the states with the highest sales: CA, FL, TX, NY. (Workbook available here)
  • There is no better way to see patterns or trends than data visualization, making expertise in this area – both technical and analytical – critical for data translators.
cezarovidiu

Tableau Software's Pat Hanrahan on "What Is a Data Scientist?" - Forbes - 0 views

  • In the contemporary enterprise, almost everyone will need to have data-science skills of some kind.
  • “When most people think of a data scientist, they think of a statistician, a guy with ‘analyst’ in his title,’” Hanrahan says. “Or, someone who works in IT and manages the data warehouses. To do these jobs, you certainly needed programming skills; you probably needed advanced statistics skills, or some combination of those skills.”
  • “At the most basic level, you are a data scientist if you have the analytical skills and the tools to ‘get’ data, manipulate it and make decisions with it,” he says.
  •  
    "What is a Data Scientist?"
cezarovidiu

Why Soft Skills Matter in Data Science - 0 views

  • 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.
  • ...4 more annotations...
  • Beware the Three-Headed Geek-Monster: Tools, Performance, and Mathematical Perfection Many things can sabotage the use of analytics within the workplace. Politics and infighting perhaps; a bad experience from a previous “enterprise, business intelligence, cloud dashboard” project; or peers who don’t want their “dark art” optimized or automated for fear that their jobs will become redundant.
  • Not all hurdles are within your control as an analytics professional. But some are. There are three primary ways I see analytics folks sabotage their own work: overly complex modeling, tool obsession, and fixation on performance.
  • In other words, work with the rest of your organization to do better business, not to do data science for its own sake.
  • Data Smart: Using Data Science to Transform Information into Insight by John W. Foreman. Copyright © 2013.
cezarovidiu

BI Brief - Four Legs of a Successful Business Intelligence (BI) Project Team - 0 views

  • 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.
  • ...2 more annotations...
  • 3. Development Team (Core Team) The core project team is divided into four sub-teams: Business requirements - This sub-team may have business people who understand IT systems, or IT people who understand the business. In either case, the team represents the business and their interests. They are responsible for gathering and prioritizing business needs; translating them into IT systems requirements; interacting with the business on the data quality and completeness; and ensuring the business provides feedback on how well the solutions generated meet their needs. BI architecture - Develops the overall BI architecture, selects the appropriate technology, creates the data models, maps the overall data workflow from source systems to BI analytics, and oversees the ETL and BI development teams from a technical perspective. ETL development - Receives the business and data requirements, as well as the target data models to be used by BI analytics. Develops the ETL code needed to gather data from the appropriate source systems into the BI databases. Often, a system analyst who is a expert in the source systems such as SAP is part of the team to provide knowledge of the data sources, customizations, and data quality. BI development - Create the reports or analytics that the business users will interact with to do their jobs. This is often a very iterative process and requires much interaction with the business users.
  • 4. Extended Project Team There are several functions required by the project team that are often accomplished through an "extended" team: Players - A group of business users are signed up to "play with" or test the BI analytics and reports as they are developed to provide feedback to the core development team. This is a virtual team that gets together at specific periods of the project but they are committed to this role during those periods. Testers - A group of resources are gathered, similarly to the virtual team above, to perform more extensive QA testing of the BI analytics, ETL processes, and overall systems testing. You may have project members test other members' work, such as the ETL team test the BI analytics and visa versa. Operators - IT operations is often separated from the development team but it is critical that they are involved from the beginning of the project to ensure that the systems are developed and deployed within your company's infrastructure. Key functions are database administration, systems administration, and networks. In addition, this extended team may also include help desk and training resources if they are usually provided outside of development.
cezarovidiu

Top Mistakes to Avoid in Analytics Implementations | StatSlice Business Intelligence an... - 0 views

  • 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.
cezarovidiu

Data Quality, Data Governance, and Master Data Management (MDM) - 0 views

  • Modern business applications produce ever more relevant and actionable information for decision makers, but in many cases the data sources are fragmented and inconsistent. Despite tremendous advancements at the application layer, nearly all IT initiatives succeed or fail based on the quality and consistency of the underlying data.
  • CIOs are responsible for making information available to their businesses in a consistent and timely basis, but in most organizations, information management is seen as a delegated set of tasks and is not the CIO’s top priority.
  • “Key initiatives such as master data management, data virtualization, data quality, data integration and data governance are employed by just a fraction of organizations that should be mastering the science of information management,”
  • ...1 more annotation...
  • While CIOs are aware that effective information management results in faster decision-making, according to the Ventana study, only 43% of organizations have undertaken information management initiatives in data governance, data integration, data quality, master data management and data virtualization during the last two years, and less than one fifth have completed those projects. The largest obstacles to completing information management projects are insufficient staffing (68%), inadequate budget (63%) and insufficient training and skills (59%).
  •  
    "Modern business applications produce ever more relevant and actionable information for decision makers, but in many cases the data sources are fragmented and inconsistent. Despite tremendous advancements at the application layer, nearly all IT initiatives succeed or fail based on the quality and consistency of the underlying data."
cezarovidiu

What is business intelligence (BI)? - Definition from WhatIs.com - 0 views

  • 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.
  • ...9 more annotations...
  • The potential benefits of business intelligence programs include accelerating and improving decision making; optimizing internal business processes; increasing operational efficiency; driving new revenues; and gaining competitive advantages over business rivals. BI systems can also help companies identify market trends and spot business problems that need to be addressed.
  • BI data can include historical information, as well as new data gathered from source systems as it is generated, enabling BI analysis to support both strategic and tactical decision-making processes.
  • BI programs can also incorporate forms of advanced analytics, such as data mining, predictive analytics, text mining, statistical analysis and big data analytics.
  • In many cases though, advanced analytics projects are conducted and managed by separate teams of data scientists, statisticians, predictive modelers and other skilled analytics professionals, while BI teams oversee more straightforward querying and analysis of business data.
  • Business intelligence data typically is stored in a data warehouse or smaller data marts that hold subsets of a company's information. In addition, Hadoop systems are increasingly being used within BI architectures as repositories or landing pads for BI and analytics data, especially for unstructured data, log files, sensor data and other types of big data. Before it's used in BI applications, raw data from different source systems must be integrated, consolidated and cleansed using data integration and data quality tools to ensure that users are analyzing accurate and consistent information.
  • In addition to BI managers, business intelligence teams generally include a mix of BI architects, BI developers, business analysts and data management professionals; business users often are also included to represent the business side and make sure its needs are met in the BI development process.
  • To help with that, a growing number of organizations are replacing traditional waterfall development with Agile BI and data warehousing approaches that use Agile software development techniques to break up BI projects into small chunks and deliver new functionality to end users on an incremental and iterative basis.
  • consultant Howard Dresner is credited with first proposing it in 1989 as an umbrella category for applying data analysis techniques to support business decision-making processes.
  • Business intelligence is sometimes used interchangeably with business analytics; in other cases, business analytics is used either more narrowly to refer to advanced data analytics or more broadly to include both BI and advanced analytics.
1 - 13 of 13
Showing 20 items per page