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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,”
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  • 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%).
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    "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

Universities Offer Courses in a Hot New Field - Data Science - NYTimes.com - 0 views

  • Data scientists are the magicians of the Big Data era. They crunch the data, use mathematical models to analyze it and create narratives or visualizations to explain it, then suggest how to use the information to make decisions.
  • Rachel Schutt, a senior research scientist at Johnson Research Labs, taught “Introduction to Data Science” last semester at Columbia (its first course with “data science” in the title). She described the data scientist this way: “a hybrid computer scientist software engineer statistician.” And added: “The best tend to be really curious people, thinkers who ask good questions and are O.K. dealing with unstructured situations and trying to find structure in them.”
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.
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  • 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

Why BI projects fail -- and how to succeed instead | InfoWorld - 0 views

  • A successful initiative starts with a good strategy, and a good strategy starts with identifying the business need.
  • The balanced scorecard is one popular methodology for linking strategy, technology, and performance management. Other methodologies, such as applied information economics, combine statistical analysis, portfolio theory, and decision science in order to help firms calculate the economic value of better information. Whether you use a published methodology or develop your own approach in-house, the important point is to make sure your BI activities are keyed to generating real business value, not merely creating pretty, but useless, dashboards and reports.
  • Next, ask: What data do we wish we had and how would that lead to different decisions? The answers to these questions form top-level requirements for any BI project.
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  • Instead a team of data experts, data analysts, and business experts must come together with the right technical expertise. This usually means bringing in outside help, though that help needs to be able to talk to management and talk tech.
  • Nothing makes an IT department more nervous than asking for a feed to a key operational system. Moreover, a lot of BI tools are resource hungry. Your requirements should dictate what, how much, and how often (that is, how “real time” you need it to be) data must be fed into your data warehousing technology.
  • In other words, you need one big feed to serve all instead of hundreds of operational, system-killing little feeds that can’t be controlled easily.
  • You'll probably need more than one tool to suit all of your use cases.
  • You did your homework, identified the use cases, picked a good team, started a data integration project, and chose the right tools.
  • Now comes the hard part: changing your business and your decisions based on the data and the reports. Managers, like other human beings, resist change.
  • oreover, BI projects shouldn't have a fixed beginning and end -- this isn't a sprint to become “data driven.”
  • A process is needed
  • and find new opportunities in the data.
  • Here's the bottom line, in a handy do's-and-don'ts format: Don’t simply run a tool-choice project Do cherry-pick the right team Do integrate the data so that it can be queried performance-wise without bringing down the house Don’t merely pick a tool -- pick the right tools for all your requirements and use cases Do let the data change your decision making and the structure of your organization itself if necessary Do have a process to weed out useless analytics and find new ones
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.
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    "What is a Data Scientist?"
cezarovidiu

Big Data ... How do I turn it on? - 0 views

  • How Do I Learn to Ride a Bike if I Don’t Own a Bike? I get it.  A lot of businesses went out and bought the bike.  In fact, some of them bought a bike that would be the envy of a Tour de France cyclist.  Now, they’re trying to learn how to ride it.
  • Hey, that’s fine.  Smart business people don’t live a linear life.  You get the tools and implement the tools – all while you’re learning the tools. 
  • There’s no “on button” for Big Data.  You need people who will put their hands into it, manipulate it and find the valuable insights.
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  • Data science is about finding insightful, meaningful relationships and correlations that can create a competitive advantage. 
  • Big data may sound like the new high-tech and flashy toy, but it’s not.  There isn’t an “on button”…it’s the data science professionals that make your data speak.
cezarovidiu

Business Intelligence Blog - The ElastiCube Chronicles - 0 views

  • SiSense’s survey finds that salaries for data professionals are on the rise across all geographies. The annual earnings of a data professional can range from an average of $55,000 USD for a data analyst to an average of $132,000 for VP Analytics. As many as 61% of the survey respondents reported higher earnings in 2012 compared to 2011, and only 12% reported lower earnings.
  • Other highlights of the survey findings include: Data professionals are highly educated. 85% of the respondents have some college degree, 39% have a Master’s degree, and 5% are Ph.D.’s. Those with doctoral degrees earn on average 65% more than those with Master’s degrees, who in turn earn 16% more than those with Bachelor’s degrees. On the job experience is even more important than education in determining salary levels. On average, professionals with ten or more years of experience earn 80% more than those with 3 years or less.
  • At the same time, the survey shows that those with 6 years or less make up as much as 59% of the data profession workforce.
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  • Most Data Professionals work in teams of up to five people. “Companies are starting to realize that Data is key to their success. The majority of them, though are not growing their Data Science teams fast enough to win. This maybe because they don’t want to or because they can’t. This is an alarming trend though and only software can come to the rescue,” noted Aziza.
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