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cezarovidiu

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

www.infoworld.com/...-fail-and-how-to-fix-that.html

bi implementare projects

shared by cezarovidiu on 28 Oct 14 - No Cached
  • 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

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

tdwi.org/...iples-analytics-rock-star.aspx

BI principles analytics

shared by cezarovidiu on 08 Feb 13 - No Cached
  • 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.
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  • 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

Is Big Data Really Working for Marketers? | ClickZ - 0 views

www.clickz.com/...a-really-working-for-marketers

shared by cezarovidiu on 09 Apr 14 - No Cached
  • Channel Optimization. Many marketers struggle to optimize each individual channel, let alone optimizing at a customer level across many channels. To the extent that Big Data can help marketers understand what is important in the moment and across touch points, that could be valuable, but it seems more of us need stronger attribution models and analytics methodologies more than access to data. Big Data does seem to be valuable if you want to understand which customers are highest value within each channel and across channels, because the platforms that manage Big Data can handle both structured and unstructured data - which is what you need to truly include Web/clickstream and social data in your analysis.
cezarovidiu

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

searchdatamanagement.techtarget.com/...business-intelligence

business intelligence definition

shared by cezarovidiu on 21 Jan 15 - Cached
  • 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.
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  • 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.
cezarovidiu

Difference between CRM lead and an opportunity - Pipeliner CRM Blog - 0 views

blog.pipelinersales.com/...en-crm-lead-and-an-opportunity

CRM documentation blog

shared by cezarovidiu on 31 Mar 15 - No Cached
  • Any individual fish or pod of fish in your sea represents one lead.
  • Your Nemo will not be the first or the second fish that you catch. At the beginning, you will have very little information about the Nemo you would like to catch. You will start to examine your fish and create some criteria as to how Nemo should look like. In other words, you are qualifying your fish.
  • Lead = Any Fish in The Sea. Opportunity = Nemo
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  • The process of examination and adding the criteria represents your sales pipeline strategy. It’s always true that: “Without a commitment to pursue working together (something that results in this company potentially buying from you) there is no opportunity.” - Anthony Iannarino
  • At the end of your examination ie. of your sales process, you will either let the fish swim back into your sea (lost opportunity) or you will put Nemo into your aquarium (won opportunity). Won Opportunity = You have found Nemo Lost Opportunity = You have not found Nemo
  • A Lead – is a contact or an account with very little information. It could be just a person who you might have met at a conference. You will need to retrieve more information regarding this lead in order to create (qualify) an opportunity in your sales pipeline.
  • A old sales rule says: “If you have never contacted your contact, it’s a lead.”
  • An Opportunity - is a contact or an account which has been qualified. This person has entered into your buying cycle and is committed to working with you. You have already contacted, called or met him and know their needs or requirements. The old sales rule says: “The opportunity is a deal that you have the possibility to close!”
  • “Think about the difference between a lead and an opportunity as an evolving process i.e. each lead needs to be qualified to an opportunity. There will always be plenty of leads in your sales territory, but only few of them will qualify to become real sales opportunity.”
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