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cezarovidiu

What's in a Tag? | ClickZ - 0 views

  • The tag-management industry is growing rapidly, as tags are critical to gathering data about your customers.
  • It's the early days for tag management, but the industry is growing rapidly because it's not so much about tags, but about the bigger challenge of using digital data.
  • Where does tag management fit in the data picture? Here's an example someone shared with me recently: He had gone to an antivirus product's website, read the reviews, and bought the software. In the days that followed, however, he suddenly began to see banner ads from that same software maker whenever he visited CNN, ESPN, and other favorite websites. The software maker knew he had visited its website, but not that he already bought the product. They were retargeting him with banner ads at unnecessary cost and no purpose.
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  • Tag management fixes this problem.
  • Most marketing teams struggle with the volume, velocity, and variety of digital data generated every time someone touches the brand. You need insights from the data. You need to understand cross-channel behavior and run predictive "what if" scenarios to improve the effectiveness of your media mix. Tag management can create a foundation to make it easier to use multichannel marketing analytics for these purposes.
  • But one of the big improvements introduced by tag management systems is this: non-technical marketers can do their own tag management.
  • No need to ask IT to deploy tags.
  • You can deploy just one tag, sometimes even just a single line of code, and then manage all the tags through a single user interface.
  • That's a big change from being forced to modify source code on your website.
  • The best tag management systems unite tagged data in one place - automatically.
  • Now the best tag management systems track a data record each time a consumer touches your brand - and deliver it to you in one place.
  • what each consumer has viewed, on what platform, how long they spent with your content, and whether they purchased anything. You get a unified view for everything the consumer has done across all marketing channels.
  • they include the right to be forgotten, easier access to your own data, explicit consent over the use of your data, and privacy by design by default.
  • And, it's clear that the best tag management systems can be a foundation for building those elusive, one-to-one relationships with customers, while using marketing analytics to further improve your marketing decisions about how, when, and where to relate to them.
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.
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  • 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

mysql - Best of MyISAM and InnoDB - Database Administrators - 0 views

  • Some people can make the table's row format FIXED using ALTER TABLE mydb.mytb ROW_FORMAT=Fixed; and can get a 20% increase in read performance without any other changes. This works and works effectively FOR MyISAM. This will not produce faster results for InnoDB because ... that's right ... you must consult the gen_clust_index each time.
cezarovidiu

PL/PDF generate and manipulate PDF with Oracle PL/SQL - 0 views

shared by cezarovidiu on 14 Feb 13 - Cached
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    "Oracle Reporting & Document Generation PL/PDF is simply the easiest and most flexible way to create professional reports from your Oracle database. The data access is the fastest and safest, because our products work in the database. There is no need for extra servers and extra costs! We provide native PL/SQL solutions which is the best way to work with the Oracle data. All Oracle developer in the PL/SQL language know and use, so no need to learn a new programming language."
cezarovidiu

Bossie Awards 2014: The best open source applications | InfoWorld - 0 views

  • SuiteCRM was forked from the 6.5.x branch of SugarCRM because a segment of the community felt that SugarCRM Inc. was paying too much attention to its commercial editions and dragging its feet on updating the community edition. In addition to packaging up the latest SugarCRM codebase, SuiteCRM added a number of third-party extensions, resulting in a new system that is comparable to SugarCRM Professional in terms of features and functionality.
cezarovidiu

13 things to consider when implementing a CRM plan | Econsultancy - 0 views

  • These are few of the benefits of implementing a good quality CRM All of your clients’ information is stored in one place, it’s easy to update and share with the whole team. Updates by colleagues should be saved immediately. Every member of your team will be able to see the exact point when your business last communicated with a client, and what the nature of that communication was. CRMs can give you instant metrics on various aspects of your business automatically.  Reports can be generated. These can also be used to forecast and plan for the future. You will be able to see the complete history of your company’s interaction with a client. Calendars and diaries can be integrated, relating important events or tasks with the relevant client.  Suitable times can be suggested to contact customers and set reminders.
  • Finding one system that will fit your needs in one package may not be possible, so be aware that you may need to customise it to fit into your company. There are infinite possibilities here so don’t get too carried away as costs will rise accordingly.
  • Ensure that the CRM works on mobile devices and can be accessed remotely. Employees aren’t necessarily sat at their desks when it needs to be used or updated. Real-time updates are necessary for ensuring that clients aren’t contacted twice with the exact same follow up.
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  • Will it work for Outlook, Gmail or whichever email provider your company uses? 
  • Does you CRM have full social media integration? It’s vital that any customers or clients interacting with you on social channels can be included in your CRM updates. You will find this happens increasingly as your public facing channels become more popular. For more detailed information download our best practice guide CRM in the social age.  
  • Do you have a fully CRM trained analytics team that can study and understand the data and reports the system will generate? It’s probably wise to implement a cleansing plan for your existing data before the new system is implemented. Sifting through contacts to remove any duplicated or defunct leads.
  • Having an extra piece of software in the company, especially one as integral as this, means there’s a lot more to manage and possibly to go wrong. Make sure you have the technical support in place to ensure its smooth running.
cezarovidiu

Gartner Positions Oracle in Leaders Quadrant for Master Data Management of Product Data... - 0 views

  • For the fourth consecutive year, Gartner, Inc. has named Oracle as a Leader in its “Magic Quadrant for Master Data Management of Product Data Solutions.” (1)
  • “MDM is a technology-enabled discipline in which business and IT staff work together to ensure the uniformity, accuracy, stewardship, semantic consistency and accountability of the enterprise's official, shared master data assets. Master data is the consistent and uniform set of identifiers and extended attributes that describes the core entities of the enterprise, such as customers, prospects, citizens, suppliers, sites, hierarchies and chart of accounts,” according to Gartner.
  • By enabling organizations to consolidate product information from heterogeneous systems, Oracle Product Hub creates a single view of product information that can be leveraged and shared across functional departments in the enterprise, as well as externally with trading partners.
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  • "In any product company, accurate product information is a foundation for all major business initiatives, and this requires a robust, comprehensive and flexible product MDM solution,” said Jon Chorley, vice president, supply chain management product strategy, Oracle. "We believe Oracle's position in Gartner's Magic Quadrant for Master Data Management of Product Data Solutions highlights our ability to provide best-in-class functionality across the industry’s most complete MDM portfolio. By using Oracle MDM solutions, companies can obtain a high-quality, common enterprise product record and are better able to support their key business initiatives.”
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    "Gartner Positions Oracle in Leaders Quadrant for Master Data Management of Product Data Solutions"
cezarovidiu

Rittman Mead Consulting - The Changing World of Business Intelligence - 0 views

  • 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.
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  • Schema on read Schema on read works on a different principle and is more common in the Big Data world. The data is not transformed in any way when it is stored, the data store acts as a big bucket. The modelling of the data only occurs when the data is read. Map/Reduce is the clearest example, the mapping is the understanding of the data structure. Hadoop is a large distributed file system, which is very good at storing large volumes of data, this is potential. It is only the mapping of this data that provides value, this is done when the data is read, not written.
  • New World Order So whereas Business Intelligence used to always be driven by the model, the ETL process to populate the model and the reporting tool to query the model, there is now an approach where the data is collected its raw form, and advanced statistical or analytical tools are used to interrogate the data. An example of one such tool is R.
  • The driver for which approach to use is often driven by what the user wants to find out. If the question is clearly formed and the sources of data that are required to answer it well understood, for example how many units of a product have we sold, then the traditional schema on write approach is best.
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.
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  • 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

Visual Business Intelligence - Naked Statistics - 0 views

  • You can’t learn data visualization by memorizing a set of rules. You must understand why things work the way they do.
  • you must be able to think statistically
  • This doesn’t mean that you must learn advanced mathematics, nor can you do this work merely by learning how to use software to calculate correlation coefficients and p-values.
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  • I am happy to announce that I’ve just found the book that does this better than any other that I’ve seen: Naked Statistics: Stripping the Dread from the Data, by Charles Wheelan (W. W. Norton & Company, 2013).
  • Wheelan teaches public policy and economics at Dartmouth College and is best known for a similar book written several years ago titled Naked Economics.
  • In Naked Statistics, he selects the most important and relevant statistical concepts that everyone should understand, especially those who work with data, and explains them in clear, entertaining, and practical terms.
  • He wrote this book specifically to help people think statistically. He shows how statistics can be used to improve our understanding of the world. He demonstrates that statistical concepts are easy to understand when they’re explained well.
  • If you read this book, you’ll come to understand statistical concepts and methods such as regression analysis and probability as never before.
  • Statistics is more important than ever before because we have more meaningful opportunities to make use of data. Yet the formulas will not tell us which uses of data are appropriate and which are not. Math cannot supplant judgment.
  • “Go forth and use data wisely and well!”
cezarovidiu

2013 ERP research: Compelling advice for the CFO : Enterprise Irregulars - 0 views

  • ERP vendor selection. As the following graph shows, the primary candidates for ERP software were SAP, Oracle, Microsoft, Epicor, and Infor:
  • The cloud question. Despite the hype, only 14 percent of respondents are using ERP delivered as Software as a Service (SaaS). Although the best cloud vendors can deliver superior security and reliability than most internal IT departments, market momentum to ERP in the cloud is not there yet, as the following diagram illustrates:
  • Important lessons. Implementing an ERP system is always complex because the deployment drives changes to both data and processes that extend across departmental boundaries inside the organization.
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  • Software projects aren’t just technical endeavors. They’re also political, financial, emotional, structural, strategic, process and people-centric initiatives. Ignoring any one of these dimensions is done at the project manager’s peril.
  • Today’s CFO must balance the demands of two competing forces: the extraordinary wave of innovation (and the process changes these bring) against the regulatory, control-driven forces who want every process, every exception, and device to be documented, controlled and secured. In recent years, CFOs have spent tens of billions of dollars (or more) with audit firms to document the control points and risks within their existing ERP solutions.
  • ERP can bring significant benefit but implementation requires careful attention to both business planning and technology activities. For this reason, achieving project success and business value demand that CFO and CIO work together as a collaborative unit.
  • Therefore, it is essential to create this partnership and show your entire organization that the business and technology teams can communicate, collaborate, and share knowledge on a systematic and consistent basis. This collaboration is the true underlying strategy for gaining maximum value from ERP or any other enterprise initiative.
cezarovidiu

Rittman Mead Consulting » Blog Archive » Using OBIEE against Transactional Sc... - 0 views

  • The best practice in business intelligence delivery is always to build a data warehouse.
  • Pure transactional reporting is problematic. There are, of course, the usual performance issues. Equally troublesome is the difficulty in distilling a physical model down to a format that is easy for business users to understand. Dimensional models are typically the way business users envision their business: simple, inclusive structures for each entity. The standard OLTP data model that takes two of the four walls in the conference room to display will never make sense to your average business user.
cezarovidiu

Top 10 Best Piano Songs Ever - YouTube - 0 views

shared by cezarovidiu on 12 Apr 13 - No Cached
cezarovidiu

Octavian Pantis, autorul cartii Musai List, vine la Start-Up Wall-Street. Afla cum sa f... - 0 views

  • Trainerul Octavian Pantis, managing director TMI Training & Consulting. El este autorul cartii "Musai List", un best-seller in domeniu.
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.
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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.
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