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  • Data Quality and Master Data Management Resources

    - by Dejan Sarka
    Many companies or organizations do regular data cleansing. When you cleanse the data, the data quality goes up to some higher level. The data quality level is determined by the amount of work invested in the cleansing. As time passes, the data quality deteriorates, and you need to repeat the cleansing process. If you spend an equal amount of effort as you did with the previous cleansing, you can expect the same level of data quality as you had after the previous cleansing. And then the data quality deteriorates over time again, and the cleansing process starts over and over again. The idea of Data Quality Services is to mitigate the cleansing process. While the amount of time you need to spend on cleansing decreases, you will achieve higher and higher levels of data quality. While cleansing, you learn what types of errors to expect, discover error patterns, find domains of correct values, etc. You don’t throw away this knowledge. You store it and use it to find and correct the same issues automatically during your next cleansing process. The following figure shows this graphically. The idea of master data management, which you can perform with Master Data Services (MDS), is to prevent data quality from deteriorating. Once you reach a particular quality level, the MDS application—together with the defined policies, people, and master data management processes—allow you to maintain this level permanently. This idea is shown in the following picture. OK, now you know what DQS and MDS are about. You can imagine the importance on maintaining the data quality. Here are some resources that help you preparing and executing the data quality (DQ) and master data management (MDM) activities. Books Dejan Sarka and Davide Mauri: Data Quality and Master Data Management with Microsoft SQL Server 2008 R2 – a general introduction to MDM, MDS, and data profiling. Matching explained in depth. Dejan Sarka, Matija Lah and Grega Jerkic: MCTS Self-Paced Training Kit (Exam 70-463): Building Data Warehouses with Microsoft SQL Server 2012 – I wrote quite a few chapters about DQ and MDM, and introduced also SQL Server 2012 DQS. Thomas Redman: Data Quality: The Field Guide – you should start with this book. Thomas Redman is the father of DQ and MDM. Tyler Graham: Microsoft SQL Server 2012 Master Data Services – MDS in depth from a product team mate. Arkady Maydanchik: Data Quality Assessment – data profiling in depth. Tamraparni Dasu, Theodore Johnson: Exploratory Data Mining and Data Cleaning – advanced data profiling with data mining. Forthcoming presentations I am presenting a DQS and MDM seminar at PASS SQL Rally Amsterdam 2013: Wednesday, November 6th, 2013: Enterprise Information Management with SQL Server 2012 – a good kick start to your first DQ and / or MDM project. Courses Data Quality and Master Data Management with SQL Server 2012 – I wrote a 2-day course for SolidQ. If you are interested in this course, which I could also deliver in a shorter seminar way, you can contact your closes SolidQ subsidiary, or, of course, me directly on addresses [email protected] or [email protected]. This course could also complement the existing courseware portfolio of training providers, which are welcome to contact me as well. Start improving the quality of your data now!

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  • 2011 PASS Board Applicants: Adam Jorgensen

    - by andyleonard
    Introduction I am interviewing 2011 PASS Board Nominee Applicants. As listed on the PASS Board Elections site the applicants are: Rob Farley Geoff Hiten Adam Jorgensen Denise McInerney Sri Sridharan Kendal Van Dyke I'm asking everyone the same questions and blogging the responses in the order received. Adam Jorgensen is next up: Interview With Adam Jorgensen 1. What's your day job? I am currently the President of Pragmatic Works Consulting ( http://www.pragmaticworks.com ). I also participate with...(read more)

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  • 2011 PASS Board Applicants: Kendal Van Dyke

    - by andyleonard
    Introduction I am interviewing 2011 PASS Board Nominee Applicants. As listed on the PASS Board Elections site the applicants are: Rob Farley Geoff Hiten Adam Jorgensen Denise McInerney Sri Sridharan Kendal Van Dyke I'm asking everyone the same questions and blogging the responses in the order received. Kendal Van Dyke is next up: Interview With Kendal Van Dyke 1. What's your day job? I'm a Senior Technical Consultant with Insource Technologies ( http://www.insource.com/ ) in Houston, TX (but I work...(read more)

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  • 2011 PASS Board Applicants: Geoff Hiten

    - by andyleonard
    Introduction I am interviewing 2011 PASS Board Nominee Applicants. As listed on the PASS Board Elections site the applicants are: Rob Farley Geoff Hiten Adam Jorgensen Denise McInerney Sri Sridharan Kendal Van Dyke I'm asking everyone the same questions and blogging the responses in the order received. Geoff Hiten is next up: Interview With Geoff Hiten 1. What's your day job? I am a Principal Consultant for Intellinet, a business technology consulting company based in Atlanta.  I work in our...(read more)

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  • 2011 PASS Board Applicants: Denise McInerney

    - by andyleonard
    Introduction I am interviewing 2011 PASS Board Nominee Applicants. As listed on the PASS Board Elections site the applicants are: Rob Farley Geoff Hiten Adam Jorgensen Denise McInerney Sri Sridharan Kendal Van Dyke I'm asking everyone the same questions and blogging the responses in the order received. Denise McInerney is next up: Interview With Denise McInerney 1. What's your day job? I'm a development DBA at Intuit. Intuit provides financial software and services to small business and consumers....(read more)

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  • 2011 PASS Board Applicants: Rob Farley

    - by andyleonard
    Introduction I am interviewing 2011 PASS Board Nominee Applicants. As listed on the PASS Board Elections site the applicants are: Rob Farley Geoff Hiten Adam Jorgensen Denise McInerney Sri Sridharan Kendal Van Dyke I'm asking everyone the same questions and blogging the responses in the order received. Rob Farley is first up: Interview With Rob Farley 1. What's your day job? I run LobsterPot Solutions out of Adelaide, Australia. We're a SQL & BI consultancy, and were the first Microsoft Partner...(read more)

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  • 2011 PASS Board Applicants: Sri Sridharan

    - by andyleonard
    Introduction I am interviewing 2011 PASS Board Nominee Applicants. As listed on the PASS Board Elections site the applicants are: Rob Farley Geoff Hiten Adam Jorgensen Denise McInerney Sri Sridharan Kendal Van Dyke I'm asking everyone the same questions and blogging the responses in the order received. Sri Sridharan is next up: Interview With Sri Sridharan 1. What's your day job? I work for VHA as a Data Architect. I am responsible for 3 main goals. · Responsible for Data Governance initiatives in...(read more)

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  • Big Data – Learning Basics of Big Data in 21 Days – Bookmark

    - by Pinal Dave
    Earlier this month I had a great time to write Bascis of Big Data series. This series received great response and lots of good comments I have received, I am going to follow up this basics series with further in-depth series in near future. Here is the consolidated blog post where you can find all the 21 days blog posts together. Bookmark this page for future reference. Big Data – Beginning Big Data – Day 1 of 21 Big Data – What is Big Data – 3 Vs of Big Data – Volume, Velocity and Variety – Day 2 of 21 Big Data – Evolution of Big Data – Day 3 of 21 Big Data – Basics of Big Data Architecture – Day 4 of 21 Big Data – Buzz Words: What is NoSQL – Day 5 of 21 Big Data – Buzz Words: What is Hadoop – Day 6 of 21 Big Data – Buzz Words: What is MapReduce – Day 7 of 21 Big Data – Buzz Words: What is HDFS – Day 8 of 21 Big Data – Buzz Words: Importance of Relational Database in Big Data World – Day 9 of 21 Big Data – Buzz Words: What is NewSQL – Day 10 of 21 Big Data – Role of Cloud Computing in Big Data – Day 11 of 21 Big Data – Operational Databases Supporting Big Data – RDBMS and NoSQL – Day 12 of 21 Big Data – Operational Databases Supporting Big Data – Key-Value Pair Databases and Document Databases – Day 13 of 21 Big Data – Operational Databases Supporting Big Data – Columnar, Graph and Spatial Database – Day 14 of 21 Big DataData Mining with Hive – What is Hive? – What is HiveQL (HQL)? – Day 15 of 21 Big Data – Interacting with Hadoop – What is PIG? – What is PIG Latin? – Day 16 of 21 Big Data – Interacting with Hadoop – What is Sqoop? – What is Zookeeper? – Day 17 of 21 Big Data – Basics of Big Data Analytics – Day 18 of 21 Big Data – How to become a Data Scientist and Learn Data Science? – Day 19 of 21 Big Data – Various Learning Resources – How to Start with Big Data? – Day 20 of 21 Big Data – Final Wrap and What Next – Day 21 of 21 Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Big Data, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL

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  • Pre-Conference Sessions at the PASS Summit

    - by andyleonard
    Introduction I have some thoughts on the selection of pre-conference and post-conference session presenters at the PASS Summit. PASS pre-conference and post-conference sessions are $395. Trainers and speakers in the various SQL Server fields (relational engine, business intelligence, etc.) are selected to deliver these day-long seminars before and (now) after each PASS Summit. I have attended a few and the quality and amount of the training easily justifies the $395 price tag. Full Disclosure I've...(read more)

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  • Blogging from the PASS Summit : Nov. 7th keynote

    - by AaronBertrand
    Bill Graziano takes the stage at 8:15. He talks about how 3,894 attendees (and 5,611 total registrants) represent 57 countries at the 14th summit. There are over 127,000 members worldwide. Note that you can watch the keynotes and many sessions through Pass TV . PASS serves SQL Server community - expertise, support, commitment. He talks about SQL Saturdays, SQL Rally, 24 Hours of PASS, and the Summit. He announces that there will be a third annual SQL Rally Nordic event next November, and that there...(read more)

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  • The PASS Elections Review Committee Needs Your Feedback

    - by andyleonard
    Introduction PASS has had an ERC (Elections Review Committee) forum running for a few months now. There's been surprisingly little feedback, though lots of reads. Here's what it looks like tonight: That's 1,662 views and 37 replies by my count. Not very many replies... Jump In! Now's the time to let PASS know what you think about the current elections process. The ERC members are good people who are trying to make things better. If you have something to add - as simple as "love it!" or "hate it!"...(read more)

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  • Data Quality Through Data Governance

    Data Quality Governance Data quality is very important to every organization, bad data cost an organization time, money, and resources that could be prevented if the proper governance was put in to place.  Data Governance Program Criteria: Support from Executive Management and all Business Units Data Stewardship Program  Cross Functional Team of Data Stewards Data Governance Committee Quality Structured Data It should go without saying but any successful project in today’s business world must get buy in from executive management and all stakeholders involved with the project. If management does not fully support a project because they see it is in there and the company’s best interest then they will remove/eliminate funding, resources and allocated time to work on the project. In essence they can render a project dead until it is official killed by the business. In addition, buy in from stake holders is also very important because they can cause delays increased spending in time, money and resources because they do not support a project. Data Stewardship programs are administered by a data steward manager who primary focus is to support, train and manage a cross functional data stewards team. A cross functional team of data stewards are pulled from various departments act to ensure that all systems work to ensure that an organization’s goals are achieved. Typically, data stewards are subject matter experts that act as mediators between their respective departments and IT. Data Quality Procedures Data Governance Committees are composed of data stewards, Upper management, IT Leadership and various subject matter experts depending on a company. The primary goal of this committee is to define strategic goals, coordinate activities, set data standards and offer data guidelines for the business. Data Quality Policies In 1997, Claudia Imhoff defined a Data Stewardship’s responsibility as to approve business naming standards, develop consistent data definitions, determine data aliases, develop standard calculations and derivations, document the business rules of the corporation, monitor the quality of the data in the data warehouse, define security requirements, and so forth. She further explains data stewards responsible for creating and enforcing polices on the following but not limited to issues. Resolving Data Integration Issues Determining Data Security Documenting Data Definitions, Calculations, Summarizations, etc. Maintaining/Updating Business Rules Analyzing and Improving Data Quality

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  • The PASS Board of Directors Q&A Session

    - by andyleonard
    Friday afternoon (18 Oct 2013), the PASS Board of Directors met with interested members of the SQL Server Community to answer questions. Paraphrases of some questions and notes I collected during the session follow (Please note: this is not a transcript): Elections Kendall Van Dyke asked about duplicate voting. The Board responded that they had looked into the matter and identified duplicate memberships based on names and addresses, but with different email addresses. After filtering for duplicate...(read more)

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  • Blogging from the PASS Summit : WIT Luncheon

    - by AaronBertrand
    SQL Sentry is very proud to sponsor the 10th annual Women in Technology Luncheon at the PASS Summit. Probably 700 people in here - pretty crowded house. This luncheon is growing year over year and is always a refreshing and interesting event to attend. Bill Graziano kicks things off and introduces our moderator, Wendy Pastrick. The panel is made up of Stefanie Higgins (actually the founder of the WIT Luncheon event), Denise McInerney, Kevin Kline, Jen Stirrup and Kendra Little. Stefanie talked about...(read more)

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  • Blogging from the PASS Summit : Nov. 8th keynote

    - by AaronBertrand
    Douglas McDowell talks about day 1, the video montage featuring folks here from all over the world, and the fiscal year. The important point I took from this is that PASS is a non-profit committed to investing its revenue back into the community. They are hiring another full-time community evangelist, adding IT resources for online resources like the SQL Saturday site, and further expanding global efforts. He introduces the new board members: Wendy Pastrick, James Rowland-Jones, and Sri Sridharan....(read more)

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  • SQL SERVER – Advanced Data Quality Services with Melissa Data – Azure Data Market

    - by pinaldave
    There has been much fanfare over the new SQL Server 2012, and especially around its new companion product Data Quality Services (DQS). Among the many new features is the addition of this integrated knowledge-driven product that enables data stewards everywhere to profile, match, and cleanse data. In addition to the homegrown rules that data stewards can design and implement, there are also connectors to third party providers that are hosted in the Azure Datamarket marketplace.  In this review, I leverage SQL Server 2012 Data Quality Services, and proceed to subscribe to a third party data cleansing product through the Datamarket to showcase this unique capability. Crucial Questions For the purposes of the review, I used a database I had in an Excel spreadsheet with name and address information. Upon a cursory inspection, there are miscellaneous problems with these records; some addresses are missing ZIP codes, others missing a city, and some records are slightly misspelled or have unparsed suites. With DQS, I can easily add a knowledge base to help standardize my values, such as for state abbreviations. But how do I know that my address is correct? And if my address is not correct, what should it be corrected to? The answer lies in a third party knowledge base by the acknowledged USPS certified address accuracy experts at Melissa Data. Reference Data Services Within DQS there is a handy feature to actually add reference data from many different third-party Reference Data Services (RDS) vendors. DQS simplifies the processes of cleansing, standardizing, and enriching data through custom rules and through service providers from the Azure Datamarket. A quick jump over to the Datamarket site shows me that there are a handful of providers that offer data directly through Data Quality Services. Upon subscribing to these services, one can attach a DQS domain or composite domain (fields in a record) to a reference data service provider, and begin using it to cleanse, standardize, and enrich that data. Besides what I am looking for (address correction and enrichment), it is possible to subscribe to a host of other services including geocoding, IP address reference, phone checking and enrichment, as well as name parsing, standardization, and genderization.  These capabilities extend the data quality that DQS has natively by quite a bit. For my current address correction review, I needed to first sign up to a reference data provider on the Azure Data Market site. For this example, I used Melissa Data’s Address Check Service. They offer free one-month trials, so if you wish to follow along, or need to add address quality to your own data, I encourage you to sign up with them. Once I subscribed to the desired Reference Data Provider, I navigated my browser to the Account Keys within My Account to view the generated account key, which I then inserted into the DQS Client – Configuration under the Administration area. Step by Step to Guide That was all it took to hook in the subscribed provider -Melissa Data- directly to my DQS Client. The next step was for me to attach and map in my Reference Data from the newly acquired reference data provider, to a domain in my knowledge base. On the DQS Client home screen, I selected “New Knowledge Base” under Knowledge Base Management on the left-hand side of the home screen. Under New Knowledge Base, I typed a Name and description of my new knowledge base, then proceeded to the Domain Management screen. Here I established a series of domains (fields) and then linked them all together as a composite domain (record set). Using the Create Domain button, I created the following domains according to the fields in my incoming data: Name Address Suite City State Zip I added a Suite column in my domain because Melissa Data has the ability to return missing Suites based on last name or company. And that’s a great benefit of using these third party providers, as they have data that the data steward would not normally have access to. The bottom line is, with these third party data providers, I can actually improve my data. Next, I created a composite domain (fulladdress) and added the (field) domains into the composite domain. This essentially groups our address fields together in a record to facilitate the full address cleansing they perform. I then selected my newly created composite domain and under the Reference Data tab, added my third party reference data provider –Melissa Data’s Address Check- and mapped in each domain that I had to the provider’s Schema. Now that my composite domain has been married to the Reference Data service, I can take the newly published knowledge base and create a project to cleanse and enrich my data. My next task was to create a new Data Quality project, mapping in my data source and matching it to the appropriate domain column, and then kick off the verification process. It took just a few minutes with some progress indicators indicating that it was working. When the process concluded, there was a helpful set of tabs that place the response records into categories: suggested; new; invalid; corrected (automatically); and correct. Accepting the suggestions provided by  Melissa Data allowed me to clean up all the records and flag the invalid ones. It is very apparent that DQS makes address data quality simplistic for any IT professional. Final Note As I have shown, DQS makes data quality very easy. Within minutes I was able to set up a data cleansing and enrichment routine within my data quality project, and ensure that my address data was clean, verified, and standardized against real reference data. As reviewed here, it’s easy to see how both SQL Server 2012 and DQS work to take what used to require a highly skilled developer, and empower an average business or database person to consume external services and clean data. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, SQL Utility, T SQL, Technology Tagged: DQS

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  • Sessions I Submitted to the PASS Summit 2010

    - by andyleonard
    Introduction I'm borrowing an idea and blog post title from Brent Ozar ( Blog - @BrentO ). I am honored the PASS Summit 2010 (Seattle, 8 - 11 Nov 2010) would consider allowing me to present. It's a truly awesome event. If you have an opportunity to attend and read this blog, please find me and introduce yourself. If you've built a cool solution to a business or technical problem; or written a script - or a bunch of scripts - to automate part of your daily / weekly / monthly routine; or have some...(read more)

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  • 24 Hours of PASS: 15 Powerful Dynamic Management Objects - Deck and Demos

    - by Adam Machanic
    Thank you to everyone who attended today's 24 Hours of PASS webcast on Dynamic Management Objects! I was shocked, awed, and somewhat scared when I saw the attendee number peak at over 800. I really appreciate your taking time out of your day to listen to me talk. It's always interesting presenting to people I can't see or hear, so I relied on Twitter for a form of nearly real-time feedback. I would like to especially thank everyone who left me tweets both during and after the presentation. Your feedback...(read more)

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  • T-SQL Tuesday : Reflections on the PASS Summit and our community

    - by AaronBertrand
    Last week I attended the PASS Summit in Seattle. I blogged from both keynotes ( Keynote #1 and Keynote #2 ), as well as the WIT Luncheon - which SQL Sentry sponsored. I had a fantastic time at the conference, even though these days I attend far fewer sessions that I used to. As a company, we were overwhelmed by the positive energy in the Expo Hall. I really liked the notebook idea, where board members were assigned notebooks to carry around and take ideas from attendees. I took full advantage when...(read more)

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  • 24 Hours of PASS: 15 Powerful Dynamic Management Objects - Deck and Demos

    - by Adam Machanic
    Thank you to everyone who attended today's 24 Hours of PASS webcast on Dynamic Management Objects! I was shocked, awed, and somewhat scared when I saw the attendee number peak at over 800. I really appreciate your taking time out of your day to listen to me talk. It's always interesting presenting to people I can't see or hear, so I relied on Twitter for a form of nearly real-time feedback. I would like to especially thank everyone who left me tweets both during and after the presentation. Your feedback...(read more)

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  • PASS Business Intelligence Virtual Chapter Upcoming Sessions (November 2013)

    - by Sergio Govoni
    Let me point out the upcoming live events, dedicated to Business Intelligence with SQL Server, that PASS Business Intelligence Virtual Chapter has scheduled for November 2013. The "Accidental Business Intelligence Project Manager"Date: Thursday 7th November - 8:00 PM GMT / 3:00 PM EST / Noon PSTSpeaker: Jen StirrupURL: https://attendee.gotowebinar.com/register/5018337449405969666 You've watched the Apprentice with Donald Trump and Lord Alan Sugar. You know that the Project Manager is usually the one gets firedYou've heard that Business Intelligence projects are prone to failureYou know that a quick Bing search for "why do Business Intelligence projects fail?" produces a search result of 25 million hits!Despite all this… you're now Business Intelligence Project Manager – now what do you do?In this session, Jen will provide a "sparks from the anvil" series of steps and working practices in Business Intelligence Project Management. What about waterfall vs agile? What is a Gantt chart anyway? Is Microsoft Project your friend or a problematic aspect of being a BI PM? Jen will give you some ideas and insights that will help you set your BI project right: assess priorities, avoid conflict, empower the BI team and generally deliver the Business Intelligence project successfully! Dimensional Modelling Design Patterns: Beyond BasicsDate: Tuesday 12th November - Noon AEDT / 1:00 AM GMT / Monday 11th November 5:00 PM PSTSpeaker: Jason Horner, Josh Fennessy and friendsURL: https://attendee.gotowebinar.com/register/852881628115426561 This session will provide a deeper dive into the art of dimensional modeling. We will look at the different types of fact tables and dimension tables, how and when to use them. We will also some approaches to creating rich hierarchies that make reporting a snap. This session promises to be very interactive and engaging, bring your toughest Dimensional Modeling quandaries. Data Vault Data Warehouse ArchitectureDate: Tuesday 19th November - 4:00 PM PST / 7 PM EST / Wednesday 20th November 11:00 PM AEDTSpeaker: Jeff Renz and Leslie WeedURL: https://attendee.gotowebinar.com/register/1571569707028142849 Data vault is a compelling architecture for an enterprise data warehouse using SQL Server 2012. A well designed data vault data warehouse facilitates fast, efficient and maintainable data integration across business systems. In this session Leslie and I will review the basics about enterprise data warehouse design, introduce you to the data vault architecture and discuss how you can leverage new features of SQL Server 2012 help make your data warehouse solution provide maximum value to your users. 

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  • T-SQL Tuesday : Reflections on the PASS Summit and our community

    - by AaronBertrand
    Last week I attended the PASS Summit in Seattle. I blogged from both keynotes ( Keynote #1 and Keynote #2 ), as well as the WIT Luncheon - which SQL Sentry sponsored. I had a fantastic time at the conference, even though these days I attend far fewer sessions that I used to. As a company, we were overwhelmed by the positive energy in the Expo Hall. I really liked the notebook idea, where board members were assigned notebooks to carry around and take ideas from attendees. I took full advantage when...(read more)

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  • pass by reference or pass by value?

    - by Sven
    When learning a new programming language, one of the possible roadblocks you might encounter is the question whether the language is, by default, pass-by-value or pass-by-reference So here is my question to all of you, in your favorite language, how is it actually done? and what are the possible pitfalls? your favorite language can, of course, be anything you have ever played with: popular, obscure, esoteric, new, old ...

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  • Big Data – Is Big Data Relevant to me? – Big Data Questionnaires – Guest Post by Vinod Kumar

    - by Pinal Dave
    This guest post is by Vinod Kumar. Vinod Kumar has worked with SQL Server extensively since joining the industry over a decade ago. Working on various versions of SQL Server 7.0, Oracle 7.3 and other database technologies – he now works with the Microsoft Technology Center (MTC) as a Technology Architect. Let us read the blog post in Vinod’s own voice. I think the series from Pinal is a good one for anyone planning to start on Big Data journey from the basics. In my daily customer interactions this buzz of “Big Data” always comes up, I react generally saying – “Sir, do you really have a ‘Big Data’ problem or do you have a big Data problem?” Generally, there is a silence in the air when I ask this question. Data is everywhere in organizations – be it big data, small data, all data and for few it is bad data which is same as no data :). Wow, don’t discount me as someone who opposes “Big Data”, I am a big supporter as much as I am a critic of the abuse of this term by the people. In this post, I wanted to let my mind flow so that you can also think in the direction I want you to see these concepts. In any case, this is not an exhaustive dump of what is in my mind – but you will surely get the drift how I am going to question Big Data terms from customers!!! Is Big Data Relevant to me? Many of my customers talk to me like blank whiteboard with no idea – “why Big Data”. They want to jump into the bandwagon of technology and they want to decipher insights from their unexplored data a.k.a. unstructured data with structured data. So what are these industry scenario’s that come to mind? Here are some of them: Financials Fraud detection: Banks and Credit cards are monitoring your spending habits on real-time basis. Customer Segmentation: applies in every industry from Banking to Retail to Aviation to Utility and others where they deal with end customer who consume their products and services. Customer Sentiment Analysis: Responding to negative brand perception on social or amplify the positive perception. Sales and Marketing Campaign: Understand the impact and get closer to customer delight. Call Center Analysis: attempt to take unstructured voice recordings and analyze them for content and sentiment. Medical Reduce Re-admissions: How to build a proactive follow-up engagements with patients. Patient Monitoring: How to track Inpatient, Out-Patient, Emergency Visits, Intensive Care Units etc. Preventive Care: Disease identification and Risk stratification is a very crucial business function for medical. Claims fraud detection: There is no precise dollars that one can put here, but this is a big thing for the medical field. Retail Customer Sentiment Analysis, Customer Care Centers, Campaign Management. Supply Chain Analysis: Every sensors and RFID data can be tracked for warehouse space optimization. Location based marketing: Based on where a check-in happens retail stores can be optimize their marketing. Telecom Price optimization and Plans, Finding Customer churn, Customer loyalty programs Call Detail Record (CDR) Analysis, Network optimizations, User Location analysis Customer Behavior Analysis Insurance Fraud Detection & Analysis, Pricing based on customer Sentiment Analysis, Loyalty Management Agents Analysis, Customer Value Management This list can go on to other areas like Utility, Manufacturing, Travel, ITES etc. So as you can see, there are obviously interesting use cases for each of these industry verticals. These are just representative list. Where to start? A lot of times I try to quiz customers on a number of dimensions before starting a Big Data conversation. Are you getting the data you need the way you want it and in a timely manner? Can you get in and analyze the data you need? How quickly is IT to respond to your BI Requests? How easily can you get at the data that you need to run your business/department/project? How are you currently measuring your business? Can you get the data you need to react WITHIN THE QUARTER to impact behaviors to meet your numbers or is it always “rear-view mirror?” How are you measuring: The Brand Customer Sentiment Your Competition Your Pricing Your performance Supply Chain Efficiencies Predictive product / service positioning What are your key challenges of driving collaboration across your global business?  What the challenges in innovation? What challenges are you facing in getting more information out of your data? Note: Garbage-in is Garbage-out. Hold good for all reporting / analytics requirements Big Data POCs? A number of customers get into the realm of setting a small team to work on Big Data – well it is a great start from an understanding point of view, but I tend to ask a number of other questions to such customers. Some of these common questions are: To what degree is your advanced analytics (natural language processing, sentiment analysis, predictive analytics and classification) paired with your Big Data’s efforts? Do you have dedicated resources exploring the possibilities of advanced analytics in Big Data for your business line? Do you plan to employ machine learning technology while doing Advanced Analytics? How is Social Media being monitored in your organization? What is your ability to scale in terms of storage and processing power? Do you have a system in place to sort incoming data in near real time by potential value, data quality, and use frequency? Do you use event-driven architecture to manage incoming data? Do you have specialized data services that can accommodate different formats, security, and the management requirements of multiple data sources? Is your organization currently using or considering in-memory analytics? To what degree are you able to correlate data from your Big Data infrastructure with that from your enterprise data warehouse? Have you extended the role of Data Stewards to include ownership of big data components? Do you prioritize data quality based on the source system (that is Facebook/Twitter data has lower quality thresholds than radio frequency identification (RFID) for a tracking system)? Do your retention policies consider the different legal responsibilities for storing Big Data for a specific amount of time? Do Data Scientists work in close collaboration with Data Stewards to ensure data quality? How is access to attributes of Big Data being given out in the organization? Are roles related to Big Data (Advanced Analyst, Data Scientist) clearly defined? How involved is risk management in the Big Data governance process? Is there a set of documented policies regarding Big Data governance? Is there an enforcement mechanism or approach to ensure that policies are followed? Who is the key sponsor for your Big Data governance program? (The CIO is best) Do you have defined policies surrounding the use of social media data for potential employees and customers, as well as the use of customer Geo-location data? How accessible are complex analytic routines to your user base? What is the level of involvement with outside vendors and third parties in regard to the planning and execution of Big Data projects? What programming technologies are utilized by your data warehouse/BI staff when working with Big Data? These are some of the important questions I ask each customer who is actively evaluating Big Data trends for their organizations. These questions give you a sense of direction where to start, what to use, how to secure, how to analyze and more. Sign off Any Big data is analysis is incomplete without a compelling story. The best way to understand this is to watch Hans Rosling – Gapminder (2:17 to 6:06) videos about the third world myths. Don’t get overwhelmed with the Big Data buzz word, the destination to what your data speaks is important. In this blog post, we did not particularly look at any Big Data technologies. This is a set of questionnaire one needs to keep in mind as they embark their journey of Big Data. I did write some of the basics in my blog: Big Data – Big Hype yet Big Opportunity. Do let me know if these questions make sense?  Reference: Pinal Dave (http://blog.sqlauthority.com)Filed under: Big Data, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL

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