Real-time Data Collaboration by Qlikview


Yesterday, I had a very nice meeting with the folks at Qlikview (thanks, @DHurd11 & Andy for making the trip).  They partnered with a local DC consulting firm by the name of Tandem Conglomerate.  I had the pleasure of working with Ben Nah from Tandem Conglomerate last year – and I can vouch that their talent is top-notch.  Qlikview is smart to find partners of this caliber – and utilize them to better serve their customers.

As a technologist, I always have my eyes open to exploring new technology.  This is always challenging with long term contracts, university politics, and an ever-changing IT landscape.  However, for me, vendors have to prove why they should remain at the top.  Competition is healthy for everyone as it makes us constantly improve.  I should also say that as long as we have a defined data model, the reporting tools that we use on top of that data model are fluid.  The point is not to constantly change and rip out what you’ve done just for the sake of redoing it, but it is important to keep an eye on the latest technology, experiment, and find what is best for your organization.  This can be done gradually with small pilot projects to prove value.  We’re actually in the process of doing one of these pilot projects with Hadoop.

Ok – so you’re probably wondering why I titled this post ‘real-time data collaboration’?  During the Qlikview presentation yesterday, I saw something that really resonated with me.  And, that was the ability to collaborate, in real-time, on the same Qlikview dashboard.

This capability is a market differentiator for Qlikview.  As many of you may have seen from Gartner and in my previous post regarding Gartner’s Magic Quadrant for BI, this is one of the reasons that Qlikview remains ahead of competitors such as Tableau.  Other dashboard vendors may provide the ability to ‘share’ with others, or embed the dashboard into a web page or part.  Don’t misunderstand.  This is NOT the same.

During their product demonstration, Qlikview demonstrated the ability in real-time to share dashboards.  This means that you can select the filters/parameters that you would like to analyze, hone in on the particular area of interest, and share it in real-time.  The recipient can then see that selection, modify it, and as they modify the shared dashboard, it will update your dashboard.  You can modify and send changes back to the recipient as well.  VERY COOL!  Kudos to Qlikview on this feature.  Below are a few screen shots to show how it works:

Click ‘Share Session’

Click ‘Start Sharing’

Choose if you want to cut/paste the link, email it, or even expire the shared session.  By the way, recipients don’t have to have any sort of Qlikview license to be able to provide feedback in real-time.

Try it out in the demo below:


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Higher Education Data Warehouse Conference (HEDW) @ Cornell University

I just returned from an excellent conference  (HEDW) which was administered by the IT staff at Cornell University. Kudos to the 2013 Conference Chair, Jeff Christen, and the staff of Cornell University for hosting this year! Below is a little bit more information about the conference:

The Higher Education Data Warehousing Forum (HEDW) is a network of higher education colleagues dedicated to promoting the sharing of knowledge and best practices regarding knowledge management in colleges and universities, including building data warehouses, developing institutional reporting strategies, and providing decision support.

The Forum meets once a year and sponsors a series of listservs to promote communication among technical developers and administrators of data access and reporting systems, data custodians, institutional researchers, and consumers of data representing a variety of internal university audiences.

There are more than 2000 active members in HEDW, representing professionals from 700+ institutions found in 38 different countries and 48 different states.

This conference has proven to be helpful to Georgetown University over the last 5 years.  It is a great opportunity to network with peers and share best practice around the latest technology tools.  And, sorry vendors, you are kept at bay.  This is important as the focus of the conference is less on technology sales – and more about relationships and sharing successes.

Cornell University Outside of Statler Conference Center

Cornell University Outside of Statler Conference Center

Personally, this was my first year in attendance.  I gained a lot of industry insight, but it was also helpful to find peer organizations that are using the same technology tools.  We are about to embark upon an Oracle Peoplesoft finance to Workday conversion.  It was helpful to connect with others that are going through similar projects.  And for me specifically, it was helpful to learn how folks are starting to extract data from Workday for business intelligence purposes.

Higher Education Data Warehouse Conference

Higher Education Data Warehouse Conference

2013 HEDW Attendee List

2013 HEDW Attendee List

My key take-aways from the conference were:

  • Business intelligence is happening with MANY tools.  We saw A LOT of technology.  Industry leaders in the higher education space still seem to be Oracle and MicrosoftOracle seemed to be embedded in more universities; however many are starting projects on the Microsoft stack – particularly with the Blackboard Analytics team standardizing on the Microsoft platform.  IBM Cognos still seemed to be the market leader in terms of operational reporting; however Microsoft’s SSRS is gaining momentum.  From an OLAP and dashboard perspective, it seemed like a mixed bag.  Some were using IBM BI Dashboards, while others were using tools such as OBIEE Dashboards, Microsoft Sharepoint’s Dashboard Designer, and an emerging product – Pyramid Analytics. Microsoft’s PowerPivot was also highly demonstrated and users like it!  PowerView was mentioned, but no one seemed to have it up and running…yet.  Tableau was also a very popular choice and highly recommended.  Several people mentioned how responsive both Microsoft and Tableau had been to their needs pre-sale.
  • Business intelligence requires a SIGNIFICANT amount of governance to be successful.  We saw presentation after presentation about the governance structures that should have been setup.  Or, projects that had to be restarted in order be governed in the appropriate way.  This includes changing business processes and ensuring that common data definitions are put in place across university silos.  A stove-piped approach does not work when you are trying to analyze data cross functionally.
  • Standardizing on one tool is difficult.  We spoke to many universities that had multiple tools in play.  This is due to the difficulty of change management and training.  It is worth making the investment for change management in order to standardize on the appropriate tool set.
  • Technology is expensive.  There is no one size fits all.  Depending on the licensing agreements that are in place at your university – there may be a clear technology choice.  Oracle is expensive, but it may already be in use to support critical ERP systems.  We also heard many universities discuss their use of Microsoft due to educational and statewide discounts available.
  • Predictive Analytics are still future state.  We had brief discussions about statistical tools like SAS and IBM’s SPSS; however, these tools were not the focus of many discussions.  It seems that most universities are trying to figure out simple ODS and EDW projects. Predictive analytics and sophisticated statistical tools are in use – but seem to be taking a back seat while IT departments get the more fundamental data models in place.  Most had an extreme amount of interest in these types of predictive analytics, but felt, “we just aren’t there yet.”  GIS data also came up in a small number of presentations, but also has interest.  In fact, one presentation displayed a dashboard with student enrollment by county.  People like to see data overlaid on a map.  I can see more universities taking advantage of this soon.
  • Business intelligence technologists are in high demand and hard to find.  It was apparent throughout the conference that many universities are challenged to find the right technology talent.  Many are in need of employees that possess business intelligence and reporting skills.
  • Hadoop remains on the shelf.  John Rome from Arizona State gave an excellent presentation about Hadoop and its functional use.  He clarified how Hadoop got its name.  The founder, Doug Cutting, named the company after his son’s stuffed yellow elephant!  John also presented a few experiments that ASU has been doing to evaluate the value that Hadoop may be able to bring the university.  In ASU’s experiments, they used Amazon’s EC2 service to quickly spin up supporting servers and configure the services necessary to support Hadoop.  This presentation was entertaining, but was almost the only mention of Hadoop during the entire conference.  It may have more use in research functions, but does not seem widely adopted in key university business intelligence efforts as of yet.  Wonder if this will change by next year?
g with Son's Stuffed Elephant

Doug Cutting with Son’s Stuffed Elephant


A Compilation of My Favorite DW Resources

Recently, I received an email as part of a listserv from a colleague at  HEDW, or Higher Education Data Warehousing Forum, is a network of higher education colleagues dedicated to promoting the sharing of knowledge and best practices regarding knowledge management in colleges and universities, including building data warehouses, developing institutional reporting strategies, and providing decision support.

In the email that I referenced above, my colleague sent a link to an IBM Redbooks publication titled, “Dimensional Modeling: In a Business Intelligence Environment.”  This is a good read for someone that wants the basics of data warehousing.  It also may be a good refresher for others.  Here’s a short description of the book:

In this IBM Redbooks publication we describe and demonstrate dimensional data modeling techniques and technology, specifically focused on business intelligence and data warehousing. It is to help the reader understand how to design, maintain, and use a dimensional model for data warehousing that can provide the data access and performance required for business intelligence.

Business intelligence is comprised of a data warehousing infrastructure, and a query, analysis, and reporting environment. Here we focus on the data warehousing infrastructure. But only a specific element of it, the data model – which we consider the base building block of the data warehouse. Or, more precisely, the topic of data modeling and its impact on the business and business applications. The objective is not to provide a treatise on dimensional modeling techniques, but to focus at a more practical level.

There is technical content for designing and maintaining such an environment, but also business content.

Dimensional Modeling: In a Business Intelligence Environment

Dimensional Modeling: In a Business Intelligence Environment

In reading through a few responses on the listserv, it compelled me to produce a list of some of my favorite BI books.  I’ll publish a part II to this post in the future, but here is an initial list that I would recommend to any BI professional.  It is also worth signing up for the Kimball Group’s Design Tips.  They are tremendously useful.

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Contain DW/BI Scope Creep and Avoid Scope Theft by Bob Becker @ Kimball Group

I just read a great article by Bob Becker at the Kimball Group.  It addresses some of the key issues of scope creep in BI projects.  I find the Kimball Group a great resource for anyone implementing or maintaining a BI system.

Read the entire article titled, “Design Tip #154 Contain DW/BI Scope Creep and Avoid Scope Theft” below:

10 Steps to Data Quality Delight!

Data quality is always an aspect of business intelligence (BI) projects that seems to be deprioritized.  It is easy to look at the beautiful visualizations and drill-through reports that are key selling features of a BI project.  However, this article is about the value of cleansing your data so that these tools will work seamlessly with the data model that you establish.  Everyone knows the IT saying, “Garbage in.  Garbage Out.”  That holds entirely true with BI projects.  If the incoming data is dirty, it is going to be very difficult to efficiently process the data and make it available for a reporting platform.  This isn’t an easy problem to solve either.  When working across multiple functional areas, you may also have different sets of users that are entering data into the system in DIFFERENT ways.  So, in this instance, you may not have a data quality issue, but a business process issue.

As I have worked through my BI projects, here are 10 steps that I have followed to work with teams to create a data-centric culture and to improve data integrity.   I hope that these are of use to you…and please feel free to share any additional best practice in the comments of this blog!  We can all learn from one another.

Data Quality Workflow

Data Quality Workflow

  • Step #1:  Build data profiling and inspection into the design of your project
    Don’t wait until you are about to go-live to start looking at the quality of your data.  From the very beginning of your project, you should start to profile the data that you are loading into your BI platform.  Depending on your technology stack, there are multiple tools that will aid you in data profiling and inspection.  You might consider tools such as Informatica Analyst, or Microsoft SSIS Data Profiler.  A quick Google search will provide many alternatives such as Talend.  Regardless of the tool, make sure that you incorporate this activity into your BI project as soon as possible.  You’ll want to do a fair amount of inspection on each system that you intend to load into your BI platform.

    Informatica Analyst

    Informatica Analyst

    Microsoft SSIS Data Profiler

    Microsoft SSIS Data Profiler

    Talend Data Quality Tool

    Talend Data Quality Tool

  • Step #2:  Don’t be afraid to discuss these data quality issues at an executive level (PRIOR TO THE EXECUTION PHASE OF THE PROJECT)
    Awareness is always a key factor for your executive team.  Executives and executive sponsors need to know about the data quality issues as soon as possible.  Why?  You will need their support not only to address the data quality issues, but sometimes these issues stem from poor business process and training.  Their support will be critical to address either issue.

  • Step #3:  Assign ownership and establish accountability
    Assign ownership for data as soon as possible.  This will assist you to not only to resolve the data quality issues, but these key data stewards may be able to help identify additional data quality issues as they may be more familiar with their data than you.  In most cases, they have inherited this bad data too, and will likely want to partner with you to fix it.  However, you must consider that it will also place a burden on them from a bandwidth perspective.  Unless dedicated to your project, they will also have a day job.  Keep this in mind during your planning and see if you can augment and support these data cleansing efforts with your team.
  • Step #4:  Define rules for the data
    One of the biggest challenges that I continue to see is when data stewards do not want to cleanse their data, they want the ETL scripts to handle the 1,001 permutations of how the data should be interpreted.  While the ETLs can handle some of this logic, the business owners need to ensure that the data is being entered into the transactional system via a single set of business processes and that it is being done consistently and completely.  Usually, the transactional systems can have business rules defined and field requirements put in place that can help to enforce these processes.  In some cases, the transaction systems are sophisticated enough to handle workflow too.  Utilize these features to your advantage and do not over-engineer the ETL processes.  Not only will this be time consuming to initially develop, but it will be a management nightmare moving forward.
  • Step #5:  Modify business process as needed
    If you are working cross-functionally, you may run into the need to revise business processes to support consistent data entry into the transactional systems.  Recently, I was working on a project across 6 HR departments.  The net of their hiring process was the same, but they had 6 different processes and unfortunately, they were utilizing the same transactional system.  We had to get their executives together and do some business process alignment work before we could proceed.  Once the business process is unified, you then have to consider the historical data.  Does it need to be cleansed or transformed?  In our case it did.  Don’t underestimate this effort!
  • Step #6:  Prioritize and make trade-offs.  Data will rarely be perfect.
    Once you have revised business process and defined data cleansing activities, you will need to prioritize them.  Rarely are you in a position where data is perfect or resources are unlimited.  If you have done your design work correctly, you will have a catalog of the most critical reports and key pieces of data.  Focus on these areas first and then expand.  Don’t try to boil the ocean.  Keep your data cleansing activities as condensed as possible and make an honest effort to try to support the business units as much as possible.  In my experience, the BI developers can generally augment the full time staff to get data cleansing and data corrections done more efficiently.  However, make sure that the business unit maintains responsibility and accountability.  You don’t want the data to become IT’s problem.  It is a shared problem and one that you will have to work very hard to maintain moving forward.
  • Step #7:  Test and make qualitative data updates
    As you prioritize and move through your data cleansing checklist, ensure that you have prioritized the efforts that will reap the largest reward.  You might be able to prioritize a few smaller wins at first to show the value of the cleansing activities.  You should then align your efforts with the primary requirements of your project.  You may be able to defer some of the data cleansing to later stages of the project, or handle it in a more gradual way.
  • Step #8:  Setup alerts and notifications for future discrepancies
    After data cleansing has occurred and you feel that you have the data in a good state, your job is not over!  Data quality is an ongoing activity.  You almost always run into future data quality issues and governance needs to be setup in order to address these.  Exception reports should be setup and made available “on-demand” to support data cleansing.  Also, one of my favorite tools is data-driven subscriptions, or report bursts.  Microsoft uses the “data-driven subscription” terminology.  IBM Cognos uses the term “report burst.”  Once you have defined the type of data integrity issues that are likely to occur (missing data, incomplete data, inaccurate data, etc.), you can setup data-driven subscriptions, or report bursts, that will prompt the data stewards when these issues occur.  Of course, at the end of the day, you still have the issue of accountability.  We’ll take a look at that in the next step.  Depending on the tool that you using, you may have the capability of sending the user an exception report with the data issue(s) listed.  In other systems, you may simply alert the user of a particular data issue and then they must take action.  These subscriptions should augment the exception reports that are available “on-demand” in your reporting portal.

    Microsoft SSRS Data-Driven Subscription

    Microsoft SSRS Data-Driven Subscription

    IBM Cognos Report Burst

    IBM Cognos Report Burst

  • Step #9:  Consider a workflow to keep data stewards accountable
    So, what now?  The user now has an inbox full of exception reports, or a portal inbox full of alerts, and they still haven’t run the manual, on-demand exception report.  Data integrity issues are causing reporting problems as the data is starting to slip in its quality.  You have a few options here.  In previous projects, I have setup a bit of workflow around the data-driven subscriptions.  The first port of call is the data steward.  They are alerted of an issue with the data and a standard SLA is set to allow them an adequate amount of time to address the issue.  After that SLA period expires, the data issue is then escalated to their line manager.  This can also be setup as a data-driven subscription.  If both steps fail (i.e. both the data steward and the line manager are ignoring the data issue), then it is time to re-engage with your executive committee.  Make the data issues visible and help the executives understand the impact of the data being inaccurate.  Paint a picture for the executive about why data is important.  To further illustrate your point, if you have an executive dashboard that is using this data…it may be worthwhile to point out how the data integrity issue may impact that dashboard.  Not many executives want to be in a position where they are making decisions on inaccurate data.
  • Step #10:  Wash, rinse, and repeat
    By the time that you have gotten to this point, it will likely be time to fold in another transactional system into your BI platform.  Remember this process and use it again!WashRinseRepeat

Busting 10 Myths about Hadoop by Philip Russom @ TDWI

Recently, I read a very informative white paper which was published by TDWI’s Philip Russom.  The research in this report was sponsored by some of the key BI players below – so it had significant backing.

Integrating Hadoop into Business Intelligence and Data Warehousing:  Research Sponsors

Integrating Hadoop into Business Intelligence and Data Warehousing: TDWI Research Sponsors

I wanted to share the top 10 myths about Hadoop from TDWI’s report.  I found them insightful and you may as well:

Credit:  Integrating Hadoop into Business Intelligence and Data Warehousing by Philip Russom @ TDWI

  • Fact #1:  Hadoop consists of multiple products
    We talk about Hadoop as if it’s one monolithic thing, but it’s actually a family of open source  products and technologies overseen by the Apache Software Foundation (ASF). (Some Hadoop  products are also available via vendor distributions; more on that later.)  The Apache Hadoop library includes (in BI priority order): the Hadoop Distributed File System  (HDFS), MapReduce, Pig, Hive, HBase, HCatalog, Ambari, Mahout, Flume, and so on. You can  combine these in various ways, but HDFS and MapReduce (perhaps with Pig, Hive, and HBase) constitute a useful technology stack for applications in BI, DW, DI, and analytics. More Hadoop projects are coming that will apply to BI/DW, including Impala, which is a much-needed SQL  engine for low-latency data access to HDFS and Hive data.
  • Fact #2:  Hadoop is open source but available from vendors, too
    Apache Hadoop’s open source software library is available from ASF at For users  desiring a more enterprise-ready package, a few vendors now offer Hadoop distributions that include  additional administrative tools, maintenance, and technical support. A handful of vendors offer their  own non-Hadoop-based implementations of MapReduce.
  • Fact #3: Hadoop is an ecosystem, not a single product
    In addition to products from Apache, the extended Hadoop ecosystem includes a growing list of  vendor products (e.g., database management systems and tools for analytics, reporting, and DI)  that integrate with or expand Hadoop technologies. One minute on your favorite search engine will reveal these.
  • Fact #4: HDFS is a file system, not a database management system (DBMS)
    Hadoop is primarily a distributed file system and therefore lacks capabilities we associate with a  DBMS, such as indexing, random access to data, support for standard SQL, and query optimization.  That’s okay, because HDFS does things DBMSs do not do as well, such as managing and processing  massive volumes of file-based, unstructured data. For minimal DBMS functionality, users can  layer HBase over HDFS and layer a query framework such as Hive or SQL-based Impala over HDFS or HBase.
  • Fact #5: Hive resembles SQL but is not standard SQL
    Many of us are handcuffed to SQL because we know it well and our tools demand it. People who  know SQL can quickly learn to hand code Hive, but that doesn’t solve compatibility issues with SQL-based tools. TDWI believes that over time, Hadoop products will support standard SQL and  SQL-based vendor tools will support Hadoop, so this issue will eventually be moot.
  • Fact #6: Hadoop and MapReduce are related but don’t require each other
    Some variations of MapReduce work with a variety of storage technologies, including HDFS, other file systems, and some relational DBMSs. Some users deploy HDFS with Hive or HBase, but not MapReduce.
  • Fact #7: MapReduce provides control for analytics, not analytics per se
    MapReduce is a general-purpose execution engine that handles the complexities of network communication, parallel programming, and fault tolerance for a wide variety of hand-coded logic  and other applications—not just analytics.
  • Fact #8: Hadoop is about data diversity, not just data volume
    Theoretically, HDFS can manage the storage and access of any data type as long as you can put the data in a file and copy that file into HDFS. As outrageously simplistic as that sounds, it’s largely true,  and it’s exactly what brings many users to Apache HDFS and related Hadoop products. After all,  many types of big data that require analysis are inherently file based, such as Web logs, XML files,  and personal productivity documents.
  • Fact #9: Hadoop complements a DW; it’s rarely a replacement
    Most organizations have designed their DWs for structured, relational data, which makes it difficult  to wring BI value from unstructured and semistructured data. Hadoop promises to complement
    DWs by handling the multi-structured data types most DWs simply weren’t designed for.  Furthermore, Hadoop can enable certain pieces of a modern DW architecture, such as massive data staging areas, archives for detailed source data, and analytic sandboxes. Some early adoptors offload as many workloads as they can to HDFS and other Hadoop technologies because they are less expensive than the average DW platform. The result is that DW resources are freed for the workloads with which they excel.
  • Fact #10: Hadoop enables many types of analytics, not just Web analytics
    Hadoop gets a lot of press about how Internet companies use it for analyzing Web logs and other  Web data, but other use cases exist. For example, consider the big data coming from sensory devices, such as robotics in manufacturing, RFID in retail, or grid monitoring in utilities. Older analytic applications that need large data samples—such as customer base segmentation, fraud detection, and  risk analysis—can benefit from the additional big data managed by Hadoop. Likewise, Hadoop’s additional data can expand 360-degree views to create a more complete and granular view of customers, financials, partners, and other business entities.

Philip also did a nice job in this white paper in clarifying the status of current HDFS implementations.  It is represented well in the graphic below.

Status of HDFS Implementations

Status of HDFS Implementations

To sort out which Hadoop products are in use today (and will be in the near future), this report’s
survey asked: Which of the following Hadoop and related technologies are in production in your
organization today? Which will go into production within three years? These
questions were answered by a subset of 48 survey respondents who claim they’ve deployed or used
HDFS. Hence, their responses are quite credible, being based on direct, hands-on experience.

HDFS and a few add-ons are the most commonly used Hadoop products today.  HDFS is near the top of
the list (67%) because most Hadoop-based applications demand HDFS as the base
platform. Certain add-on Hadoop tools are regularly layered atop HDFS today:

  • MapReduce (69%) for the distributed processing of hand-coded logic, whether for analytics or for fast data loading and ingestion
  • Hive (60%) for projecting structure onto Hadoop data so it can be queried using a SQL-like language called HiveQL
  • HBase (54%) for simple, record-store database functions against HDFS’s data

If this information has been helpful to you, check out the full report from TDWI below.  How is your organization using Hadoop?

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Benefit of Column-store Indexes

With the release of Microsoft SQL Server 2012, Microsoft has bought into the concept of column-store indexes with its VertiPaq technology.  This is similar to the approach taken by Vertica.  In a very simplistic example, below is a standard data set:

LastName FirstName BusinessUnit JobTitle
Johnson Ben Finance Director
Stevens George HR Recruiter
Evans Bryce Advancement Major Gift Officer

In a common row-store, this might be stored on the disk as follows:

  • Johnson, Ben, Finance, Director
  • Stevens, George, HR, Recruiter
  • Evans, Bryce, Advancement, Major Gift Officer

Column-store indexes store each column’s data together.  In a column-store, you may find the information stored on the disk in this format:

  • Johnson, Stevens, Evans
  • Ben, George, Bryce
  • Finance, HR, Advancement
  • Director, Recruiter, Major Gift Officer

The important thing to notice is that the columns are stored individually.  If your queries are just doing SELECT LastName, FirstName then you don’t need to read the business unit or job title.  Effectively, you read less off the disk.  This is fantastic for data warehouses where query performance is paramount.   In their white paper, Microsoft uses the illustration below to better explain how columnstore indexes work:

Microsoft's Columnstore Index Illustration

Microsoft’s Columnstore Index Illustration

The benefits of using a non-clustered columnstore index are:

  • Only the columns needed to solve a query are fetched from disk (this is often fewer than 15% of the columns in a typical fact table)
  • It is easier to compress the data due to the redundancy of data within a column
  • Buffer hit rates are improved because data is highly compressed, and frequently accessed parts of commonly used columns remain in memory, while infrequently used parts are paged out.

Microsoft recommends that columnstore indexes are used for fact tables in datawarehouses, for large dimensions (say with more than 10 millions of records), and any large tables designated to be used as read-only.  When introducing a columnstore index, tables do become read only.  Some may see this as a disadvantage.  To learn more about Microsoft’s columnstore indexes, read their article titled, “Columstore Indexes Described.”

So let’s take a quick look at how to do this in the 2012 SQL Server Management Studio.  If you need to add a new non-clustered column store index, you can do so as seen below:

Columnstore index in MS SQL Server 2012

Creating a new non-clustered columnstore index in Microsoft SQL Server 2012

To ensure that the non-clustered columnstore index is achieving the right result for you, test it.  Under the Query menu, ensure that you enable the “Display Estimated Execution Plan.”

Displaying the Query Execution Plan in SQL Server 2012

Displaying the Query Execution Plan in SQL Server 2012

Analyzing the Query Execution Plan in SQL Server 2012

Analyzing the Query Execution Plan in SQL Server 2012

You should now be in a position to analyze your old and new queries to determine which is optimal from a performance perspective.  You’ll likely see a great reduction in query time by using the non-clustered columnstore index.

What are your thoughts?  Are you using columnstore indexes in your environment?

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Squeezing Value From Data – Higher Education Institutions: What do you think?

SAS sponsored this report which draws on a survey of 752 executives, as well as in-depth interviews with 17 senior executives and experts who are regarded as data pioneers.  I would love to hear from other higher education institutions in regard to “the importance of data to company functions” section.  This report focuses on a more corporate audience, but I imagine that in a university setting you might see Finance (Audit & Regulatory Compliance) and Advancement added to the list below.  And, perhaps, more mission-driven data such as Learning data (student/teacher performance).  Blackboard Analytics has a neat product in that arena (see screen shot below):

Blackboard Learn Analytics

Blackboard Learn Analytics

Overall, I think this infographic represents a comprehensive list of some of the key challenges of big data and business intelligence.  What is the most compelling bit of information in this infographic for you?

Squeezing Value From Data Infographic (credit:  SAS, All Analytics)

Squeezing Value From Data Infographic (credit: SAS, All Analytics)

I found the following two questions of particular interest.  These questions provoke thought about having the right type of talent within your organization and ensure that you have achieved the right type of outcome for your end-user.

Economist Intelligence Unit Survey:  Skills Gap

A skills gap question from the Economist Intelligence Unit survey in March 2012

Economist Intelligence Unit Survey:  Data Processing Speed

A data processing speed increase question from the Economist Intelligence Unit survey conducted in March 2012.

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Gartner releases 2013 Business Intelligence & Analytics Magic Quadrant

Last month, Gartner released the 2013 version of their Business Intelligence & Analytics Platform Magic Quadrant.  I always look forward to the release of Gartner’s magic quadrants as they are tremendously helpful in understanding the landscape of specific technology tools.

Gartner Magic Quadrant for Business Intelligence & Analytics - Comparison of 2012 to 2013

This year, I was pleased to observe the following:

  • Microsoft has improved its overall ability to execute.  Overall, it seems that Microsoft is moving in the right direction with their SQL Server 2012 product.  I’m excited about the enhancements to Master Data Services and I like where they are headed with PowerPivot and Power Views.  A full list of new features can be found at Microsoft’s website.  I’m a big Microsoft fan and I’m excited about Office 2013 and the impact that it will have on BI.
  • IBM has maintained, and slightly increased, its market position.  IBM continues to expand upon the features of their key acquisitions (Cognos, SPSS).  They have done a nice job of migrating customers from the old Cognos 8 platform to IBM Cognos 10.x.  This has increased customer satisfaction.  I also really like their Analytic Answers offering.  In my opinion, BI will continue to become more service oriented – so a big applause for IBM’s analytics as a service offering.
  • Tableau has moved into the top right square.  Tableau deserves to be here and I’m excited to see this movement.  Tableau’s customer support and product quality has been consistently high.  They have also set a benchmark in terms of how straightforward it is to move to their platform and upgrade to the latest version release.
  • There is plenty of competition at the bottom of the market.  Niche players like Jaspersoft and Pentaho are at each others heels.  Competition is healthy!

The only thing that surprised me is that I didn’t see Pyramid Analytics on this list.  Microsoft acquired ProClarity back in 2006.  Extended support for ProClarity will soon end in 2017.  Given that Microsoft has not migrated all of the ProClarity features to PerformancePoint, I am speaking to many users that are jumping ship on the ProClarity front and moving toward Pyramid Analytics.  Pyramid Analytics has done a nice job to aid customers moving from ProClarity.  Keep an eye out.  We might see them on the list next year.

If you are interested in reading the full 2013 report, you may preview the online version here.