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Business Intelligence Symposium VI Focuses on Innovative Analytics

LÛCRUM Inc., in conjunction with the University of Cincinnati’s Carl H. Lindner College of Business, has announced that their 6^th Business Intelligence Symposium is scheduled for December 13, 2011, at the Carl H. Lindner College of Business focusing on Innovative Analytics. LÛCRUM Inc. and the Carl H. Lindner College of Business have been organizing these events for business executives and IT professionals that use and need analytical data to provide a forum for sharing ideas, stories, experiences and business cards.

Confirmed speakers for the event are:

Drew Boyd

Drew Boyd is a recognized authority, thought leader, educator, and practitioner in the fields of innovation, persuasion, and social media. He is the Executive Director of the Master of Science in Marketing Program and Assistant Professor of Marketing and Innovation at the University of Cincinnati.

Ginny Walker

Virginia (Ginny) Walker is currently GE Aviation’s Chief Enterprise Architect, reporting to the CIO. She grew up in Michigan, obtained her undergraduate degree in Systems Analysis at Miami (OH) University, an MBA from Xavier (OH), and has worked 25 years for GE in Aviation, Corporate and Energy. She has held a diverse span of Information Technology roles…from application development, to data warehouse and data management, to strategic planning, outsourcing, data center management, and ERP implementations.

Jim Goetz

Jim is the Chief Information Officer at Convergys.  He is responsible for the planning, development, and delivery of the technology products and services for the Company globally, including external and internal clients. He reports to Jeff Fox, president and chief executive officer of Convergys.

Mike Cholak

Mike Cholak is the Vice President of Customer Intelligence Services at Convergys.  He leads a team dedicated to delivering a full suite of consulting services to the Company’s clients that help leverage customer intelligence and feedback to optimize long- term customer loyalty and profitability.

For more information:

Data Vault: The Preferred “flavor” for DW Architecture in BI – Part II

In Part-I, I explained the place of Data Vault (DV) in Enterprise Data Warehouse Architecture. Now let’s look at different DV entities, rules for each entity and why Dan Lindstedt calls DV a “hybrid” approach. This minimal understanding is necessary before diving into the differences between the various modeling techniques.

The main entities of Data Vault are Hub, Link and Satellite.

HUB Entity (HUB_): This is a defining entity. It contains a unique list of business keys. These are the keys that businesses utilize in everyday operations. For example, employee number, SSN number, Product Code. So the attributes of HUB are:

• Surrogate Key – This is a Primary Key of hub and holds 1-to-1 relationship with the Business Keys.
• Business Key – This is a Primary Key of the source system. This can be a composite key. ETL checks this key’s existence in the hub table and inserts one if it doesn’t exist.
• Load Date Time – The datetime of the key / record when it was first loaded into the table.
• Record Source – The name of the source the record originated from. This is useful for data traceability.
• Record Begin Date Time – The datetime when the record became active in the source (if available) or the datetime when ETL has been run.
• Record End Date Time – The datetime when the record is closed. This can only be detected if the logical deletes are supplied or derived in some manner.

LINK Entity (LINK_): LINKS are constructed once all the HUBS are identified. Links are relationship entities.  These are the physical representation of m-to-m 3NF relationship. It represents the relationship or transaction between hubs. The link table contains the unique list of relationships between hub keys. When a relationship arrives, it simply gets loaded into the table if doesn’t exist. Typically, the link tables translate into fact tables in the datamart access layer. For example, the link between employee number and the project number. The other attributes of LINK are:

• Surrogate Key – This is a Primary Key of the table and is useful when a link contains more than two hub keys as composite key might cause performance problems. This is also
  useful when the granularity of the link changes (a hub key is added) or history needs to be maintained on the relationships.
• Hub Key 1 to Hub Key N – The surrogate keys from the hub tables that are involved in the relationship.
• Load Date Time- The datetime when the record was loaded into the table.
• Record Source – The source system name from where the record or relationship was loaded from.

SAT Entity (SAT_): SATS holds descriptive information about the hub keys or the relationships. The satellite is most closely resembles Type 2 Dimension. When the data changes, a delta record is inserted into the table and if the certain columns changes faster than others then these can be split into two different tables to avoid data replication. For example, employee details such as employee name, address, phone number, email address in the satellite off of hub  or time spent by an employee on a certain project in satellite off of LINK that stores the relationship between an employees and projects. The other attributes of SAT are:

• Hub or Link Surrogate Key from HUB or LINK table. This is part of the primary key.
• Load Date Time – The datetime when the record was inserted into the table. This is part of the primary key.
• Surrogate Key – This is optional. It is useful when satellites have multiple values such as multiple home addresses.
• Record Source – The name of the source.
• Record Begin Date Time – The datetime when the record became active in the source (if known) or the datetime when ETL has been run.
• Record End Date Time – The datetime when the record is closed.

And stand-alone tables such as calendars, time, code and description tables may be used.

 

Below is a snippet of a Data Vault Model housing borrowers who have taken out Student Loans:

 

 

Modeling Rules for Each Part of the Entity:

FOR HUBS:

• Hubs keys cannot migrate into other hubs (no parent/child like HUBS).
• Hubs must be connected through links.
• More than two hubs can be connected through links.
• Surrogate keys may be used.
• Business keys are 1 to 1 relationship with surrogate keys.
• Hubs primary keys always migrate outward.
• Hub business keys and primary keys never change.
• If a hub has two or more satellites, then a point-in-time table can be built for ease of joins.
• An ‘UNKNOWN’ business key record can be inserted into Hub that can be used to tie other data in links and sats that has no business keys in source. This kind of data is usually a bad/incomplete source data.

FOR LINKS:

• Links can be connected to other links.
• Links must have atleast two hubs associated with them in order to instantiated.
• Surrogate keys may be used.
• The combination of surrogate business keys made a unique key.
• Does not contain descriptive data.
• Does not contain begin and end dates.

FOR SATS:

• Satellites may be connected to hubs or links.
• Have 1 and only 1 parent table.
• Satellites always contain either a load date-time stamp, or a numeric reference to a stand-alone load date-time sequence table.
• Primary key is a combination of ‘surrogate key’ from either hub or link and the load datetime stamp.
• Surrogate keys may not be used.
• Must have a Load End Date to indicate when the CHANGE to the data set has occurred.
• Satellites are always delta driven. Duplicate rows should not appear.
• Data is separated into satellite structures based on 1) type of information 2) rate of change.

DV model utilize bits of both 3^rd Normal Form and Dimension Modeling concepts.  This approach has made the model simple, flexible, expandable, adaptable and consistent.

• Adapted many-to-many physical relationship structure from 3NF that became a LINK table.
• The LINK table is also similar to factless fact in Start Schema.
• Adapted the notion of 1 to 1 (business key to surrogate key) tracking from dimensional modeling (type 1 dimension).
• Adapted the notion of “data over time in a separate table/structure” from dimensional modeling (type 2 dimension). This resulted in a SAT table however it is fundamentally
  different, in that it is a child dependent table, whereas the dimension is a parent table to the facts.

This is it for now. In next post(s) we will look into some examples which will show how Data Vault technique overcomes the limitations of 3NF and Dimensional Model structures when applied as an Enterprise Data Warehouse.

- Jyothi

 

Source: tdan.com, danlinstedt.com

Better BI on Bigger Data

“Can we do BI without a significant infrastructure effort?”

What’s the most common BI tool? Microsoft EXCEL! If you are considering a DW initiative, you are likely encountering resistance from your hard-core, Excel based, analysts. Taking away Excel from them means that they will have less flexibility and spend more time waiting on changes. I’m guessing that they may not be on board with your vision.

1010Data is a zero-footprint, browser based data warehousing product. It has an Excel like feel, but it can handle billions of rows of data (not thousands like Excel). It’s fast, flexible and requires no infrastructure support from IT (if you are in IT, my guess is that you may have already stopped reading this article). The system is not an OLAP system, it requires no design, and not even indexing strategies – but it’s so fast!!

This is a new paradigm shift: from ETL to ELTAR – extract, load and transform as required. Hmmm….cleansing and summarization at query run time; views are used for data governance.

Don’t think it’s true? Dollar General is using the platform and was up and running in 5 weeks to 115 users with a 100% ROI.

What could you do for your customers if you could have their DW up in just a few weeks instead of a few months??

- Jodie

Data Mining – We want it Now!

“We are drowning in information but starving for knowledge.”

What do you really understand about data mining? Dr. Candace Gunnarsson was formerly a professor of statistics at Xavier University. Her view of data mining is all about getting information out of the systems you have today and predicting future results. Her view is also about needing a DW modeled in a way that makes the mining experience easier and more meaningful.

Her view is that there is prescriptive (automated) data mining and descriptive data mining. I think most people think of data mining to be an automated process. Truly, the manual exploration must happen first. What do I think the drivers should be? If it’s predicting a purchase, I need to understand you as a customer. What magazines do you read? How old are you? Male/female? How often do you come to my store? All of these become data points in an algorithm that will lead to prescriptive data mining. If I can predict that you will make a purchase, I then need to be able to test my theory and provide results back to my model. Data Mining is Avery iterative process.

To have truly effective data mining, you need to have a multi-disciplinary team. Be sure to bring in your IT, Marketing, Finance, and operations-focused team members. They will all have different views of your customers and will understand their transactional data better than anyone else. Use all of these views to create a better view.

BIG Question – can you start data mining before you have your data organized in a DW?

I’d be interested in hearing your data mining stories.

- Jodie

The Science of Visual Analytics

Today LUCRUM hosted our second BI Symposium. Once again, it was well attended and we had some great speakers! I’m hoping that this becomes a regular event. If you have yet to attend, I encourage you to come to our next event (to be scheduled).

Our first speaker was Mr. Stuart Woodward, President OD OcuCue. (http://ocucue.com/) OcuCue is an interesting start-up that’s all focused on data visualization. I always love listening to visual experts. There is such a science to visual design. It’s about understanding the psychology of how users think and perceive what they see. If you are creating a dashboard, you have to design it in the way people think – we read from left to right, heavy color should be at the lower left hand side, etc.

“Good design has two key elements. Graphical elegance is often found in the simplicity of design and complexity of data.” – Edward Tufte

Mr. Woodward’s company creates meaningful dashboards that are icon based. They go beyond speedometers and graphs and actually create a customized dashboards with icons that are meaningful to the company using them. One example that he showed was for a hospital. There are some rooms that can only take female patients or only male patients. To show bed availability, their dashboard has a pink pillow or a blue pillow to represent which rooms are available. Hmmm…never thought of that!

How are you presenting data to your users? Are you simplifying the message? Setting up the information from left to right? Are your colors meaningful? (ie Red should mean bad, green is good)

OK…gotta run and listen to the next speaker!

- Jodie

Making Information Available

I’m not sure if you’ve noticed, but I’ve not been blogging with the same gusto as of late. Ah the life of a Consultant. I have been working with a local financial institution creating financial models this summer. (It leaves me with little time for blogging.) I did happen to stop by our 7755 Montgomery Road office today and checked my mailbox. In it was this month’s Information Management mag.  I was immediately drawn to this month’s Snapshot:  Making Information Available.  Here’s some stats for you to consider:

61% of respondents are less than satisfied with their current process of creating information applications and are only lukewarm about their current information application technology.  Here are their complaints:

• It takes too long to assemble and deploy applications.
• It is too difficult to assemble and view information into a simple view.
• There are not enough capabilities to integreate and normalize information from disparate applications.

WOW!  I ask all of you fellow BI folks out there…what are you doing to solve this problem???  Why is it with all of the tools available today, our users are finding it too difficult to use them!!  What are WE doing wrong?

As I mentioned, I am working with a customer on Financial Models this summer.  I am fortunate to work with some SUPER SMART people in this group.  They have come up with the most ingenious ways of getting their data out of old clunky systems.  They can create some of the most INSANE Excel formulas to manipulate data!  Their Excel sheets are visually appealing and get data to their management in a timely manner.  I’ve had some spreadsheets that have taken me days to figure out the Excel formulas (and I’m a guru!).  They are awaiting IT to “build them a DW” to make their lives easier.  Here’s to hoping that it can deliver on their expectations!  Here’s what I would do to ensure that it does:

1.  Use an iterative methodology to build the DW.  Recreate existing Excel reports from the DW as you go.

2.  Implement a user-friendly reporting tool that allows them to create their own reporting.  Give ‘em lots of drag and drop functionality and make sure it can Export to Excel.

3.  Create a request process that allows the DW to change with the Business.  Creating a process that queues up the work for months and months does not help the business user to create the financial package that’s needed at the end of the month.

4.  Keep the model flexible.  Doing this will ensure that you can always add a new organziation, hierarchy or measurement.

5.  Build cubes!  These users are smart cookies and they aren’t afraid of a Pivot Table.  Give them the flexibility and performance of a cube and let them start to uncover their data.

Hmmm…what’s missing from my list?  What would you add?

Happy building!

 - Jodie

On Not Reinventing the Wheel, Part 1: Take Your SharePoint Data With You

At some point in every project, I’m usually confronted with the question – should we build or should we buy?  The pendulum of thought on this topic swings back and forth.  As a consultant that regularly advises clients on these kinds of questions, my current read is that the mindset has moved toward the “buy” side on some key fronts.  The trends underlying this, and their impact on both business and technology, are complex and evolving.

Another way to frame the discussion is, “Has someone already solved that problem?”  When the answer is yes, the existing solutions deserve a good look.  In my experience, a combination of both buy and build often turns out to be the right answer.

Let’s take the issue of getting more out of all that data that has been loaded into your company’s Microsoft SharePoint-based collaboration site.  Someone (probably many someones) has spent lots of time getting thousands of documents, contact lists, project plans, and other kinds of essential data into this amazingly versatile tool.  And if your portal has been well-designed, you probably find it an invaluable resource while you’re using your laptop or desktop at the office.

As soon as you try to get this information out using your mobile device, let’s say using your iPhone, it becomes a different story.  Even if you manage to navigate to the right place, the functional richness of the interface is not ideal when you move into the realm of finger touches and gestures .

So what to do?  Build a new, mobile version of your SharePoint site (probably a tough sell given the current economy)?  Or has someone already solved this problem?

As it turns out, the creators of SharePoint provided a shortcut to get to stored data directly.  This method uses web services – in human terms we might call it the “just give me the data, please ” service – to make the data available without the need to navigate through a web page.  It’s up to the application you’re using to determine how the data should look and be presented.

But until recently, not many people had solved the other part of the problem – taking the SharePoint web services and building an iPhone app to make it useable on a mobile device.

When he put two technologies together (buy + build), it increased the value of the data we already had, by making it more portable and our staff more productive.  Kudos to him for seeing the opportunity and going for it!

A growing number of developers have followed this lead to build iPhone apps that put all of your SharePoint data at your fingertips.  There are several choices now available in the Apple App Store, including (in alphabetical order):

• Attache: SharePoint Client (LUCRUM)
• iShare (Spyk Software)
• iSharePhone (Webstate)
• iSP-Browser (iSP Apps)
• Moshare (Moprise)
• SharePlus Office Mobile Client (SouthLabs)
• Sharetica (Celltica)

I’ll expand further on some of the topics touched on here in later posts, including more thoughts on the “build vs. buy” decision, the changing dynamics in data visualization, and how business intelligence solutions on mobile devices are impacting the way we work.

-Eric

Using Business Intelligence to Drive your own Recovery.

eWeek published a video describing the value of using Business Intelligence to find and exploit market and revenue opportunities.  Great point, and very well worth the 6:49 it takes to view it.  Many organizations are using BI to understand some of the basic historical results of their business.  It’s the next level of organization who begins to answer questions like the below using their BI toolset:

• What are my customer’s buying is a basic question, but moreover, what products do they buy together?
• Which products do they buy when times are tough?
• What did they buy during the last recovery?
• What aren’t they buying, and what should I recommend they buy?

All great questions, and clearly a value add of a strong BI platform.

eWeek – Using-Business-Intelligence-to-Find-Your-Economic-Recovery

LUCRUM powers the new Cleveland Museum of Art website

It’s a treat when we have the opportunity to publicly showcase work from our portfolio.  This week, we invite you to view the results of our most recent assignment with the Cleveland Museum of Art and their new website.  

 This event marks the successful completion of a two year effort to set a new standard for how museums engage with their visitors.  The engagement has followed the LUCRUM iStream methodology – starting with our high-value Stakeholder Alignment Session, though a process of analysis, collaboration, transformation, and now launch – to result in this groundbreaking experience.  The website was developed in partnership with the museum staff and award-winning Pentagram Design, based in New York.  

One of the most compelling features of the new website is enhanced access to the museum’s encyclopedic collection of over 40,000 objects - many of these iconic works of art are now accessible online, as well as woven into the pages of the site.  The objects become part of the fabric of the experience.  Large images and a wide range of search options make it easy to wander for hours in the online collection – I encourage you to give it a try!  

Visualization: Rules for BI

“In God we trust; all others must bring data.”
- W. Edwards Deming

At our BI Symposium on May 6, 2010, Jeff Shaffer provided us with great insight on how the way we present our data can be just as important as what we present.  Jeff is not a big fan of pie charts.  In fact he has 4 rules:

1. Don’t use pie charts.
2. If you use pie charts, be careful in chosing the number of items you chart.
3. If you use pie charts, be sure they are “centered at noon”.
4. If you use pie charts, make sure that they sum to 100%

Jeff shared lots of bad charts, lots of REALLY BAD charts and summed it up with some great looking dashboards. I encourage you to check out his presentation!

- Jodie

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