Don't Let Budgets Hold You Back

Just read an article from the Data Warehousing Institute on how to move forward with data warehousing / business intelligence initiatives when you have limited funding and resources available to you.  The idea is to use these techniques to provide visible business wins that should translate into additional funding being available for your BI program.

This is actually a 3 part article that looks at the problem from 3 perspectives (click to see article):

• Technology & Tools - How to leverage what you already have / expanding on existing vendor relationships.
• People - The typical roles required on a data warehousing project and how to combine them
• Project Selection - Steer around projects that require significant resources and have high complexity, instead go for the quick wins or low hanging fruits.

Good read for anyone in the field, we don't need a 10 pound sledgehammer to nail a picture to the wall!

Mark

BI's Place in the IT Portfolio

Good morning, ran across an interesting article on where Business Intelligence fits within an overall IT portfolio.  The below is an interesting picture of this:

Nice to see this on top of the chain isn't it?  8)  Here is the link:

http://www.b-eye-network.com/view/index.php?cid=7498

Creating a Positive Future

If you have spent any time on the Internet researching a topic like Business Intelligence / Knowledge Management, it can be very overwhelming when you consider all the pieces that need to be implemented to have your organization on the leading edge in this field.  This is not just a technology evolution, you also must consider the people and process components to implement a successful Business Intelligence program.  Especially in today's business world, an organization has to find a balance between keeping the current operations running to respond to the latest market opportunity, and building strategic capabilities to support the future and provide competitive advantage.

This is a daunting task, so where to begin?  If you have done any reading on the power of positive thinking you may have run across references to "Creating a Positive Future".  The idea is that if you can en-vision what you would like things look like in the future, then you can work backwards to get there.  Think of it as a top-down rather than a bottom-up approach.

Your first step is to approach this from the experience you want your clients to have.  You must start by talking to your users to understand what business pain points you can solve for them.  Now if you are in a situation where they don't currently have a lot of BI capabilities, you will likely here a lot about what you are not providing now.  That is ok, you need to understand these pain points to ensure you cover them off in your future state.  In an organization more mature in leveraging information, you will hopefully hear about some great new opportunities that BI technology can help with.

Start by working from a what is possible perspective.  Don't burden yourself with the current limitations of people, funding or technology as this will in turn limit your vision.  Build a vision based on your experiences, research on what other companies are doing, but most importantly spend time with your clients to ensure you fully understand what they are looking for.

If you focus on what is possible, instead of what you aren't doing today you can build a positive future for the implementation of BI in your organization.  Watch out for future posts on the next steps to develop your BI roadmap once you have this vision.

P.S.  This is my first post using Windows Live Writer to post to Blogger.com.  Works great!  A big thank you to my colleague Frank Vandongen for suggesting it.

Turning Data into Information is Just the Beginning

Final day for me at the Semantic Technology conference in San Jose. I ended this trip with a renewed appreciation for the bigger picture of knowledge management to create and leverage true Intelligence for an organization. In some organizations we are so focused on turning raw data into usable information, we sometimes lose sight of how we can continue to add value to our information assets to turn them into a true competitive advantage.

The following picture shows the full story on the "Intelligence Pyramid" that will add exponentially more value to an organization. I won't take credit for this pyramid, sometimes referred to the "Wisdom Pyramid", but I will share my personal recreation of it.



Lets walk through these 4 stages starting at the bottom, and provide some additional context:

• Data - This can be described as the raw data that would typically support a company's operational processes. For all you ETL buffs, the "Source System". - "What is happening?"


• Information - Transformed information that is not in IT terms, but in business terms and is consumable by the business for reporting and analytical needs. - "What does it mean?"


• Knowledge - Taking the Information we have prepared in the previous step and enriching it with relationships and correlations that start to "tell the story" of what an organization's data contains. - "What do we already know and why?"


• Intelligence - The holy grail, we have leveraged all the previous stages to put all this data to work to develop action plans to resolve the business problem or challenge. - "What do we do?"

This is the true power of data! This goal can not be accomplished by traditional BI tools and techniques alone. We need to add a mix of other technologies that are better at representing relationships and data (i.e. semantic technology), and most importantly to bring together the right people to look at all this data from their different perspectives. With the right mix, we can deliver value to the organization that will pay back the investment in data many, many times over.

Mark

Ontologies Applied to Business Intelligence

After a full day of seminars at the Semantic Technology conference, I ran into an interesting tool that is "new to me". This tool is called Ontology.

An ontology is a formal representation of a set of concepts within a domain and the relationships between those concepts.

OK, I have to admit this is a pretty high level and cerebral topic, lets put this in the context of an example to make it real. Lets say we have a business problem (i.e. the domain) that involves displaying a customer's invoice online. If you think about it, this problem typically has the following entities involved:

• Customer
• Product
• Marketing Strategy
• Person
• Invoice

Does this seem like an exercise in Entity Relationship (ER) modelling? Well to a certain extent it is, but the value an ontology adds on top of this is how these entities are related. This still seems like ER modelling, but lets see how this plays out...

• A Customer subscribes to Products
• A Customer is a Person
• A Marketing Strategy acquires Customers
• A Marketing Strategy sells Products
• An Invoice belongs to a Customer
• An Invoice has Products
• An Invoice includes Marketing Strategies (Bill Messages)
• A Person creates a Marketing Strategy

Are all you data modellers out there feeling confused by all these relationships? 8) Real world relationships seldom fall into the typical hierarchical relationships so common in ER modelling. To fully describe the richness of all relationships we have to step back from the physical data structure and build out a separate meta data store that does not care about the structure, but does care about the context of the data and its relationships. This is typically stored within a database as a Triplestore, which breaks down relationships into "subject" "predicate" and "object".

A Person (subject) creates (predicate) a Marketing Strategy (object)

If each of these 3 pieces had a Uniform Resource Identifier (URI) that uniquely identified each piece, a deceptively simple data model can be created to handle any object and any relationship. In this way any object in the system can be related to any other object using 1 data model.

Once we have this ontology, the next logical step is to map this ontology to the physical data. This not only helps business users navigate data, it is a great tool to facilitate data integration efforts as we can map any data source against the business focused ontology.

What is the value of this for Business Intelligence? In the BI field we are constantly striving to take data and turn it into actionable intelligence. If we had a rich meta data layer that contained a validated ontology we could use this data to uncover correlations in data that would not be uncovered through a simple ER model. As the ontology is in business language it serves as a great tool to bridge the gap between physical data and business entities. This facilitates communication of the organization's data assets between business and IT to ensure no bit or byte goes un-leveraged.

This is but one tool to allow us to bring our users into the development of their applications, better yet lets get the business to own this "layer" since they know it best!

Mark

Comments Opened

Just a quick note to let you know that I have removed the requirement for users to be signed in to post comments to hopefully encourage more participation in this blog. You will have to do one of those pesky words embedded within images thought...8)

Semantic Technology Conference 2008 - Questions

Well, I’m in sunny San Jose now, and am preparing for my first day at the Semantic Technology Conference. Before I dive into the seminars, I think it is important to step back and understand what specific questions I am trying to answer by attending this conference.

Here we go:

· How can we bridge the gap between information and human understanding to make information actionable?

· How do we build a business model that represents how the organization perceives it’s products, service and customers? How can we put such a business model in place to help us organize information in a way that it is accessible and takes us to the next level of comprehension?

· How can we leverage user-generated semantic data to enhance applications to add context?

· How can we create a data discovery process that is intuitive and returns results?

· How does Semantic Technology apply to both Business Intelligence and a Service Oriented Architecture?

I’ll throw these questions out for now, and will look to answer them over the next 3 days. I’ll be posting updates to this blog as I have time between sessions. If you have any input on the above, please comment away!

Mark

Foundational Concept for Performance Management and Data Quality

I was going through my reading, and ran across this article on the B-Eye-Network that talks about the foundational concepts that need to ground a Data Quality program. Part of this article discussed the Deming Cycle (PDSA Cycle). The PDSA acronym stands for the following:

• Plan - Plan for the future desired state
• Do - Execute actions to get to future state
• Study - Check the results against desired state
• Act - Act to correct towards desired state

This is a foundational concept that we can apply to both Data Quality programs and the use of Key Performance Indicators in a performance management system. In the data quality program we would apply the PDSA model as follows:

• Plan - Define data quality issues in a given data source
• Do - Put in place monitoring tools to analyze data quality
• Study - Analyze the results to understand issues and identify root causes
• Act - Act on root causes (typically process issues) and fine tune monitoring tools

Similarily, when we define and use Key Performance Indicators we might approach it this way:

• Plan - Define a set of KPIs that are thought to be critical to business success
• Do - Collect data on actual performance against the KPIs
• Study - Analyze the results to validate KPI effectiveness and to search for other supporting factors of success
• Act - Fine tune KPIs from learnings to improve overall performance management

If we look at this type of process as a framework, we can use it to build more effective processes that are rooted in an iterative approach to improving quality and value. With this type of execution you can climb any hill by taking small, measured steps. A word of caution however, you may suddenly realize you are climbing the wrong hill...8)

Good night,

Mark

Links for May 11, 2008

An interesting article that questions the need for a data warehouse for every Business Intelligence project. This parallel's the move to Web 2.0 in BI technology as this data is not likely to be stored in the data warehouse.

http://www.b-eye-network.com/view/index.php?cid=7344

Heading to the Semantic Technology Conference on May 19th, should have some good posts from the road. Check out the website at:

http://www.semantic-conference.com

Mark

Dealing with Culture

How does the culture of the organization impact the adoption of Business Intelligence?

This is a great question we need to ask ourselves as we move the vision of Business Intelligence forward in our organizations. Depending on the organization, your BI program will be successful based on how the program aligns to the culture of the company.

For example, lets envision a culture where finding cost efficiencies is a core mission of the organization. Think of some type of commoditites industry where margins are razor sharp, and every .001% improvement can be what makes or breaks the organization's profitability that year.

In this type of pragmatic organization, you would entirely miss the boat if you came in with a solution that provides demographics on their customer base and opens up new opportunities for them. In this type of culture, they may not be looking for new opportunites. We just want to procure it, process it and sell it on the open market! Beware those BI practitioners that stray from a services industry...8)

This is a pretty crude way to point out that you need to know your organization and it's culture to understand how to position your BI strategy. Tailor your approach using vocabulary and outcomes that align with organizational culture and you will be saying the right things to be successful.

Have a good night!

Mark