Strategic Planning | Enterprise Architecture in Higher Education

Business Analytics Anyone?

enterprise architecture, higher education, strategic planning No Responses »

Jan 112012

Today, I participated in a focus group to help start up the BCIT School of Business Business Analytics Centre of Excellence. The room was full of Business Intelligence/Analytics/Insight leaders from around Vancouver. We were brought together by Ed Bosman and Karen Plesner both instructors in the BCIT School of Business. Karen facilitated a two hour discussion on a series of topics. The group provided advice on the skills expected of graduates in the various business analytic roles – consumers, artisans/analysts and systems technicians. The other major focus was on what a “centre of excellence” for business analytics should provide and deliver to industry.

We were provided with a definition of Business Analytics as the seed for the discussion:

Business Analytics: the skills, technologies, applications and practices for continuous iterative exploration and investigation of past business performance to gain insight and drive business planning (Davenport and Harris, 2007)

This definition generated a very good discussion and the consensus was that this definition was too narrow. It failed to address real-time analytics for operational performance management and web analytics for customer behaviour management.

We had a good discussion about master data management and data standards. One of the great quotes of the day came from an panel member. He was referring to a discussion about how confident and accurate your numbers need to be. I really like this pragmatic approach.

Business Analytics augments your gut

The another panel member introduced the group to a model used by Davenport and Harris. Here is what it looks like:

Davenport and Harris Model	Information	Insight
Past
Present
Future

The model is a measure of where business analytics efforts are focused. This would be a good model for us to look at the maturity of our Business Intelligence/Analytics practices.

This table contains the lists of topics and themes I noted during our focus group. There are many topics and themes below that will warrant future blog posts.

Trends	Tools	BI/BA Type	Audience
Web Analytics	Excel	Operational “Real time”	Consumers
Mobile	Access	Tactical “Just in Time”	Artisans
Bring Your Own Device	Qlikview	Strategic “Points in Time”	Analysts
Security	Tableau	Compliance	Authors
Privacy	SAP	Predictive	Systems Technicians
Predictive	IBM Cognos
MDM	MS Analysis Services
Big Data	SAS
Information Overload	SPSS

I am looking forward to the next steps in the process and hope to contribute to the effort.

Davenport, Thomas H.; Harris, Jeanne G. (2007). Competing on Analytics: The New Science of Winning. Harvard Business School Press

Microsoft Canadian Colleges Exec Briefing – Institutional Effectiveness

enterprise architecture, higher education, it governance, strategic planning No Responses »

Feb 042011

David Raney MD, CEO Nuventive

How do you achieve Institutional Effectiveness?

What questions do you need to answer?

are your core learning outcomes improving?
how do you assess institutional performance?
how do you measure strategic goals?
…

The Growing Challenges of Accountability – many challenges here centred around disconnect between data and planning

What is Institutional Effectiveness?

Efficiencies – business goals
Achievement – academic & administrative outcomes
Culture of Data Driven Decision Making

Nuventive tool – tracdat now being developed in SharePoint and iWebfolio for individual assessments

Microsoft Platform for Institutional Effectiveness (MPIE) – aligned, balanced, pervasive

Closing the Loop – plan, collect, analyze, utilize, follow-up then repeat

What is seen in a typical assessment cycle is that lots of time is spent on planning and very little time spent using the data to learn what happened and how it can influence the plan.

Microsoft Canadian Colleges Exec Briefing – Data Center Management

enterprise architecture, higher education, it governance, strategic planning No Responses »

Feb 022011

Rick Bakken, Senior Director, Data Center Evangelism, Microsoft Corporation

Delivering Infrastructure to the Cloud

Data Center Innovation – asked the question “Why are we building giant refrigerators for our data centers?” In the Chicago data center, there are two floors. Top floor is standard raised floor and the bottom floor is container based servers. Made a decision to run the data centers hotter. Microsoft buys 5% of the servers bought annually around the world.

Timeline – PC era, Portal era, Online App era, Web Services era, Cloud Computing era –>

opened first data center in 1989
launched microsoft.com in 1994-95
added Trustworthy Computing in 2002
added Security development lifecycle in 2004
Windows Live launched in 2008
Windows Azure cloud service launched in 2010

Microsoft runs on a huge global scale 24 x 7 with over 200 cloud services running today (2011). Microsoft has more than 10 and less than 100 data centers world wide. The Network Architecture is really the key to cloud based computing scalability. Geo-redundancy is a feature of this style of network architecture. This is a combination of few High Density data centers and large numbers of edge nodes. Microsoft Global Foundation Services works from layer 0 to 4 of the OSI model. They have some serious process and procedures to bring a low cost data center service to market. Microsoft is investing a huge amount of capital in the Global Network and particularly Dark Fibre.

Sustainability Evolution

Generation 1 – 1989 – 2005 – co-location with server based architecture – $25M per MW
Generation 2 – 2007 – density with rack based architecture – $17M per MW
Generation 3 – 2008 – containers and pods architecture
Generation 4 – 2010+ – modular with ITPAC (pre-assebmled components) architecture – $4 to 8M per MW
Generation 5 – ? – remove all moving parts from the data center servers
Generation 6 – ? – completely green data center – all components fully recyclable

Server Hardware Design Considerations

the data center is the server
performance/watt/dollar – PUE is no longer a useful measure need a new way to measure
drive change in the industry through strong partnerships
deliver value to online service partners through customized designs at the application layer
think about infrastructure refreshes using OpEx instead of CapEx

CTO “Journey to the Cloud’ 2008 to 2020

IT Workload Analysis – what is running where?
Virtualized Data Center – efficiency gains
Legacy Migration- cloud rehost, rewrite ancient code, reduce power
Cloud – on/off premise utility model
Data Sovereignty appliance - Azure Appliance refreshed on a OpEx cycle every 3 years