Sunday, March 29, 2015

EDW 2015: Data Strategy

This session was presented by Lewis Broome (from Data Blueprint) and Brian Cassel (from the Massey Cancer Center).

Lewis presented a his strategic model and roadmap using the case study of a logistics company implementing an ambitious project.

Brian spoke about the challenges he faced within the cancer center as he implemented analytics across data hidden in silos. This was primarily culture based but once he was past that he was able to use the existing Data Analytics hub to build a specialized data mart to support strategic review of the data.

This was really great stuff and my summary doesn't do it justice by a long shot.
 
Data Strategy
Business Needs
In order to get anywhere with discussions about data and mays to improve it throughout the organization, the value of the effort has to be made clear. Clean data may seem like the most obvious need in the world, but that view is too low level to make it on to the radar of senior management. Instead, it needs to clearly address a business need.

There are three aspects to consider
  • How will mesh with the company Mission and Brand Promises?
    • Ex. FedEx: Your package will get there overnight. Guaranteed.
  • Does it improve the company's market position / provide a competitive advantage?
    • Michael Porter's Market Positioning Framework and his Competitive Advantage Framework provide a good way to think about this.
  • Will it improve the operating model and support the company's objectives?
    • Operating models improve by changing the degree of business integration or standardization.

If the data changes do not address any of these areas, it will not gain the support needed to succeed.

New capabilities that do not meet a business need aren't a program, they are a science project.

Current State of the Business
The current state assessment looks at
  • Existing Assets and Capabilities
  • Gaps in Assets and Capabilities
  • Constraints and Interdependencies
    • This can be the toughest stuff to identify.
    • BEWARE SHADOW SYSTEMS typically excel spreadsheets with macros or access data fixing before feeding it into the next step of a process.
  • Cultural Readiness

Cultural Readiness
Cultural Readiness depends on 5 different areas
  • Vision
    • A clear message of what the program is expected to achieve
  • Skills
    • Ensure that the right people are part of the program
  • Incentive
    • The value and importance of the program should be clear to all of the participants
  • Resources
    • Backing the program will require more than just good will, tools, environments and training may all be required
  • Action Plan
    • The system boundaries being developed should be clearly defined

Capability Maturity Model Levels
  1. Starting point
    1. There is some data in a pile over there.
  2. Repeatable Process
    1. This is how we sweep the data into a pile and remove the bits of junk we find.
  3. Defined Process
    1. Sweep from left to right, avoid the dead bugs. Leave data in a pile.
  4. Managed Process
    1. The entire team has the same brooms and the dead bugs are highlighted and automatically avoided by the brooms.
  5. Optimizing
    1. Maybe we can add rules to avoid sweeping twigs into the pile as well.

Roadmap
The Roadmap establishes the path of the Data Management Program to achieve the strategic goals.

Leadership and Planning
    • Planning and Business Strategy Alignment
    • Program Management
    • Clearly Defined Imperatives, Tactics and KPI
    • Accountable to CDO
Project Development
    • Outcome Based Targets
    • Business Case and Project Scope
    • Program Execution

Project Model
Big Projects tend to fail, at least twice sometimes more than that as the business learns what it really needs.

Always start with crawling and walking before going to running.
  • Governance should start with a small 'g', where it matters most. There are commonly 5-10 critical data elements, take care of those before setting targets higher.
  • Data Strategy as top-down approach works best. Otherwise it is uncoordinated and is only capable of supporting tactical initiatives.
  • Data Architecture must focus on the business needs, not individual systems or applications.

2 comments:

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