Most organizations, like most humans, have a tendency to jump to solutions, especially when they are under pressure. They do not have time or patient to understand the problem before jumping to a solution. That is why we see projects solving the wrong problems or using the wrong solutions! To a person with a hammer, everything in the world looks like a nail!
More disturbing is when the organizational climate and culture discourage people who might see the mismatch between solution and problem to speak out. The Emperor’s new cloth story becomes real!
above argument applies to enterprises too.
Before selecting any solution, one needs to understand the problem. It starts with asking the right
questions. Below are samples of questions
that many EA teams deal with. Finding the
right, practical, and proven answer for them helps EA teams to be effective and
focus on the right areas. Working on IBM's internal transformation, helping many clients, and doing research on enterprise computing have given me many opportunities to deal with questions like these.
- How do I get started on EA?
- How do I convince my executives that we need EA?
- How can I get the business team excited about EA?
- How do I know that my team is focusing on the right areas? Some EA teams have lost funding since they have not been considered to be focusing on what is critical to the enterprise.
- How can I build trust in the EA team?
- How can I convince the enterprise that areas like security and architecture must be dealt with at the enterprise level not local/department level?
- My technical team completed a project. We were so excited that we had created an excellent capability. But my clients were not as excited as we were. It seemed they didn’t care/need the new capability! What do I need to do to avoid developing the wrong solution?
- What types of skills do I need in my EA team? How can I build the right set of skills in my organization? Is there a systematic way to transfer skills?
- How can I measure and track the ROI in EA and IT projects?
- What is the best way to deal with merger and acquisition (e.g. integrating new data and applications, difference in business model, redundancy and duplication)?
- How to operationalize the strategy and EA concepts?
- How to leverage the investments in so called legacy systems?
- How to get more from my current IT investments?
- How do we know what we have (e.g. IT assets, data, resources) and what we are spending on them? How should we track them at the enterprise level? How do I know which of them should be kept and which ones should be retired?
- How can I determine what to outsource?
- How to deal with challenges of global teams?
- Increase collaborations
- Increase knowledge and asset sharing
- Help them to feel they have a career
- Help them to feel that they are an equal member of global team regardless of where they are
- How to create a culture of innovation and invention in the organization?
- How to balance control and autonomy within the organization?
- How to use analytics to optimize the enterprise operations?
- How to replace legacy and silo systems with an ERP system?
- We have a big data mess. How should we deal with it in a systematic manner?
- We don’t know what we have
- Who owns it
- How much is spent at the enterprise level
- It is hard to say if we are safeguarding our data correctly. It also costs us lots money to comply with all legal requirements (e.g. security, privacy, backup, disaster recovery).
- We have incidents that our data was compromised and lost and had a big negative on our reputation.
- Poor data quality. We are spending a lot money on it but it seems it is not getting better
- Lots of duplication and redundancy
- It is difficult to get actionable information out of our data
- We are having problem to integrate data from different sources (internal and external)
- We have put lots of our data into packages applications. Now it is difficult to use them for other needs and integrate them with the data that reside outside those packaged applications
- There is a lack of trust on data
- Many applications blame our data bases for their performance issues.
- Data is growing fast and we do not have a strategy how to deal with our data in a systematic manner.