Enterprise / DevOps

We recommend factoring in the following considerations when preparing a POC. Goals and scope: Describe the purpose of the solution POC and the functional areas it will address. For example, the project team could decide to limit the POC to a single functional area, or a specific set of requirements or features. Source data: Identify what data will be used in the POC. Depending on the solution, the project team might decide to use different types of data, such as: Production (real) data Sample data Generated synthetic data that resembles actual data volumes and complexity observed in production environments Demonstration: Describe how and when the project team will demonstrate the POC to stakeholders and users. Demonstrations could be given during regular updates, or when the POC fulfills specific functional criteria. Environment: Describe where the project team will build the POC. A good approach is to use a distinct sandbox environment for the POC, and deploy it to a development environment when it's ready. A sandbox environment has more flexible policies and fluid content, and it's focused on producing quick results. In contrast, a development environment follows more structured processes that enable collaboration, and it focuses on completing specific tasks.

Solutions: Systems or tools built to directly address specific business problems or data needs for users. Examples of solutions include:

Initiatives: Processes, training resources, and policies that support your key results. Initiatives are typically non-technical instruments that support users or processes. Examples of initiatives include:

When curating this implementation backlog, consider the following points.

While the solutions you plan aim to address the business data needs, it's unlikely you'll be able to address all of these needs immediately. Ensure that you plan to mitigate the potential impact of unmet business data needs that you won't address now. Try to assess the impact of these data needs and plan to either partially address them with quick wins or even stopgap solutions to at least temporarily alleviate the business impact.

Once you've defined key results, solutions, and initiatives, you should get approval from executives and the key stakeholders before enacting your tactical planning. Present the outcomes of tactical planning to executives and key stakeholders. Highlight the expected benefits and relevant outcomes for the business should tactical planning be successful. Also, explain how the described BI key results support the business objectives and data needs identified in BI strategic planning. Use any feedback to adjust tactical planning, where necessary.

To ensure BI and business strategic alignment, you should establish continuous improvement cycles. These cycles should be influenced by the success criteria (your KPIs or OKRs) and the feedback that you regularly collect to evaluate progress.

In the last step of BI strategic planning, for each of the prioritized areas, the working team usually defines several goals to work toward in the next 12-18 months. Typically, these goals represent the desired outcomes and maturity level growth.

While the working team should be involved in clarifying and documenting goals and priorities, it isn't responsible for defining them. The executive sponsor and equivalent decision makers own these decisions

While the working team should be involved in clarifying and documenting goals and priorities, it isn't responsible for defining them.

In the last step of BI strategic planning, for each of the prioritized areas, the working team usually defines several goals to work toward in the next 12-18 months. Typically, these goals represent the desired outcomes and maturity level growth.

There has been a ton of interesting innovation in the BI space the last few years, but they are all point solutions attempting to solve specific, narrow-scope problems

They do it elegantly and create important new ways to tell stories and collaborate with data, but they aren’t trying to upend the enterprise BI market and it’s hard to see how they could evolve in that direction.

The first iteration of such an effort is usually a valuable discovery method and learning experience. Great… treat it as such; take notes, make note of the good parts and then throw it away and start over! In Fredrick Brooks’ “The Mythical Man Month“, he cites that for most engineering projects, the first six attempts should be abandoned before the team will be prepared to start over and complete the work successfully. Brooks was a chemical engineer before working for IBM; and hopefully, our methods in the data engineering business are more effective then his 6-to-1 rule. But, this makes the case the prototypes and proof-of-concept projects are a critical part of the learning path.

I thought it would be interesting to highlight a few of the feature additions and enhancements present in the current preview version (version 0.1.7).

Microsoft has provided several online resources related to the new SDK:

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