The explicit measures podcast unpacks what does DevOps mean. It’s not a software it is a way of thinking. Matthias is the creator and inventor of our now beloved TMDL format. This conversation will blow your mind as you learn about DevOps with Microsoft Fabric.
Playlist Overview
Total Videos: 4
Theme: DevOps strategies and practices in Power BI
Guest: Mathias Thierbach
Audience: Power BI professionals and teams interested in CI/CD, DataOps, and DevOps methodologies
Top Down and Bottom Up DevOps – Ep.434 Explores different approaches to implementing DevOps in Power BI environments, from leadership-driven to grassroots efforts.
Starting a center of excellence (COE) can feel daunting. We face political challenges. This article pushes to explore the challenges of a COE and some recommendations to handle these challenges.
The Importance of Attention to Detail
Microsoft does a great job in outlining the key aspects of COE. For more details on this topic check out the Fabric adoption roadmap found here. A summary of those items are in the list below:
Pick a COE structure, does your company work more like a centralized, a unified, federated or decentralized team.
I strongly feel that documenting the result of these conversations is a huge win. The documentation can be used to show leadership that you have a solid plan. Discussing these topics pushes towards a health data culture. Lastly, when you bring documentation to leadership you show thought of aspects that drive success.
Foundational Attributes for Success
The optics of the COE matter. COE performance and leadership are crucial, as they can impact the entire organization. Don’t underestimate the value of setting clear goals. Taking time to identify pain points with your current organization structure help with planning process for the COE.
Setting clear goals
Addressing pain points that you see, plan to solve those pain points
Just start, don’t worry about making the COE perfect, plan for adjustments
Sometimes I feel that people try to over plan. Therefore, read up on the best practices provided by Microsoft’s documentation, write down your decisions then get moving! I have observed just by communicating and developing the plan really creates some momentum. Bear in mind it won’t be perfect in the first iteration. Plan on being flexible to adjust the COE to the organizations needs.
Recommendations for Overcoming Challenges
Attention to Detail: Paying attention to aspect you can control of the COE’s performance. Engage leadership so they support the development of the COE. Remember the COE is a vote in the direction of better data culture for your company.
Setting Clear Goals: Defining clear goals helps the team align towards a unified direction. Address pain points that could derail or distract from the creation of the COE. Connect the success of the COE to Objectives and Key Results (OKRs) outlined by the leadership team.
Regular Communication with Executives: Regular communication with the executive team helps remove mis-aligned expectations. When you win let leadership know, they can promote your success. Success means more buy-in from the company.
Feedback: Gathering feedback and pivot. Have empath for the process and be willing to adjust. If something is not working within the COE try something new. Ask others involved in the COE for recommendations, some of the smartest people are the ones you already work with.
For more thoughts on the COE and overcoming those challenges check out our episode on the explicit measures podcast.
In podcast #286 we take the time to review an older video of John Cleese giving a talk about the Creative Process in Management. We thought this would be an outstanding conversation to draw parallels to the Business Intelligence world. There are so many different areas we can apply creative thinking in implementing Fabric & Power BI solutions.
What is Creativity, and what strategies can we use to be more creative?
Discussion centered around the concept of being creative in handling data, building reports and fostering a data culture.
Emphasize that creativity is not a talent but a mode of operation and discussed its relevance in the field of Business Intelligence.
Key Topics:
Where does creativity lie:
Discussed the distinction between open and closed states.
Strategies to foster creativity and measure its benefits.
Dedicated time for creative thinking, challenging the conventional to-do list approach.
Creating an oasis of quiet for pondering and problem-solving.
Embracing humor as a catalyst for transitioning from closed to open thinking.
Overcoming Barriers:
Identifying common obstacles to creativity within organizations dealing with technology and data.
Evaluating the balance between analytical rigor and creative exploration in Power BI.
Applications and Tips:
Exploring creativity in the adoption of BI practices, report building, and its application in day-to-day operations.
Building reports with an open mindset.
Allowing time for pondering before making decisions.
Encouraging positive collaboration within a community.
Applying the “art of the possible” by exploring new ideas.
Meeting Creativity:
Examining the impact and value of injecting creativity into BI processes for organizational growth.
Introducing humor in meetings to foster creativity.
Building on ideas without fear of right or wrong.
Utilizing random connections for innovative solutions.
Creating a positive environment by avoiding negativity.
Report Building Process:
Front-loading creativity in requirements gathering.
Incorporating creative thinking in model design and building calculations for visuals.
This podcast episode is a treasure trove of insights for BI professionals looking to infuse creativity into their work, ultimately contributing to more innovative and effective business intelligence solutions. You can listen to the full conversation on the Explicit Measures podcast here:
As a special add-on for your enjoyment Tommy came up with a whole slew of jokes in the same vein that John Cleese told in the presentation. Feel free to use them in your next creative meeting!
Jokes Created by Tommy Puglia:
How many data scientists does it take to change a light bulb? Three. One to replace the bulb, and two to model whether it was the most cost-effective light bulb choice.
How many machine learning experts does it take to change a light bulb? Just one, but it will take thousands of tries to learn how to do it properly.
How many business analysts does it take to screw in a light bulb? Two. One to assure everyone that everything is going according to the plan while the other screws the bulb into the water faucet.
How many BI consultants does it take to screw in a light bulb? Only one, but they’ll first conduct a cost-benefit analysis to determine if the light bulb change will add value.
How many marketing analysts does it take to change a light bulb? One, but they’ll also rebrand the room to make it look brighter.
How many sales analysts does it take to change a light bulb? Just one, but they’ll convince you to upgrade to a smart bulb with a subscription plan.
How many data warehouse architects does it take to change a light bulb? Two: one to change the bulb and another to ensure it integrates seamlessly with the existing lighting infrastructure.
How many AI developers does it take to change a light bulb? They won’t. They’ll train a neural network to predict when the bulb will burn out and preemptively send a drone to replace it.
How many cloud storage experts does it take to change a light bulb? None. They’ll just store light in the cloud and access it as needed.
How many Business Analysts does it take to screw in a light bulb? Just one, but they will first interview everyone in the room to define the requirements for the ‘ideal light’ experience.
How many Data Analysts does it take to change a light bulb? Two. One to replace the bulb, and the other to tell everyone how much brighter it could be with just a few more data points.
How many stakeholders does it take to change a light bulb? Four. One to ask for a greener bulb, one to demand a cost-effective solution, one to insist on a smart bulb, and one to question why the bulb needs changing at all.
How many report requesters does it take to change a light bulb? None. They’ll just ask for a daily report on the status of the light bulb but never actually replace it.
How many Data Analysts does it take to screw in a light bulb? One, but by the time they’ve finished analyzing the best method, the technology for light bulbs has already changed.
How many stakeholders does it take to change a light bulb? Five. One to change it and four to form a committee that debates whether it was better the old way.
How many report requesters does it take to change a light bulb? None. They just keep requesting status updates on the darkness.
How many BI Consultants does it take to screw in a light bulb? Two. One to assure the client that they’re leveraging cutting-edge lightbulb technology, and the other to outsource the actual screwing in to an intern.
How many BI Consultants does it take to change a light bulb at a large corporation? An entire team, but the project will take three years and by the end, they’ll switch to a completely different kind of bulb.
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