Archives January 2021

2021 – A year of consolidation

As we look to move on from 2020 and the disruption that it held, it is clear that 2021 will be a year of admin activities. Across 2020 we all took decisions rapidly and the IT landscape of many businesses went through three to five years of change in a concise period of time – days if not hours for some businesses. The net result is that we will all have to normalise our new businesses practices at some point.

The pace of change required within many businesses last year was unprecedented; it has convinced the world that rapid change is not bad and that it is the path to success. I am already hearing that 2021 is going to be the year that DevOps fully comes of age. What does that mean for us in the world of Data Analytics? Does DevOps really apply to us?

To answer that question, we need to look at the origin and methodology behind true DevOps; Google is credited with determining what DevOps should be and what the principal should be. It falls to the idea of planning, preparation and modularity.


By documenting your process and being clear about what should be happening and when it becomes possible to determine what activities need to be undertaken


Looking at the plan allows you to code solutions; solutions can be for positive activities and prepare for negative events.


Code must be lightweight and able to be retooled/reused with little or no human involvement.

Code Team “Owns” the whole management cycle


The net result of these three is an agile solution that is as self-correcting as possible with context (parameters) being used to allow multiple uses of all elements, i.e. having a single “Write to log” module, with parameters being used to determine Title, Description, Start Status, End Status. As you use simple components to make rapid minor changes to keep performance within agreed specifications. So DevOps is about Automation. In addition to this (and many would say the crux of DevOps) as code becomes more critical and code being used/reused, traditional Change Management methodologies cannot effectively manage the solution instead the coding team begins to monitor their own work and determine the delivery pipeline… This whole situation’s implications are that the business hands over control, management, and accountability of their process to a code-focused team (typically IT).

For Analytics?

Power BI works remarkably well with the DevOps mindset; however, it must be made clear that there s a divide and that divide becomes the biggest challenge to balancing a lasting Power BI Enterprise Grade solution.

DevOps in Power BI

So Enterprise Analytics can benefit from a DevOps mentality. When done correctly, the bonds formed between the main Business Community and the Power BI Team have wider benefits. To achieve excellence in your Analytics Vision requires close integration with report consumers and those carrying out the activities being focused upon. In other words, the value in a “Sales Report” is only provided because the report shows the Sales Director what she needs to see in terms of success and failure while being clearly aligned to the sales team’s working patterns.

2020 is now firmly behind us. Looking at the future, we must all now consolidate on the rapid business changes implemented to keep the business going and transition back into a “Business as Usual” (BAU) mindset. DevOps is excellent for BAU in an uncertain world because it embraces the principle that everything is not always perfect. The principal of inbuilt error or issue management enables your business to carry on. The simplest example of this within Power BI is the refresh model. A parallel refresh is used so there is no downtime during the data load; additionally, should the refresh fail the last known good version remains up and available. A clear example that Microsoft has developed the platform with DevOps at the centre.

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