But before we let our minds wander into chaos, let's grasp the meaning behind this vogue word and how it influences the realm of Power BI implementations.
Suppose you have a jigsaw puzzle box and you scatter its pieces across a table. Hypothetically, the pieces could miraculously land in their proper positions, resulting in an assembled puzzle right from the get-go.
However, in reality, this scenario is nearly impossible.
Why?
Simply put, the probability is overwhelmingly against such an occurrence. Each piece needs to land in the exact correct location to form a finished puzzle. Although there is a singular state of complete order, there are nearly infinite states of disorder. From a mathematical perspective, the likelihood of a spontaneous orderly result is exceptionally slim.
We can apply this law to our lives, sport, puzzles, or in our case - projects. Entropy manifests in analytical projects through unseen symptoms that sneak into our best-laid plans. As we embark on our journey through the universe of numerical, categorical, and geospatial in an attempt to deliver outcomes for a client, we lose a day here, need to refactor code there and before you know it, you've blown a project deadline, and your PM is blowing up the phone.
However, there is a way to combat the insidious nature of entropy without surrendering to its chaos; it revolves around recognising the potential for events and devising plans to address them.
We do this by keeping the big picture at the fore and constantly reviewing where we're at in terms of project timelines.
Entropy can manifest itself in any project-based implementation not just Power BI Projects.
Let's chat about how:
Challenges with Data Quality Picture this, you devote time creating an appealing dashboard to discover that the data behind it contains issues like anomalies missing values, or duplicates. Dealing with these data quality problems upfront is crucial for ensuring the accuracy and reliability of your analysis.
Unexpected Changes in Data Sources - Data sources undergo changes as time goes by. External APIs may be updated data formats might be. Data providers could change their delivery methods. These subtle modifications in data sources can cause chaos in your Power BI models. Introduce challenges.
Adapting to Evolving Business Requirements - Like data sources, business objectives evolve and so do the associated analytics needs. The unpredictable nature of the business can lead to shifting priorities and new insights sought by stakeholders. Data practitioners must be agile and adaptable to keep up and almost anticipate these changing demands.
Dealing with Performance Issues - Performance bottlenecks can arise as datasets grow. Reports that used to respond may now become unresponsive. Understanding the root causes of these performance issues and optimising Power BI is vital for maintaining a user experience.
So how do we manage entropy - every challenge presents an opportunity, right?
Mastery of Data Preparation - Before diving into your Power BI journey, investing time in data preparation is crucial. This involves cleansing, transforming and validating your data to establish a foundation for your analytics endeavours. Getting the data bang on at the start of the project will serve you wellk through the project lifecycle.
Building Robust Data Models - When designing your data models, it's important to have foresight. Consider changes in the data. Incorporate flexibility into your models. A structured model can gracefully adapt to modifications.
Continuous Monitoring and Maintenance - Regularly monitor data sources and performance metrics to identify any anomalies that might arise promptly. Setting up alerts and notifications will provide warnings of disruptions caused by entropy.
Promoting Collaboration and Communication - Foster open communication with stakeholders and data providers. By aligning expectations and staying informed about changes, you can better prepare for the surprises that entropy may bring.Embracing Iteration - Entropy is a reminder that no data analytics project's ever truly "complete." Embrace a mindset of iteration and continuous improvement as a part of your organisation's data culture.
Entropy - the hidden force that complicates life - via Farnam Street
To sum up, anticipating entropy in any deployment let alone Power BI deployments, will in turn keep it in check. And you can manage entropy by simply keeping it in mind.
Knowing of and being aware of the natural draw toward disorder automatiucally allows you to defend and see the forect from the trees.
I've been on the wrong end of disorder in my professional life - I wish I knew about this topic a long time ago.
