Yet another in our series of posts building the business case for data quality. This one looks at the cost to BI.

Introduction

Business intelligence (BI) has the potential to revolutionize decision-making, operational efficiency, and revenue generation for organizations. However, the success of BI hinges on the quality of the underlying data and the validity of assumptions made.

This article explores the reasons behind BI failures and highlights the significance of data integrity in achieving reliable outcomes.

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Super Rugby 2015: A Case in Point

Predicting the outcomes of sporting events, such as the opening round of Super Rugby, can be challenging due to the reliance on historical data. In 2015, the weekend results defied expectations and demonstrated the limitations of using historical form as the sole basis for predictions. Notably, several underdog teams secured unexpected victories, surprising both pundits and fans alike.

The Weekend Results:

Predicting the first round of any sporting event is always a bit of a gamble – largely because one is looking at historical data.

In many cases, this can be a sound strategy. In the absence of form, which cannot be demonstrated until the players are on the field, I made my predictions based on the two two principle factors — the hypothetical strength of the respective teams on paper, and, where this was inconclusive, on which team had the home ground advantage.

What a mistake!

The historical form had no role to play in the weekend results.

Game One: The reigning seven-time champion Crusaders, boasting multiple All Blacks players, suffered a defeat at home against the perennial wooden spoonists, the Rebels. Only 2% of punters on Superbru anticipated this outcome.

The Lions, considered strong contenders, faced an upset at home against the Hurricanes, with only 35% of punters predicting it.

In another surprising turn, the Sharks, a highly regarded team, lost at home to the ostensibly weaker Cheetahs. This outcome was anticipated by a mere 6% of punters. Similarly, the Bulls were outplayed at home by the Stormers, contrary to the predictions of 31% of punters.

Out of the seven games played, only two followed the expected form. The Brumbies and Chiefs emerged victorious against the Reds and Blues, respectively, aligning with my correct predictions. The final game witnessed last year’s champions, the Waratahs, being humbled at home by a seemingly inferior Force team. Only 6% of punters foresaw this outcome.

Lessons for Business Intelligence:

The unpredictability of Super Rugby exemplifies the inherent risks associated with relying solely on historical data for decision-making. This holds true for business intelligence as well. Faulty assumptions, missing or inaccurate data, and biases can undermine the effectiveness of BI initiatives, leading to unreliable insights.

Data Quality: The Key to Reliable Business Intelligence

To overcome the pitfalls of BI, organizations must prioritize data quality. By ensuring that the data used for decision-making is current, complete, and accurate, businesses can enhance the reliability and validity of their intelligence. Robust data governance practices, data validation, and enrichment techniques play crucial roles in maintaining data integrity.

Consider this: Why is Data quality critical for BI? It’s not just a checkbox; it’s the foundation of reliable insights.

Moreover, what is the true cost of poor quality data? Understanding this unveils the hidden toll it takes on your organization.

Conclusion:

Business intelligence can provide substantial benefits to organizations, but its success depends on the quality of data and the avoidance of faulty assumptions. Explore the top benefits that businesses derive from business intelligence.

Super Rugby 2015 serves as a reminder that historical data alone are insufficient for predicting outcomes. By prioritizing data quality and adopting sound data management practices, such as DataOps, businesses can unlock the true potential of business intelligence. Learn the difference between DevOps, DataOps, and MLOps and why DataOps is the right approach for BI.

Image sourced from http://en.wikipedia.org/wiki/List_of_Super_Rugby_champions