Debunking data analysis myths – Part 2


There are many myths surrounding data analysis, especially when it comes to identifying the best ways to drive business growth and competitive advantage. Last month, I mentioned this Forbes article that examines 14 myths in this regard, with my discussion turning to six of them. In my blog article this month, I will examine the remaining eight. Let me jump right in and start with number 7 of the myths listed.

7. Survey responses are fully reliable data

Survey data can certainly provide us with better insights into our customers’ perceptions of our business, support provided, available products, and so on. However, this can only happen if the survey has been designed correctly. There is a science to designing it in such a way that the submitted data is processable and can be analysed. So, even though opinion and free text fields are nice to get customer views, they can be a nightmare to extract quantifiable information from.

8. All data is good data

9. Data must be 100% accurate to be useful

In my view, these two myths are linked. All data is not necessarily good data. And what one department considers good might not be applicable to another department in the business. There are a myriad of ways to measure the quality of data. This includes accuracy, completeness, timeliness, and whether it is fit for purpose. The adage of ‘you cannot manage what you do not measure’ applies to data as well.

10. Once you’ve set up the model, you’re done

This is a myth that has been busted millions of times over. Analytical models are developed to improve business processes and decision-making. These improvements will affect the values of the variables used in the model. Over time, the insights of the model will diminish in value as the variables get affected by changing business processes. The models must therefore be recalibrated to point out new variables to focus on.

11. The questions to be answered must be settled up front

I believe that there are two aspects to this. Firstly, the question must be phrased correctly to get the analytical model or reporting component to answer it effectively. If the question is vaguely defined, then the insights will be vague as well. Secondly, discovery can also be done through exploratory analytics and visualisations. As the name suggests, a business must have a more open mindset to the data in this instance and let it show the ‘stories’ without applying preconceived notions to it.

12. Data analytics is a destination

Even though an organisation may have a specific question that needs answering, once that is done and the changes implemented, there will likely be more questions that must be addressed. Additionally, many companies do not begin with a destination in mind. It is more a case of starting and seeing where they end up. In this case, my view is that data exploration truly becomes a journey.

13. Knowing ‘the numbers’ is enough

While a data culture and mindset are important, interpretation and understanding are also required. Approaches like data visualisation become the ‘language of data’ putting it in a way that the company understands.

14. Our data is unique

While I agree with the author that the business should focus on the insights that would differentiate it, there are organisations (even though they are in the minority) that are different to those ‘standard’ companies. For instance, for the company I work for, at one stage we were trying to map standard insurance data models to businesses in both South Africa and Australia. However, the business models were so different that the data models did not fit. The same applies to analytical models. Sometimes they fit, but sometimes the data of an organisation is truly unique. It is therefore important to detect the difference and not force a square peg down a round hole.

As more businesses embark on digital transformation journeys, data analysis will continue to show its importance in leveraging insights that can be used by a business to create a sustainable competitive advantage. Understanding the role of data analysis and debunking the myths that exist become important to ensuring its success.

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