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Debunking data analytics myths – Part 1

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Experts in the data space know the importance of data and its analysis to help drive business growth and competitive advantage. Of course, data analytics is not something that can just be switched on and happen overnight. It does come with challenges, and overcoming those challenges are instrumental to a project’s success.

Further complicating matters are the many myths that surround this strategic function. I recently came across this Forbes article that highlights 14 of them. I found these to be interesting and the insights valuable, and so wanted to share my further views. I will examine the six in this article and finish up next month with the remaining eight.

1. The data will confirm what I already know

On the one hand, this is a good think. The data may confirm what the business already thinks it knows if it needs validation of that ‘gut feel’. However, when it comes to conducting data discovery, predictive analytics, and other forms of analytics that generate new insights, it is about gaining access to these new insights that were not previously known, that could improve business processes and strategy.

2. We can’t do this without a data scientist

The article rightly notes that there are plenty of tools and solutions available to perform data analysis without requiring a specialist – and this is certainly valid. Yet, I do believe that there comes a tipping point where a company cannot get any more insights from off-the-shelf, pre-configured analytics, visualisations and reporting solutions. In my experience, this is especially the case if data is spread across multiple systems where it is not always possible to integrate it without the expertise of a data scientist. Ultimately, to achieve a higher level of data insight a business does require someone with those data scientist skills.

3. Following where the data leads is scary

If analytics generates ‘scary’ or impossible-to-achieve insights, then my view would be that it is not performed correctly. ‘Good’ analytics therefore specifies that analytical insights should be actionable and related to the business strategy – and this should not be scary. While some analytical insights do affect the business strategy, if it expects too significant a change then it simply will not be considered actionable. An absurd example is if the analytics shows a retailer that it will make more profit per unit if it starts selling motor vehicles or real estate properties as opposed to food or other fast moving consumer goods.

4. Getting insights from data is simple

What a business gets out of its data analytics equates to what it puts in. Simple reporting and visualisation will yield simple results. Advanced analytics on complex data on the other hand can be difficult and will require skilled resources to get it right and to extract the most value for the business.

5. The more data, the better

As the article states, more data is not always better – something I strongly agree with. There seems to be a massive drive in the industry – maybe fuelled by the cloud storage vendors – to collect as much data as possible these days. However, this does not necessarily contribute to good insights. Prioritisation, relevance, and the quality of data are key.

6. Data equals knowledge

I believe that there is a big gap from data to information to insights to knowledge. Additionally, there is also the matter of data/information/insight maturity in the organisation to consider. A company that does not have a good grip on managing its data, can hardly be considered to have the ability to deal with advanced insights and the knowledge of how best to apply those.

Join me in part 2 next month when I will examine the rest of these identified myths associated with data analytics.

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