As we’ve seen since the beginning of the data analytics and BI onslaught, managing source data and ensuring its accuracy and consistent format is paramount.

The validity of the data ‘going in’ determines the usefulness of the data ‘coming out.’ As companies rely more heavily on this information to run their businesses, finding a way to solve this problem becomes non-negotiable.

Add to the challenge the continued increase in new data sources from omnichannel interactions, the ever-growing threat of security issues, and a public that is more aware and more cautious than ever about their privacy, and you can easily recognize the need for a significant investment in time and resources to address this complexity which we rely on as business intelligence.

Business Intelligence has long been the fore-front of Data Analytics that required very unique, specific, and hard to acquire skillset in the past. It is, however, a lot more mainstream nowadays, thanks to the rise of Business Intelligence tools and infrastructures over the years.

In the near future, traditional reporting Business Intelligence will become more and more obsolete with repetitive tasks that soon to be fully automated, and it is important for one to focus on either of these to be relevant in the future:

  • Data Analyst path: acquiring the core skillset of extracting insights, deep analysis and create recommendations, business decisions out of data, or
  • Data Engineer path:the engineering aspect of it, to develop, setup and maintain the automated analytics systems, or
  • Data Science path: somewhat a combination of the above  

As for businesses, investing in proper Business Intelligence infrastructure and personnel is always a must, and especially, much more important to establish early on, before moving towards Data Science and Machine Learning products. 

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