Business Intelligence

Paving the road for Augmented Analytics

Hugo Cordeiro, Closer Consulting

The new generation of BI tools allow greater agility in implementing solutions and in their use by adopting intuitive interfaces. This evolution is continuous and looking a little into the not-too-distant future, we will see the consolidation of the concept of Augmented Analytics with the appearance of virtual BI assistants, analogous to the personal assistants we already know, such as Alexa, Siri, or Google Assistant. However, instead of looking on the internet for answers to the questions they are asked, they will do so in the analytical models of organizations.

The great challenge that this technology poses is that in order to maximize its use, the analytical models must be previously prepared to answer the pretended questions. In practice, it means that we need to know a priori what kind of questions are going to be asked.

Without knowing what questions we want to ask in the future, an alternative will be to adapt or enrich the models a posteriori, after identifying the questions for which we seek answers, which essentially corresponds to the approach that has been followed in the recent years.

It is a more reactive approach - which works -, but which conditions the BI time to market. Once the question has been identified, the system will have to be upgraded, enriching it with more data or new dimensions of analysis to get the answer. The truth is that these development activities are, in fact, necessary and inevitable (until the tools have the required intelligence to do it for us). Still, the question is: what if they cannot be done before the problem arises?

Perhaps by changing the ownership of who has to define the requirements for Business Intelligence systems. Instead of being the people in the business - because they, like everyone else, focus on the needs of the present -, it should be the data to do so because it is the data that delimit the scope of the answers that can be given. And suppose one day my virtual BI assistant is unable to answer a question. In that case, I hope it happens because my organization's data is insufficient to provide the answer, not because the data exists but it’s not available in my Business Intelligence system.

The reason why this approach is not the one in vogue is due to the fact that it is not efficient in terms of its cost/benefit ratio. Essentially, because even though BI tools have evolved a lot, they still require some degree of specialization and time invested in exploring the data. In other words, searching for the answer in self-service mode is still somewhat expensive, which means that the eventual wealth of available data is not fully explored. That is why we stick to the most pressing needs, and the evolution of BI systems is done cyclically as new needs are identified. It is an evolution in batch and not in real-time.

But with the technological evolution of Business Intelligence tools, in particular with the development of Augmented Analytics, it is no longer we who research the answer, but our virtual assistant, and then we can ask the questions we want because the "price" of the response is “cheaper."

The way forward is the integration of the organizations' operating and business systems with the BI systems, the curation of the collected data, their organization in informational domains, and the enrichment of the models, all continuously and proactively to maintain the data manageable and available to trigger their full potential on demand, at any time.

The time the machines will do this work for us, in an abstract and seamless way, will come one day. Until then, it is up to organizations to take the initiative to prepare for the future BI paradigm and benefit from all the potential that technology offers to leverage the success of their business.

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