In the 50s, we started to see SQL databases with only one type of format. Then moving along, larger databases such as Oracle and Informix appeared. I started to use Dbase as a relevant tool in the early 80s. Today the variety of formats like pictures, videos, texts, engineering data, spreadsheets, mobile data, social media and emails require a different database format. This is why NoSQL started to exist (not only SQL). Data and the amount of data are more and more available. The power of data also started to become clear in the early 50s. However, at that time we did not know how to gather data and more importantly how to use data. Today, data has great value to all companies. Until recently, we had never thought of putting together all the different types of data. Over time companies have started to realize that they could use more and more their data to answer many questions throughout the company. Financial, sales, marketing, customer support questions were answered in a coherent way. For example, when does a customer tend to order? What is the profile of our customers? When do they pay? How have we approached them, etc.? Marketing departments would try to answer this, but it was not precise enough for salespeople to optimize. When an employee would leave the company, they would take away with them a lot of know-how and sometimes companies would struggle. Then the idea of real time operating systems based on data came into play. Robots were created and used historical data to progress and behave more and more like an animal for some or like a human for others. These robots created data like no other. Anything they saw or heard, any movement they made and any interaction they had were recorded and reused to ensure the robot would continually develop. In parallel, research around data and organization of data developed.
Changes in our world
Companies with CDOs (Chief Data Officers) and analysts need to aggregate the data to start making changes and transform the way we work, live and interact. Moreover, they need to make data speak and make sense so it can be used by all.
Creating smart technologies (large databases – data organization, data clusters, algorithms…) based on historical data and real time data gathering mechanism will lead to some sort of intelligence around data. Then the more the data is used, the more intelligent the algorithm will become. It will only copy human intelligence but because it is not yet capable of feelings, intuition or empathy, it will remain as artificial intelligence (machine intelligence vs human intelligence). This is the closest we are going to get for now to intelligence.
Innovation and disruption
Companies that start thinking outside of the box and optimize AI through data will be able to have more precise analytics (past and predictive) and will be able to be augmented because their decisions will be based on algorithms never seen before. In this digital world where you need to be first it is the only way to market dominance, this will ensure companies drive their market as leaders.
This is an exciting time because all companies can score big on the AI and data front. A lot needs to be done to make sure the AI strategy is in place. Companies need to protect data, ensure strict data privacy laws and make technology scalable to get as much data as possible over a period of time without suffering from lack of space. Furthermore, they must work to get rid of silos within the company, so all departments communicate with fluidity. Finally, they must recruit analysists, AI algorithms programmers and big data specialists.
AI friend or foe?
Some people are fearful of AI because of the association between robots and cinegraphic end of the world scenarios. However, this is not reality. AI needs to be a way to augment and compliment humans. For example, when we make big decisions as a CEO, surgeon or head of state, we need to have raw data and understand all possibilities without emotional interference. This is what AI provides. Humans can decide to add empathy or any type of feeling and augment the power of their decision-making abilities.
In general, the journey to AI requires a digital transformation within the company. Policies need to change as well as mindset. Using professional technology suppliers can help this process and ensure a smooth transition for the benefit of the company. Digital transformation creates cost optimization as well as full engagement of resources. This transformation will lead to transparent decision making due to the new ability to see data from all angles. Technology will also be updated with state-of-the-art capabilities.