The journey to AI part II

Data relevance

In the early 50s, the power of data started to become clear. We could sense that data was something of value, but nobody knew how to use it and what kind of information could be valuable.

In 2012, according to the Guardian, only 0.5% of all data was analyzed. In 2017, the Economist claimed that data would replace oil as the world’s most valuable source. Even if many people would compare data and oil, they did not mention the fact that data was very different from oil. In fact, data can be extracted, analyzed, shared, stored with endless means. Moreover, anyone can use data endless times and get very different type of insights from it.In 2012 a report from IDC about big data statistics claimed that only 22% of all the data had the potential for analysis. It also claimed that by 2020, the percentage of useful data (that can be analyzed, shared, stored and that has the potential for analysis) would climb to 37%.

Today with the rise of Chief Data Officers, data scientists and big data technology, there are vendors capable of providing solutions that allow easy and efficient access to data.

The technology that has impressed me the most is the IBM one. The IBM journey to AI (have a look at allows a very efficient way to connect data with cognitive capabilities under one platform. What I like the most is the fact that it takes into consideration the existing client’s architecture and ensures that even with different starting points all users can arrive at their respective destination with their own personal journey. IBM flexible technology, approach and unique customer service will allow anyone to start with basic data infrastructure and continue to perform with great AI algorithms. The journey to AI is challenging but through the IBM approach, it will ensure success throughout all steps.

Many people will ask why AI is now so popular. AI will allow data to be optimized and used in a way that you probably have not expected yet. Thanks to AI you will be able to predict and organize models that will then give you more potential. Thanks to AI you will think of disruptive ways of doing business. You will optimize your resources, and finally ensure that processes, experiences and decisions are automatized based on intelligent models.

IBM offers an analytic tool (IBM planning analytics) that helps iron out all mundane tasks such as manual spreadsheets integration and ensuring all data are considered. With this tool you will ensure a much more accurate forecast.

IBM’s approach to use 4 steps that allow an easy understanding of what is required is very efficient and transparent. Please do not assume that you need to change your way of working. IBM adapts to its customers’ needs and ways of working.

As a first step you will need to collect data. You probably have a lot of data available through your company. IBM will help you audit what is available and ensure you can collect new types of data while combining data already stored. IBM will ensure all data remains with the same architecture and that all data can be used.

Once data has been collected, it is critical to get it organized. Using big data architecture and databases allowing the 5 Vs (Velocity, Volume, Value, Variety and Veracity), IBM technology will ensure data is prepared for analysis and organized in an intelligent way so data specialists can get their algorithms optimized.

Analyzing data is the next step. Data scientists will be able to determine what kind of data is needed and how to analyze it. Once the infrastructure and the architecture are in place, data scientists can operate their magic and work with the IBM team to ensure data analysis optimization.

Once data has been collected, organized and analyzed, you will need to get it Infused (as per IBM lingo). This means build trust, transparency, compliancy and ensuring model based decision making and explanations are in place. Bias is a challenge for all AI algorithms. What kind of data should be used; how should we use them; could just one data scientist make these decisions; would bias use of data create some issues with AI algorithms? IBM infuse step will answer all these very important questions and ensure you make the right decision for your business.

Data relevance is key to all AI projects. The right data in any format with the right architecture and analyzed with the right data scientists will ensure success in your project.

According to New Vantage, 97.2% of organizations are investing in big data and AI today. In the same report from New Vantage, you can read that 62.5% of survey participants said their organization appointed a Chief Data Officer (CDO) with data scientists recruited as well. Companies are transforming their business; it is very important everyone thinks about a new strategy to ensure your company survives the next 5 years.

As a conclusion, I personally like the IBM approach because in a challenging world, they make their strategy clear, efficient and easy to understand. Their teams are always available to support client needs. If you are hesitating, why don’t you go ahead and give their free planning analytics trial a chance at ? In any case your first investment will be softened by a 12K cloud credit at .  What are you waiting for?