Why is AI not everywhere?
When I talk to Sogeti clients about AI and Data, they usually tell me that they are “already doing AI” or “we have a Data Science team”. I hear practically every organisation stating that AI and Data is important. And I see analyst agencies and research companies talking about how important it is and how organisations which leverage AI and Data do better then the competition. So why is AI not everywhere in these organisations? Something is missing, and I think that the following 3 Bad Data Habits account for a lot of the issues.
Habit 1: “We already have Data Scientists and we use AI”
If you have Data Scientists and value their work, why does everyone in an organisation not have access to it?
- Most organisations who leverage Data and AI, do it in a tactical way. A point solution which answers one specific issue.
- Data Scientists are actually IT resources or other business resources working as enthusiastic amateurs. Or they have training but no experience bigger data teams.
- Analysis and Reports take a long time to create and share.
- Data scientists and Data analysts don’t distribute their work, because they have no platform to do so.
Habit 2: “We need to collect data in a central location”
The thought process goes, if you have data in a central location, your data scientist and analysts will have access to the data easier and as a result, they can do more and better work. “We need a Data Lake” to improve our use of data.
For many reasons, this is just wrong. I am not saying that you should not do this but adding new technology will not solve the real problems. A technology solution could contribute to new problems. Organisations need to know who owns the data. This is enabled by a Data Culture in an organisation. A Data Culture, enables organisations to distribute ownership of Data sources and systems, distribute data analysis, share knowledge and most importantly for data to be reliable and trustable.
When organisations add a Data Lake (or database or similar), security and access rights need to be organised, data quality needs to be monitored, data dictionaries need to be created, etc… And this is something else that IT needs to manage.
Habit 3: “But we are unique, and you don’t understand our business”
There might be something to this, but we have analysed and mostly, this is untrue
- Experienced data professionals understand data.
- Data professionals understand data patterns.
- Data tells its own story.
But domain expertise does help. However, we use standardised patterns, Data Science can often take advantage of not understanding the domain and simply analysing the data. And we have also seen similar data patterns across industries (I have examples of similar data flows in Banking Loans and Drug Research and Development, for instance).
How can my organisation take advantage of AI and Data?
Ask yourself these questions:
- Does my organization have a data culture?
- Does the whole organisation have easy access to data and simple reports?
If the answer to either question is No, you are not ready for AI and probably don’t understand its value anyway.
The most successful Data Driven Organisations are good at AI and leveraging data because they do the following well:
- Data Democratization
- Data Governance
- Data Security
- Business Ownership of Data and Data Sources
- and a Companywide strategy driven from senior executives down.
It is these points that help an organization be successful because they are the most basic foundation building blocks. After all, if you want to see this technology everywhere in your organization, you simply need to enable your organization to use and understand the data advantage.