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Is your data ready for AI

Updated: Feb 8



Often, many organizations deploy data warehouse strategies and solutions to focus on historical and statistical data to predict a few business variables. However, is the data present in the warehouse does it suit the sophisticated systems that use Artificial Intelligence (AI) and Machine Learning (ML) algorithms ?


Business leaders understand the data warehouse and the application layer on top of it and they constantly look at various permutations and combinations of data to meet their business needs. Few data warehouses have almost the same data at the data warehouse and the application layer. The middle layer where there is lot of scope of data transform is not present. This is the opportunity where an AI or ML API should interfere and make the data more useful to the business needs.


And coming to the availability of data to AI and ML is the biggest gap to be filled by your data warehouse strategy.


How do we ensure that our data warehouse is ready for AI?


Data Readiness for AI is crucial for any AI Implementation Project.


These are few checklists which will ensure that your data is compatible to the sophisticated systems and companies can follow to prepare their data for AI implementations.


Target:

You need to quantify what is the amount of data being used, from where it is being used and for what purpose are you building this dataset. Although it is quiet evident that AI uses predetermined datasets, this will absolutely reduce the size of the data that you may have to go thru.


Data Management:

You need to ensure that data categorized, labelled and kept in a proper format and each of this dataset should be available on demand.

For few companies, where the data is still not organized properly, it is going to be difficult to categorize and remove all the data redundancies.




Correctness:

Few companies where bots key in their data on several websites which may skew your website traffic. And it is highly common that you find error in manual entry job done by human. Ensure that the data is entered in correct format and it is accurate. Mainly in CRMs and Master Data Management should undergo regular audit checks.


Architecture:

It is crucial to have an open architecture system, where you have proper data management channel, data repository, data transform and all this can key in data to your AI and ML algorithms. This will ensure that there are minimal changes to your systems for it to be accessible to the sophisticated systems.


Conclusion:

Even after following all the steps provided below there is always room for improvement. Reason being the multitude of the giant AI machine. You need to be in a constant learning loop with respect to data to be churned when working with AI.

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