The surge in AI interest this year has steered a lot of companies to invest more and more on the technology, onboard vendors to help build predictive analytics, and bring data-driven solutions to problems which are still heavily intuition driven. But the hurdle a lot of these companies have faced is not related to finding the best classification or predictive algorithm or any external dearth of data sources to supplement their internal data but in fact their internal data infrastructure and sources.

Here are the three major challenges hindering AI readiness and how companies can address them:

a) Data aggregation: AI/ML models perform better on aggregated data and build insights out of it. There is no doubt that big companies have tons of historical data to train the algorithms but these companies also have the issue that their databases and data sources are scattered across multiple systems and they need to be aggregated for AI models to make sense of it. Data needs to be cleaned, rid of noise or any anomalies, and be consistent with the need of the models — this will take time and process-driven effort from the companies to bring everything together.

AI, in fact, can also be used to improve data aggregation processes.
It can be used to automatically clean data by finding patterns to detect anomalies in the datasets and bring harmony across the sources

b) Data contextualization — Now bringing everything together does not mean that you dump everything at one place and create disconnected datasets. Datasets need to be more contextualized — sales data can be near finance data and demand data, and logistics data can be near manufacturing data; the more relevant information is fed into the algorithms, the better the relationships between data can be understood. This also helps in educating the end users of AI models to make sense of the results spit out at the end.

Data science teams and business functions must work together from the beginning to build more contextualized data. Understanding of datasets and use of it from a business lens can inform the data scientists better on how to integrate sources, bring contextual data more closer for the AI models to run on.

c) Data standardization — Big companies have a whole lot of data across multiple functions but it also can mean that all the datasets are not standardized, they might follow different schema, some data is manually entered, some are automated through systems, units of measurements, date formats all can be very different across systems. Relationships between those datasets will be complex to create and data retrieval will be time consuming. Hence, having consistent format across systems can increase data quality across organization, and make it simpler for the teams to integrate data from different sources.

Once the organizations reflect internally and take a data-first approach, the ROI through AI can be exponential and aid to predict sales, forecast demand with high accuracy, create targeted customer segments to enhance profits and a lot more…