How Wide Data, Not Big Data, Provides More Prescriptive Recommendations.

By Anand Mahurkar, Founder and CEO of Findability Sciences

For companies to effectively use their data for AI for predictive purposes, a variety of data, and especially wide data, must be used.

In today’s fast-paced digital world, the usage of data is constantly evolving to help companies better understand the insights from massive amounts of both structured and unstructured data repositories. However, while big data can provide business-critical analytics, the data mostly serves to showcase what has happened. Past tense. Increasingly, wide data must be taken into account when performing predictive and prescriptive analytics.

That is where artificial intelligence can help and where the needs of big data for AI diverge. Breaking it down, big data is defined by three factors: volume, velocity, and variety. Volume refers to the size of data available, velocity, on the other hand, refers to the speed at which data arrives and is processed.

However, for companies to effectively use their data for AI for predictive purposes, the need is for a variety of data. With the rise in the adoption of AI across sectors, the ability to access diverse sets of data is paramount and a catalyst for AI algorithms. In other words, keep the data from being too vanilla and spice it up with much more variety.

We call this data with variety “wide data,” which is sourced from an organization’s internal, external, structured, and unstructured data. This is crucial because, in the globalized economy, business performance depends on many parameters.

An example of wide data is to look at two manufacturing plants designing products in different parts of the United States. The geographic location of these two plants will have an impact on production, especially if there are natural occurrences, like snowstorms. Taking into consideration weather and several other disparate external factors, combined with internal data to feed the AI algorithms, will result in more accurate predictions related to inventory, supply chain, and demand for each manufacturing organization. Variety of data offers more correlations and hence better learning for AI algorithms to provide accurate results.

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