Data-driven Decisions Powered by Gen AI

Are you missing the Generative AI train?

?

Have you already tried a few things and been super impressed with the possibilities or been disappointed and thought: What’s all the hype about anyway?

?

Maybe there is an explanation for each of these outcomes.

?

Gen AI can’t help with structured data analysis much more than a spreadsheet can (at least not yet).

?

If you think, given the effort needed, getting insights from structured data is hard the analysis of unstructured data is a lot more complex as it comprises text, images, videos, and social media feeds and there has been no technology that could do this so far.

??

This is where Gen AI comes to the rescue. The beauty of Gen AI is that it can help you make sense of unstructured data using natural language processing (NLP), computer vision, and sentiment analysis. This was the hardest part of data analysis until Gen AI started providing insights from unstructured data. The benefit of getting insights from unstructured data is that you can now tie insights from structured data to insights from unstructured data and make much better data-driven decisions.

?

For example, you could use raw customer engagement data in its unstructured format and have Gen AI do data analysis, identify patterns, summarize feedback, identify requests for new features, and issues with your current product and services. Then you can tie this with your structured data to get a holistic picture of what your customers are looking for and your opportunities.

?

That is all well and good but solid, relevant, reliable data… structured and unstructured… needs to be at the core of every decision-making process. If you are not there yet, you can get there with a good data strategy that is practical and meets your immediate and long-term needs.

?

Certainly, some organizations are spending the time and effort getting their data to a good place, however are still not making decisions based on that good data. This could be because of a need for cultural change towards data-driven decisions or the need to build trust in the data.

?

Then other organizations are just storing data and not putting it to any good use.? Stored and unused data is worse than destroyed data as you still need to figure out security measures around access and storage of it.

?

Getting data to provide meaningful insights takes a village (or two). This village should consist of your data stewards, custodians, governance processes, quality, measurement techniques, data owners, data architects, IT teams, and tying outcomes to insights.

?

There is no “easy button” to resolve your data issues if you have not invested in the process and culture. Investing in a data strategy is the trusted way to get your data to a good spot so you can do serious descriptive, predictive, and prescriptive analytics.

?

Imagine being able to hold your Business, Product and IT leaders accountable for clearer outcomes and KPIs using Gen AI strategically!