Overlooking Data Priorities over Artificial Intelligence Initiatives

Data is one of the most important assets that companies can utilize for their processes. The type of data includes a broad category of files that comprises raw records of sales, customer feedback, and statistical information among many others. Comapnies dedicate a lot of effort to define how to collect enormous amounts of data from current systems in-house and from external sources.

Companies have dedicated many efforts in different fields to secure their assets and improve their perfomance. Investments in operations, security, ad hoc, marketing etc., but probably one of the most important ones is automation. Here is where Artificial Intelligence (AI) has become very important nowadays. 

AI can be the central pillar to elevate the way we used to execute processes getting them from manual tasks to automated self-learned activities. Because of its nature, it can perform frequent, high-volume tasks without breaks or fatigue. And has the potential to extrapolate any existing business rule and findings to create new conjectures that will provide insights to the companies based on the data they are collecting. 

There is no doubt that compenies are focusing and investing laboriously in AI. Nevertheless, it is important to highlight that these technologies are only good as long as the data they are trained on. 

Currently, there is a paradox of companies needing help to become data-driven despite the enormous investment in Big Data and AI. 

Most of the problems are related to a lack of a systematic approach to understanding what data to collect and how to collect it. For example, a beverage company might assume they have sufficient data to understand their customers like sales numbers, sentiment data from social netweoks, and a survey made years ago. However, to understand correctly their customer, they might have to execute advanced techniques to understand what they perceive as a high-quality product that could create customer loyalty. 

This is one of the reasons why companies must continue to invest in input-collecting efforts and work with specialists in this field. Our members at Kansei Analytics, focus on bringing advanced techniques that go beyond traditional methods to make you achieve your business goal. 

Are you interested in knowing how you could use Kansei in your business, send us your inquiry here.

Sources:

Rosen, H. (2022). Why Good Data is Critical To Making Informed Business Decisions, Forbes. 

Businessolution (2022). AI in Busness Statistics 2023 [Adoption, Use Cases, Market Size], Businessolution.

McKendrick, J. (2021). The Data Paradox: Artificial Intelligence Needs Data; Data Needs AI, Forbes.