The data is that of a company most powerful asset. Yet many companies cannibalize this valuable asset by selling it to third parties when they should be using it to make their business stronger and more sustainable.
Almost all digital businesses collect some type of data from their users, so privacy groups are increasingly concerned about how that data is used. Yet data collection is not inherently bad. It’s the why, how and what to do with it that matters most when it comes to building a profitable and sustainable business that simultaneously respects the privacy of its users.
In the majority of cases, there is no evil man behind the curtain collecting data for evil. Most companies collect as much data as possible on the assumption that you never know when and how the data might be useful at any given time.
Fortunately, that is starting to change, and data scientists in data-driven companies are leading the charge. Collecting data based on a vague hypothetical scenario indicates a lack of intuitive understanding of the types of data that are really important to obtain from users, but smart businesses rightly only ask for the data needed to deliver products and services to people. end users.
Invading user privacy by collecting data just to sell it is a waste of time and unimaginative business intelligence.
Make data work for you with AI and a data fabric
Instead of selling user data to make money, data-driven companies have chosen to analyze that data to understand how to get the most useful insights. Know Your Customer (KYC) initiatives rely on data, using artificial intelligence (AI) to analyze information to uncover preferences that users might not talk about in online reviews.
Companies like Pepsi leads the way in using AI for consumer product development purposes, and digital businesses can and should follow suit. Online platforms that wish to take this route should strengthen their internal capacities by hiring more data scientists and AI experts.
In addition to helping improve the customer experience by enabling better personalization and personalization options, AI can help make the onboarding process smoother and more transparent for products and services.
As data becomes more complex, organizations attempt to use their data treasures more efficiently by implementing a data fabric – an interconnected layer of data and processes that supports composite data and analytics, as well. than their various components.
A data fabric allows organizations to reuse and combine different styles of data science, allowing them to reduce integration design time by up to 30%, deployment by up to 30%, and downtime. load up to 70%. In addition, a data fabric allows companies to use the skills and technologies of existing data hubs, data lakes and data warehouses, as well as introduce new approaches and tools for the future. .