Data collection isn’t the problem: It’s what companies are doing with it

Data is a company’s most powerful asset. Yet, many businesses cannibalize this valuable asset by selling it to third parties when they should be using it to make their businesses stronger and more sustainable.

Nearly all digital businesses collect some type of data from their users, so there has been growing concern from privacy rights groups about how that data is used. Yet, data collection is not wrong in and of itself. It’s the why, how and what is done 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 nefarious man behind the curtain collecting data for evil. Most companies rake in as much data as they can under the assumption that you never know when and how data might be useful at some point down the line.

Thankfully, this is starting to change, and data scientists at data-driven companies are leading the charge. Collecting data based on a vague hypothetical scenario indicates a lack of intuitive understanding of what kinds of data are actually important to have from users, but smart companies are rightly asking only for the data that is needed to provide products and services to the end users.

Invading user privacy by collecting data just to sell it is an unimaginative waste of time and business intelligence.

Making data work for you through AI and a data fabric

Instead of selling user data to make money, data-driven companies have opted to analyze this data to understand how to gain the most useful insights. Know Your Customer (KYC) initiatives are dependent on data, using artificial intelligence (AI) to analyze the information to uncover preferences that users might not be talking about in online reviews.

Companies like Pepsi are leading the way in using AI for consumer product development purposes, and digital businesses can and should follow suit. Online platforms that want to go this route should beef up their in-house capabilities by hiring more data scientists and AI experts.

In addition to helping improve customer experience by enabling better personalization and customization options, AI can assist in making the onboarding process smoother and seamless for products and services.

As data becomes more complex, companies are trying to make more efficient use of their troves of data by implementing a data fabric — an interconnected layer of data and processes that supports composite data and analytics, as well as their various components.

A data fabric lets companies reuse and combine different styles of data science, enabling them to reduce integration design time by up to 30%, deployment by up to 30% and support by as much as 70%. In addition, a data fabric allows firms to use existing skills and technologies from data hubs, data lakes and data warehouses, as well as introduce new approaches and tools for the future.