Implementing AI? Make sure you start with your data

We recently attended an interesting session at BrandWeek by Mackenzie Knapp of Cart.com entitled Leveraging AI to Create Wildly Profitable Businesses. But it begs the question, how do you start an AI journey in your business?

For the past decade, AI and ML have been at the forefront of new developments in various fields; from manufacturing, to financial and cyber security, to marketing, and much more besides.

AI enables business teams to get insights from “big data” that we never have before – the sheer computational power means we can solve big problems, with prescriptive answers, in many different areas of business.

That being said, AI and ML can seem so abstract to those in corporate leadership, who may not have prior experience in data science. So it can be challenging to think about the benefits AI can offer, and how to start implementing it within business practices.

In almost any vertical, AI can improve business decision-making, marketing efforts, data efficiency, and employee productivity. But how do you begin? You start with your data.

Starting an AI journey with a data-first approach

Without data, ML is meaningless – rich data is the very thing it uses in order to generate patterns, and turn into useful insights for your business. So you need to go back to basics and look at what data you’re collecting and how you collect it, as well as where you store and analyze it.

To start with, you need enough tangible data from disparate sources to make it worthwhile to analyze. It’s no use analyzing petabytes of data from the same source that tells you all the same information.

The good news is that most businesses will have data coming in from more than one source without having even thought about it – it naturally happens as the business grows and you add new tools to your tech stack. The not-so-good news is that it’s very difficult, if not impossible, to gain meaningful insights from data silos, even with AI.

So then, of course, you have several sources of data you need to unify; which means aggregating, normalizing, and labeling before being able to make any sense of it all.

However, aggregating and normalizing data from disparate sources manually, without a solid data automation system in place to do the heavy lifting quickly becomes unsustainable. Even if you hired an army of data scientists it would still be a huge challenge – so it’s therefore impossible to derive meaningful insights without first automating the process.

Data automation for effective AI

The basic premise of making your data useful through automation is having a software tool, such as Switchboard, that helps you aggregate the disparate data sources for you, then normalizes it, before unifying it in a data warehouse storage solution – ready for both AI and human analysis.

This process is referred to as ETL – Extract, Transform, and Load.

Extract refers to pulling the raw data you collect from multiple sources into an interim storage solution. It’s important to set this first stage up correctly as mistakes can impact the rest of the data pipeline – consider time zones, backfilling missing data, handling various APIs, and data security.

Transform refers to the process where the raw data is converted into foundational data within the interim storage. Here you build rules (we call them data recipes) and apply them to the raw data ready for its intended purpose. This involves cleaning, standardizing, verifying, formatting, sorting, labeling, and protecting the data.

The final phase, Load, is when your transformed data is transferred to its final destination – a data warehouse or data lake where you can use it to derive insights and build AI models.

While it’s possible to build a custom-built software solution to help you complete this process, using an existing enterprise solution can help your business make use of your data faster, enabling you to tap into a customer success team for additional domain expertise when you need it.

Switchboard is a data automation platform that helps you complete the whole ETL process fast and reliably. It can give you access to real-time data for accurate forecasts, with data in the right format in a no-code environment. What’s more, it scales with your data (because as your business grows, so does your data and the need to organize it).

Using a data-first approach is the easiest, most efficient way to begin an AI journey within your business – from there you can build models for whatever your business needs.