Today, companies all over the world are generating unprecedented amounts of data. While data has always developed naturally as a by-product of economic and business activity, nowadays, as more and more of our personal and professional lives take place online, humans are creating an abundance daily. of data. In fact, 90% of all global internet data has been created since 2016.
For more than a decade, only so-called FAANG companies (Facebook, Apple, Amazon, Netflix and Google) were able to take advantage of collecting large amounts of data at scale. For these companies, data is the main product and is inherent to their value proposition. So they invested early in AI teams, servers, network infrastructure, and so on. Such intensive resource allocation was nearly impossible for non-tech companies that had other pressing spending and spending demands.
More recently, cloud computing platforms, improvements in general tooling, and the democratization of machine learning models have brought advanced data capabilities within reach of more businesses. By the end of 2021, more than half of all companies had adopted artificial intelligence in at least one business function, and more than a quarter of all companies report that at least 5% of their EBITDA was attributable to the adoption of AI. Mass-produced machine learning models are ubiquitous.
It is a significant change. With AI tools, non-tech companies can use the data they already have to improve sales, logistics, and operations overall.
Getting the right tools is not enough, however. To achieve long-term benefits and competitive advantage, companies must optimize their data and learn how to use it strategically. Their leaders must prioritize the development of data processes as a central component of the business. To do this, they must take the following steps.
1. Learn about data usage
The first step to understanding how to use your data is understanding what data you already have. Take an inventory of your business processes to determine which ones naturally create data. Ask: What does the company log and record? What is‘t we connect and why not? What information do we discard that we might retain?
Once you’ve taken inventory of your data, learn about data usage by looking at how other companies store and use similar data to improve their business functions.
For example, how do other companies use their quality assurance records? Do they build machine learning algorithms to find out which sales pitches work best and train their reps based on what they find? And what about supply chain and logistics data? Are other companies using this data to create optimization programs that route inventory more efficiently?
For example, we know that other companies have started using historical data on utilities and building maintenance to save on future expenses. Consider what Google achieved when it connected its energy consumption data to DeepMind AI. By taking historical data on temperature, power, pump speed, etc. that had been collected by thousands of sensors and using them to train a cluster of deep neural networks, DeepMind AI developed a series of recommendations that reduced the amount of energy used to cool Google’s data centers by 40% .
Also think critically about the data other companies collect publicly and use that information to better understand the problems they are trying to solve. For example, what image is Google asking you to tag in its CAPTCHA and why? While most recent CAPTCHAs have involved low-light conditions for cars, it’s likely that Google wants this information to address edge cases in its training data for self-driving vehicle models. Observing and reasoning about the data other companies collect will help you better understand which data processes you should keep and invest in.
2. Copy and Paste
After understanding how companies use data, learn how the latest tech startups are leveraging data. These companies can offer a cheat sheet for using data by helping executives understand how the people who work with data as part of their core business are monetizing it.
Consider engaging early-stage startups with proof-of-concept contracts or creating data-sharing agreements with early-stage startups to understand the innovations happening in these companies. Sponsor enterprise hackathons that attract tech talent and help you find data-centric AI solutions for your long-standing business challenges.
Read the news sources startup founders and influential developers read — such as Hacker News and ML Substacks — to learn about the latest products and cutting-edge ideas. After all, 10 years ago Stripe didn’t launch their product at a Fortune 500 conference. They launched it on Hacker News.
Take a look at these apps and see if you can translate them into your business. Don’t ignore disruptive technologies — think about how to use them for your business needs.
3. Buy don’t build
For many problems related to data capture and management, SaaS solutions already exist. Unfortunately, companies often try to solve these problems in-house rather than buying an off-the-shelf solution. Many large companies build data management tools in-house, leading to cumbersome and slow infrastructure that doesn’t scale alongside other technologies. And when new companies attempt to build these tools in-house, they increase their time to market and risk losing their first mover advantage.
Don’t fool yourself into thinking that your use case is so specific that it requires special internal infrastructure. Building data infrastructure tools in-house takes months, is expensive to maintain, and often the results aren’t as good as a product already on the market.
Whenever possible, you should buy, not build, the tools you need to structure and manage data. If the tools aren’t critical to your business, don’t rebuild them in-house. It will slow down your machine learning model development, and it is the product that will save you money and help you stay ahead of the competition.
4. Start building a data moat
Collecting large amounts of data as part of normal business functions can help companies begin to create a structural data moat that can be used for higher value generating activities. Eventually, that gap could get so big it’s too wide for other companies to cross, so data gives you a competitive advantage.
Take the example of Waymo and Tesla, two major players in the autonomous vehicle market.
The former spends a significant amount of resources sifting through and processing thousands of video hours of street driving footage to capture appropriate data to train its models.
The latter – having sold nearly 2 million electric vehicles – can leverage data readily available from the thousands of Tesla owners who use their vehicles’ self-driving software. The company has access to information on accidents, human behavior, etc. Having this real-world data at scale sets Tesla apart from the competition. Additionally, if Tesla decides to abandon its AV aspirations, the company could continue to make money by selling its valuable data inventory to other AV companies.
So don’t throw away your data. Collect it and store it until later when you can use it to achieve your future business goals.
Think of the history of Rockefeller and crude oil by-products. Most refinery owners considered the byproducts of converting crude oil into kerosene as waste and threw them away. Rockefeller, however, saw their value: he collected paraffin wax to sell to candle makers and petroleum jelly to sell to medical supply companies.
Be like Rockefeller. Keep your data so you can monetize it later. Don’t treat it as a useless byproduct just because it’s not your main product right now.
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Gone are the days when AI and machine learning were luxuries only available to big tech companies. But as powerful new tools become more accessible than ever, businesses need to learn how to use them strategically and think about the data that powers them. learn to do this This is where you will really find the competitive advantage of AI.