Data as a Business Model
Back when I was just a little startup tyke, data was something companies collected but didn’t use. It just sat around gathering digital dust in spreadsheets and databases. But over the years, things have changed faster than my baby pictures faded.
Now, data is one of the most valuable assets a business can have.
Some companies build their whole money-making strategy around data – they turn boring old numbers and statistics into cold, hard cash.
What is Data, Anyway?
Before we dive in, let’s step back for a second and knead out what exactly data is.
Data is just a fancy term for facts and statistics. Every time you search Google, buy something online, play a game, click a link, or even just turn on your phone, you’re creating bits of data.
Data can be numbers like how many minutes you were on Instagram today or words like the lyrics to your favorite Broadway musical.
Companies collect this data to help them make better-pickled products and marketing decisions.
How Companies Turn Data into Dough
Now let’s get back to how savvy businesses bake up profits from bland data. There are a few common money-making ingredients:
1. Selling Data
Some companies make banks simply by selling the data they’ve collected. For example, information about what soccer ball you just searched for on Amazon can be mighty valuable to soccer companies salivating for sales.
Online quiz results, browsing history, purchase patterns – it’s all dough in the data bank!
2. Targeted Advertising
From boring data comes dynamic advertising.
If Company X knows you recently searched for soccer shin guards, they can serve up ads for the best shin protectors on the block. By showing you stuff you actually want, they sell more and boast their bottom line. Sweet!
3. Improving Products
Data can also help companies whip up products users truly love and crave.
If a video app notices 80% of teenagers fast forward through the intros, they can nix long-winded openings. Short and sweet video snacks mean more happy watchers, more engagement, and ultimately more money-making opportunities. Cha-Ching!
Turning On the Data Tap
By now you see data can flow like a money river for savvy businesses.
But first, they need users to turn on the data taps. Most customers happily share data because they get something yummy in return like free search engines, addictive social apps, or online stores that instantly gratify their shopping whims. It’s a fair sugar-for-data trade.
Data Responsibility
With great data comes great responsibility. Like Spiderman’s Uncle Ben once said, “With great power comes great responsibility.”
Companies need to handle user data like eggs – gently and ethically. They must be transparent in how they collect and use data. And keep it safe from break-ins and yucky misuse. Responsible data handling maintains trust and ensures the data dollars keep pouring.
The Future of Data
We’ve come a long way from dusty spreadsheets! Data is now a crucial ingredient for baking up solid business models and scrumptious profits.
Moving forward, expect companies to value high-quality user data like gold. Equally, consumers will demand greater transparency and care.
Ultimately, responsible data strategies that respect people while also making companies money will rule the future.
Data as a Service (DaaS)
There’s a hot new data trend in town – it’s called DaaS (Data as a Service). DaaS providers offer on-demand access to data sets over the internet, instead of selling actual data ownership. Well-known DaaS companies include Xignite for financial data, D&B Hoovers for business data, and Snowflake’s cloud data marketplace.
The DaaS business model is all about convenience. Providers source valuable data from places like government agencies, research firms, and private companies. They then package this data into platforms and APIs that customers can easily access on-demand. DaaS offers datasets ranging from structured items like financials or demographics to unstructured feeds like social media and sensor data.
These data middlemen generate revenue through subscription fees or pay-per-use charges. Some also provide value-adds like analysis, integration, and visualization to help make sense of all those raw numbers.
The DaaS model pays off through its scalability. Providers can easily spin up more data and bandwidth as customer needs grow, with lower overheads than traditional data sellers. It also saves customers money compared to buying data outright. And most importantly, it allows easy experimentation with data-driven ideas that would be too costly or complex to build in-house.
Examples of Companies
Here are some examples of companies that have successfully built business models around data:
Facebook has leveraged the data it collects on users’ interests, relationships, and online activity to offer highly targeted advertising.
By precisely targeting ads based on data like users’ hobbies, relationship status, and browsing history,
Facebook made over $85 billion in advertising revenue in 2020 alone.
As the dominant search engine, Google has unprecedented access to data about what people are interested in and searching for.
Google offers advertisers immense value by allowing extremely targeted ads alongside relevant search results. If you search for a new tennis racket, you may see ads for sports equipment companies on the side of your screen.
Amazon
Amazon’s core retail business runs on a constant flow of customer data – what people search for, click, purchase, view, and review. This allows Amazon to tune its product recommendations and prominently display products it predicts an individual customer will want to buy.
Data also informs Amazon’s inventory levels, logistics infrastructure, and sensor-optimized warehouses.
Netflix
Netflix leverages viewership data like watch history and ratings to serve up ultra-personalized content recommendations for each subscriber. This data-driven approach keeps users engaged and content turnover high.
Netflix also applies data analysis to determine what new shows and movies to invest millions of dollars into producing.
23andMe
23andMe offers personal DNA analysis. Not only can individuals purchase kits to learn about their genetic ancestry and traits, but 23andMe also uses the aggregated data for research.
Pharmaceutical firms are willing to pay for access to 23andMe’s massive unique genetic dataset.
The opportunities are endless for creative data monetization. With smart data strategies, companies both big and small can turn simple numbers into dollars and cents.
So in conclusion, by churning data into decisions, dollar bills, and delight, companies can rise like perfect souffles – light as air yet rich with value. So data, which starts dry as a bone, can end up being the juicy center of a new business model.
