Being data-driven helps businesses to cut costs and produce higher returns on investments, increasing their financial viability in the fight for a piece of the market pie. But *becoming* data-driven is a more labor-intensive process. In the same way that companies must align themselves around business objectives, data professionals must align their data around data models. In other words: if you want to run a successful data-driven operation, you need to model your data first.

Over the last 100 years alone, artificial intelligence has achieved what was once believed to be science fiction: cars that drive themselves, machine learning models that diagnose heart disease better than doctors can, and predictive customer analytics that lead to companies knowing their customers better than their parents do. This machine learning revolution was sparked by a simple question: can a computer learn without explicitly being told how?

Back in the old days, marketing was ridden with a lot of guesswork. Sometimes, unexpected campaigns brought new leads and converted prospects into customers. Other times, the best-designed campaigns flopped, the market remained unmoved and all you could hear after the launch of a campaign was silence. Data-driven marketing rose from the pains of this insecurity and took on the overwhelming growth of data for its support.

Being more productive than your super competitive peer group is hard. Being 10 times more productive might sound like an impossibility, an exaggeration.... or even a myth (unicorn, you say?). A 10x data scientist is literally 10 times more productive than the average data scientist. The skillsets of these data scientists create better career opportunities, higher peer recognition, and more interesting projects to work on.

On one of our webinars in April 2020 we talked about the developer portal and how our developer community are pushing the Keboola Connection platform into places that often surprise our own core team. Our partners often are the creative ones, adding their knowledge and expertise to expand our platform in service of our shared customers and their varying needs. This is a guest post, written by Johnathan Brook, Solutions Architect at 4 Mile Analytics.

Companies use predictive and business analytics to gain an advantage over their competitors and claim a bigger share of the market. But with the accelerated proliferation of data volume, speed and variety, establishing a system to make sense of this data is posing ever-increasing challenges. Several data solutions - from databases to data lakes - have emerged to empower companies of all sizes to take over their data and use it to accelerate growth.

Bonus Material: PostgreSQL vs MySQL complete comparison table ‍ PostgreSQL (or Postgres) and MySQL are both relational database management systems (RDBMS for short). They are complex technological inventions designed to simplify your data operations across a wide variety of business use cases. The “relational” part of the name refers to the way in which they structure data as relations between rows and columns.

While digging through data, Anna spots an interesting trend - some customers buy 3 times more than others. A segment of super-high spenders? This could make it rain for the company! She rushes to her boss, to show them the data, only to hear: “Yeah, I know. We have a bug, which inserts every order three times in the database. Were you not paying attention during our daily meeting?” Aw-kward.

Being data-driven is the holy grail of modern business. It allows you to grow 8x faster than your competition, boosts your company’s net earnings by 30% and will have VCs throwing money at you if your organization relies on AI. So, what strategy does one use to become data-driven? Well, it’s actually quite simple: If you follow this recipe to the T, you can have your data cake and eat it.

Every time you write another piece of code that picks up data from an FTP server, a small piece of you dies. As a developer in the data space, you know what we’re talking about. 80% of your time can be taken by building and improving the environment and tools, maintenance tasks, and pieces of functionality. That's simply too much time dedicated away from tackling more important issues.