The 2020 Data Landscape Chart Is Out! How Should You Feel About It?
For me, it’s a mix of excitement and a degree of overwhelmed.
In what’s become an annual tradition, Matt Turck — VC at First Mark Capital and host of the Data Driven NYC meetup — released the 2020 version of his yearly chart of the Data+AI Landscape.
The first version of this visualization appeared in back in the year 2012. And although the concept of placing a gaggle of logos on a slide remains the same; the explosion of categories, companies, and technologies is stark reminder of how far we’ve come.
As Matt explains in his article announcing the chart (which also contains a link to a full-size version of the image), data in 2020 has seen a number of new trends. Check out his article for a more detailed discussion, I’ll only preview that he mentions:
In the realm of data infrastructure: the modern data stack going mainstream, the ETL paradigm becoming ELT, automated data engineering, the rise of the data analyst, merging of data lakes and data warehouses, and the complexity in analytic architectures.
In the realm of analytics and ML/AI: the rise of ML platforms, AI being embedded into more products, the field of decision science, and advancements in NLP.
In terms of the visualization itself there was the addition of a new category, Data Governance, populated by companies like Soda Data, Monte Carlo, and Talend.
There’s only so many times I can bug my teammates about whether a certain dataset is accurate or actually used, before the value of “metadata about your data” tools these companies are building becomes clear. In a couples year’s time, people joining the field might wonder how we ever worked in data without having usage statistics about warehouse tables at our fingertips.
Anyways back to the question posed in the title, “How should we feel about the data landscape in 2020?” My feelings are a combination of excitement and overwhelmed.
Why excited?
Businesses remain enthused to leverage data, and increasingly view it as a table-stakes capability. More data roles are moving beyond simple metric accounting, and are involved in strategic and product decision making.
As a discipline, we are aware of the problems we face and are capable of building specialized tools to solve them. The influx of capital to incentivize those building data-tooling products certainly doesn’t hurt.
Overall the value provided and output produced by someone knowledgeable in the modern data stack is generally increasing, and many predict this trend is likely to continue, if not accelerate.
Why overwhelmed?
Hellooo! Do you see how many logos are on the chart? How am I supposed to keep up with all of that, maintain a social life, find a mate, and live a happy life?
Kidding aside, the obvious proliferation does prevent a challenge to anyone who dares to call him or herself an expert in the field. Subconsciously I’d guess most ambitious people feel their only two options are to 1) commit to a certain stack of technologies and risk becoming obsolete or 2) go down the rabbit hole of staying on top of all the trends and throwing their personal life to the wind.
My prescription to this concern is to not get caught up in the specific names on the chart. But rather develop an understanding of the deeper concepts they encapsulate. Understanding how a tool works under the hood is the most direct way to do this; most products are simpler than they seem at first glance.
Of course, there’s also no substitute to actually trying a tool out for yourself. Being able to quickly, yet meaningfully, complete a proof of concept (“POC”) with a technology is critical to knowing what something is all about.
How do you feel about the data ecosystem in the present? Share your thoughts below 😄
