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By providing this context, Alation helps people find the data and the knowledge they need to understand the data. We do this by leveraging all the subtle signals that already exist inside of an enterprise. Similar to the way that LinkedIn and Google contextualized Venky, Alation exists to contextualize data, to make the right data easy to find, easy to understand, and easy to use. Armed with the right context, at the right time, I was able to find a co-founder to start a company. Before I met him, I knew that he could be a co-founder, that meeting him would be fruitful. Then Google allowed me to find the patents and papers that he had written. I could do this because LinkedIn contextualized Venky-where he worked, who he knew, and what he knew. Two and a half years ago, I found one of my co-founders, Venky, on LinkedIn. On the other hand, having the right context can be tremendously powerful. Imagine deciding to build a brand new mobile site because you thought that 42% of the traffic came on mobile, where the real number was 4.2% There is a massive potential cost to errors and we bear these costs every single day. But, imagine running a $2.6m marketing campaign segmented by age, where you have the wrong ages.
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Sometimes you can decode the data by reading stale documentation, finding the right colleague, or just trying to reverse engineer the knowledge based on what you see in the data itself. You don’t have the context to find the information you want or make sense of the information you find. The data is effectively encoded, gated, locked away. The programmer forgot to tell anyone that users that did not enter their date of birth would be assigned a default value of January 1, 1960.Īs a consequence, if you want to consume this data to learn about the world, you have to re-discover all of this knowledge.
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Second, the assumptions used to populate the data are also opaque. The guy who created a column named “DB” knew that it meant date of birth nobody else did. And this complexity, in turn, makes data inaccessible.įirst, the content of these containers is written in a language known only to the programmers or the analysts who created them. All of these containers come with a lot of complexity, a lot of implicit knowledge. Every time we write a mobile app, build a web application, write a data pipeline, build a dashboard, or develop a sensor, we create a new container of data. People tend to think about this data production in terms of size-the number of terabytes or petabytes, but people rarely think about the fact that we’re also producing more containers of data every day. We’re all aware that the world is producing more data every single day. There certainly are many data technologies in the world today-and that’s actually part of the problem. Don’t databases and BI platforms already make data accessible to anyone that can write a query or even open a link to a dashboard? Aren’t there already about 300 companies that store, process, and visualize data? Stated differently, data is largely inaccessible to most people. These truths may well be evident, but they are actually realized by a small number of people who access a relatively small amount of data for a small percentage of the time. Data cuts through fiction, empowering people to work together around the facts. Data allows people to build amazing things. Data helps people learn about the world around them. Data guides people to make better decisions. It’s likely that anyone reading this post believes in the power of data.