# Prophecy: Teamwork's Data Lake

DevFeed: [Prophecy: Teamwork's Data Lake](<https://devfeed.tech/articles/prophecy-teamwork-s-data-lake-35101.md>)

Original publisher: [Read original article](<https://engineroom.teamwork.com/prophecy-teamworks-data-lake-1a8ebb6dd3ae?source=rss----cea4eecd5960---4>)

Author: Joe Minichino

Published: 2020-07-17T11:41:10Z

Content type: opinion

Language: en

Sources: [Teamwork](<https://devfeed.tech/sources/teamwork.md>)

Topics: [data lake](<https://devfeed.tech/topics/data-lake.md>), [data-engineering](<https://devfeed.tech/topics/data-engineering.md>), [data](<https://devfeed.tech/topics/data.md>), [decision-making](<https://devfeed.tech/topics/decision-making.md>), [big-data](<https://devfeed.tech/topics/big-data.md>), [Databases](<https://devfeed.tech/topics/databases.md>), [Google Analytics](<https://devfeed.tech/topics/google-analytics.md>), [stripe](<https://devfeed.tech/topics/stripe.md>)

Tags: [analytics](<https://devfeed.tech/tags/analytics.md>), [aws](<https://devfeed.tech/tags/aws.md>), [big-data](<https://devfeed.tech/tags/big-data.md>), [data](<https://devfeed.tech/tags/data.md>), [data-engineering](<https://devfeed.tech/tags/data-engineering.md>), [data-lake](<https://devfeed.tech/tags/data-lake.md>), [databases](<https://devfeed.tech/tags/databases.md>), [decision-making](<https://devfeed.tech/tags/decision-making.md>), [google-analytics](<https://devfeed.tech/tags/google-analytics.md>), [stripe](<https://devfeed.tech/tags/stripe.md>)

## AI overview

Teamwork describes reevaluating its analytics approach because data was distributed across product databases and third-party services, making it difficult to connect leads, product usage, and revenue. The article argues that a data lake can make analytics and data-informed decision-making more practical, while clarifying that data lakes and big data are not limited to enterprise-scale environments.

## Source excerpt

You need a Data Lake. The Context Teamwork has been around for more than 10 years. Starting out as a project management and work collaboration platform and later expanding into other areas, such as help-desk, chat, document management and CRM software. As the company has grown and evolved, data has grown, changed, expanded, diversified, fragmented, then changed again. Analytics in this landscape are not for the faint of heart. The Problem The issue at the base of the decision to start re-evaluating our analytics approach, at Teamwork, was because we have way too many data sources to make sense of all the data we collect. Much of the data ends up being "dark", underutilized, unleveraged. We have multiple databases shards, for each product, along with platform databases containing global customer information; and then we have 3rd parties such as Stripe, Marketo, Chart Mogul and Google Analytics to name the most important ones. We realized that making a connection between sales leads in Marketo, their usage of our products (GA and our DBs) and their revenue (our DBs, Stripe, Chart Mogul) was impossible. Well, it was possible, but incredibly painful and poorly automated, making it slow and error prone. And by addressing this problem we are getting a large number of benefits back, practically for free. You need Analytics Let's cut to the chase: you need analytics. No matter how big or small your business or enterprise is, you need analytics to take more informed business decisions. I do not doubt there are individuals with great gut-driven decision-making skills, but by and large, it's better if you look at data to make decisions. That's why you need a Data Lake. It could be a really small lake, it could be a pond or a puddle. But you need it for better decision making. Data Lakes, Big Data and other buzzwords Let's clear the air on a couple of misconceptions. The terms "Big Data" and "Data Lake" absolutely scream of corporate, of enterprise, of governance, compliance, r