IT Science Case Study: How to Optimize the Data Scientist Function

Disqus adopted Looker in 2015 as its primary BI tool. It immediately became an instrumental tool that allowed the company to operate the business more efficiently.

Here is the latest article in an eWEEK feature series called IT Science, in which we look at what actually happens at the intersection of new-gen IT and legacy systems.

Unless it’s brand new and right off various assembly lines, servers, storage and networking inside every IT system can be considered “legacy.” This is because the iteration of both hardware and software products is speeding up all the time. It’s not unusual for an app-maker, for example, to update and/or patch for security purposes an application a few times a month, or even a week. Some apps are updated daily! Hardware moves a little slower, but manufacturing cycles are also speeding up.

These articles describe new-gen industry solutions. The idea is to look at real-world examples of how new-gen IT products and services are making a difference in production each day. Most of them are success stories, but there will also be others about projects that blew up. We’ll have IT integrators, system consultants, analysts and other experts helping us with these as needed.

Today’s Topic: How to Use BI to Optimize the Data Scientist Function

Name the problem to be solved: After the data analyst was declared by D.J. Patil of LinkedIn and Jeff Hammerbacher of Facebook in 2008 to be the “sexiest job of the 21st century,” most successful companies increased their investments in data and aimed to foster data-driven cultures. This lead to a high demand for data analysts and scientists who needed to sift through data in search of value.

Due to the lack of widespread data accessibility and transparency within companies, too often analyst work became frustrating and monotonous, consisting of one-off requests, menial data-validation tasks and requests to run similar queries over and over. The inefficiencies meant co-workers competed for resources and prioritization, kept companies from getting the most out of their analysts and made achieving the benefits of a data-driven culture more difficult.

Describe the strategy that went into finding the solution: Disqus has been in a progressive evolution determining how to use data to optimize its business. The company built a business and strategy around understanding data signals that the market relays on a regular basis and acted quickly to make sure everyone in the company was able to access easily digestible information on the progress of any project or product. Disqus conducted an internal survey in 2017 that found that almost all of its employees utilize some form of data or analytics resource to do their job, which is truly representative of their data-driven culture.

Obviously, this didn’t happen overnight. The change was gradual, sometimes painfully so, and not met without challenges. However, with persistence, the support of Disqus’ leadership team, and a devoted data and analytics team, the company was able to influence all Disqus employees into embracing the data culture.

List the key components in the solution: Disqus utilized data analytics services provider Looker’s scheduled email reporting to send updates to the entire team first thing every morning about the most important topline metrics.

For more detail on topline metrics or other project-specific metrics, Disqus created easy-to-remember URLs that redirect to important Looker Dashboards, so that anyone in the company could track progress of any project or product at any time.

Describe how the deployment went, how long it took, and if it came off as planned: Disqus adopted Looker in 2015 as its primary BI tool. It immediately became an instrumental tool that allowed the company to operate the business more efficiently. Colleagues were no longer at the mercy of analysts to pull data or perform simple analyses. Operational burdens for the analytics team were greatly reduced and productivity was increased overall. Teams are now making more informed decisions and the company has widespread alignment across the company.

Describe the result, new efficiencies gained, and what was learned from the project: Now that Disqus has a company of data-literate individuals, the question is did the company work its data analysts out of their jobs? Analysts and the data team are actually needed more than ever. Since so many Disqus employees rely on data analysis on a daily basis, the team devoted more resources to improving data infrastructure and performance. Now that Disqus employees are all experts on utilizing data, they are able to push forward on more interesting and innovative projects that drive further company growth.

Describe ROI, carbon footprint savings, and staff time savings, if any: 94 percent of employees are now able to access data on their own through Looker without help from an analyst. Adoption and internal training of Looker lead to 10-20 hours per week of time saving for analysts, opening up more time for the data and analytics team to focus on opportunities for growth and improved infrastructure.

If you have a suggestion for an IT Science case study, email [email protected]

Chris Preimesberger

Chris J. Preimesberger

Chris J. Preimesberger is Editor-in-Chief of eWEEK and responsible for all the publication's coverage. In his 15 years and more than 4,000 articles at eWEEK, he has distinguished himself in reporting...