When I first started my career in analytics, Data Analyst was declared the “Sexiest Job of the 21st Century.” Buzz around “big data” was on the rise as the most successful companies were increasing their investments in data and striving to foster data-driven cultures. This led to a high demand for data analysts and scientists who were needed to sift through that data in search of value. However, due to the lack of widespread data accessibility and transparency within companies, too often analyst work becomes frustrating and monotonous, consisting of one-off requests for stakeholders, data-validation, running similar queries over and over, and having co-workers compete for resources and prioritization. These inefficiencies keep companies from getting the most out of their analysts and make achieving the benefits of a data-driven culture more difficult.
Here at Disqus, we’ve been in a progressive evolution around how we leverage data to optimize our business. We’ve built our business and strategy around understanding the data signals that the market relays to us on a regular basis and being able to act quickly to take advantage of opportunities.
Disqus adopted Looker in 2015 as our primary BI tool. It immediately became an instrumental tool that allowed us to operate our business more efficiently. Our colleagues were no longer at the mercy of analysts to pull data and or perform simple analyses. Operational burdens for the analytics team were greatly reduced and productivity increased overall. Teams were making more informed decisions and we had widespread alignment across the company.
An internal survey we conducted earlier this year found that 94% of our employees utilize some form of data or analytics resource to do their job, which is truly representative of a 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 our leadership team, and a devoted data and analytics team, we were able to influence our co-workers into caring about data as much as we do.
Here are some best practices that we learned along the way:
Be transparent about KPIs and progress towards goals
Make sure everyone in the team understands overall company KPIs and why they matter. At Disqus, we talk about KPIs openly and frequently. Something that we put into practice years ago is a weekly KPI meeting run by the analytics team. While the meeting started as an executive summary for the leadership team, it is now open to the entire company. Analysts disseminate metrics, pacing, progress towards goals--if and why changes occurred, and any follow-ups or action items needed. Other attendees are encouraged to be curious about data and ask questions, add context, or call-out specific projects that they think may have shifted KPIs (bug fixes, feature releases, new partnerships, etc.). This promotes team accountability and allows individuals to deduce how their individual contributions have played a role in moving company metrics. In return, the analytics team gets feedback and insight into which metrics are critical and which ones need iteration. Key product-driven insights are provided that drive engagement around KPIs.
“Branding” key metrics for the entire company
There was a time at Disqus asking two different people to define a metric would likely produce two different answers. An inventory metric for the Sales team might be different than the inventory metric for Marketing and an Engineer might not even know to what an inventory metric is. As a way to alleviate confusion and prevent poor assumptions, we cataloged all key terms, measures, and metrics in the data dictionary. Every entry in the data dictionary has four key elements:
- An internal definition: what this measures and why it is important for tracking success
- An external definition: how this metric might be interpreted by 3rd parties
- A technical definition: how the metric is calculated and where it lives in our database
- A practical example, usually a link to a Looker report that the company is already familiar with via the KPI meetings
Metrics are often driven by context. The purpose of the data dictionary is not necessarily to be dogmatic. We know that metrics and definitions can change over time as a business does. The purpose is to remove ambiguity from metrics and rectify different interpretations. It is a great resource for all employees so they can understand differences between metrics. Additionally, it serves as a great learning device, especially for those who might be less quantitative or technically savvy. Your team won’t care about data unless they understand how to read and interpret it. The data dictionary is a tool they need to do just that.
Make metrics easily accessible
Not everyone at Disqus necessarily needs to look at data every day to do their job, but they should. Find a way to make KPIs a part of everyone’s daily routine--like checking the weather.
For the most important topline metrics, we utilize Looker’s scheduled email reporting to send updates to the entire team first thing every morning. For more detail on topline metrics or other project-specific metrics, we create easy-to-remember URLs that redirect to important Looker Dashboards, so that anyone in the company can track progress of any project or product at any time. We have also released an FAQ that contains links to important looks and dashboards based on high frequency requests for data. Part of generating a data-driving culture is creating a team that wants to seek out data. Make it easy for them to find.
Define success metrics for all new projects
Anytime a new project kicks-off, we identify one or more metrics to track success. We also set expectations of how success in this project will move company KPIs. Prior to kick-off, the project team takes inventory of everything that is measurable and then distinguishes actionable metrics from potentially distracting ones--a distracting metric being something that is measurable, might be interesting, but not necessarily actionable. Opening these discussions from the get go and defining project goals set us up for later success. When we go into the project with a goal in mind and measure against that goal throughout, we are able to make deliberate decisions and adapt quickly. Whether the metric results in expected outcomes or not, we have a clear signal of what actions should be taken. Do we stay the course of change strategies? Do we need to allocate more resources? Maybe it’s not clear on the surface so we commit to digging into data more. Whatever it may be, it forces a decision to be made.
Teach them to fish.
In reality, most of our team at Disqus does not necessarily require an analyst to do most ad-hoc requests. They’re perfectly capable of looking at the data and drawing their own conclusions as long as they can trust they’re looking at the right data. Enter Looker. It allowed us analysts to set up a playground where end-users have a self-servable way to load and explore data. And while we might expect “if you build it, they will come” to apply here, it certainly does not happen overnight. Honestly, in the beginning, our team was conditioned to rely on analysts for data pulls and analysis. Even though this really powerful analytics tool was available to them, end-users weren’t quick to jump into the deep end. So, we committed to providing them with the tools and training to help them understand how to work with data and bolster their confidence to perform their own analysis. When someone requests a data pull, sit down with them and walk them through building a report from scratch. And don’t just have them look over your shoulder while you do it--seriously, make them do it. With time, they will become more proficient, gain more confidence, ask more questions, and most importantly, have the ability to find the answers themselves.
So, now that we have a company of data-literate individuals, did we just work ourselves out of a job? Actually, analysts and the data team are needed more than ever. Since so many of us at Disqus rely on it on a daily basis, we have devoted more resources to improving data infrastructure and performance. Now that we’re all experts on utilizing data, we’re able to push forward on more interesting and innovative projects that that drive further growth.