A featured contribution from Leadership Perspectives, a curated forum for enterprise technology leaders, nominated by our subscribers and vetted by the CIOApplications Editorial Board.

TripAdvisor

Three Habits of Highly Effective Analytics

I have always been a fan of Steven Covey’s “Seven Habits of Highly Effective People.” This is an attempt to share just three habits which help tune your analytics efforts further. While I could have discussed data architectures and the merits of cloud-based solutions, in many instances I have found adherence to a few sound principles practiced well will carry your teams further. In this vein, I am sure you can think of more good habits, but these three have been key to our success over time.

Habit 1: Begin with the data. Have a shared vision for the future of your data strategy and reporting, align next steps accordingly.

I am sure you have seen your teams generate lists of reports or analysis needed. These lists then generate more lists of data requirements. Each analyst then spends several weeks creating a new set of tables for each report. This model allows autonomy and speed for the analytics, but eventually, the infrastructure and support become overwhelming as the number of reports grows. In many cases, the data needed for new or updated reports was just a few more added columns to an existing table. Before implementing a shared vision and strategy around data structures, this was the norm. In our case, our tools became over-whelmed and many reports took minutes to run.

While several models for organizing data analytics exist, our focus was to create a centralized data governance strategy to develop and align a data strategy across teams. This central support model defines ownership for each of the data and serves as a “council” to review new data requests, update data definitions and ensure consistency.

Habit 2: Be VERY proactive. Make analytics and measuring success part of the product development lifecycle.

As priorities and focus changes, the ability to leverage these habits should help your analytics move faster through the change curve


Does this sound too familiar? Your new product launches with urgency across the business. Several shortcuts are taken to develop the product quickly. As a result, one of the short-cuts to launching was to only develop a few basis reports used to determine if the product was delivering. Report for management, sales, marketing, and even finance were assumed to fast follow. The first few months were great, but with any new product, gaps started to appear. What should have taken a couple of weeks to identify, analyze and report eventually turned into months. As the teams triage the data issues, the fallout continued as the fast follow reports were also delayed.

The issues are basic in hindsight, the data needed wasn’t available and what data was collected couldn’t be trusted. Your data should be a strategic asset to manage your business and drive insights. Your data quickly loses the strategic nature unless the data matches the current and future state of the business. Had the analytics been proactive in defining the success measures and data needed, several of the post-implementation challenges should have been avoided. In fact, the data could have proactively further defined the gaps with the ability to pinpoint issues as the product is launched.

When analytics teams are proactively engaged through the product development cycle, the data and analysis is part of defining the expected results. This has helped speed up the cycle for report development as feedback based on data is leveraged earlier in the overall process. A key benefit to our analyst team is a deeper understanding of the business and better analysis on impact.

Habit 3: Data has a “Name”. Understanding your data, definitions and systems architecture should be documented and socialized first. The reporting comes second.

How often do you question reports that don’t tie out or find yourself wondering which parts of them can be trusted? This is a common flaw when the data and sources lack governance and are misinterpreted. Data is initially interpreted using one set of criteria and can easily create limitations and gaps when the data is then used for other analysis.

While simple, having good data governance is hard to implement and refine; especially in distributed or matrixed teams. Often to drive a complete analysis, data is combined and repurposed from other uses with little insights into data quality, gaps, or limitations. Our team has to combine and repurpose data that comes from different teams and uses. This has led to re-building our governance model from the top down. We have seen early wins by having the agreement on data definitions and sources. This is a challenge worth taking and winning.

Hopefully, these habits help in your success. I call these habits as they require on-going effort and training to get right. As priorities and focus changes, the ability to leverage these habits should help your analytics move faster through the change curve.

The articles from these contributors are based on their personal expertise and viewpoints, and do not necessarily reflect the opinions of their employers or affiliated organizations.