Tips for Developing a Data-Driven Team Mindset

Ten Steps to Creating a Data-Driven Culture - Harvard Business Review 1. Start the data-driven culture from the top (C-suite) 2. Choose metrics with care (business outcome -focused) 3. Don’t pigeonhole your data scientists (embed across Firm) 4. Fix basic data-access issues quickly (bad data in, bad data out) 5. Quantify uncertainty (be explicit and quantitative about levels of uncertainty) 6. Make proofs of concept robust and straightforward, not fancy and brittle 7. Offer specialized training at the appropriate time 8. Use analytics to help employees as well as customers 9. Trade flexibility for consistency willingly - at least in the short term 10. Explain analytical choices as a matter of course "For many companies, a strong, data-driven culture remains elusive, and data are rarely the universal basis for decision making. Why is it so hard? The biggest obstacles to creating data-based businesses aren’t technical; they’re cultural. Companies - and the divisions and individuals that comprise them - often fall back on habit, because alternatives look too risky. Data can provide a form of evidence to back up hypotheses, giving managers the confidence to jump into new areas and processes without taking a leap in the dark. But simply aspiring to be data-driven is not enough. To be driven by data, companies need to develop cultures in which this mindset can flourish. Leaders can promote this shift through example, by practicing new habits and creating expectations for what it really means to root decisions in data." - David Waller #DataDriven #Culture #Transformation Olaf J Groth, PhD University of California, Berkeley, Haas School of Business Berkeley Chief Technology Officer Program https://lnkd.in/eNurB-kb

Here's the process we use to keep our entire 600+ person team up-to-speed on analytics. Data is the nucleus for all of our media services. It's critical that ALL employees understand metrics, data tools, and how data impacts our clients' business. At Power Digital, we have hundreds of employees. ...engineers, client services, media buyers, executives, finance, etc. They ALL need to speak the language of CAC and LTV. Employees must be confident when talking to clients about complex topics like incrementality and media mix modeling. Here's what we do: 1.) Mass Training & Certification: When we roll out a new data product (like the NOVA intelligence suite launched last month) we require EVERY employee be trained and certified in the tool. Certification can include: - Modularized training (prepared by our data and engineering teams) - Final quiz - 2-minute self-recorded session talking through the tool/concept Now the "fun part" is that a specific set of ~30-50 members of the sr. team (account directors, executives) need to do an additional LIVE role-play and certification. Each member must role-play as if they're presenting to a client. Then, a leader from our data team will judge and coach (and hopefull "pass") them on the delivery. 2.) Centralized FAQs, SOPs, and Documentation We use a mix of Intercom, Google Drive, and Loom for this. - FAQs on all tools and common analysis questions - Shared library of SOPs (templates, walk-thrus, videos) 3.) Data "Office Hours" & Lunch-n-learns We periodically record internal webinars, 30-60 min deep dives on topics like: - GA4 migration - HIPAA Compliance - Customer Data Platform 101 - Channel Measurement: Attribution vs. Contribution - Modern Data Stack - etc. 4.) Company-wide Slack Channels for ad-hoc Q&A We have a "no question is dumb" mentality here. Folks can ask questions and the data/engineering teams can reply in real time. This forum is helpful, as I suspect many others learn on the sidelines by reading these threads. If we see frequently asked questions, we'll create something more formal (see SOP, Webinar, FAQ process above) As with any process, it is constantly evolving. How are you fostering data maturity and education across your team? #dataanalytics #digitalamarketing #marketinganalytics

Have you ever said or done something based on bad data from others? I have, and I don't want to be there again. How do you build a data-driven dream team that makes smarter choices? We've all been there. Someone throws out a statistic from a "highly reliable source" (read: Jenny heard it from Sam...), or a "surefire" conversion tweak boasts impressive results based on... a sample size of 5. As managers, these become the data sources for your decisions, and the resulting bad decisions doesn’t just hurt the team, it can irreparably dent your credibility. Here are 3 ways to improve the quality of data used in decision making. 1. Embrace "Trust But Verify" for Data-Driven Decisions. Think of it like your favorite hiking buddy. You trust them, but you still check your own compass, right? The same goes for data. Trust your team's work, but double-check for accuracy, especially for high-stakes decisions. Ask clarifying questions: Who collected the data? What was the methodology? Are there any limitations?   2. Invest in Data Savvy, Not Just Data. Be clear with your team that they are expected to analyze the data first hand before making recommendations. Invest in training and resources - for example, a data validation checklist - that equip them to analyze data rigorously and extract its true meaning. 3. Recognition: The Fuel for a Data-Driven Culture. When someone embodies these desired behaviors, recognize them publicly. Recognition fosters motivation for others on the team. Building a culture of trust, verification and continuous learning raises the collective decision making of teams. What steps can we take where questioning and verifying data is encouraged, rather than seen as a sign of distrust?