data leader

Data Leader Digest - 8/19/2022

All the data and analytics insights for data leaders we've scooped up this week.

Are you spending more time cooking your data or tasting it?

Chefs finishing their salads in culinary class in kitchen

According to Stéphane Kirchacker, vice-president EMEA at Sinequa, orgs are spending way moretime preparing data than they are actually analyzing it. Or said another way: “Lots of cooking and little tasting”. Even if you like cooking, you have to admit that savoring it is the best part, right?

Data leaders and the C-Suite should be looking for ways to flip the script. She poses a more optimal breakdown of 80% analysis and 20% preparation. What do you think is the optimal breakdown? What amount of time do you spend on each?

Read the article --> 

Is your company truly data-driven? 

There are a lot of stats out there that suggest a large majority of executives and senior leaders are not seeing the ROI of analytics investments. Here are a few:

39% of executives believe their organizations manage data as an asset.

24% of them would say their companies are data-driven.

And only 13% of them believe their organizations are delivering on their data strategy.

Where does your company stand? The authors suggest taking an “enterprise-wide reality check” of your company’s current capabilities with a small, diverse group of people representing different functions and seniority.

Analytics capabilities chart from HBRAnalytics Capabilities Chart, Harvard Business Review

Read the article --> 

Improving the value of data

“Data in itself has zero value. It's how you use the data that drives value.”

Bill Schmarzo, deemed the "Dean of Big Data", authored a book called The Economics of Data, Analytics, and Digital Transformation. In the book, Schmarzo dives into the lessons he’s learned on improving data value. Some of them include:

  • The value of data comes from sharing it and continuously refining it. (Which makes data siloes very, very bad.)
  • The value of data decays over time when you get new and better data. But the value doesn’t drop to zero, because you need to use past data to make predictions.
  • Build a culture of trust. Empower employees. Decentralize decision-making. Democratize ideation.

Read the article -->

Translating data speak to business speak

Here’s a really in-depth article by Brandon Lisk that explores how to communicate data insights to non-technical teams. We know sending a link to a new dashboard doesn’t do the job, but what are some practical tips?

Brandon suggests bucketing your stakeholders into 3 separate groups. These groupings help you as the data leader think about what’s important/relevant to each of them:

  1. Product-Focused (Think Product, Engineering & Project Managers)
  2. User-Facing: (Customer Success, Marketing, Sales)
  3. Business-Focused: (C-Suite, Executive Team)

Then he gives 3 tips for communicating:

  • KISS: Keep it simple, stupid! Make it easy and quick to understand the key insight with your report.
  • Add a TL;DR: Make it even easier. Highlight and summarize key insights and why it matters at the very top.
  • Avoid Trivia: Focus on what’s actionable. If it’s not actionable, it’s better used as a fun fact for a trivia game this Friday.

Some of these tips may seem overly simplistic and maybe even basic. But it’s definitely easy to forget who your audience is and what they care about, and this is a great piece that re-frames your thought process.

Read the article -->

Data Scientists are not Pizza

Image of man taking pizza from oven

Folks at the Analytics Engineering Podcast interviewed Twitter’s Data Science Manager Katie Bauer to pick her brain on how managers can help data science teams thrive. Katie dives into why data teams are necessary, the difference between insights vs knowledge, how to structure your data team, and why data scientists are not pizza 🍕!!

Listen to the podcast -->

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