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18 Things I Learned from Winning with Data

A few weeks back I read Winning with Data by Tomasz Tunguz. It’s a short, but insightful book that I’d recommend to all entrepreneurs. From creating a data-driven culture to operationalizing your data, there’s useful advice by the truckload. 

Here are 18 of the things I learned:

1. The data that companies store today has exploded.
According to IDC, from 2013 to 2020, the digital universe will grow by a factor of 10, from 4.4 trillion to 44 trillion gigabytes. It more than doubles every 2 years.

Why it Matters:  

With so much data to analyze, cutting through the clutter to unearth real insights is a challenge.

2. Data is cool...and should be immediately accessible.

Why it matters:  

Data has never been cooler with today’s millennial workforce driving that feeling. Since many of them grew up on the internet, they expect answers to be at their fingertips (Siri, Alexa, Google).

3. Starved for insight, employees substitute instinct, gut, back-of-the-envelope calculations, estimates, and other short-circuited research to decide.

Why it matters: 

The HIPPO (Highest Paid Person’s Opinion) is just another opinion if not backed up by data. Data forces decisions to based on merit alone.

4. Company “data brawls” are very common and the source is always the same - there is no single definition of each metric the company uses, and there’s no canonical place to access that data.

Why it matters: 

Without a universal lexicon, confusion is inevitable and conflict unavoidable.

5. A Data Dictionary is a universal set of definitions of your company’s most important metrics.

Why it matters: 

The first step in becoming data driven is defining key metrics, getting agreement across the team on those definitions, and making those definitions readily available in a centralized, living document. This “Rosetta Stone” allows for productive conversations about the data and accelerates decisions.

6. Data must be part of every important discussion and decision.

Why it matters: 

When data is a part of every important discussion and decision, anyone in the company can contribute (and defend) their idea. This type of culture is empowering to employees by giving them the tools these need to research, explore, and argue their ideas.

7. Data analysis is the norm at Facebook. Of the company’s 4,600 employees, at least 1,000 query data daily. Leveraging the infrastructure to help the company make critical business decisions, drive product changes, and push the company forward.

Why it matters: 

Facebook is pretty successful. Perhaps we should look for ways to emulate the culture they’ve built?

8. The Best Companies Run on Data

Why it matters: 

The best performing companies not only use data as a historical tool to better understand what happened in the past and why it happened, but they truly operationalize their data.

9. Operationalizing your data is using data to improve the business’s performance.

Why it matters: 

Operationalizing the data means you’ll consult the data (through reporting or running specific custom queries) before making material changes. It means changing the way a company operates in the afternoon based on the data from the morning.  It’s empowering everyone from front line employees to executives to use data throughout their day to tune their actions.

10. We have observed that the very best companies create repeatable decision-making practices.

Why it matters: 

A repeatable process is important because when backed into a corner a decision maker will irrationally defend his decision (and protect his reputation) even if the decision is clearly the wrong one. Data relieves people of this prideful irrationality.

11. Culture is the single biggest contributor to becoming a data-driven company.

Why it matters: 

When hiring you should look for the following traits in candidates: curiosity, collaboration, and a desire to use data for decisions.

12. One of the most important consequences of becoming a data-driven organization is that anyone in the company can contribute, a trajectory-altering idea and defend it with data.

Why it matters: 

Empowering and addictive, this type of collaboration invites meritocracy.

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13. Parkinson’s Law of Triviality is the reality that companies spend a disproportionate amount of time on trivial issues. Incisive data combats this vortex.

Why it matters: 

Ever sat in a trivial meeting that never ended? Data slashes debate time in meetings by focusing the team on the right questions.

14. The results don’t always need to be incredibly accurate. Oftentimes, directional data provides just as much value as very high resolution data.

Why it matters: 

Many times, a quick approximation provides just the guidance the team needs to decide the relative priority of an effort or new project. Ballparking (estimating the order of magnitude of a result) can save you significant amounts of time and effort to determine if you  should proceed with calculating a more precise answer.

15. Data pushes us to experiment and experimentation is at the core of innovation because it equips every employee to support their argument with data.

Why it matters: 

When data is democratized and employees are encouraged to answer their questions, any employee can birth and back up the next great idea for your company.

16. Data education, data literacy, and data tooling are the 3 key ingredients to evolving a company’s culture to becoming more data-driven.

Why it matters: 

Everyone wants to know how to build a data-driven culture. Well, here’s the blueprint: 1) Develop the right metrics and language 2) Educate the team to analyze data without bias 3) Reward curiosity with the best tools to find the right information 4) Maximize the speed of the company to decide

17. Operationalizing data will be the competitive advantage of the future.

Why it matters: 

Change today is more complex, faster, and harder to predict than ever before. Consequently competition in business has never been more demanding. The only antidote to this increasing volatility is data.

18. Actionability is a key attribute of useful data

Why it matters: 

Tracking metrics for the sake of tracking metrics is useless and time consuming. The data should always inform an action. Ask yourself, “what decisions will doing this analysis inform?”

Want to get more from your data? We can help.

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September 14, 2016
by
Operations

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