Data Practices:

2.1 How to Build a Data-Driven Culture

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History

Values and Principles for Data Practices

What set of values and principles describes the most effective, ethical, and modern approach to data teamwork?

4 core values

12 principles

39 authors

1,600+ signatories

datapractices.org/manifesto

Four core values

datapractices.org/manifesto

Inclusion

Maximize diversity, connectivity, and accessibility, amoung data sources, colaboration, and outputs.

Experimentation

Ephasise continuously iterative testing and data analysis.

Accountability

Behave ethically and transparently, fix mistakes quickly, and hold ourselves and others accountable.

Impact

Prioritize projects with well-defined goals, and design them to achieve measurable, substantive outcomes.

Supported by leaders of the data community

39 authors, including:

  • Eric Colson, Chief Algorithms Officer, StitchFix
  • Amy Gershkoff, former Chief Data Scientist, Ancestry.com
  • Fernando Perez, creator of iPython, Assistant Professor, Statistics, UC Berkeley
  • Andrew Therriault, Chief Data Officer, City of Boston
  • Therese Couture, Human Trafficking Data Analyst, Polaris
  • Wes McKinney, BDFL, Pandas

1,600+ signatories, including:

  • DJ Patil, former Chief Data Scientist of the United States
  • Monica Rogati, former VP of Data Science, Jawbone
  • Kirk Borne, Principal Data Scientist, Booz Allen Hamilton
  • Tricia Wang, Fellow, Harvard Berkman Center
  • Jonathan Albright, Research Director, Tow Center for Digital Journalism
  • Gregory Piatetsky, founder, KDnuggets.com

Getting Started

Topics Covered

  1. Framing the Problem
  2. Pillars of a Data-Driven Company
  3. Data-Driven Leadership
  4. Decision Making
  5. Treat Data Like an Asset!
  6. Data Governance isn't a Dirty Word
  7. Break Down Silos
  8. Ask Questions
  9. The "Culture" of Your Data-Driven Organization

Exercise 0: Outline your challenges

As it relates to data, write down what your primary organizational challenges are. Consider:

  • Infrastructure

  • Tooling

  • Culture

  • Creation

  • Curation



5 minute exercise (solo)

Framing the Problem

Definition: Data-Driven

Making Data and Analysis a central part of business decisions, systems, processes, and overall culture.

Ultimately being “data-driven” is about people and not just the technology.

Play Bigger

The book “Play Bigger” was targeted primarily at businesses and how they can build a category. Many of these lessons can be applied to the challenge of building good data practice within an organization.

  • What do you need to solve?
  • Key component to the problem?
  • Define the "villain"

Winning With Data

The book “Winning with Data explores the cultural changes big data brings to business, and shows you how to adapt your organization to leverage data to maximum effect. Authors Tomasz Tunguz and Frank Bien draw on extensive background in big data, business intelligence, and business strategy to provide a blueprint for companies looking to move head-on into the data wave.”

Defining Your Data Problems

Breadlines

Obscurity

Fragmentation

Brawls

data.world POV

Data is dramatically shaping the future of how decisions are made. Sitting at the confluence of governments, industry, and community, data.world has been watching the ecosystem evolve.

  • Data + Community
  • Data Governance & Tool Fatigue
  • Transformation Through Data
  • Change is People-Powered
  • What's YOUR Version of Open?

Exercise 1: Refine your challenges

Having heard how others frame the problem, refine your original outline. Consider:

  • What problem(s) does your org need to solve? (breadlines, obscurity, etc)

  • What percentage of your workforce uses data on a daily basis to make informed decisions?

  • What things does your organization do well?

  • What tools / processes do you use?

  • What questions do you use data to answer?

  • What questions SHOULD you be using data to answer?



5 minute exercise (solo)

Pillars of a Data-Driven Company

Data Infrastructure

There are many different aspects to data infrastructure, and each company will need to solve their own needs here (there is no silver bullet). The real key here is that it solves for issues like accessibility, interoperability, ease of use, etc.

  • Data Sources
    • Data lakes
    • Data warehouses
    • Databases
    • Applications (streaming or API data)
    • Spreadsheets / flat files
    • Dark data
  • Data access / dictionary / single source or truth

Data Governance

Data governance isn’t a dirty word! (more on that later)

Consider:

  • Integrity
  • Security
  • Availability
  • Usability

Data Literacy

While tools and processes are important, it's the people that make your organization successful.

There are more practitioners than you think. Awaken that hidden data workforce!

  • Examples (Facebook, AirBnB)
  • Speaking the same data language
  • Who? (spoiler: you!)

Data-Driven Leadership

Leading the Change

While it is the practitioners who will typically do the bulk of the transformative work, leadership needs to be both aware of what transformations need to happen as well as lead by example.

  • Be patient, change takes time
  • Data isn’t everything, avoid paralysis
  • Hold employees accountable
  • Create quantifiable goals
  • Avoid "expert syndrome"

Exercise 2: Socialize your challenges

Now that we've thought about organizational structure and leadership, take advantage of a partner who can help act as a sounding board to focus your thinking.

  1. Describe your data process (do you have one?) to your partner end-to-end. Include everything from kickoff to post-mortem.

  2. Highlight what you think you do well, and how many people / business units / disciplines / etc participate in this process. Is it siloed or collaborative?

  3. Describe your current problems or inefficiencies. Do you already have thoughts on how to fix them? Do you still have questions? Are your problems more technical (tools/infrastructure/supply) or cultural (literacy/collaboration/communication)?



30 minute exercise (Pairs)

Data Decision Making

The trick of making meaningful and informed decisions from your data is making sure that you’re asking real questions, and actively pursuing the answers, rather than creating frivolous dashboards that aren’t used for actual decision making.

Practitioners

Responsibilities:

  • Data collection / pipeline
  • Data prep / validation
  • Deep analysis

SME / Non-Technical

Responsibilities:

  • Data catalog
  • Data limitations
  • Familiar analysis

Treat data like an asset

Data isn't just a cost center! (or if it is, you're missing out!)


Data can be:

  • Operationalized asset
  • Revenue Stream
  • Barter

Data Governance isn't a dirty word.

Data governance used to be a luxury (or complication) that only large organizations needed. With increasing focus and importance on data and analytics it’s becoming an organizational necessity for everyone.

  • Building consistency
  • Ensuring data quality
  • Establishing common lexicon
  • Regulatory requirements (ex: GDPR)
  • Democratizing access

Break down silos

Ask Questions

Asking questions of your org/team/colleagues is the best way to ensure constant growth and focus.

  • Do you have a data portal/platform?
  • What percentage of your employees use it each week?
  • What tools and training are available?
  • What documentation do you have in place to support your data?

Culture

Define your version of "open"

Building culture through practice.

Organizational plumbing

Exercise 3: Finalize your plan

Now that you have a good understanding of all the pieces, finalize your problem statement and action plan. Think in broad strokes, this isn't your finished strategy, but the framework to develop one from later.

  1. What tools/infrastructure do you have in place now for data? Consider any/all of the following:

    • Data Catalog / Governance
    • Data Science / Analysis
    • Data Visualization / Dashboarding / Reporting
    • Others
  2. What is your organizational data process currently? Do you have one? What needs to change?

  3. What is the culture around data in your organization like? Would it support change and evolution? Should it?

  4. What two changes would make the most impact? Select one that is short term (ex: tool/process) and one that would be longer term (ex: education).


10 minute exercise (solo)

Exercise 4: Present your plan

Volunteer members of the workshop present their outlines / plans and participate in collaborative Q&A / brainstorming.



Remainder (Group)

Want to run a workshop like this at your company?

[email protected]



Don't forget to sign the values and principles! https://datapractices.org/manifesto