avatarBruno Aziza

Summary

The website content provides insights into the evolving landscape of data products, the significance of data leadership, and upcoming events in the data and analytics community.

Abstract

The web content delves into the concept of data products and their impact on the industry, highlighting key lessons in growth akin to "Breaking Bad" but in the data domain. It emphasizes the importance of data heroes like Sanjay Chaudhary, who exemplify commitment to data leadership. The article also previews the 2023 Gartner Data & Analytics Summit, discussing priority topics such as Data Fabric, large-scale data architectures, and data engineering. It offers a comprehensive guide to the summit's sessions, emphasizing the need for financial governance, talent development, and the adoption of machine learning. Additionally, the content covers Marc Andreessen's new Substack, where he discusses the influence of AI on the economy, and shares the do's and don'ts of data leaders from former Jaguar Land Rover's Data Leader Benny Benford. The piece concludes with a discussion on the state of data leadership success, the average tenure of data leaders, and invites readers to engage in upcoming events in Paris and London.

Opinions

  • The author expresses gratitude towards data leaders like Sanjay Chaudhary for their unwavering commitment and guidance in the world of data.
  • There is an emphasis on the importance of understanding and implementing data products as valuable artifacts for data organizations.
  • The author suggests that success with data is contingent upon the ability to bridge gaps across technology, teams, and clouds, highlighting the necessity of operating within an ecosystem.
  • Philip Russom's insights are highly regarded, with the author adding their own top recommendations for the Gartner Data & Analytics Summit sessions.
  • The author conveys excitement about Marc Andreessen's new Substack and his perspective on AI's economic impact, while also noting his unique writing style.
  • Benny Benford's advice on the do's and don'ts for data leaders is well-received, with the author endorsing the shift from Chief Data Officer (CDO) to Chief Data and Analytics Officer (CDAO) to ensure analytics ownership.
  • The author points out the challenges faced by data leaders, including the short average tenure and the misunderstanding of their roles within organizations.
  • There is a call to action for readers to engage with the content and participate in the upcoming data and analytics community events.

What’s a Data Product?!

Lessons in Growth | “Breaking Bad” [Data Edition] | Where To Meet Next (Paris, London and Gartner DA).

More than a customer. More than a friend. A Data Hero!

When I told Sanjay Chaudhary that I was meeting with data customers, he went out of his way to make time and drive up to meet us in person! He shared his experience, unfiltered and provided his guidance. I couldn’t be more thankful to work with such committed data leaders like Sanjay. Thanks for your work. Thanks for what you do for the world of data!

Engage here

1) ON AIR in the Briefing Room!

Congrats Eric Kavanagh for celebrating your 13th year anniversary of the show….and thanks for having me to talk about customer use cases and what’s impacting the world of Data, AI and Analytics!

Catch the recording here and engage here.

We discussed what a Data Product is….Watch & Subscribe to the CarCast to get the break down!

2) What To Expect at the 2023 Gartner Data & Analytics Summit.

A MUST READ by Philip Russom, Ph.D. Full piece in comment below. My highlights of Philip’s piece below + my own TOP recommendations!

  1. The Data Fabric has indeed become a high priority topic for Data Leaders. As I noted in a recent post, data integration, data quality, lineage, metadata discovery management are a top area of spend for organizations in 2023 (ahead of culture!). Per philip , look for Ehtisham Zaidi, Mark Beyer and Robert Thanaraj’s sessions!
  2. Large-Scale Data Architectures and Ecosystems. Eric Kavanagh and I talked about this this week. You success with data is predicated by your ability to bridge gaps across technology, teams and clouds. We live in an ecosystem and limitless scale is the name of the game! Catch Merv Adrian, Donald Feinberg and Ramke Ramakrishnan’s sessions!
  3. Data Engineering. What can I add here?! Joe Reis, who wrote the book can explain it better than I can. This is the leading trend of 2023. Data leaders are building teams who’s job is to create data products as the artifacts of value for the data organizations. To get the download on best practices, catch Sumit Pal, Ehtisham and Robert’s sessions!
  4. Convergence! Lakes, Lakehouses…etc. Donald Feinberg has a few sessions on this topic but my favorite will probably be “Avoid Data Lake Failures”. Love his style and guidance. If you haven’t attended his sessions before, you need to bookmark that one.

Finally, Philip Russom, if you don’t mind, I think there are a few sessions and topics that are worth considering in addition to the ones you highlighted:

  1. Financial Governance and Recession Proofing Your Data Strategy with Adam Ronthal
  2. The CDO roundtables with Carlie Idoine, Saul Judah and Lydia A. Clougherty Jones on talent, culture and inclusion
  3. Create a RACI Matrix with Aura (we met in Sydney and had a great session on Data Strategy!
  4. What You Can’t Ignore in Machine Learning with Svetlana Sicular. I still remember attending her first session on this topic years ago. Always full of stats and customer stories.
  5. 10 great examples of Analytics in Action with Gareth Herschel. If you’re looking for case studies in success, sign up for this one!
  6. Ask the Experts series, particularly the one on Cost Optimization with Allison Adams
  7. BakeOffs! Cindi Howson might remember those! ;) Rita Sallam Georgia O’Callaghan, Ph.D. and Aura Popa, I’m sure are planning a very entertainment session there! :)
  8. Five Steps to Create a Productive Analytics and AI COE with Joe Antelmi. Cari Goodrich, Tait Kirkham and Skander Larbi — this is a session not to miss.
  9. Jorgen Heizenberg, Jim Hare and Debra Logan on Data team organizations and efficiencies.

And #10, sessions with our own Ani Jain, Stuart Moncada, Ritika Gunnar featuring great customers….more to come in the next few weeks!

Engage here.

3) Marc Andreessen on Substack?!

Marc Andreessen who wrote the “Why Software Is Eating the World” Manifesto some 12 years ago is at it again, starting a NEW Substack. Great blog to follow. In his latest posts, he makes the case that AI will NOT eat the economy. See more below. Also, be warned about cadence, style, consistency…in his first post, namely:

“How will I write? Generally I won’t edit my copy, I won’t cite my sources, and I won’t try to be consistent. My motto is “strong views weakly held”, and you’ll see that on display here. Anything I say today I may disagree with tomorrow, in fact I frequently won’t even remember tomorrow. Ours is a moment of small, pinched, bitter minds hurling accusations of hypocrisy and inconsistency; I declare my freedom from all such criticism up front. I don’t even know what I think most of the time, why should you? Interpret all statements as questions and all declarations as explorations.”

🤔

Engage here

4) Do’s and Don’ts of Data Leaders

Former Jaguar Land Rover’s Data Leader Benny Benford shares his 5 do’s and 5 don’ts on how to succeed with the Data Office. Thanks Benny for the guidance! My favorites include: 1) CDAO not CDO. My take: Too many CDOs don’t own Analytics. I think that’s a mistake. Benny seems to agree. 2) Data Products: “Products products products: establishing new capabilities is essential, but you can never pivot to a product-led approach too soon”.

3) Make the Data Office accountable: “Data offices should report to a SteerCo of peers from around the business. Accountability can be uncomfortable, but without it, you lose relevance” 4) Data is about people more than data or tech. “Build networks at every level. Data sponsors at the director level, data champions at the individual contributor level and everything in between”. 5) Plan for role creep: CDO roles are new, and the scope of the role will creep. Be prepared for role creep and respond with the additional investment and resources needed.

Engage here

5) The Good, The Bad and The Ugly of Data

Look who’s on VentureBeat! My friend Derek Zanutto, General Partner at CapitalG. In this post he highlights the good, the bad and the ugly of the world of data. Check out the full piece below. Some highlights:

  1. The good: The data organization is now a value organization: 70% of data leaders report to the company’s president, CEO, COO or CIO & nearly 40% of them are adopting a #productmanagement orientation to their #datastrategy, hiring data product managers to ensure that members of a data product team don’t just create algorithms, but instead collaborate in deploying entire business-critical applications.
  2. The bad: Data leaders are misunderstood. While 92% of firms say they are seeing returns from data and #AI investments, only 40% of companies said the CDO role is currently #successful within their organization.
  3. The ugly: The average tenure of data leaders is less than 950 days. This compares to 7 years for the typical CEO and just over 4.5 years for the average CIO.

Now What?!

  1. Shift to data lake houses to cost-effectively support the rising volume, variety, and velocity of data and reduce time-consuming and expensive data movement.
  2. Transition from siloed business intelligence dashboards to data products that work at enterprise-grade (globally available, highly-reliable and optimized for high data volume) and live up to consumer-grade scenarios (fast and responsive, optimized for high concurrency and work in real-time, all the time).
  3. Increasing focus on real time and AI operationalization. Providing compelling customer experiences requires that an organization’s data and analytics infrastructure be optimized for decisions in real-time.

Engage here.

WANT TO MEET IRL?! (AKA “In Real Life”). Join the events below!

Paris on Tuesday March 14

London on Thursday, March 16

EXTRAS

Last week, we “BROKE BAD DATA…BAD”. I put together a quick playlist of the highlights of my interview with FirstMark’s Matt Turck below along with 2 essential highlights. Be sure to engage via the Linkedin Posts below!

Breaking Down the MAD Landscape

1 — Financings, M&A And IPOs:

  1. Public Market: IPO window has been shut, with little visibility on when it might re-open. IPO volume sank 78% in 2022. Public data & infrastructure companies pull back -51% vs. -19% S&P 500 in 2022. Companies like MariaDB are now worth less than the $$$ they raised before going public.
  2. Private Market: Great VC Pullback. In 2022, startups raised an aggregate ~$238B, a drop of 31% compared to 2021. But, some startups raised large rounds before the market dried up. Some examples include: Dataiku, raised $200M (F) at a $3.7B valuation, Alation $123M (E) at $1.7B, DataStax, $115M (F-II) at $1.6B, Cribl, $150M (D) at $2.5B, Monte Carlo, $135M (D) at $1.6B, dbt Labs, $222M (D) at $4.2B, Starburst, $250M (D) at $3.35B, Dremio, $160M (E) at $2B — this, excluding the #GenerativeAI funding “mini-bubble”: 110 deals, $2.6B+ in 2022.
  3. Good News: McKinsey & Company’s survey shows 63% will increase investment in #AI over next three years

Full post @ https://lnkd.in/gMCKgveX

Engage here

2 — Trends In Data Infrastructure

Authors point to 7 trends. IMHO, there are at least 3 everyone needs to make sure they pay attention to:

  1. Consolidation: Authors predict consolidation amongst the ocean of “single feature” data infrastructure startups, typically young (1–4 years), with good customers, but not a resounding product market-fit just yet (often sub $5M ARR, often raised at 50x-200x ARR, with now a cash runway of 6–36 months). Note potential for extensions across categories pursued by larger players — either organically or inorganically.
  2. Converge & Fold: ETL+Reverse ETL, Data Quality+Observability, OLTP+OLAP (aka HTAP), Data Catalogs fold under Data Governance platforms. MLOps fold under AI platforms.
  3. MDS under pressure. In a world of tight budgets and rationalization, authors argue that the Modern Data Stack can be complex (customers need to stitch everything together and deal with multiple vendors), expensive (lots of copying and moving data) and elitist (best-in-breed).

Topics like CDP, Data Mesh, Data Products and Data Contracts are also handled. Missing include Industry Clouds and Data Clean Rooms?

Full post @ https://lnkd.in/gyAM4bhV

Engage here

The “MAD” Playlist:

1) Machine Learning, Artificial Intelligence & Data Landscape (aka MAD). What You Should Know | 50 seconds

2) How It Works? The Machine Learning, Artificial Intelligence & Data Landscape (aka MAD) | 23 seconds

3) Financings, M&A And IPOs | 4 mins, 47 seconds

4) Under Pressure: The Modern Data Stack | 18 seconds

5) Is Hadoop Dead?! | 25 seconds

6) Le MAD….In French?! | A Special one for Tony Baer | 30 Seconds

Subscribe to the full playlist here

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