Masthead Data is a no-code data monitoring solution that analyzes logs and metadata to identify freshness and volume anomalies, schema changes in tables, pipeline errors, and their blast radius effects on businesses. It provides a data observability platform that detects anomalies in Google Cloud in real time, using logs and metadata, without integrating with ETL.

Masthead Data

Masthead Data is a data reliability platform that catches anomalies in Google BigQuery in real time. It utilizes machine learning to understand which data inputs are falling out of their expected behavior. Masthead observes data from the moment it enters GBQ to the moment it reaches the data consumer in Looker or Data Studio. Masthead is designed to pinpoint what anomalies have happened and where helping to reduce root cause analysis for data engineers and data analysts to a few minutes. This helps data teams catch anomalies before they end up in C-Suite dashboards and cause harm.

What makes Masthead stand out is its high-security standards. Masthead does not need direct access to the client's data and the data never leaves the client's environment. This means we are HIPAA compliant and do not require any downgrades to client security standards. 

 If you'd like to learn more, feel free to shoot us a message and check out our website! We would love to chat and see how we can help you reach your milestones!

Visit our website to learn more: https://mastheadata.com/
Twitter: @mastheaddata

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DEPO Ventures

🚨New #Virtual Event Alert🚨

Join us for #2Learn: Starting with Data Contracts: Initial Steps and Key Pitfalls on February 13.

Yuliia Tkachova, Co-Founder and #CEO at Masthead Data, and Jean-Georges Perrin, Co-Founder & Chief #Innovation Officer at AbeaData, will discuss the necessity of data contracts and when companies should invest.

Register for the #webinar today: https://lnkd.in/g9VcF563

Brand != Value it brings, even though it often implies a premium price.

Here's the case: One of our clients was testing the lineage available within #gcp, which cost them a few thousand dollars a month, although they were using Lineage at Masthead Data. They found Masthead Lineage more valuable because it provides column-level lineage and shows an extended version with dependencies down to their Looker dashboard.

The thing to embrace is that no vendor on the market can be a silver bullet to solve all the problems you might encounter when building a data platform. Tom De Wolf highlighted a fundamental principle to follow when architecting a data platform - composability. 

The reason for this is that there is no single solution to address all possible use cases for your business and data platform; each one is unique. Therefore, when constructing a data platform architecture, you need to ensure that you don't tie yourself to a single vendor, as none of them can address all the problems you might encounter, including those related to future business cases. You can find the link to Tom's post in the first comment.

Often, using a smaller vendor to address a specific problem and not locking yourself into multi-year commitments with a larger company, which prioritizes revenue over product performance and potential use cases, is a smarter approach. 🤓 

The pictures above are screenshots from Masthead and GCP lineage for the same data flows. 😬  We can visualize column-level lineage within 30 minutes after deployment and don't require access to read or edit your data (god forbid).

Feel free to ping me in DMs if you'd like to arrange a test drive. 

#dataplatform #googlecloud #datalineage #dataengineer

Ready to boost your organization's #datareliability game? 📈

Join us for a #virtual #2Learn session on December 12th where you’ll learn from industry leaders Yuliia Tkachova, Co-Founder & CEO at Masthead Data, and Dumky de Wilde, Senior Analytics Engineer at Xebia Data! 📊 

Register today: https://lnkd.in/gNV5SwRv

#ProTips #DataReliability #GoogleCloud

Call out to all Toronto Data folks! 

In collaboration with C2C, Masthead Data is excited to host our first meet-up at the Google office in Toronto. A low-key in-person event with cocktails, beers, and pizza. 🍕 🍸 🍻 

Additionally, I'll do a lightning talk with Derek Nogiec, focusing on #Looker and #BigQuery. And how to ensure data consumers get the highest quality through reliability length for both systems.

The meet-up is very informal; the idea is to get anyone to share an interesting story or challenge they had. Let us know if you want to give a talk!

When: December 6 at 5 PM
Where: Google Office, 100 Adelaide St W, Toronto
Just so you know, registration closes by the end of this Thursday. You know where the link is. 

CC Eric Broda, Zamira Khamidova, Arman Didandeh, Glenn Wiebe, Slava Lenskyy, Gaurav J 🇨🇦🇮🇳

Data teams need product managers' roles/skills to succeed. (TL;DR)

Yet, we hear that Airbnb has eliminated PM positions, and other startups do not plan to hire any PMs.

Let's start from the basics: what do PMs do? The answer greatly depends on the organization. You cannot expect the same level of responsibilities from a Google Cloud Product Manager as from a 20-person startup's PM. I'm not saying it's easier or harder; it's different. However, the expectation to own and lead the product is common in both roles, regardless of company size. This involves prioritizing tasks, communicating and keeping stakeholders in the loop, identifying potential strategic opportunities, and making things happen.

In Airbnb's case, their CEO first announced that they would merge PM functions with Product Designers' functions, which makes much sense for them. A vast portion of their product is UX/UI. In this case, consolidating the PM's function within a role that owns the product's visual and behavioral aspects is a brilliant decision. This was possible due to the maturity of the product and the team working with it. Later, they announced that the PM role was merged with the Product Marketing Manager. What happened is that the PM's responsibilities were distributed between the product designer and the product marketing manager. My conviction that this was possible is based on clear product-market fit, a long history of successful product iteration and experimentation, long-standing staff, clear responsibilities between teams, and a team culture where members were encouraged to take ownership and leadership of initiatives. The bottom line is the maturity of culture, people, processes, and products. 

When talking about identifying data products, data quantum, and domains in an organization to start building a data mesh, the expectation is that someone from the data team will start doing it. My take is that data teams have been too long overlooked and undervalued. Going out there and proactively talking to business teams, identifying potential data products, and asking questions are viewed today as extra. I'm not saying it doesn't happen; it just doesn't happen in 9 out of 10 cases.

One way to address this status is to have a data product manager role. This person would eventually take ownership and leadership for shipping data products and maintain a bigger picture. They would also coach the data team in product thinking skills, motivating them to collaborate with business users more meaningfully.

Data mesh initiatives need a data product manager role, as most companies are in the early stages of adopting data mesh. There need to be lessons learned organization-wise, cultural maturity, and process-ready foundation before we can hand this responsibility to any data team members handling data products. 

Read my article, why product thinking is the key to success with data products. 

#datamesh #productthinking #dataproducts

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