Evergage is killing it!

So thrilled with this announcement by Evergage.

I remember the first time I met Karl Wirth when he was at Red Hat and knew from almost the first hour that he was going to do great things.  I’m so proud to be a small part of their story as a seed investor.

Well done team Evergage!

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Web Development in Cambridge : Grafton Studio

Two of my favorite things (startups and Cambridge) merge at Grafton Studio – a lean web agency led by my friend Sean Treacy, based right in Harvard Square (in fact, their office is visible from the main image on Koa’s landing page). This team of web designers and developers eat and breathe webtech, and have cut their teeth with some impressive clients as a result.

Having completed projects for MIT and Harvard, they most recently launched the Princeton and Slavery website, the culmination of a 5-year investigation into Princeton and greater New Jersey’s involvement with the institution of slavery.

For Boston Latin School, they developed a scalable booking and analytics app for their school’s library, guidance and nurse centers, featuring separate student and faculty dashboards.

Beyond established academic institutions, and tech giants like Google ISP, the Grafton Studios team have also worked with startups like Nedap, a retail article intelligence brand out of the Netherlands, and have just begun work with exciting concussion tech player Brain Power. They also designed and developed a website for AirToken (AIR), the cryptocurrency of Cambridge startup, Airfox.

If you’re looking for help with user experience design, visual design, front end and/or back end development, or if you would like to learn more about their experience, get in touch with Sean at sean@graftonstudio.com.


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DataOps: A Unique Moment in Time for Next Generation Data Engineering

Check out the original LinkedIn post here

Judging from the engagement on my latest post from across the spectrum of business leaders, data engineers, data scientists and thought leaders, DataOps is clearly picking up steam. There were too many comments for me to respond to them all on LinkedIn, so I decided to post this follow up.

My overall takeaway? The sense of optimism that people are feeling about next-generation data engineering in the enterprise. It finally feels like large organizations have embraced their “data debt” and are figuring out how to monetize their data. It helps when they realize that great analytics depend on great data.

We’re at a unique point in time when fundamental changes in data management in the enterprise, the low barrier to cloud migration and the volume and value of enterprise data combine into a massive opportunity to create next generation data engineering pathways.


Figure 1: A unique moment in time for Data as an Asset

One of the foundational ideas behind DataOps, and the reason Mike and I started Tamr, is that enterprise technology and the people responsible for data have finally reached a place where companies that are not data-native can start achieving the types of analytical outcomes that have allowed Google, Facebook and Amazon to sprint so far ahead of the competition. These companies achieved their primacy because they could devote vast amounts of technical resources to solving the problems of software and data engineering at scale when it was a seemingly impossible task. DataOps technologies and processes have the potential to bring Google/Amazon-level data engineering to rest of us in the Global 2000.

Below, I’ve taken a few threads from comments I received and addressed them. I love the discussion — so let’s keep it up!

Starting with outcomes

A common point was expertly summed up: “putting [this] into practice will be a long journey, and will need to bring cultural change.” This is right on the nose. While developing a data-driven engineering infrastructure will take organizations a long time, getting started won’t. Building a foundation for the repeatable delivery of production-ready data starts with a single use case, and is often preceded by shockingly simple but shockingly hard-to-answer questions from business users: “How many customers do we have?” “How much do we pay each of our suppliers?” Answering these questions well is the best way to get started on DataOps. By attacking the question not as a one-and-done project, but as an analytic that should be readily available to many users at any point in time, the principles of DataOps necessarily come into focus. You can’t answer that question for many users over time without integrating data from many different sources, without involving users and without automation. DataOps will be successful when driven by use cases with clear business value — whether it’s generating more revenue through cross-sell and upsell, preparing for GDPR or finding savings in the supply chain. The success of these analytics is addictive; once an organization is successful for one project; the next most important use case is immediately apparent.

Isn’t DataOps just a new marketing name for an old trend?

This was a common and valid comment. As someone who has been on both sides of the CIO/vendor table for over three decades, I’ve been in the midst of every new attempt at solving this problem. My partner Mike gives a great talk on the generations of data curation.

DataOps is a discipline that allows organizations to repeatedly create production-ready data from a wide variety of sources for a wide variety of use cases. There are two reasons why DataOps is materially different than the generations of data curation which came before it. The first differentiator is the is the ability to create bi-directionality between end users at the point of consumption into the data models themselves. The key to repeatability is the tight coupling of feedback from users at the point of consumption into the creation of the data models themselves. The quintessential example, in my mind, is Google Maps. Google keeps informational incredibly up-to-date simply by asking a few users to add a few data points. Integrating this feedback into its architecture is at the core of why Google Maps is so good and constantly getting better. When we’re setting up next-generation data engineering processes, we have to keep this bi-directionality in mind; it’s the only way to scale data curation. The technology is there to allow closely-coupled integration between data production and consumption, and coupling them allows repeatability and scale.

The second differentiator is automation. The use of technologies like machine learning on very tactical but predictable chores like transformations or schema-mapping allow DataOps-ground data engineering pipelines to do the vast majority of work — upwards of 95% — without human intervention; saving the tough work for humans. Combined with scale-out computing and the easy availability of cloud resources, we are at a place where the rest of us can get a handle on the crazy amounts of siloed enterprise data.

Data Quality: Cold War or Overt Conflict within your organization? ?

At the heart of the problem is an age-old battle — the desire of IT folks to not let systems and data get out of hand, and the desire of business users to have useful, efficient operations with the data they need to get the job done. Business process automation has been spewing out data for decades; until recently, we thought of this as as a type of exhaust. As business users realize how valuable that data is, the desire to treat data as an asset has highlighted just how messy enterprise data systems are. DataOps projects need to acknowledge that business users need some freedom to operate — but that data needs to be handled and treated like an asset. Engineering for these realities, rather than ignoring them, is a key component of DataOps. A great example comes from my friends at Novartis Institute for Biomedical Research, who realized that while they wanted to glean insights from the tens of thousands of experiments, forcing data entry standards on hundreds of scientists was a non-starter. Instead, they had to adjust their data engineering to accommodate how scientists actually produce data, and use the machine to prepare the data how the business wants to use it.

In some companies, this tension is overt; in others, it’s a cold war. In these cases, technological changes should be deployed to facilitate cultural changes. In every DataOps project I’ve been a part of, the people side of things is much harder than the technology side. Giving business users a taste of the possible analytical outcomes from having unified, trusted data givens them a foundation of trust to give up data hoarding and political games. It’s tough, but integrating users into the process as experts, and seeing the results of their feedback improve the data they have access to, is a powerful tool for detente.

Isn’t another system for copying data just adding to the problem?

Data isn’t code; as one commenter mentioned, the instant you copy data it becomes corrupted. This is why Master Data Management, for example, failed to reach its promise. Again, DataOps is about acknowledging reality. Data is going to multiply, duplicate and live where it’s not suppose to. A common system of reference — a unified data hub — tackles this problem by allowing the data models to be flexible enough to accommodate duplication and resolve uncertainties by incorporating experts for data curation. Only processes that acknowledge and account for how data is produced and used have any hope of success. The alternatives have all been tried: imposing standards, mastering data, system consolidations, etc. They are necessary but insufficient, because as soon as they become cumbersome to business users, those users ignore them, circumvent them or procure their own, new system.

DataOps done poorly will just result in another competing golden record. This happens when the tools and process takes primacy over results. When DataOps projects are focused on driving transformational and repeatable analytical outcomes, users will clamor to gain access for their own use cases. I’ve seen this virtuous cycle over and over again.

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DataOps: Building A Next Generation Data Engineering Organization

See the original post here

Large enterprises are experiencing a foundational shift in how they view their data and structure their data engineering teams. As organizations capture more data than ever before, and store it in an ever-increasing variety of data stores, the prospect of competing on analytics becomes more tantalizing than ever. The challenges of using data at scale, however, is rooted in the “data debt” accumulated over time by enterprises struggling to manage the extreme volume and variety of their data. Paying down this data debt is the proverbial long pole in the tent for competing on analytics.

If you ask most employees, they likely believe their company’s data is neatly organized and easily accessible. As enterprise data professionals know, the reality is that a typical data environment resembles a “random data salad.” For decades, companies have been idiosyncratically deploying systems for business process automation, with the data generated from these deployments treated mainly as “exhaust” to the business processes. The resulting data environment is deeply fragmented and virtually impossible to integrate at scale — crushing the hopes of companies that want to develop an analytical advantage.

Indeed, even basic questions about the business, like “Who are my customers?” can’t be answered consistently and completely. The realities of doing business, like merger and acquisitions, further complicate data management. Companies need to start managing their data as an asset, much like they would their own money, if they hope to compete on analytics. It’s time to rethink priorities and start putting the “data horse in front of the analytics cart.”

Legacy Approaches to Data Engineering Are Failing

Legacy approaches to data management, such as ETL and MDM, are acceptable tools for small-scale or static environments; however, their rules-based approach can’t handle a large-scale, highly dynamic data environment (see this whitepaper by my co-founder Mike Stonebraker). Each question asked from the business would result in a long, complicated process of acquiring and manually preparing the data for consumption — providing little repeatability or scalability. Standardization is another approach that isn’t practical for large enterprises. Forcing top-down naming conventions for customers, suppliers, or other entities is hard to monitor and harder still to enforce. These complicated data management challenges are the reasons why we see such a high failure rate of so many potentially game-changing analytic projects.

Data Operations (DataOps) As An Engineering Methodology 

We envision a world where enterprise data customers have ready access to high-quality, cross-silo, unified enterprise data for all of their core logical entities. Data Operations (DataOps) is a methodology consisting of people, processes, tools, and services for enterprises to rapidly, repeatedly, and reliably deliver production-ready data from the vast array of enterprise data sources.

DataOps was inspired by the concept of DevOps, a term pioneered decades ago by internet startups to describe one of the main ways they were trying to beat their much better-resourced established competitors . The concept is that the best way to increase velocity of new features being delivered in software is by using a continuous build, test, and release process that has a strong emphasis on QA automation. If you’re Google working on Maps, a sustained faster build, test, and release cycle creates a competitive advantage over Yahoo!.

Similarly, enterprises expecting to compete on the basis of analytics need to empower analysts with easy access to up-to-date, unified data organized logically for them. Only by implementing an integrated, repeatable, scalable data curation process is it possible for a business to achieve the analytic velocity necessary to create a competitive advantage.

DataOps Capabilities & Principles

DataOps methodologies promote rapid, logical organization of all enterprise data, consumable downstream by a wide variety of methods. Its capabilities and principles are built for data engineering organizations that need to manage an architecture of constant change in data sources and uses.


(Figure 1. Open “DataOps” Ecosystem: Catalogs and Hubs)


DataOps teams need to consider the following capabilities (See Figure 1.):

  • Raw Source Catalog — Catalog all critical data sources, including raw physical attributes and records from the operational sources across an enterprise. Before you serve up insight, you need to know what you have.
  • Movement / Logging / Provenance — These capabilities are required to move the data efficiently from Point A to Point B. Today’s ETL platforms, having been refined and hardened over the past two decades, offer highly optimized movement and manipulation of data. Core features, such as end-to-end data lineage and dependency analysis as well as data quality and operational metadata capture, make your raw data sources and feeds highly accessible and reliable.
  • Logical Models — Well-designed logical models deliver on three fronts:
  1. Flexible Format: Fundamentally, a single logical entity can be completely described by a collection of simple key-value pairs. A flexible format must hold up to both RDBMS design and highly normalized document search engines.
  2. Abstract: The model must successfully abstract the many varied physical data sources and feeds. Consumers of data care about analyzing entities like customers, suppliers, products or purchases and not a collection of source attributes.
  3. Semantically Meaningful: A logical model speaks the language of the consumer, allowing them to understand the underlying physical data, consume it, and provide feedback.
  • Unified Data Hub — An analytical system of record for key enterprise entities that is accessed and curated by any employee in an organization. Within the data hub, you gain a holistic view of your data landscape. This includes the entities that have been organized, the sources contributing to each entity, the applications utilizing the unified data, and the individuals who are curating and consuming the data.
  • Feedback — Provide users with a feedback mechanism to identify where problems in the data exist. This will improve the upstream system dynamically and continuously as well as eliminate the need for consumers to go to people physically managing data to provide feedback or put in requests. Critically, feedback is enabled in the unified data hub in the language of the logical model.

These capabilities deliver clean, unified views of data organized in a way that consumers would want it — while respecting the extreme amount of variety in both sources and uses. Users can consume a much higher quality of data faster while eliminating the need to idiosyncratically prepare the data themselves.


The DataOps principles underpinning these capabilities are equally as important to consider. These principles don’t just apply to tools and technology, but also to people, process and services. DataOps is ultimately an ecosystem, and changing organizational behavior is just as important as altering technological choices.

  • Interoperable: Open, Best-of-Breed, FOSS & Proprietary, Table In / Table Out — An ideal data engineering architecture should include technologies that are best-of-breed and open. Allowing mega-vendors “guided tours of your wallet” needs to be a thing of the past. Delivering clean, complete data to consumers when they want it and how they want it requires piecing together multiple technologies from different vendors, whether they be large tech companies or startups. Moreover, there can’t be an aversion to open source if it can best solve the problem. Technologies and processes need to interoperate and follow the basic premise that they should be able to easily accept a table in and produce a table out.
  • Social: Bi-Directional, Collaborative, Extreme Distributed Curation — Consumers want to use a wide variety of tools to interact with their data. They want to visualize information in their favorite analytic tool or wiki page and provide feedback directly so physical interaction or request tickets aren’t necessary. The communication paradigm needs to shift: information flow shouldn’t always be from source to consumption. Rather it needs to be bi-directional. It’s equally important that source owners receive feedback from users as users receive data from source owners. Moreover, like the modern internet experience, this collaboration needs to be enterprise-wide. Users are continually being conditioned to be responsible for data curation in their personal lives and data engineering teams should leverage this.
  • Modern: Hybrid, Service-Oriented, Scale-Out Architecture — Flexibility is critical for next-generation data engineering teams. When creating a repeatable architecture to service data consumers, ensure the backend has the capabilities to scale out as projects broaden in scope. Also, using a microservices-based architecture when developing key capabilities is critical, as changes often need to occur in some specific functionality and re-architecting the entire system isn’t desirable. Finally, make use of the cloud for scalability when needed but consider that a hybrid approach of cloud and on-premise capabilities is likely the way to go.

DataOps Wave Will Transform Data Engineering Environments

Applying a DataOps methodology to your data engineering organization will allow you to achieve transformational analytic outcomes and “capture low-hanging analytical fruit” that will save your company money and drive additional revenue. Quickly delivering clean data from the vast array of enterprise data sources to the vast array of consumption use cases in a repeatable manner will build your “company IQ.” DataOps is the transformational change data engineering teams have been waiting for to fulfill their aspirations of enabling their business to gain analytic advantage through the use of clean, complete, current data.

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Think3 – $1B Fund for SaaS Founders who want to move onto next project

What happens when the SaaS company you started has grown beyond startup phase and your ready to go do your next startup.  If your company has been successful – but is not going to end up being a unicorn or doesn’t have an obvious strategic buyer – sometimes you can stall for years.  There are a ton of my fellow founders who end up in this state.  As posted last week on TechCrunch here – the team at Think3 has $1B and perhaps more importantly – are actual software operating people who will provide you with a fair exit that enables you to take on your next project knowing that what you built will continue in good hands.  If you are interested ping Andy Tryba @ Think3 – andy.tryba@think3.com.

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Twine Health acquired by Fitbit

Twine Health has been acquired by Fitbit.  As a user, investor & Founding BOD Member @ Twine Health I’m thrilled about the potential of this deal for patients.

The mission at Twine is powerful and the impact of Twine @ scale could be transformational.  I was one of the first users of Twine and – as posted here – Twine enabled me to reduce my A1C by 7.1 in less than 4 months.  The Twine Health team should be able to bring dramatic health benefits to the Fitbit user community.  John Moore, Scott Gilroy & Frank Moss have great partners over the past 5 years – it’s been an honor to be part of such a great team.

Following the lead of leaders like Eric Topol, MD and Dave Chase – I believe that the time is right for a significant change in healthcare.  We can dramatically improve outcomes and reduce cost if we embrace the patient as consumer and information liquidity as the key lever.

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LZR in Austin is AWESOME!

Had a chance to swing by LZR – the rebranded event space formerly the music venue La Zona Rosa – reference article here.

John Price has done an incredible job of building the type of space and community that creates great startups.  Not only is the space cool, comfortable and flexible – but the diversity of the people & interests is inspirational.  I think I know where I’ll be hanging out during the day now that Amy and I have a pied-à- in downtown Austin just a few blocks from LZR.

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