Premarket Mover: Mongodb Inc (MDB) Down 2.72% – InvestorsObserver

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Wednesday, August 02, 2023 08:45 AM | InvestorsObserver Analysts

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Premarket Mover: Mongodb Inc (MDB) Down 2.72%

Mongodb Inc (MDB) is down Wednesday morning, with the stock falling -2.72% in pre-market trading to 413.03.

MDB’s short-term technical score of 67 indicates that the stock has traded more bullishly over the last month than 67% of stocks on the market. In the Software – Infrastructure industry, which ranks 88 out of 146 industries, MDB ranks higher than 71% of stocks.

Mongodb Inc has risen 3.30% over the past month, closing at $409.57 on July 5. During this period of time, the stock fell as low as $388.62 and as high as $439.00. MDB has an average analyst recommendation of Strong Buy. The company has an average price target of $399.43.

Overall Score - 52
MDB has an Overall Score of 52. Find out what this means to you and get the rest of the rankings on MDB!

Mongodb Inc has a Long-Term Technical rank of 85. This means that trading over the last 200 trading days has placed the company in the upper half of stocks with 15% of the market scoring higher. In the Software – Infrastructure industry which is number 67 by this metric, MDB ranks better than 67% of stocks.

Important Dates for Investors in MDB:

-Mongodb Inc is set to release earnings on 8/30/2023. Over the last 12 months, the company has reported EPS of $-8.77.

-We do not have a set dividend date for Mongodb Inc at this time.

Click Here To Get The Full Report on Mongodb Inc (MDB)

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Victory Capital Management Inc. Sells 6163 Shares of MongoDB, Inc. (NASDAQ:MDB)

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Victory Capital Management Inc. lessened its stake in shares of MongoDB, Inc. (NASDAQ:MDBFree Report) by 15.7% during the first quarter, according to the company in its most recent filing with the Securities and Exchange Commission (SEC). The firm owned 33,172 shares of the company’s stock after selling 6,163 shares during the period. Victory Capital Management Inc.’s holdings in MongoDB were worth $7,733,000 at the end of the most recent reporting period.

A number of other hedge funds have also recently added to or reduced their stakes in MDB. Bessemer Group Inc. bought a new position in shares of MongoDB in the 4th quarter valued at $29,000. BI Asset Management Fondsmaeglerselskab A S bought a new position in shares of MongoDB in the 4th quarter valued at $30,000. Lindbrook Capital LLC boosted its holdings in shares of MongoDB by 350.0% during the 4th quarter. Lindbrook Capital LLC now owns 171 shares of the company’s stock valued at $34,000 after acquiring an additional 133 shares in the last quarter. Y.D. More Investments Ltd acquired a new stake in shares of MongoDB during the 4th quarter valued at $36,000. Finally, CI Investments Inc. boosted its holdings in shares of MongoDB by 126.8% during the 4th quarter. CI Investments Inc. now owns 186 shares of the company’s stock valued at $37,000 after acquiring an additional 104 shares in the last quarter. Institutional investors own 89.22% of the company’s stock.

MongoDB Trading Up 0.3 %

MongoDB stock opened at $424.57 on Wednesday. The stock has a market cap of $29.97 billion, a P/E ratio of -90.91 and a beta of 1.13. MongoDB, Inc. has a fifty-two week low of $135.15 and a fifty-two week high of $439.00. The firm’s fifty day moving average price is $381.38 and its 200-day moving average price is $279.71. The company has a debt-to-equity ratio of 1.44, a quick ratio of 4.19 and a current ratio of 4.19.

MongoDB (NASDAQ:MDBGet Free Report) last released its quarterly earnings results on Thursday, June 1st. The company reported $0.56 earnings per share for the quarter, topping the consensus estimate of $0.18 by $0.38. The firm had revenue of $368.28 million for the quarter, compared to analysts’ expectations of $347.77 million. MongoDB had a negative net margin of 23.58% and a negative return on equity of 43.25%. The business’s quarterly revenue was up 29.0% on a year-over-year basis. During the same quarter in the prior year, the firm posted ($1.15) earnings per share. Equities research analysts forecast that MongoDB, Inc. will post -2.8 earnings per share for the current fiscal year.

Insider Buying and Selling at MongoDB

In other MongoDB news, CAO Thomas Bull sold 516 shares of the company’s stock in a transaction dated Monday, July 3rd. The shares were sold at an average price of $406.78, for a total transaction of $209,898.48. Following the completion of the sale, the chief accounting officer now directly owns 17,190 shares in the company, valued at $6,992,548.20. The transaction was disclosed in a document filed with the SEC, which is accessible through this link. In other news, Director Dwight A. Merriman sold 2,000 shares of the stock in a transaction dated Thursday, May 4th. The shares were sold at an average price of $240.00, for a total transaction of $480,000.00. Following the completion of the sale, the director now directly owns 1,223,954 shares in the company, valued at $293,748,960. The transaction was disclosed in a document filed with the SEC, which is accessible through the SEC website. Also, CAO Thomas Bull sold 516 shares of the stock in a transaction dated Monday, July 3rd. The stock was sold at an average price of $406.78, for a total transaction of $209,898.48. Following the sale, the chief accounting officer now owns 17,190 shares of the company’s stock, valued at $6,992,548.20. The disclosure for this sale can be found here. Insiders sold a total of 116,427 shares of company stock worth $41,304,961 over the last three months. Company insiders own 4.80% of the company’s stock.

Analyst Ratings Changes

Several research analysts have commented on the company. Citigroup lifted their target price on MongoDB from $363.00 to $430.00 in a report on Friday, June 2nd. Capital One Financial assumed coverage on MongoDB in a report on Monday, June 26th. They set an “equal weight” rating and a $396.00 target price on the stock. Royal Bank of Canada lifted their target price on MongoDB from $400.00 to $445.00 in a report on Friday, June 23rd. Robert W. Baird lifted their price objective on MongoDB from $390.00 to $430.00 in a research report on Friday, June 23rd. Finally, Needham & Company LLC lifted their price objective on MongoDB from $250.00 to $430.00 in a research report on Friday, June 2nd. One analyst has rated the stock with a sell rating, three have issued a hold rating and twenty have issued a buy rating to the stock. Based on data from MarketBeat, the company currently has an average rating of “Moderate Buy” and a consensus price target of $378.09.

About MongoDB

(Free Report)

MongoDB, Inc provides general purpose database platform worldwide. The company offers MongoDB Atlas, a hosted multi-cloud database-as-a-service solution; MongoDB Enterprise Advanced, a commercial database server for enterprise customers to run in the cloud, on-premise, or in a hybrid environment; and Community Server, a free-to-download version of its database, which includes the functionality that developers need to get started with MongoDB.

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Institutional Ownership by Quarter for MongoDB (NASDAQ:MDB)



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Los Angeles Capital Management Shows Confidence in MongoDB, Inc. with Significant …

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August 1, 2023 – Los Angeles Capital Management LLC, a prominent investment management firm, recently revealed that it has significantly increased its stake in MongoDB, Inc. (NASDAQ:MDB) during the first quarter of this year. According to a filing with the Securities and Exchange Commission (SEC), the company raised its holdings in MongoDB by an impressive 193.9%. This move showcases the firm’s confidence in the technology company’s prospects.

Los Angeles Capital Management now owns approximately 41,400 shares of MongoDB after purchasing an additional 27,315 shares during the period under review. The total value of their stake amounts to a noteworthy $9,651,000, making them a substantial shareholder in the company. At present, Los Angeles Capital Management holds about 0.06% of MongoDB.

MongoDB is an internationally renowned provider of a general-purpose database platform. Their flagship product includes various offerings such as MongoDB Atlas – a highly regarded multi-cloud database-as-a-service solution. Additionally, they provide MongoDB Enterprise Advanced which caters primarily to enterprise customers seeking to run their databases either on-premise, in the cloud or through hybrid environments. The company also offers Community Server for developers seeking to leverage MongoDB’s capabilities without any cost implications.

The recent news about Los Angeles Capital Management increasing their position in MongoDB comes after the company released its quarterly earnings data on June 1st of this year. The financial results turned heads as they outperformed analysts’ consensus estimates significantly.

During that quarter, MongoDB reported earnings per share (EPS) of $0.56, exceeding analysts’ projected figure of $0.18 by an impressive margin of $0.38 per share. Moreover, contrary to expectations given its negative return on equity and net margin figures last year; this time around it posted improved figures for these metrics as well – a positive sign for investors.

MongoDB’s revenue during this period also garnered attention; reaching $368.28 million, surpassing analysts’ estimates pegged at $347.77 million. This indicated a solid increase of 29.0% in revenue on a year-over-year basis.

Looking forward, equities research analysts remain optimistic about MongoDB’s future prospects, with a consensus forecast projecting that the company will record -2.8 EPS for the current fiscal year.

Overall, Los Angeles Capital Management’s increased stake in MongoDB indicates not only their confidence in the company’s outlook but also highlights their belief in the potential growth opportunities that lie ahead for the technology sector as a whole. With MongoDB’s innovative products and strong financial performance, it remains well-positioned to navigate the evolving business landscape and deliver value to its shareholders.

MongoDB, Inc.

MDB

Buy

Updated on: 02/08/2023

Price Target

Current $424.57

Concensus $388.06


Low $180.00

Median $406.50

High $630.00

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Social Sentiments

We did not find social sentiment data for this stock

Analyst Ratings

Analyst / firm Rating
Miller Jump
Truist Financial
Sell
Mike Cikos
Needham
Buy
Rishi Jaluria
RBC Capital
Sell
Ittai Kidron
Oppenheimer
Sell
Matthew Broome
Mizuho Securities
Sell

Show more

Institutional Investors Show Growing Confidence in MongoDB’s Future Growth and Profitability


MongoDB, Inc. (NASDAQ: MDB), a global provider of general-purpose database platforms, has seen a significant shift in institutional investor holdings recently. Several prominent investors have made adjustments to their stakes in the company, indicating a growing interest in its potential for future growth and profitability.

Bessemer Group Inc., a renowned investment firm, purchased a new stake in MongoDB during the fourth quarter of 2023. The purchase was valued at approximately $29,000, reflecting Bessemer Group’s confidence in MongoDB’s value proposition and long-term prospects.

Similarly, BI Asset Management Fondsmaeglerselskab A S also acquired a new stake in MongoDB during the same period. The investment was worth around $30,000, underlining their belief in the company’s ability to deliver returns and generate shareholder value.

In yet another testament to MongoDB’s appeal as an investment opportunity, Lindbrook Capital LLC significantly increased its holdings in shares of the company by 350% during the fourth quarter. Lindbrook Capital now owns 171 shares of MongoDB stock worth $34,000 after purchasing an additional 133 shares.

Y.D. More Investments Ltd., too, recognized the potential of MongoDB and acquired a new position in the fourth quarter valued at approximately $36,000. This move demonstrates yet another institutional investor’s confidence in MongoDB’s ability to deliver results and generate positive returns.

Lastly, CI Investments Inc., a notable asset management firm, lifted its holdings in MongoDB by an impressive 126.8% during the fourth quarter. Presently owning 186 shares of the company’s stock valued at $37,000 after acquiring an additional 104 shares during this period.

The strong interest from hedge funds and other institutional investors signifies their recognition of MongoDB as an attractive investment opportunity. In fact, these groups currently own about 89.22% of the company’s outstanding stock.

On August 1st, MDB shares on NASDAQ traded down $0.55 during mid-day trading, reaching $422.85. Approximately 193,517 shares were traded, compared to the average volume of 1,735,221. With a market capitalization of $29.84 billion and a beta of 1.13, MongoDB’s performance in the market has been noteworthy.

The company offers several products including MongoDB Atlas, a hosted multi-cloud database-as-a-service solution; MongoDB Enterprise Advanced, a commercial database server for enterprise customers; and Community Server, a free-to-download version of its database intended for developers.

A number of brokerages have recently issued reports on MDB. VNET Group maintained its rating on shares of MongoDB in a research note back in June 2023. Needham & Company LLC raised their price target to $430.00 from $250.00 in early June as they recognized the growth potential of the company.

Furthermore, Tigress Financial raised their price objective from $365.00 to $490.00 in late June due to their positive outlook on MongoDB’s future prospects.

Overall, out of the analysts who have rated the stock recently, one analyst has given it a sell rating while three have rated it as hold and twenty have assigned it as a buy rating. Bloomberg data reveals that the company currently holds an average rating of “Moderate Buy” with a consensus target price of $378.09.

In recent news related to insider trading activity within the company, Director Dwight A. Merriman sold 1,000 shares of MongoDB’s stock at an average price of $420 per share on July 18th. As per filings with the Securities & Exchange Commission (SEC), Merriman now owns more than 1 million shares in the firm valued at over half a billion dollars.

Additionally, Chief Revenue Officer Cedric Pech also sold 360 shares at an average price of $406.79 per share on July 3rd. Following the sale, Pech owns 37,156 shares valued at approximately $15 million.

These transactions highlight the confidence and commitment of MongoDB’s senior management in their own company.

In conclusion, MongoDB’s recent surge in institutional investor interest speaks volumes about its growing prominence within the technology sector. The company’s ability to provide innovative database solutions has captured the attention of prominent investors who recognize its potential for future growth and profitability. With positive ratings from analysts and a consensus target price above its current trading value, MongoDB has firmly established itself as a formidable player in the market.

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Vast Data Intentionally Blurs The Line Between Storage And Database – The Next Platform

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Depending on how you look at it, a database is a kind of sophisticated storage system or storage is a kind of a reduction of a database. In the real world, where databases and storage are separate, there is a continuum of cooperation between the two, for sure. There is no question that relational databases drove the creation of storage systems every bit as much – and drove them in very different directions – as file serving and then object serving workloads have.

What if you didn’t have to make such choices? What if your storage was a real, bona fide, honest to goodness database? What if Vast Data, the upstart maker of all-flash storage clusters that speak Network File System better and with vastly more scale than more complex (and less useful) NoSQL or object stores, was thinking about this from the very moment it was founded, that creating a new kind of storage to drive a new kind of embedded database, was always the plan? What if AI was always the plan, and HPC simulation and modeling could come along for the ride?

Well, the Vast Data Platform, as this storage-database hybrid is now called, was always the plan. And that plan was always more than the Universal Storage that was conceived of in early 2016 by co-founders, Renen Hallak, the company’s chief executive officer, Shachar Fienblit, vice president of research and development, and Jeff Denworth, vice president of products and chief marketing officer, and launched in February 2019. This is a next platform in its own right, which means that it will have to do clever things with compute as well. So maybe, in the end, it will just be called the Vast Platform? But let’s not get ahead of ourselves.

Then again, why not? The co-founders of Vast Data did way back when.

“Back in 2015, in my pitch deck, there was one slide about storage in that entire deck, which had maybe fifteen slides,” Hallak tells The Next Platform. “One of them had storage in it, the rest of them had other parts that needed to be built in order for this AI revolution to really happen in the way that it should. Eight years ago, AI was cats in YouTube videos being identified as cats. It was not close to what it is today. But it was very clear that if anything big was going to happen in the IT sector over the next twenty years, it would be AI and we wanted to be a part of it. We wanted to lead it. We wanted to enable others to take part in this revolution that looked like it might be confined to a few very large organizations. And we didn’t like that. We want to democratize this technology.”

And that means more than just creating a next-generation, massively scalable NFS file system and object storage system based on flash. It means thinking at ever-higher levels in the stack, and bringing together the concepts of data storage and a database against the large datasets from the natural world that are increasingly underpinning AI applications.

Data in no longer restricted to limited amounts of text and numbers in rows or columns in a database, but high resolution data – video, sound, genomics, whatever – that would break a normal relational database. AI workloads need enormous amounts of data to build models, and lots of performance to drive the training of models and sometimes an enormous amount of compute to run inference on new data as it enters the model. All of this puts tremendous pressure on the storage system to deliver information – something that Vast Data’s Universal Storage, a disaggregated shared nothing implementation of NFS that has a very fine-grained quasi-object store underneath it, can handle.

“Data has a lot more gravity than compute does,” Hallack adds. “It’s bigger and it’s harder to move around. And so for us to play in that AI space, we cannot confine ourselves just to the data piece. We have to know something and have an opinion about how the data is organized. It is about the breaking of tradeoffs, and it is not just a storage thing. If you take out that word storage, and put in the word database, the same type of challenges apply. Cost, performance, scale, resilience, ease of use – these are not storage terms. They’re very generic computer science terms.”

The first inklings of the Vast Data Platform were unveiled in the Vast Catalog, introduced in February of this year, which basically put a SQL front end and semantic system on top of the NFS file system and object storage underpinning the Universal Storage. This was the first hint that a new engine was underneath the covers of the Universal Storage that supported SQL queries. Now, Vast Data is taking the covers completely off, revealing how the data storage and database have been converged into a single platform and how it will eventually have a compute layer.

And as such, we are going to treat the Vast Data Platform announcement just like we would a server compute engine announcement, giving it an overview to start (that would be this story you are reading) and then a deep dive after we do some digging into the architecture. Technically, we are on vacation at the beach in Hilton Head Island, South Carolina and have children to play with on the beach. . . .

A Full Stack Problem, Indeed

As Jensen Huang, co-founder of Nvidia, is fond of saying, AI is a full stack problem and Vast Data, like Nvidia, has been thinking about the full stack from Day One. As far as we can tell, Vast Data has no interest in making hardware for compute, storage, or networking and is perfectly happy to leave that to others. Because, quite frankly, it has better things to do.

Like mashing up exascale class storage with a native database to get rid of AI workflows like this one, from Amazon Web Services:

But it is more than that. It is about making sense of truly vast amounts of data.

“GPT-3 was trained on about 45 terabytes of data, which I don’t think is a lot of data in the grand context,” Denworth tells The Next Platform. “We are now working with a series of people that are building foundation models – so organizations of the ilk of Inflection AI – and we are starting to see plans for multi-exabyte single datastores. Some of the biggest business I’ve ever seen in my life is happening in the span of around eight weeks. And one of the considerations is that as you move beyond text, to the data of the natural world, the corpus grows by orders of magnitude. At the moment, there are only a few organizations on Earth that can capture that much unique information and make sense of it, and the question is: Why?”

The answer is that this is all too hard and too expensive, and there has to be a way to make it easier, faster, and cheaper. Something that looks more like this:

The first time we know where someone tried to create a data platform like this was a long, long time ago – relative to the timeframes of the computer industry at least — and it worked within its own context and limitations to a certain extent. The second example we know of was an abject failure, and the third had such poor performance that no one talks about it anymore.

Way back in 1978, when IBM created the relational database, it did not commercialize it first on the venerable System/370 mainframes of the time, but on a little used but architecturally significant machine called the System/38. The brilliance of this machine is that the operating system had a relational database embedded within it, and it was accessed just like a flatfile datastore but it had all of these SQL extensions that allowed users to query the data in ways that you cannot actually do in a flatfile datastore. In effect, the relational database was the file system, and there was never a way to store data that was not able to be queried. The only trouble with this approach is that it took a lot of computation, and MIPS for MIPS, the System/38 loaded up with a relational database stack was 2X to 3X more expensive than a System/370 mainframe of the time. It wasn’t until the AS/400 was announced by IBM in 1988 that the cost of compute came down enough for this to be more practical, but it was still a slow file system. And by the late 1990s, IBM grafted the OS/2 Parallel File System to the OS/400 operating system so it could have a proper Internet file system and the database was relegated to being just a database.

Big Blue had the right idea, but it was ahead of the computational budget of the time. Just like AI algorithms created in the 1980s more or less worked, but they needed orders of magnitude more data and orders of magnitude more compute to drive the neural network to actually work.

Microsoft also had the right idea with the Object File System that was part of the “Cairo” Windows and Windows Server kernel in the 1990s, which was reborn as WinFS with the “Longhorn” Windows and Windows Server release in the early 2000s. Microsoft, too, understood that we all needed to store structured, semi-structured, and unstructured data in the same database/datastore and allow it to be queried using SQL.

And finally, there was Hadoop, the clone of the Google MapReduce data querying algorithm and massively distributed, unstructured datastore. Eventually, various SQL overlays were added to Haoop, including Hive, HBase, Impala, and Hawq, and while these worked, the performance was abysmal. Relational databases could not scale anywhere as far as Hadoop, and Hadoop was orders of magnitude slower at querying data than a relational database – and that is not particularly fast in the scheme of things.

Which brings us all the way to today and the Vast Data Platform. The team at Vast Data is taking another run at the idea, and they have a unique storage architecture that just might bring this age-old vision to fruition.

We look forward to getting into the weeds and figuring out how and why.

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Westpac Banking Corp Reduces Holdings in MongoDB, Inc. (NASDAQ:MDB) – MarketBeat

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Westpac Banking Corp decreased its holdings in MongoDB, Inc. (NASDAQ:MDBFree Report) by 2.8% during the first quarter, according to the company in its most recent disclosure with the Securities and Exchange Commission. The fund owned 10,479 shares of the company’s stock after selling 300 shares during the quarter. Westpac Banking Corp’s holdings in MongoDB were worth $2,443,000 at the end of the most recent reporting period.

A number of other institutional investors and hedge funds also recently made changes to their positions in MDB. 1832 Asset Management L.P. raised its position in shares of MongoDB by 3,283,771.0% during the fourth quarter. 1832 Asset Management L.P. now owns 1,018,000 shares of the company’s stock valued at $200,383,000 after buying an additional 1,017,969 shares during the last quarter. Renaissance Technologies LLC increased its position in MongoDB by 493.2% in the fourth quarter. Renaissance Technologies LLC now owns 918,200 shares of the company’s stock worth $180,738,000 after purchasing an additional 763,400 shares during the last quarter. Norges Bank bought a new stake in MongoDB in the fourth quarter worth $147,735,000. William Blair Investment Management LLC increased its position in MongoDB by 2,354.2% in the fourth quarter. William Blair Investment Management LLC now owns 387,366 shares of the company’s stock worth $76,249,000 after purchasing an additional 371,582 shares during the last quarter. Finally, First Trust Advisors LP increased its position in MongoDB by 72.9% in the fourth quarter. First Trust Advisors LP now owns 613,818 shares of the company’s stock worth $120,935,000 after purchasing an additional 258,783 shares during the last quarter. Hedge funds and other institutional investors own 89.22% of the company’s stock.

Insiders Place Their Bets

In other news, Director Dwight A. Merriman sold 1,000 shares of MongoDB stock in a transaction that occurred on Tuesday, July 18th. The shares were sold at an average price of $420.00, for a total transaction of $420,000.00. Following the completion of the transaction, the director now directly owns 1,213,159 shares of the company’s stock, valued at approximately $509,526,780. The transaction was disclosed in a legal filing with the SEC, which is available through the SEC website. In related news, Director Dwight A. Merriman sold 1,000 shares of MongoDB stock in a transaction on Tuesday, July 18th. The shares were sold at an average price of $420.00, for a total value of $420,000.00. Following the sale, the director now owns 1,213,159 shares of the company’s stock, valued at approximately $509,526,780. The transaction was disclosed in a filing with the Securities & Exchange Commission, which is available through this hyperlink. Also, Director Dwight A. Merriman sold 606 shares of MongoDB stock in a transaction on Monday, July 10th. The shares were sold at an average price of $382.41, for a total transaction of $231,740.46. Following the completion of the sale, the director now directly owns 1,214,159 shares in the company, valued at approximately $464,306,543.19. The disclosure for this sale can be found here. In the last ninety days, insiders sold 116,427 shares of company stock valued at $41,304,961. 4.80% of the stock is owned by insiders.

MongoDB Price Performance

Shares of MongoDB stock opened at $424.57 on Wednesday. The company has a quick ratio of 4.19, a current ratio of 4.19 and a debt-to-equity ratio of 1.44. The company has a 50 day moving average price of $381.38 and a 200 day moving average price of $279.71. MongoDB, Inc. has a 52 week low of $135.15 and a 52 week high of $439.00.

MongoDB (NASDAQ:MDBGet Free Report) last posted its quarterly earnings results on Thursday, June 1st. The company reported $0.56 earnings per share (EPS) for the quarter, topping analysts’ consensus estimates of $0.18 by $0.38. MongoDB had a negative net margin of 23.58% and a negative return on equity of 43.25%. The company had revenue of $368.28 million during the quarter, compared to analyst estimates of $347.77 million. During the same period last year, the firm posted ($1.15) EPS. MongoDB’s revenue for the quarter was up 29.0% compared to the same quarter last year. As a group, equities research analysts predict that MongoDB, Inc. will post -2.8 earnings per share for the current fiscal year.

Analysts Set New Price Targets

Several brokerages recently weighed in on MDB. Tigress Financial boosted their price target on shares of MongoDB from $365.00 to $490.00 in a research report on Wednesday, June 28th. William Blair reiterated an “outperform” rating on shares of MongoDB in a research report on Friday, June 2nd. Guggenheim cut shares of MongoDB from a “neutral” rating to a “sell” rating and boosted their price objective for the company from $205.00 to $210.00 in a research report on Thursday, May 25th. They noted that the move was a valuation call. Morgan Stanley boosted their price objective on shares of MongoDB from $270.00 to $440.00 in a research report on Friday, June 23rd. Finally, Oppenheimer boosted their price objective on shares of MongoDB from $270.00 to $430.00 in a research report on Friday, June 2nd. One investment analyst has rated the stock with a sell rating, three have assigned a hold rating and twenty have issued a buy rating to the stock. According to data from MarketBeat.com, the company presently has a consensus rating of “Moderate Buy” and a consensus price target of $378.09.

About MongoDB

(Free Report)

MongoDB, Inc provides general purpose database platform worldwide. The company offers MongoDB Atlas, a hosted multi-cloud database-as-a-service solution; MongoDB Enterprise Advanced, a commercial database server for enterprise customers to run in the cloud, on-premise, or in a hybrid environment; and Community Server, a free-to-download version of its database, which includes the functionality that developers need to get started with MongoDB.

See Also

Want to see what other hedge funds are holding MDB? Visit HoldingsChannel.com to get the latest 13F filings and insider trades for MongoDB, Inc. (NASDAQ:MDBFree Report).

Institutional Ownership by Quarter for MongoDB (NASDAQ:MDB)

This instant news alert was generated by narrative science technology and financial data from MarketBeat in order to provide readers with the fastest and most accurate reporting. This story was reviewed by MarketBeat’s editorial team prior to publication. Please send any questions or comments about this story to contact@marketbeat.com.

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AWS Introduces a Generative AI-Powered Clinical Documentation Tool with HealthScribe in Preview

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Article originally posted on InfoQ. Visit InfoQ

AWS recently announced a new HIPAA-eligible service called AWS HealthScribe in a preview that uses speech recognition and generative AI (powered by Amazon Bedrock) to generate clinical documentation.

The company describes AWS HealthScribe as a combination of conversational and generative Artificial Intelligence (AI) to reduce the burden of clinical documentation and improve the consultation experience. By leveraging the service, users gain access to a comprehensive set of AI-powered functionalities tailored to expedite clinical documentation within their clinical application.

AWS HealthScribe offers healthcare software providers a single API to automatically create robust transcripts, extract key details (e.g., medical terms and medications), and create summaries from doctor-patient discussions that can be entered into an electronic health record (EHR) system.

For instance, notes created in HealthScribe are augmented by AI, including details such as the reason(s) for a visit, the history of the present illness, assessment, and follow-up.

Example of the application experience that healthcare developers can provide users with AWS HealthScribe (Source: AWS for Industries blog post)

The authors of an AWS for Industries blog post mention the benefits of AWS HealthScribe:

By consolidating these capabilities, AWS HealthScribe reduces the need for training, optimizing, integrating separate AI services, and building custom models, allowing for faster implementation. Customers can focus on delivering value to their end users without worrying about optimizing individual AI components.

On the other hand, while the service is HIPAA-eligible, companies must sign a contract known as a business associate addendum, which AWS’ documentation covers in detail to become fully compliant.

Besides AWS, Microsoft, and Google have healthcare offerings like AWS HealthScribe. For example, Microsoft Healthcare Bot is a cloud service that enables healthcare organizations to build and deploy conversational agents that can be used for various purposes, such as triage and symptom checking. Or the Google Cloud Healthcare API provides a suite of healthcare-specific products and services built on the Google Cloud Platform.

Bertalan Meskó, a Director at the Medical Futurist Institute, Ph.D. and MD, commented in a LinkedIn post:

It’s exciting to see tech giants marching into healthcare, and we should all be happy about it as they are much better at creating technologies people want to use than healthcare/pharma companies.

In addition, Simon Dawlat, a CEO at Batch, tweeted:

Gold rush of AI-based clinical doc APIs in full swing with Amazon joining Microsoft/Google with the launch of HealthScribe—yet all those FAANG-powered products feel awkward compared to what laser-focused cos like @NablaTech have to offer.

Race is on!

Other comparable solutions are available from companies such as Nuance, offering conversational AI for healthcare and customer engagement, and Cerner Corporation (Oracle), a supplier of health information technology solutions, services, devices, and hardware.

Lastly, AWS HealthScribe is currently available in US East (N. Virginia) region only, and customers can sign up via a form to access the service. Its pricing details can be found on the pricing page, and other details in the documentation.

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New C# 12 Preview Features Available

MMS Founder
MMS Arthur Casals

Article originally posted on InfoQ. Visit InfoQ

Last month, together with Visual Studio 17.7 Preview 3, Microsoft released new preview features for C# 12. The new language version will be the default for .NET 8, expected to be released later this year.

The latest available features for C# 12 are inline arrays and interceptors. Inline arrays, often utilized by the runtime team and various library creators, are tools to enhance the speed of your applications. They allow developers to set up fixed-size arrays within a struct. While you might not frequently declare your own inline arrays, you’ll encounter them seamlessly as either System.Span or System.ReadOnlySpan when working with runtime APIs, for example.

Starting with C# 12, you can declare inline arrays as a struct type. According to the official language reference documentation:

An inline array is a structure that contains a contiguous block of N elements of the same type. It’s a safe-code equivalent of the fixed buffer declaration available only in unsafe code. [..] Inline arrays are an advanced language feature, intended for high-performance scenarios where an inline, contiguous block of elements is faster than other alternative data structures.

A struct equipped with an inline buffer should offer performance akin to an unsafe fixed-size buffer. Also, an inline array doesn’t have a specific layout, except that it contains only a single field. They are defined as annotated structs, functioning much like standard arrays:
 

[System.Runtime.CompilerServices.InlineArray(10)]

public struct CharBuffer

{

private char _firstElement;

}

Source: Microsoft

The compiler validates the System.Runtime.CompilerServices.InlineArrayAttribute attribute as follows: its length must be greater than zero, and its target type must be a struct.

In most cases, an inline array can be accessed like an array, both to read and write values. In addition, you can use the range and index operators. As for the type of the structure’s single field, the restrictions are minimal: it can’t be a pointer type. You can find more about inline arrays in its feature speclet.

Interceptors are an experimental compiler feature, available only in preview mode. Being an experimental feature means that it is subject to change or even removal in the near future. In a nutshell, an interceptor is a method that, during compile time, can seamlessly replace a call intended for an interceptable method with a call to itself. It achieves this by designating the original call locations it intends to replace. Essentially, interceptors offer a mechanism to alter the behavior of pre-existing code by introducing new code during compilation, often through a source generator.

When using an interceptor within a source generator, the goal is to modify existing code instead of merely adding to it. In this process, the source generator will replace calls targeting an interceptable method with those directed at the interceptor method. The interceptor feature specification provides a detailed design of the new feature, including code samples and usage restrictions. In order to use this new feature in your C# project, you must explicitly set the InterceptorsPreview element in your project file.

In addition to these, other C# 12 features are already available for developers. Primary constructors, introduced in C# 9, are no longer restricted to record types. Since Visual Studio 17.6 preview 2, they can now be created in any class and struct, which means it is possible to add parameters to the class or struct declaration and use these values inside the type body.

Other available C# 12 features are the possibility of adding optional parameters to lambda expressions and using the using alias directive to alias any sort of type (not just named types). This allows you to create semantic aliases for tuple types, array types, pointer types, or any other unsafe types.

You can find more about the available C# 12 features in the official language documentation page. You can get C# 12 by installing either the latest Visual Studio preview (Windows)  or the latest version of the .NET 8 SDK (Windows, macOS, and Linux). You will also need to set the language version of your project to preview:
 



   preview




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MongoDB: The 2-Step Rule For Attractive Returns (NASDAQ:MDB) | Seeking Alpha

MMS Founder
MMS RSS

Posted on nosqlgooglealerts. Visit nosqlgooglealerts

laptop sign with magnify glass on dark background 3d render concept for

Sakibul Hasan/iStock via Getty Images

Investment Thesis

MongoDB, Inc. (NASDAQ:MDB) has strong prospects and is expected to deliver both compelling growth rates and attractive profitability into the end of its fiscal year. Note, MongoDB is about

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Presentation: Generous, High Fidelity Communication Is the Key to a Safe, Effective Team

MMS Founder
MMS Denise Yu

Article originally posted on InfoQ. Visit InfoQ

Transcript

Yu: My name is Denise. This is generous, high-fidelity communication, and how that is the key to a safe and effective team. I’m actually on a career break right now, just taking a couple months off to unwind from a very long pandemic. Earlier this year, I was a senior engineering manager at GitHub, working in a department called Communities. We are the org that builds tools that power open source communities on GitHub. I worked specifically on GitHub Sponsors, which lets people give money directly to the maintainers of open source projects that they depend on.

Being an engineering manager has been by far my favorite job in my career so far. I loved being a senior engineer, but being a manager really stretched my abilities in ways that I could not anticipate. I went into the job with a lot of leadership experience, a lot of experience running projects and being in charge of things. Nothing truly prepared me for the process of building a great team. Because that process wasn’t just about putting the right energy out into the world. It’s not just about nailing your top-down communications. The process of building an amazing team is messy. It’s incremental. It’s humbling. It’s every little decision you make. It’s like every Slack DM you choose to send, every choice you make about when to give feedback, how to give it.

Just Culture

I want to explore how communication skills within a team can make or break that team, and why the team as a unit matters in terms of building a resilient and just culture. I want to start by contextualizing this talk against just culture, because this track is dedicated to a deeper examination of that concept. The idea of just culture comes from academic research in safety critical industries, such as medicine, aviation, manufacturing. We’ve adopted some of the ideas into software engineering in recent years. I think that just culture is a philosophical approach to improving an organization’s ability to manage incidents through a balanced examination of both environmental/cultural/organizational factors, as well as individual accountability. In recent years, more tech organizations have been trying to apply the principles of just culture as we approach the limits of what traditional safety management practices can give us. Of course, not all of us work on safety critical systems where all of this research originated. We don’t necessarily have the same constraints and requirements that medicine or aviation has, to be very worried about mistakes. Our organizations are still deeply invested in wanting things to go right and learning from when they don’t. A lot of the practical applications of just culture have been described through Sidney Dekker’s work. I’ll be referencing his work a lot.

In order to improve and make our systems more resilient, just culture seeks a balanced examination of the environmental factors that contributed to a mistake, as well as individual accountability from the person who was directly involved in the incident. I want to pause here and just point out that just culture’s definition of accountability is quite narrow. It might be a bit different from how we generally think of the term. In just culture, accountability doesn’t mean blame. Rather, it means that we literally want the person who was involved in the incident to be able to talk to the rest of the org about it so that we can all learn from it to literally give an account of what happened. In just culture, it’s really tricky to get this balance right when you look to both environmental, or cultural factors, and individual accountability, if our goal here is to improve the overall system. Because on the one hand, you don’t want to place too much accountability on an individual, because if you push an individual to own up to too much, they’ll feel penalized. The long-term effect is that fewer people are going to speak up when they see something going wrong. In other words, you just suppress reporting. Any information that potentially could have helped avoid or mitigate a problem just gets pushed underground. This does not make your system safer. On the other hand, if you place too little emphasis on individual accountability, you potentially create a culture of increased carelessness in the future, because the people perceive that their managers don’t really care about things going wrong, then, why should they? In both of these situations, we lose out on opportunities to learn from the failure and improve the overall resilience of our systems.

Decision Making Frameworks

Dekker’s research showed that when we’re trying to get this balance right, leaders in our org are generally looking at human choices that led up to a mistake. They’re making a judgment call. Was this action an honest mistake, or was it at risk behavior, somewhere in the middle, or was it straight up negligence? What kinds of actions fall into each of these boxes? That depends entirely on your organization. Figuring out where these boundaries should be requires really specialized knowledge about the work that you’re doing. It’s a difficult task, even when you do have all the information. Sometimes organizations use devices like decision trees to add some structure and transparency to this judgment making process. Here’s an example from the aviation industry. This is a series of bubbles that you traverse by answering yes or no at each juncture. The further you make it to the right, it means that you took more precautions along the way, and therefore, the person involved in the incident is less likely to be held to account. I think it can be sometimes useful to formalize decision making like this, but at the same time, creating a diagram that’s useful and generalizable, necessarily requires us to buff away a lot of the finer details and edge cases. The problem with that is that, of course, life happens in these edge cases in finer details and reality is messy and is full of surprises.

As I learn more about decision making frameworks, another example that popped into my head was engineering career progression frameworks, or career ladders, which attempt to systematize the growth of engineering ICs. If you make your career matrix really detailed and concrete, what can sometimes happen is that very ambitious ICs can turn them into a box ticking exercise. It stops being a meaningful framework for both engineers and their managers to help them grow their skills and impact. Another thing that both decision trees and career ladders have in common is that they were both written by a group of people who had a certain set of opinions at a certain point in time about what exactly is an honest mistake, or a senior engineer. People are going to bake in their own biases, experiences, and outlook at a particular snapshot in time. I think that while these tools can be helpful, if we truly want to create organizations that can learn and adapt quickly, we should not place too much emphasis or too much confidence in what’s already been written, and instead, continually reassess whether these tools are still serving their intended purpose. When they stop doing so, we can change them, we can iterate them, or we can thank them for their service and let them go.

Every time we try to formalize an inherently subjective idea, like an honest mistake, or a senior engineer, we have to expect that we’re just going to get it wrong sometimes. I used to give a talk about why distributed computing is so difficult, both in theory and in practice. One of the core messages was that with large scale distributed computer systems, we honestly should just expect things to go wrong at some point because our systems will be designed more robustly if we build in the possibility that they could fail. It turns out that big socio-technical systems are no different. Humans working together towards a complex goal, like building an organization that can learn from mistakes well, is not all that different from a large system of computers executing instructions to stay online as much as possible. If we assume that we’re going to get things wrong sometimes and we design for failure, this is how we build resilience into big and unpredictable systems. Dekker’s work over the years has found that when an organization tries to decide what types of actions require accountability and oversight, who should be held to account, when, to what degree? We’re just going to get it wrong sometimes. It might be a more interesting question to examine who is making these judgments rather than the specifics of like, what actions go in which box. It also turns out that we humans really like defaulting to negligence. We really like boiling down complex problems to statements like, this person did something wrong, which sometimes creates organizational impacts that don’t actually benefit us in the long run. Because even the best managers and leaders among us are only human. That’s ok, because, like we can design against network partitions in distributed systems, we can design for this mistake in judgment making. We can tolerate mistakes in judgment calls, as long as we have a mechanism for making things right. That’s where teams come in.

Table Stakes When Implementing a Just Culture

Table stakes for implementing a just culture is that psychological safety has to be present. Psychological safety was first coined by Dr. Amy Edmondson. The classic definition is that it is a shared belief held by members of a team that the team is safe for interpersonal risk taking. Today, we commonly think of risk taking as speaking up, bringing up difficult issues, and being vulnerable. Vulnerable enough to say, for example, I made a mistake and here’s how I’m going to fix it. When you ask for individual accountability, the only way you’re going to get an honest and insightful account is if the person telling the story feels like their job, or their reputation won’t be in jeopardy afterwards. In my experience, and I think in a lot of people’s experiences, your team is your primary social environment at work. Most engineers learn about cultural norms at the team. Most engineers pick up on cues about how to be successful at this organization from their teammates. For most of us, our happiness and our safety at work is directly influenced by how safe and supported we feel around our teammates. If you’re on a team where you feel like you can speak freely and disagree without fear of consequences, then I believe that you have the idea of social support that Dekker talks extensively about. This kind of social support is vital to, number one, psychologically recovering from mistakes we make. Even if you know you’re not going to get in trouble, it still sucks to be the person who was at the wheel when prod went down, or whatever it was. Also, secondly, social support is really important for being able to effectively take accountability for those mistakes, and to help the wider org actually learn from them.

As an engineering manager, if your org is pursuing a just culture model, or even if they’re just trying to learn more from incidents, I think that the best investment you can make is in your team. Even if none of the just culture stuff earlier resonated with you, I think that investing in your team is still worth your while because it’s realistically probably the only thing within your direct control. I believe that there’s one behavior in particular that teams with high psychological safety do really well, which is that they disagree productively. In terms of building a just culture, having teams that can navigate conflict effectively will form your strong foundation for a resilient organization.

Disagreements in Communication

I’m going to shift gears a bit into the second half of this talk, which is more focused on communication. We’re going to dive a lot deeper into what a good disagreement looks like. A good disagreement, in my opinion, boils down to three characteristics. A good disagreement is engaging, fair, and generous. A long time ago, I was a competitive debater in university, so I understand the mechanics of a good debate pretty well. I noticed over the past couple of years that I started leaving conversations with coworkers and friends, sometimes with a nagging sense of incompleteness, a feeling like I hadn’t really been hurt at all. I realized that this was the same feeling I got when I finished a really bad round of competitive debating. A lot of disagreements at work are missed opportunities to find a better solution. Instead, they can hurt relationships, and perhaps create a chilling effect in the future, if, for example, one person feels like they really were not listened to, that they’re never listened to. I spent a lot of time reflecting over the years about what great disagreements look like. It turns out, they look pretty similar to great debates. Good disagreements are engaging, first of all. By engaging, I mean that everyone is actually taking time to respond to the arguments being presented by their peers, instead of just waiting to say their own piece.

Let me give you an example of a conversation where engagement is not happening. Meet our two engineers, Katherine on the left and Jimmy on the right. Jimmy and Katherine are going to disagree about something. Katherine says, “I think we should transition away from our hosted app analytics solution called Datacat and look into building our own in-house monitoring solution, because we’re hitting the limits of what we can learn from Datacat’s current analytics tools.” Jimmy responds, “That makes no sense. We’re struggling to staff up our core product teams. I don’t think there’s any way we can get headcount or budget to maintain a whole in-house monitoring solution when Datacat gets us 95% of the way there.” Katherine replies to that, “If the gap is 5% today, it’s only going to get bigger in the next year, because our sales teams have already told us what our projected growth is going to be, just over the next 12 months.” At this point, Jimmy is getting frustrated. Jimmy says, “I think we should look into other alternatives. I think we should explore other off-the-shelf solutions.” It concludes with Katherine saying, “You’re not really listening to me. I have looked at other vendored solutions, and no one is selling a tool that can keep up with our scale.”

I’m going to explain what happened. First, I want all of us to take a beat and reflect. Does this disagreement sound familiar? When is the last time that you saw or took part in a disagreement like this? How did it make you feel? How would you imagine the other participants felt? In debate parlance, we refer to this type of conversation as two ships passing in the night. Katherine’s argument is that Datacat is becoming a poor solution to her team’s specific problems. Jimmy is making an argument about resource allocation. Both of these people are experts. They’re equally familiar with the problem, but they have different ideas about what’s the most important aspect of the problem. Jimmy didn’t disagree with Katherine’s premise that this company is going to outgrow a vendored monitoring solution. Katherine also didn’t engage with Jimmy’s concerns about staffing and budget being a finite resource. The outcome here is that both Jimmy and Katherine will walk away from this interaction, feeling pretty frustrated. There’s also no sense of resolution if you’re a listener. For example, if I was a judge, and this is a real debate round, this would be a pretty terrible round, both of them would get really poor scores. This conversation might have gone differently if Jimmy had first paused to consider Katherine’s concerns, before offering up his own arguments, and likewise for Katherine in her later response.

I see this dynamic happening in async settings, especially, because it’s so easy. The barrier is so low to go into someone’s Google Doc or someone’s written document, whatever it is, and drop in 30 different comments each along the lines of, but what about this other alternative? What about this? What about that? Without ever having engaged with what the author did right. In doing so, you’re not engaging with their ideas. Secondly, you’re also not giving them any credit for having done some critical thinking, because maybe they did consider some alternatives but this is the best version that made it onto the paper. Engage with what’s there. Give people credit for having done critical thinking. Listen as much as you speak, and I think you’ll find that the quality of your disagreements will really improve. Second, a good disagreement is a fair disagreement. By that I mean that team members can equitably participate in the discussion. Sometimes a bad disagreement happens because one person never actually had as loud of a voice as the others in the conversation. This can result in one person’s ideas being shut out, and they eventually lose trust in the rest of their team to fairly engage with their ideas. Maybe they’ll eventually withdraw from trying to participate all together. This is a big loss. I think this is something that we as managers and leaders should be very worried about. In the long term, this will have a negative impact on the morale of the team.

Some debates are inherently unfair. This one is really tricky for a lot of managers and leaders, because it requires knowledge of power dynamics. There are certain aspects of structural power that we can see, things like job titles, things like years of experience, seniority on the team or at the company, and so on. There are also social and interpersonal dynamics that are less obvious, and as managers sometimes we are especially poorly positioned to see those dynamics. Because sometimes when people are creating a negative impact on the team, they shield us from that. Sarah Milstein did an excellent talk at the LeadDev New York this past year, called, “Harassers are nice to me.” I highly recommend you go watch this talk and check out this blog post in its entirety. The thrust of this is, as you gain more formal power in your organization, people who are really sneaky are increasingly likely to be perfectly sweet and civil to our faces, because they know that they have to be in order to get what they want. As much as we try to pay attention in our one-to-one conversations, it can be really hard for managers to know who has soft influence over whom. Sometimes you don’t necessarily need malice or sneakiness involved, things can still be unfair without anyone deliberately trying to be harmful.

Let me tell you a little story. This is a true story. I told you already that I was a competitive debater in university. There are a lot of different debate formats that use different styles for winning. There’s a format of debate called policy debate, which rewards teams that can overwhelm their opponents by speaking really fast, and bringing a ton of evidence to back up their arguments. By evidence, I literally need paper printouts of news articles, that sort of thing. They seem to bring boxes of paper to every single debate tournament, like you would expect from a lawyer arguing a real-life court case. It takes a lot of effort to compile all this evidence. It literally takes hundreds or thousands of hours of work. Many of the more privileged teams on these debate circuits will hire full time debate coaches to help them compile all of this evidence. For several years, the top ranks of policy debate were completely dominated by the wealthiest schools who could afford to pay for the best debate coaches.

Unsurprisingly, a group of black debaters from schools who cannot afford to pay for coaches found that they were consistently not performing well at tournaments, no matter how much they practiced. Instead of competing by the rules of the game, they decided to confront the rules. Instead of coming with boxes of evidence, they started making a new argument in debate rounds that the long-standing expectations of policy debate were inherently unfair to less privileged teams. Policy debate as a whole should reject these old rules and instead evolve towards something more creative and engaging. They argue that being a great debater shouldn’t be about being able to hire the most expensive coach. It should be about developing your critical thinking skills, winning people over to your cause, and eventually using that power to do some good in the world. What message are we sending then, when only the most privileged teams receive all the opportunities? Today, this type of argument about the rules or the procedures themselves, is called meta-debate. It’s super common across all formats. If you want to learn more about the story, RadioLab did a great episode called “Debatable” about it.

I told you the story, first of all, because I think it’s an interesting story. Second, to illustrate that sometimes the rules of engagement are unfair, and they’re worthy of reexamination. This is a simplified model that I came up with to encourage managers to be inwardly critical of what we’re asking from our teams when we ask them to communicate or to disagree with each other. On the x-axis, we can ask for communication in a slow or fast style. On the vertical, we can ask them to do it together or alone. To add a bit more color to this, I want you to think about what your rules of engagement are. Are you having a lot of team meetings where folks are asked to spontaneously respond to new information and come up with an opinion on the spot? That’s a fast and together style. Or, do you give your team written materials to review ahead of time and explicitly block out time for everyone to do it, which is more of a slow and alone, or maybe eventually a slow and together style. Some other examples of communication-oriented tasks include asking your team to develop opinions on something really high context, brainstorming ideas together, giving their opinions, and responding to each other’s ideas. If you’re finding that your facilitation style falls heavily in one quadrant of this model, consider mixing it up once in a while. Consider sending out reading material ahead of time if that’s not something you normally do, or give people explicit time at the start of the meeting to review the material. Or maybe let people brainstorm independently before sharing out their ideas.

If you sense that some people are being unfairly shut out from a conversation, it’s our responsibility as managers and leaders to stop the conversation all together. Then do some critical thinking and examination about what made it unfair. For me, personally, I call it a day. I give people time to cool off. Then I go and check in with the people who I think were being shut out. This is one of those opportunities where your actions as a leader establish your culture. This is one of those brick-by-brick things. You can either choose to plow forward and to continue to shut out your most marginalized voices, or you can use your structural power to try to make things right. You can explicitly tell those folks that their ideas matter, and that you will back them up, and, of course, actually do that the next time you get the team together to continue the conversation. I’m going to repeat this part because I think it’s really important. As a manager or a leader, you have structural power and privilege here. It is a fantastic use of that power to use it to equalize the playing field for your team. Let’s pause and reflect again. Now that you know a little bit more about the different ways that disagreements can be fair or unfair, when’s the last time you noticed someone getting shut out? How might they have felt? What is something that you could have done from your position of power to include them more effectively?

Third and final characteristic of good disagreements. Good disagreements are generous. In philosophy and in debating, actually, there is something called the principle of charity, which is that when you are debating an opponent, you should always try to understand their statements in the most rational way possible, as if they made the strongest possible version of their argument. Here’s an example of ungenerous and then generous interpretation. Say you have a staff engineer on your team, and you’re presenting the architecture for a new project. She points out something that looks like a load balancer in the diagram, and she says, why do we need a load balancer? There’s actually a lot of different ways to interpret this question. An ungenerous interpretation would be to assume that she has zero knowledge of load balancers generally, and then launch into a 101 level explanation of how load balancers work. I’ve actually seen this happen before, and it was as awkward as it sounds. A far more generous interpretation is to assume that she’s deployed a load balancer before, she understands her general purpose. Instead, she’s asking a very high context question about why this particular system might need load balancing around a particular set of services. Maybe she knows something about typical traffic patterns in this scenario that you might not have thought of yet. Even better than trying to guess how generous or how ungenerous, even better is when teams actually check for understanding. It gave me a lot of energy to see teams doing this.

In that example, the person presenting the architecture might ask a clarifying question before trying to answer. That clarifying question might be something like, just so I understand your question, are you looking for more clarity around the assumptions we made about traffic to these services or are you asking a different question? Getting everyone on the same page when you work in a really high context environment is really difficult. Disagreements tend to be a lot more productive, though, if you can take a couple of extra beats, and just make sure you’re always treating each other with dignity and generosity. One more quick round of reflection. When’s the last time you noticed someone interpreting the words of a teammate in a really ungenerous way? How do you think they felt? Has this happened to you? What impact did that ungenerous interpretation have on the overall tone of the conversation? A good disagreement is engaging, fair, and generous.

Takeaways

Now that you’ve seen a couple of examples of bad disagreements, we’ll wrap up with a couple of actionable things for you to take back with you. Of course, it’s one thing to observe and to recognize, but that’s not enough. Our job as managers is to play an active role in shaping the culture we want for our teams. That involves rolling up our sleeves and doing. First, I think we have to model graciousness and humility when people disagree with us, because we have the most formal power on the team. It’s important to show that you as a manager can deal with being told that you’re wrong. As managers, we get it wrong all the time. If someone disagrees with me in private, and it results in a conversation where we get to a better solution, I get their consent to share what their objection was at the next team sync or just in the team channel. I make it super clear that their feedback was welcome, relevant, and impactful. In fact, I am changing my plan because they made a compelling and productive argument, and I’m going to be partnering with that person on the follow through. I might write a message like this, “Hey Team. I know I said this morning that we will be doing X next, but Amanda made a really great point to me earlier that we’re spreading ourselves too thin. She also shared with me some new context that I didn’t have that one of our tracks is delayed because of the deployment problems last week. I’ll be working with her over the next few hours to figure out a new game plan for our coming weeks, let me know if you have input that we should know about.” My goal here is to give credit to good disagreements and to engage the team in crafting the solution together. If you’re having interactions like this with your team regularly, it means that you’ve likely done a decent job at creating some psychological safety. I think that’s something you should be proud of. Because when your team feels comfortable bringing up problems to you, you can get in front of potential issues before they happen. That’s exactly how just culture seeks to make things better. Summarizing and naming things, also a very useful tactic. Try this out.

To go back to the two ships passing in the night example, if you see that people are talking past each other, you can try to intervene with something like, “Katherine, it sounds like you’re really worried about the problem solution fit of our monitoring solutions in the coming years. It sounds like Jimmy is really worried about resourcing. I think these are both really valid concerns and I love that we’re getting as deep as we are into this. I’d love to hear a little more from Jimmy on Katherine’s core idea. I want to make sure that everyone’s ideas are properly engaged with before we move on.” Nothing too fancy. When you see these bad disagreements happen, you have structural power to bring things back on track. You can use that power to level the playing field to amplify voices who you suspect are being shut out. As a leader, it’s far more important for us to create space than to take up space. Anyone can take up space, actually. As leaders, we are way better positioned to be the ones creating space. As I learned more about team dynamics, I learned that everything I say inherently has more weight when we’re trying to find a solution because I’m the manager. At first, that was really scary to me because I was like, I don’t know what I’m doing. I’m just making things up. I’m just guessing. Here’s the thing, when you’re just making things up, or you’re just speculating as a manager, at best, it can be distracting. At worst, it can actually be harmful because people sometimes think that you’re privy to a lot more information than you actually are. When in reality, the engineers directly working on the problem are the experts. Recall earlier that in order for just culture to be effectively implemented, we need to make space for the people who have the most direct knowledge so that they can creatively and effectively solve problems.

To mitigate this phenomenon, where I noticed that every time I spoke, I took up all the oxygen in the room, I started doing two things. First, I worked on developing my facilitation skills. Secondly, when it comes to substantial contributions, like actually coming up with solutions, talking about the problem, I started speaking last. It’s important to note that this isn’t about trying to get the last word in. Speaking order is just one tool. The most effective thing you can do as a leader is learn to facilitate conversations in an inclusive and productive way. Finally, a great thing you can always do if you sense that your team is in disagreement territory, you can always reaffirm shared common ground and remind everyone that we are on the same side. It’s the whole team versus the problem.

Conclusion

Building a healthy and safe team is a really hard thing to do. Hopefully, by the end, you’ll have a clear idea of some of the concrete behaviors on a psychologically safe team, how these teams communicate, and some ideas of what you can do to help your team get there. If an organization has a lot of independently strong teams, my bet is that the org will be so much better equipped to pursue a just culture, because the cost of getting it wrong is so much lower. When we design for failure, when we think about building resilience into our organizational systems, I think that we end up with building stronger systems overall. Ultimately, that comes down to having really strong teams that can support each other, provide social support, and know how to navigate conflict.

Questions and Answers

Brush: You mentioned psychological safety and what it means. Sometimes folks confuse psychological safety for the ability to not hear critical feedback, to not have to get that direct feedback. In your experience, how do you handle that situation?

Yu: I think one misnomer that a lot of folks have about psychological safety is that if you’re happy and healthy and safe on a team, you never push back on anything, the team never disagrees and nothing spicy ever happens. I actually don’t think that that is really how healthy teams operate. Because as long as you have a group of people with different experiences and different ideas in a room, which is always the case when you have a group of software engineers where the group is larger than one, I’ve never seen two people agree perfectly on everything. Some level of natural disagreement is always going to happen. The question is, how is the team responding to those disagreements and recovering from that? Just culture’s whole idea here is that social support, like when these disagreements happen, you have enough of a social foundation so that you can recover from that disagreement. So that me disagreeing with your idea doesn’t come across as Denise hates Michelle, or something like that. Because we’re on the same page there. We’re just here to figure out what the best solution is to this problem that we both have to solve together. The key really is to build a lot of trust within the team, which is, of course, way easier said than done in all circumstances, especially if you’re beginning from a low-trust environment. It’s like, how do you know if you’re even moving the needle when no one is saying anything to you in team meetings, or one to ones, or anything like that? I think it’s really tough.

Brush: I think the key thing was focus on, how do people respond to the feedback? How do they feel supported during receiving the feedback and not about trying to avoid it?

The most ungenerous thing happening to me is when an opponent starts doing something else when you try to make your point. This is about not having respect. Then sometimes this is the manager themselves. How would you respond in that situation?

Yu: That’s really rough if that’s your manager, because that’s supposed to be the person that’s looking out for you. That’s the person with formal power, who should be establishing that the norms of communication are based in respect, and being on the same page about what we’re here to do. It sounds like this is an experience that might have happened to you. That really sucks. I just want to validate that that is a terrible thing to have to experience. In the case where it’s a teammate, I do expect that team leads and managers, the people who have the formal authority, take some feedback on board about how they’re running meetings, about how they’re facilitating meetings. If this is a regular recurring thing, that’s some feedback that needs to flow from the managerial level down to those ICs who are consistently displaying that lack of respect. Other things I would consider doing if I was someone in charge of that situation is changing of the meeting structure, just as an experiment. There’s no one-size-fits-all thing here. If you’re constantly in a situation where it’s like one person lecturing, try to change up the meeting format to be more dynamic and more interactive. Try to create situations where people have to be engaged, people have to sit with their ideas, and they have to collaborate with others to mesh their ideas together before just saying their piece.

There’s different facilitation tactics, if you’re really interested in this. I learned a lot of this when I was at Pivotal from the designers. They all were very immersed in the world of design thinking, which I don’t know super a lot about, but I know that’s where a lot of those facilitation tips and tricks came from. I think if it’s your manager straight up being on their phone all the time while you’re talking. That really sucks. At that point, I think upward management techniques are things that you would have to look into, if it’s not an option to switch managers or switch teams. I know that that’s rarely a feasible option. If I suspect that someone’s not paying attention with me, I do the thing where I say something, and I check in with them. I’m like, just so we’re on the same page, you agree to do this and that. Just get verbal assent from them that they heard you, that they understood your point, and then just operate as if you have their consent for as much as possible. It’s difficult to collaborate with people who are fundamentally not interested in collaborating with you, but I would try really hard to. I heard the phrase a long time ago, like very early in my career, if you’re in a situation where you don’t have a lot of control over things, you can do two things. You can either change your environment, or you can change your environment, which is like, depending on where the emphasis is on those words, it’s two different things. That’s a tough situation, for sure.

Brush: How would you handle the situation when there’s a request coming from upper management that the team doesn’t agree on?

Yu: I think this happens to everyone. At some point, sooner or later in your career, you’re going to be asked to build something that you don’t just agree with. In my experience, there are times when it’s productive to push back on this and bring data upwards and try to appeal to upper management, speaking their language. Figure out what is the thing that they’re most concerned about, and try to appeal to them on those grounds. There are other times in my career that I’ve just had to suck it up and just deliver the thing, because that builds enough trust for me to fight the next battle more effectively. Picking and choosing your battles, incredibly difficult, especially if you’re someone who’s very strong willed and opinionated, like I am. I’ve had to learn. When you want to stay at a company for a couple of years, for whatever reason, you’re there to grow, you’re golden handcuffed, whatever it is: it’s a marathon, it’s not a sprint. If you go out swinging in every single fight you’re in, you’re going to run out of energy, probably before the end of the first year. Pick your battles and find out how wrong can upper management afford to be? Is this a company tanking mistake? Is this a mistake that will cost a couple of customers? Will it erode all of your customer trust? Figure out what’s the worth of pushing back before you decide to push back or not.

Brush: The thing I always add to that is like, how hard is it to change the decision later? If it’s easy, then maybe that’s the one you let them learn the hard way.

Yu: There’s a really good blog post by Camille Fournier. The name of the blog post is called, Other People’s Problems. Essentially, it’s like, you only have so much energy when you’re at any size organization, you need to be deliberate about where you direct that energy. If you keep throwing your energy at things that are outside of your control, you’re just going to set yourself up for failure. Also, one of the other outcomes of that blog post is one of the things that is usually within your control is focusing on your team, because often you’re not part of the decision-making structure for things that are two or three titles above you.

Brush: When the team is speaking their mind, how can we make sure they are not judged or held accountable for what they said. I’m assuming accountable in a bad way in this sense.

Yu: I think a lot of the negative accountability happens when a person’s comments are taken out of context. That usually is like, someone maybe said something in a channel that was meant to be for the team, but it’s actually public, and now it’s been shared. We’re all on social media. I think we’ve all seen the scenario where a set of comments gets taken out of context. You can’t control how other people interpret your comments, but you actually can control the flow of information. You can set up norms and structures so that someone’s feedback or someone’s comments don’t spiral out of control, and they don’t get continually taken out of context. One of the best ways to do that is have conversations as a team first. Make your team the safe space for people to communicate. Do it ideally, on Zoom, ideally, in real time, like on video, or at least over audio. Because when things get written down, that increases the risk of people misinterpreting or start taking out of context. Just make sure that disagreements firstly happen within the team, they happen among the people who are closest to the problem, and most in charge of delivering the solution. Beyond that, things don’t have to ever get shared outside. What you could do as a team leader, as a manager, if you have a culture where teams share what they’re doing to the broader company, which I think is actually a really good culture that I’ve seen at a lot of different places, make sure that whatever you’re posting outward, gets run by the people who originally made those comments. If someone made a really great idea, and that makes it into like a new iteration of this feature, make sure that person is appropriately credited, but understand that you as the leader, you’re going to be the person that goes to bat for defending that idea.

Sometimes the people are like staff level or super senior level, they’re comfortable with being the person to also defend their ideas, or there’s career reasons to also want to do that. I think a good default, as the manager, as the leader, you’re accountable for your team’s decisions, and you’re first-line support, you’re first-line of defense for any possible repercussions from the outer org. I know that’s only one variety of the negative accountability, things being taken out of context. I think that theme can extend outward. Protect your team, as someone with formal power, protect your people. That’s how you build trust within the team. That’s how you create an environment where people actually come to you and say things like, “We’re heading towards a really bad incident, I need to talk to you.” That’s exactly what just culture wants to happen. Show with both your words and your actions that you have your team’s back. You will protect them. You will make sure that their ideas and their feedback and anything that they raise will be interpreted and recorded in the most generous interpretation possible.

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