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With the right tools, predicting startup revenue is possible
For a long time, “revenue” seemed to be a taboo word in the startup world. Fortunately, things have changed with the rise of SaaS and alternative funding sources such as revenue-based investing VCs. Still, revenue modeling remains a challenge for founders. How do you predict earnings when you are still figuring it out?
The answer is twofold: You need to make your revenue predictable, repeatable and scalable in the first place, plus make use of tools that will help you create projections based on your data. Here, we’ll suggest some ways you can get more visibility into your revenue, find the data that really matter and figure out how to put a process in place to make forecasts about it.
You need to make your revenue predictable, repeatable and scalable in the first place, plus make use of tools that will help you create projections based on your data.
Base projections on repeatable, scalable results
Aaron Ross is a co-author of “Predictable Revenue,” a book based on his experience of creating a process and team that helped grow Salesforce’s revenue by more than $100 million. “Predictable” is the key word here: “You want growth that doesn’t require guessing, hope and frantic last-minute deal-hustling every quarter- and year-end,” he says.
This makes recurring revenue particularly desirable, though it is by no means the be-all-end-all of predictable revenue. On one hand, there is always the risk that recurring revenue won’t last, as customers may churn and organic growth runs out of gas. On the other, there is a broader picture for predictable revenue that goes beyond subscription-based models.
Ross and his co-author, Marylou Tyler, outline three steps to predictable revenue: predictable lead generation, a dedicated sales development team and consistent sales systems. They wrote an entire book about it, so it would be hard to sum it up here. So what’s the takeaway? You shouldn’t base your projections on processes and results that aren’t repeatable and scalable.
Cross the hot coals
In their early days, startups usually grow via word of mouth, luck and sheer hustle. The problem is that it likely won’t lead to sustainable growth; as the saying goes, what got you here won’t get you there. In between, there is typically a phase of uncertainty and missed results that Ross refers to as “the hot coals.”
Before the hot coals, predicting revenue is vain at best, and oftentimes impossible. I, for one, remember being at a loss when an old-school investor asked me for five-year profit-and-loss projections when my now-defunct startup was nowhere near a stable money-making path. Not all seed investors expect this, so there was obviously a mismatch here, but the challenge is still the same for most founders: How do you bridge the gap between traditional projections and the reality of a startup?
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Meroxa raises $15M Series A for its real-time data platform
Meroxa, a startup that makes it easier for businesses to build the data pipelines to power both their analytics and operational workflows, today announced that it has raised a $15 million Series A funding round led by Drive Capital. Existing investors Root, Amplify and Hustle Fund also participated in this round, which together with the company’s previously undisclosed $4.2 million seed round now brings total funding in the company to $19.2 million.
The promise of Meroxa is that businesses can use a single platform for their various data needs and won’t need a team of experts to build their infrastructure and then manage it. At its core, Meroxa provides a single software-as-a-service solution that connects relational databases to data warehouses and then helps businesses operationalize that data.
Image Credits: Meroxa
“The interesting thing is that we are focusing squarely on relational and NoSQL databases into data warehouse,” Meroxa co-founder and CEO DeVaris Brown told me. “Honestly, people come to us as a real-time FiveTran or real-time data warehouse sink. Because, you know, the industry has moved to this [extract, load, transform] format. But the beautiful part about us is, because we do change data capture, we get that granular data as it happens.” And businesses want this very granular data to be reflected inside of their data warehouses, Brown noted, but he also stressed that Meroxa can expose this stream of data as an API endpoint or point it to a Webhook.
The company is able to do this because its core architecture is somewhat different from other data pipeline and integration services that, at first glance, seem to offer a similar solution. Because of this, users can use the service to connect different tools to their data warehouse but also build real-time tools on top of these data streams.
Image Credits: Meroxa
“We aren’t a point-to-point solution,” Meroxa co-founder and CTO Ali Hamidi explained. “When you set up the connection, you aren’t taking data from Postgres and only putting it into Snowflake. What’s really happening is that it’s going into our intermediate stream. Once it’s in that stream, you can then start hanging off connectors and say, ‘Okay, well, I also want to peek into the stream, I want to transfer my data, I want to filter out some things, I want to put it into S3.’ ”
Because of this, users can use the service to connect different tools to their data warehouse but also build real-time tools to utilize the real-time data stream. With this flexibility, Hamidi noted, a lot of the company’s customers start with a pretty standard use case and then quickly expand into other areas as well.
Brown and Hamidi met during their time at Heroku, where Brown was a director of product management and Hamidi a lead software engineer. But while Heroku made it very easy for developers to publish their web apps, there wasn’t anything comparable in the highly fragmented database space. The team acknowledges that there are a lot of tools that aim to solve these data problems, but few of them focus on the user experience.
Image Credits: Meroxa
“When we talk to customers now, it’s still very much an unsolved problem,” Hamidi said. “It seems kind of insane to me that this is such a common thing and there is no ‘oh, of course you use this tool because it addresses all my problems.’ And so the angle that we’re taking is that we see user experience not as a nice-to-have, it’s really an enabler, it is something that enables a software engineer or someone who isn’t a data engineer with 10 years of experience in wrangling Kafka and Postgres and all these things. […] That’s a transformative kind of change.”
It’s worth noting that Meroxa uses a lot of open-source tools but the company has also committed to open-sourcing everything in its data plane as well. “This has multiple wins for us, but one of the biggest incentives is in terms of the customer, we’re really committed to having our agenda aligned. Because if we don’t do well, we don’t serve the customer. If we do a crappy job, they can just keep all of those components and run it themselves,” Hamidi explained.
Today, Meroxa, which the team founded in early 2020, has more than 24 employees (and is 100% remote). “I really think we’re building one of the most talented and most inclusive teams possible,” Brown told me. “Inclusion and diversity are very, very high on our radar. Our team is 50% black and brown. Over 40% are women. Our management team is 90% underrepresented. So not only are we building a great product, we’re building a great company, we’re building a great business.”
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Startups must curb bureaucracy to ensure agile data governance
By now, all companies are fundamentally data driven. This is true regardless of whether they operate in the tech space. Therefore, it makes sense to examine the role data management plays in bolstering — and, for that matter, hampering — productivity and collaboration within organizations.
While the term “data management” inevitably conjures up mental images of vast server farms, the basic tenets predate the computer age. From censuses and elections to the dawn of banking, individuals and organizations have long grappled with the acquisition and analysis of data.
By understanding the needs of all stakeholders, organizations can start to figure out how to remove blockages.
One oft-quoted example is Florence Nightingale, a British nurse who, during the Crimean war, recorded and visualized patient records to highlight the dismal conditions in frontline hospitals. Over a century later, Nightingale is regarded not just as a humanitarian, but also as one of the world’s first data scientists.
As technology began to play a greater role, and the size of data sets began to swell, data management ultimately became codified in a number of formal roles, with names like “database analyst” and “chief data officer.” New challenges followed that formalization, particularly from the regulatory side of things, as legislators introduced tough new data protection rules — most notably the EU’s GDPR legislation.
This inevitably led many organizations to perceive data management as being akin to data governance, where responsibilities are centered around establishing controls and audit procedures, and things are viewed from a defensive lens.
That defensiveness is admittedly justified, particularly given the potential financial and reputational damages caused by data mismanagement and leakage. Nonetheless, there’s an element of myopia here, and being excessively cautious can prevent organizations from realizing the benefits of data-driven collaboration, particularly when it comes to software and product development.
Taking the offense
Data defensiveness manifests itself in bureaucracy. You start creating roles like “data steward” and “data custodian” to handle internal requests. A “governance council” sits above them, whose members issue diktats and establish operating procedures — while not actually working in the trenches. Before long, blockages emerge.
Blockages are never good for business. The first sign of trouble comes in the form of “data breadlines.” Employees seeking crucial data find themselves having to make their case to whoever is responsible. Time gets wasted.
By itself, this is catastrophic. But the cultural impact is much worse. People are natural problem-solvers. That’s doubly true for software engineers. So, they start figuring out how to circumvent established procedures, hoarding data in their own “silos.” Collaboration falters. Inconsistencies creep in as teams inevitably find themselves working from different versions of the same data set.
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Dataminr raises $475M on a $4.1B valuation for real-time insights based on 100k sources of public data
Significant funding news today for one of the startups making a business out of tapping huge, noisy troves of publicly available data across social media, news sites, undisclosed filings and more. Dataminr, which ingests information from a mix of 100,000 public data sources, and then based on that provides customers real-time insights into ongoing events and new developments, has closed on $475 million in new funding. Dataminr has confirmed that this Series F values the company at $4.1 billion as it gears up for an IPO in 2023.
This Series F is coming from a mix of investors including Eldridge (a firm that owns the LA Dodgers but also makes a bunch of other sports, media, tech and other investments), Valor Equity Partners (the firm behind Tesla and many tech startups), MSD Capital (Michael Dell’s fund), Reinvent Capital (Mark Pincus and Reid Hoffman’s firm), ArrowMark Partners, IVP, Eden Global and investment funds managed by Morgan Stanley Tactical Value, among others.
To put its valuation into some context, the New York-based company last raised money in 2018 at a $1.6 billion valuation. And with this latest round, it has now raised over $1 billion in outside funding, based on PitchBook data. This latest round has been in the works for a while and was rumored last week at a lower valuation than what Dataminr ultimately got.
The funding is coming at a critical moment, both for the company and for the world at large.
In terms of the company, Dataminr has been seeing a huge surge of business.
Ted Bailey, the founder and CEO, said in an interview that it will be using the money to continue growing its business in existing areas: adding more corporate customers, expanding in international sales and expanding its AI platform as it gears up for an IPO, most likely in 2023. In addition to being used journalists and newsrooms, NGOs and other public organizations, its corporate business today, Bailey said, includes half of the Fortune 50 and a number of large public sector organizations. Over the last year that large enterprise segment of its customers doubled in revenue growth.
“Whether it’s for physical safety, reputation risk or crisis management, or business intelligence or cybersecurity, we’re providing critical insights on a daily basis,” he said. “All of the events of the recent year have created a sense of urgency, and demand has really surged.”
Activity on the many platforms that Dataminr taps to ingest information has been on the rise for years, but it has grown exponentially in the last year especially as more people spend more time at home and online and away from physically interacting with each other: that means more data for Dataminr to crawl, but also, quite possibly, more at stake for all of us as a result: there is so much more out there than before, and as a result so much more to be gleaned out of that information.
That also means that the wider context of Dataminr’s growth is not quite so clear cut.
The company’s data tools have indeed usefully helped first responders react in crisis situations, feeding them data faster than even their own channels might do; and it provides a number of useful, market-impacting insights to businesses.
But Dataminr’s role in helping its customers — which include policing forces — connect the dots on certain issues has not always been seen as a positive. One controversial accusation made last year was that Dataminr data was being used by police for racial profiling. In years past, it has been barred by specific partners like Twitter from sharing data with intelligence agencies. Twitter used to be a 5% shareholder in the company. Bailey confirmed to me that it no longer is but remains a key partner for data. I’ve contacted Twitter to see if I can get more detail on this and will update the story if and when I learn more. Twitter made $509 million in revenues from services like data licensing in 2020, up by about $45 million on the year before.
In defense of Dataminr, Bailey that the negative spins on what it does result from “misperceptions,” since it can’t track people or do anything proactive. “We deliver alerts on events and it’s [about] a time advantage,” he said, likening it to the Associated Press, but “just earlier.”
“The product can’t be used for surveillance,” Bailey added. “It is prohibited.”
Of course, in the ongoing debate about surveillance, it’s more about how Dataminr’s customers might ultimately use the data that they get through Dataminr’s tools, so the criticism is more about what it might enable rather than what it does directly.
Despite some of those persistent questions about the ethics of AI and other tools and how they are implemented by end users, backers are bullish on the opportunities for Dataminr to continue growing.
Eden Global Partners served as strategic partner for the Series F capital round.
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No-code business intelligence service y42 raises $2.9M seed round
Berlin-based y42 (formerly known as Datos Intelligence), a data warehouse-centric business intelligence service that promises to give businesses access to an enterprise-level data stack that’s as simple to use as a spreadsheet, today announced that it has raised a $2.9 million seed funding round led by La Famiglia VC. Additional investors include the co-founders of Foodspring, Personio and Petlab.
The service, which was founded in 2020, integrates with more than 100 data sources, covering all the standard B2B SaaS tools, from Airtable to Shopify and Zendesk, as well as database services like Google’s BigQuery. Users can then transform and visualize this data, orchestrate their data pipelines and trigger automated workflows based on this data (think sending Slack notifications when revenue drops or emailing customers based on your own custom criteria).
Like similar startups, y42 extends the idea data warehouse, which was traditionally used for analytics, and helps businesses operationalize this data. At the core of the service is a lot of open source and the company, for example, contributes to GitLabs’ Meltano platform for building data pipelines.
y42 founder and CEO Hung Dang. Image Credits: y42
“We’re taking the best of breed open-source software. What we really want to accomplish is to create a tool that is so easy to understand and that enables everyone to work with their data effectively,” Y42 founder and CEO Hung Dang told me. “We’re extremely UX obsessed and I would describe us as a no-code/low-code BI tool — but with the power of an enterprise-level data stack and the simplicity of Google Sheets.”
Before y42, Vietnam-born Dang co-founded a major events company that operated in more than 10 countries and made millions in revenue (but with very thin margins), all while finishing up his studies with a focus on business analytics. And that in turn led him to also found a second company that focused on B2B data analytics.
Image Credits: y42
Even while building his events company, he noted, he was always very product- and data-driven. “I was implementing data pipelines to collect customer feedback and merge it with operational data — and it was really a big pain at that time,” he said. “I was using tools like Tableau and Alteryx, and it was really hard to glue them together — and they were quite expensive. So out of that frustration, I decided to develop an internal tool that was actually quite usable and in 2016, I decided to turn it into an actual company. ”
He then sold this company to a major publicly listed German company. An NDA prevents him from talking about the details of this transaction, but maybe you can draw some conclusions from the fact that he spent time at Eventim before founding y42.
Given his background, it’s maybe no surprise that y42’s focus is on making life easier for data engineers and, at the same time, putting the power of these platforms in the hands of business analysts. Dang noted that y42 typically provides some consulting work when it onboards new clients, but that’s mostly to give them a head start. Given the no-code/low-code nature of the product, most analysts are able to get started pretty quickly — and for more complex queries, customers can opt to drop down from the graphical interface to y42’s low-code level and write queries in the service’s SQL dialect.
The service itself runs on Google Cloud and the 25-people team manages about 50,000 jobs per day for its clients. The company’s customers include the likes of LifeMD, Petlab and Everdrop.
Until raising this round, Dang self-funded the company and had also raised some money from angel investors. But La Famiglia felt like the right fit for y42, especially due to its focus on connecting startups with more traditional enterprise companies.
“When we first saw the product demo, it struck us how on top of analytical excellence, a lot of product development has gone into the y42 platform,” said Judith Dada, general partner at LaFamiglia VC. “More and more work with data today means that data silos within organizations multiply, resulting in chaos or incorrect data. y42 is a powerful single source of truth for data experts and non-data experts alike. As former data scientists and analysts, we wish that we had y42 capabilities back then.”
Dang tells me he could have raised more but decided that he didn’t want to dilute the team’s stake too much at this point. “It’s a small round, but this round forces us to set up the right structure. For the Series A, which we plan to be towards the end of this year, we’re talking about a dimension which is 10x,” he told me.
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It’s time to abandon business intelligence tools
Organizations spend ungodly amounts of money — millions of dollars — on business intelligence (BI) tools. Yet, adoption rates are still below 30%. Why is this the case? Because BI has failed businesses.
Logi Analytics’ 2021 State of Analytics: Why Users Demand Better survey showed that knowledge workers spend more than five hours a day in analytics, and more than 99% consider analytics very to extremely valuable when making critical decisions. Unfortunately, many are dissatisfied with their current tools due to the loss of productivity, multiple “sources of truth,” and the lack of integration with their current tools and systems.
A gap exists between the functionalities provided by current BI and data discovery tools and what users want and need.
Throughout my career, I’ve spoken with many executives who wonder why BI continues to fail them, especially when data discovery tools like Qlik and Tableau have gained such momentum. The reality is, these tools are great for a very limited set of use cases among a limited audience of users — and the adoption rates reflect that reality.
Data discovery applications allow analysts to link with data sources and perform self-service analysis, but still come with major pitfalls. Lack of self-service customization, the inability to integrate into workflows with other applications, and an overall lack of flexibility seriously impacts the ability for most users (who aren’t data analysts) to derive meaningful information from these tools.
BI platforms and data discovery applications are supposed to launch insight into action, informing decisions at every level of the organization. But many are instead left with costly investments that actually create inefficiencies, hinder workflows and exclude the vast majority of employees who could benefit from those operational insights. Now that’s what I like to call a lack of ROI.
Business leaders across a variety of industries — including “legacy” sectors like manufacturing, healthcare and financial services — are demanding better and, in my opinion, they should have gotten it long ago.
It’s time to abandon BI — at least as we currently know it.
Here’s what I’ve learned over the years about why traditional BI platforms and newer tools like data discovery applications fail and what I’ve gathered from companies that moved away from them.
The inefficiency breakdown is killing your company
Traditional BI platforms and data discovery applications require users to exit their workflow to attempt data collection. And, as you can guess, stalling teams in the middle of their workflow creates massive inefficiencies. Instead of having the data you need to make a decision readily available to you, instead, you have to exit the application, enter another application, secure the data and then reenter the original application.
According to the 2021 State of Analytics report, 99% of knowledge workers had to spend additional time searching for information they couldn’t easily locate in their analytics solution.
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Noogata raises $12M seed round for its no-code enterprise AI platform
Noogata, a startup that offers a no-code AI solution for enterprises, today announced that it has raised a $12 million seed round led by Team8, with participation from Skylake Capital. The company, which was founded in 2019 and counts Colgate and PepsiCo among its customers, currently focuses on e-commerce, retail and financial services, but it notes that it will use the new funding to power its product development and expand into new industries.
The company’s platform offers a collection of what are essentially pre-built AI building blocks that enterprises can then connect to third-party tools like their data warehouse, Salesforce, Stripe and other data sources. An e-commerce retailer could use this to optimize its pricing, for example, thanks to recommendations from the Noogata platform, while a brick-and-mortar retailer could use it to plan which assortment to allocate to a given location.
Image Credits: Noogata
“We believe data teams are at the epicenter of digital transformation and that to drive impact, they need to be able to unlock the value of data. They need access to relevant, continuous and explainable insights and predictions that are reliable and up-to-date,” said Noogata co-founder and CEO Assaf Egozi. “Noogata unlocks the value of data by providing contextual, business-focused blocks that integrate seamlessly into enterprise data environments to generate actionable insights, predictions and recommendations. This empowers users to go far beyond traditional business intelligence by leveraging AI in their self-serve analytics as well as in their data solutions.”
Image Credits: Noogata
We’ve obviously seen a plethora of startups in this space lately. The proliferation of data — and the advent of data warehousing — means that most businesses now have the fuel to create machine learning-based predictions. What’s often lacking, though, is the talent. There’s still a shortage of data scientists and developers who can build these models from scratch, so it’s no surprise that we’re seeing more startups that are creating no-code/low-code services in this space. The well-funded Abacus.ai, for example, targets about the same market as Noogata.
“Noogata is perfectly positioned to address the significant market need for a best-in-class, no-code data analytics platform to drive decision-making,” writes Team8 managing partner Yuval Shachar. “The innovative platform replaces the need for internal build, which is complex and costly, or the use of out-of-the-box vendor solutions which are limited. The company’s ability to unlock the value of data through AI is a game-changer. Add to that a stellar founding team, and there is no doubt in my mind that Noogata will be enormously successful.”
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Kleeen raises $3.8M to make front-end design for business applications easy
Building a front-end for business applications is often a matter of reinventing the wheel, but because every business’ needs are slightly different, it’s also hard to automate. Kleeen is the latest startup to attempt this, with a focus on building the user interface and experience for today’s data-centric applications. The service, which was founded by a team that previously ran a UI/UX studio in the Bay Area, uses a wizard-like interface to build the routine elements of the app and frees a company’s designers and developers to focus on the more custom elements of an application.
The company today announced that it has raised a $3.8 million seed round led by First Ray Venture Partners. Leslie Ventures, Silicon Valley Data Capital, WestWave Capital, Neotribe Ventures, AI Fund and a group of angel investors also participated in the round. Neotribe also led Kleeen’s $1.6 million pre-seed round, bringing the company’s total funding to $5.3 million.
Image Credits: Kleeen
After the startup he worked at sold, Kleeen co-founder, CPO and President Joshua Hailpern told me, he started his own B2B design studio, which focused on front-end design and engineering.
“What we ended up seeing was the same pattern that would happen over and over again,” he said. “We would go into a client, and they would be like: ‘we have the greatest idea ever. We want to do this, this, this and this.’ And they would tell us all these really cool things and we were: ‘hey, we want to be part of that.’ But then what we would end up doing was not that. Because when building products — there’s the showcase of the product and there’s all these parts that support that product that are necessary but you’re not going to win a deal because someone loved that config screen.”
The idea behind Kleeen is that you can essentially tell the system what you are trying to do and what the users need to be able to accomplish — because at the end of the day, there are some variations in what companies need from these basic building blocks, but not a ton. Kleeen can then generate this user interface and workflow for you — and generate the sample data to make this mock-up come to life.
Once that work is done, likely after a few iterations, Kleeen can generate React code, which development teams can then take and work with directly.
Image Credits: Kleeen
As Kleeen co-founder and CEO Matt Fox noted, the platform explicitly doesn’t want to be everything to everybody.
“In the no-code space, to say that you can build any app probably means that you’re not building any app very well if you’re just going to cover every use case. If someone wants to build a Bumble-style phone app where they swipe right and swipe left and find their next mate, we’re not the application platform for you. We’re focused on really data-intensive workflows.” He noted that Kleeen is at its best when developers use it to build applications that help a company analyze and monitor information and, crucially, take action on that information within the app. It’s this last part that also clearly sets it apart from a standard business intelligence platform.
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Census raises $16M Series A to help companies put their data warehouses to work
Census, a startup that helps businesses sync their customer data from their data warehouses to their various business tools like Salesforce and Marketo, today announced that it has raised a $16 million Series A round led by Sequoia Capital. Other participants in this round include Andreessen Horowitz, which led the company’s $4.3 million seed round last year, as well as several notable angles, including Figma CEO Dylan Field, GitHub CTO Jason Warner, Notion COO Akshay Kothari and Rippling CEO Parker Conrad.
The company is part of a new crop of startups that are building on top of data warehouses. The general idea behind Census is to help businesses operationalize the data in their data warehouses, which was traditionally only used for analytics and reporting use cases. But as businesses realized that all the data they needed was already available in their data warehouses and that they could use that as a single source of truth without having to build additional integrations, an ecosystem of companies that operationalize this data started to form.
The company argues that the modern data stack, with data warehouses like Amazon Redshift, Google BigQuery and Snowflake at its core, offers all of the tools a business needs to extract and transform data (like Fivetran, dbt) and then visualize it (think Looker).
Tools like Census then essentially function as a new layer that sits between the data warehouse and the business tools that can help companies extract value from this data. With that, users can easily sync their product data into a marketing tool like Marketo or a CRM service like Salesforce, for example.
Image Credits: Census
“Three years ago, we were the first to ask, ‘Why are we relying on a clumsy tangle of wires connecting every app when everything we need is already in the warehouse? What if you could leverage your data team to drive operations?’ When the data warehouse is connected to the rest of the business, the possibilities are limitless,” Census explains in today’s announcement. “When we launched, our focus was enabling product-led companies like Figma, Canva, and Notion to drive better marketing, sales, and customer success. Along the way, our customers have pulled Census into more and more scenarios, like auto-prioritizing support tickets in Zendesk, automating invoices in Netsuite, or even integrating with HR systems.“
Census already integrates with dozens of different services and data tools and its customers include the likes of Clearbit, Figma, Fivetran, LogDNA, Loom and Notion.
Looking ahead, Census plans to use the new funding to launch new features like deeper data validation and a visual query experience. In addition, it also plans to launch code-based orchestration to make Census workflows versionable and make it easier to integrate them into an enterprise orchestration system.
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Use Git data to optimize your developers’ annual reviews
The end of the year is looming and with it one of your most important tasks as a manager. Summarizing the performance of 10, 20 or 50 developers over the past 12 months, offering personalized advice and having the facts to back it up — is no small task.
We believe that the only unbiased, accurate and insightful way to understand how your developers are working, progressing and — last but definitely not least — how they’re feeling, is with data. Data can provide more objective insights into employee activity than could ever be gathered by a human.
It’s still very hard for many managers to fully understand that all employees work at different paces and levels.
Consider this: Over two-thirds of employees say they would put more effort into their work if they felt more appreciated, and 90% want a manager who’s fair to all employees.
Let’s be honest. It’s hard to judge all of your employees fairly if you’re (1) unable to work physically side-by-side with them, meaning you’ll inevitably have more contact with the some over others (e.g., those you’re more friendly with); and (2) you’re relying on manual trackers to keep on top of everyone’s work, which can get lost and take a lot of effort to process and analyze; (3) you expect engineers to self-report their progress, which is far from objective.
It’s also unlikely, especially with the quieter ones, that on top of all that you’ll have identified areas for them to expand their talents by upskilling or reskilling. But it’s that kind of personal attention that will make employees feel appreciated and able to progress professionally with you. Absent that, they’re likely to take the next best job opportunity that shows up.
So here’s a run down of why you need data to set up a fair annual review process; if not this year, then you can kick-start it for 2021.
1. Use data to set next year’s goals
The best way to track your developers’ progress automatically is by using Git Analytics tools, which track the performance of individuals by aggregating historical Git data and then feeding that information back to managers in minute detail.
This data will clearly show you if one of your engineers is over capacity or underworked and the types of projects they excel in. If you’re assessing an engineering manager and the team members they’re responsible for have been taking longer to push their code to the shared repository, causing a backlog of tasks, it may mean that they’re not delegating tasks properly. An appropriate goal here would be to track and divide their team’s responsibilities more efficiently, which can be tracked using the same metrics, or cross-training members of other teams to assist with their tasks.
Another example is that of an engineer who is dipping their toe into multiple projects. Indicators of where they’ve performed best include churn (we’ll get to that later), coworkers repeatedly asking that same employee to assist them in new tasks and of course positive feedback for senior staff, which can easily be integrated into Git analytics tools. These are clear signs that next year, your engineer could be maximizing their talents in these alternative areas, and you could diversify their tasks accordingly.
Once you know what targets to set, you can use analytics tools to create automatic targets for each engineer. That means that after you’ve set it up, it will be updated regularly on the engineer’s progress using indicators directly from the code repository. It won’t need time-consuming input from either you or your employee, allowing you both to focus on more important tasks. As a manager you’ll receive full reports once the deadline of the task is reached and get notified whenever metrics start dropping or the goal has been met.
This is important — you’ll be able to keep on top of those goals yourself, without having to delegate that responsibility or depend on self-reporting by the engineer. It will keep employee monitoring honest and transparent.
2. Three Git metrics can help you understand true performance quality
The easiest way for managers to “conclude” how an engineer has performed is by looking at superficial output: the number of completed pull requests submitted per week, the number of commits per day, etc. Especially for nontechnical managers, this is a grave but common error. When something is done, it doesn’t mean it’s been done well or that it is even productive or usable.
Instead, look at these data points to determine the actual quality of your engineer’s work:
- Churn is your number-one red flag, telling you how many times someone has modified their code in the first 21 days after it has been checked in. The more churn, the less of an engineer’s code is actually productive, with good longevity. Churn is a natural and healthy part of the software development process, but we’ve identified that any churn level above the normal 15%-30% indicates that an engineer is struggling with assignments.
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