Companies should utilize real-time compensation data to ensure equal pay

Diversity, equity and inclusion (DEI) initiatives are often thought to be an issue that can be solved by intuition by some segment of the HR team. However, in reality, it needs to come from a data-driven approach that encompasses the entire workforce.

The primary aspect that companies usually look to, in terms of treating employees fairly, is remuneration. However, having the conversation and agreeing on the need for equality doesn’t mean it will be achieved on an organizational scale.

Particular attention should be paid to addressing inequities in the areas of attracting and hiring candidates, integration, performance assessment, compensation and promotion.

In a recent survey from Mercer that included data from more than 1,000 companies in 54 countries, 81% agreed that it was important to have a plan for advancing gender equality, but just 42% actually had one in place. This points toward a tokenism attitude indicating companies are happy to talk around the issue without addressing it directly.

Despite the fact that women make up roughly half of all college-educated workers in the United States, they are underrepresented in positions of power — just 8% of Fortune 500 companies are led by women, and, incredibly, just 1% by women of color. Furthermore, the last U.S. census revealed that women who are employed full time are paid on average 17% less than men.

While there have been steps to ensure equal pay, such as Canada’s Pay Equity Act, which states that men and women in the public sector should be paid equally, it does not cover the private sector. Given that the Institute for Women’s Policy Research estimates that equal pay will not be reached until 2059, there is still plenty of work to be done.

Particular attention should be paid to addressing inequities in the areas of attracting and hiring candidates, integration, performance assessment, compensation and promotion. Companies need to think about initiatives that are supported by objective tools to drive progress, identify problems and strategize solutions. This is where data can be a great tool to provide insight into DEI: by highlighting shortcomings and areas where there is bias.

Start with data collection

The first step is to create a data set so that tangible metrics can be utilized and turned into actionable decisions. To do this, diversity and inclusion officers need to be given the opportunity to weed out bias.

Obviously, the data would drive decisions on areas such as compensation. But far too often, director-level discussions don’t involve the talent acquisition team. To eradicate the pay gap and ensure compensation is equalized on individual merit, this needs to change. Line managers and talent acquisition teams have the best knowledge of their staff and are well placed to procure the right information to help senior managers make equitable decisions.

Productivity startup Time is Ltd raises $5.6M to be the ‘Google Analytics for company time’

Productivity analytics startup Time is Ltd wants to be the Google Analytics for company time. Or perhaps a sort of “Apple Screen Time” for companies. Whatever the case, the founders reckon that if you can map how time is spent in a company enormous productivity gains can be unlocked and, money better spent.

It’s now raised a $5.6 million late seed funding round led by Mike Chalfen, of London-based Chalfen Ventures, with participation from Illuminate Financial Management and existing investor Accel. Acequia Capital and former Seal Software chairman Paul Sallaberry are also contributing to the new round, as is former Seal board member Clark Golestani. Furthermore, Ulf Zetterberg, founder and former CEO of contract discovery and analytics company Seal Software, is joining as President and co-founder.

The venture is the latest from serial entrepreneur Jan Rezab, better known for founding SocialBakers, which was acquired last year.

We are all familiar with inefficient meetings, pestering notifications chat, video conferencing tools and the deluge of emails. Time is Ltd. says it plans to address this by acquiring insights and data platforms such as Microsoft 365, Google Workspace, Zoom, Webex, MS Teams, Slack, and more. The data and insights gathered would then help managers to understand and take a new approach to measure productivity, engagement, and collaboration, the startup says.

The startup says it has now gathered 400 indicators that companies can choose from. For example, a task set by The Wall Street Journal for Time is Ltd. found the average response time for Slack users vs. email was 16.3 minutes, comparing to emails which was 72 minutes.

Chalfen commented: “Measuring hybrid and distributed work patterns is critical for every business. Time Is Ltd.’s platform makes such measurement easily available and actionable for so many different types of organizations that I believe it could make work better for every business in the world.”

Rezab said: “The opportunity to analyze these kinds of collaboration and communication data in a privacy-compliant way alongside existing business metrics is the future of understanding the heartbeat of every company – I believe in 10 years time we will be looking at how we could have ignored insights from these platforms.”

Tomas Cupr, Founder and Group CEO of Rohlik Group, the European leader of e-grocery, said: “Alongside our traditional BI approaches using performance data, we use Time is Ltd. to help improve the way we collaborate in our teams and improve the way we work both internally and with our vendors – data that Time is Ltd. provides is a must-have for business leaders.”

Climate risk platform Cervest raises $30M Series A led by Draper Esprit

Cervest – a startup with a platform that claims to quantify climate risk across multiple decades and threats down to the asset level – has raised a $30 million Series A round led by Draper Esprit. Previous investors Astanor Ventures, Lowercarbon Capital (Chris Sacca), and Future Positive Capital also participated in the round, and were joined by new investors UNTITLED, the venture fund of Magnus Rausing, and TIME Ventures, the venture fund of Marc Benioff. Cervest’s total funding now stands at $36.2 million. It previously raised $5.2M in 2019.

Cervest’s competitors include Jupiter Intelligence, which has raised $35M to Series B level, but Cervest claims it has a more data + AI approach.

The company will use the new funding to expand in the U.S. and European markets through its freemium model
It’s widely accepted now, with unpredictable weather patterns and clear climate “weirding” that these weather events are of huge risk to trillions of dollars of physical assets.

Cervest says its “Climate Intelligence” platform has been built through peer-reviewed research over the five years and combines public and private data sources (i.e. NOAA, ECMWF, CMIP6), machine learning, and statistical science to come up with a view of climate risks to assets.

‘EarthScan’ will be its first product, giving enterprises and governments a view on how flooding, droughts, and extreme temperatures can impact the assets they own or manage,going back 50 years and looking forward 80 years.

Iggy Bassi, Founder and CEO of Cervest said: “Climate Intelligence is Business Intelligence for managing climate risk. Climate volatility has thrown us into a new era where Climate Intelligence needs to be integrated into all decisions. Organizations that fail to do so risk being blindsided by climate events such as the recent floods and fires in Australia, the droughts in Europe, and the winter freeze in Texas. Much of the spotlight is on decarbonization today. While this is absolutely necessary, it is not sufficient to build asset-level resilience.”

Vinoth Jayakumar, Partner and Fintech Practice Lead at Draper Esprit added: “Climate Tech has grabbed a lot of attention recently, with good reason… Cervest’s pioneering approach to quantifying risk, in a way that was never before possible, means we can better understand the economics of the problem and bring real-world market solutions to bear.”

Analytics as a service: Why more enterprises should consider outsourcing

With an increasing number of enterprise systems, growing teams, a rising proliferation of the web and multiple digital initiatives, companies of all sizes are creating loads of data every day. This data contains excellent business insights and immense opportunities, but it has become impossible for companies to derive actionable insights from this data consistently due to its sheer volume.

According to Verified Market Research, the analytics-as-a-service (AaaS) market is expected to grow to $101.29 billion by 2026. Organizations that have not started on their analytics journey or are spending scarce data engineer resources to resolve issues with analytics implementations are not identifying actionable data insights. Through AaaS, managed services providers (MSPs) can help organizations get started on their analytics journey immediately without extravagant capital investment.

MSPs can take ownership of the company’s immediate data analytics needs, resolve ongoing challenges and integrate new data sources to manage dashboard visualizations, reporting and predictive modeling — enabling companies to make data-driven decisions every day.

AaaS could come bundled with multiple business-intelligence-related services. Primarily, the service includes (1) services for data warehouses; (2) services for visualizations and reports; and (3) services for predictive analytics, artificial intelligence (AI) and machine learning (ML). When a company partners with an MSP for analytics as a service, organizations are able to tap into business intelligence easily, instantly and at a lower cost of ownership than doing it in-house. This empowers the enterprise to focus on delivering better customer experiences, be unencumbered with decision-making and build data-driven strategies.

Organizations that have not started on their analytics journey or are spending scarce data engineer resources to resolve issues with analytics implementations are not identifying actionable data insights.

In today’s world, where customers value experiences over transactions, AaaS helps businesses dig deeper into their psyche and tap insights to build long-term winning strategies. It also enables enterprises to forecast and predict business trends by looking at their data and allows employees at every level to make informed decisions.

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?

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.

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 over 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

y42 founder and CEO Hung Dang.

“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 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 over 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.

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.

Appfire, provider of Atlassian apps, raises $100M to continue its buying spree

Appfire, a Boston-based provider of software development apps, announced Tuesday that it has received a $100 million investment from growth private equity firm TA Associates.

Founded in 2005, Appfire was bootstrapped until it got $49 million from Silversmith Capital Partners last May. Since that time, Appfire has acquired six companies in the Atlassian “ecosystem,” including Botron, Beecom, Innovalog, Navarambh, Artemis and Bolo.

The Boston-based company has been profitable for over a decade, according to Randall Ward, co-founder and CEO of Appfire. And while Ward declined to reveal valuation or hard revenue numbers, he did say that Appfire has seen its ARR more than double over the past year.

Since last June alone, the company says it has experienced:

  • A 103% year over year increase in ARR.
  • A 258% YOY increase in enterprise subscription revenue (data center only). 
  • A 182% YOY increase in all subscription revenue (data center and cloud).  

So why the need for institutional capital? With the latest funding, Appfire intends to extend its buying spree of complementary apps. 

Appfire has been acquiring businesses every six to eight weeks, and it plans to continue scooping them up at that pace, according to Ward.

It’s also looking to let shareholders cash in on their options.

Fun fact: Atlassian itself was bootstrapped for nearly a decade. The Australian enterprise software company was profitable from its inception in 2001 before taking its first round of external capital, a $60 million financing led by Accel, in July 2010. The financing was primarily secondary.

Some context

Appfire was initially a professional services company before transitioning into products in 2013. The company says it has “developed domain expertise in creating, launching and distributing apps” through the Atlassian marketplace. Today, the company has 85 products on that marketplace and more than 110,000 active installations globally spanning workflow automation, business intelligence, publishing and administrative tools. 

Specifically, the company’s Bob Swift, Feed Three and Wittified brand apps aim to help companies like Google, Amazon and Starbucks streamline product development through improved collaboration, security, reporting and automation.

“We started this business 15 years ago with the goal of building software applications for customers,” Ward told TechCrunch. “At that time, there were no marketplaces, so iTunes marketplace didn’t exist, Google Play didn’t exist, but yet we were seeing that applications were getting smaller in size, Mozilla was putting out plugins. My co-founder and I were sitting on the floor of a warehouse in Maynard, Massachusetts and we conceived of this company called Appfire, and boy did we pick the right name.”

The pair then stumbled upon a project by which a friend of a friend was looking for them to integrate two pieces of software with software from Atlassian.

“It was brand new to us — we had never heard of it — a software called JIRA and another piece of software called Confluence,” Ward recalls. “About three months later we launched a project and then got introduced to the co-founders of Atlassian.”

In 2017, Appfire decided it wanted to focus full time on becoming “the biggest app platform and aggregator.”

“So we decided to wind down all the other little special side projects for Atlassian delivering services to customers, and really put all of our eggs in this marketplace basket,” Ward recalls. 

It was at that point the company began looking for external capital. With this last raise, though, Ward says Appfire was not necessarily looking for more cash.

When approached by TA, Appfire asked if it could create more employee equity programs so the company could be an employee-led business. It also asked if it could take 1% of its equity and contribute to the Pledge 1% initiative.

“They said yes,” Ward said. “So that led us to this latest funding.”

Appfire is also moving into business intelligence and data analytics apps for Tableau and Microsoft Power BI.

As mentioned above, some of its latest funding will go back to existing shareholders, Ward said. The remainder will go into continuing to grow the business.

“We have a lot of organic and inorganic growth opportunities,” he added. “…That obviously takes some momentum.”

Michael Libert, a principal of TA Associates, said his firm had been tracking Appfire’s progress for “quite some time.” The company’s apps, he said, do not require complex training, allowing customers to improve productivity “at a low cost,” leading to further customer adoption and enabling “a solid land-and-expand strategy.”

“We found the company’s high-quality business model, impressive organic growth and recent significant acquisitive activity particularly attractive,” Libert told TechCrunch.

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.”