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Implementing a data-driven approach to guarantee fair, equitable and transparent employee pay

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Your pay is important. It’s usually something most people don’t understand — why are we paid what we’re paid? Ultimately, this lack of clarity can lead to confusion and negative feelings that affect our productivity and relationships with our employers. You may have encountered situations when you felt your pay was unfairly biased by your manager, recruiter, HR or company policies.

You may know or suspect instances in which your pay has been determined based on someone else’s preferences for background, or stereotypes about your gender, race, ethnicity, identity or abilities. It can even feel unfair based on your own confidence in your ability to negotiate.

What do we think is the right thing to do, and how do we aim to achieve it here at Plastiq? Paying employees fairly, equitably and competitively is what’s right. Being transparent about our philosophy and practices is the commitment we’ve made to achieve this goal.

In designing our compensation philosophy, the Plastiq leadership team agreed that fair pay and transparency would be our guiding principles. Then it was all about the data.

The first step was to understand everyone’s work: their job function, the scale and scope of their work, and their day-to-day responsibilities. This led us to being able to identify if someone was working in accounting or financial forecasting, software development or product management, recruiting or people operations, contributing as a recent graduate/new person to the workforce, a seasoned individual contributor, a senior team lead, an experienced people manager or a more strategic cross-functional vice president.

Next we invested in access to market data from a credible resource — one that we know is used by other companies we respect — with comparable market, industry and size to Plastiq. Because companies have to participate in the benchmark survey to be able to purchase and access the survey data, we knew we were getting accurate, verified information we could trust. This ensures a few things: no subjective self-reported data, accurate alignment in assessing the scale and scope of all the roles, as well as mutual interest by the user base to make sure the data reporting and retrieval was reliable. For Plastiq, the most relevant data centered around what other companies in San Francisco and Boston pay their talent. We also cared about paying as well or better than other tech companies — in particular fintech companies — that were not yet publicly traded.

These distinctions are important for any business when planning pay. To use another small business as an example — let’s say a food truck looking to hire cashiers and cooks — one might evaluate how much to pay its employees using several factors. For example, there may be a difference in pay for food trucks operating out of Austin versus Seattle; the type of food truck (savory or sweet) may influence the level of skill required to prepare or serve the food; margins may be vastly different, meaning the business may be able to employ many or only a few. If you were planning to staff and pay a large-scale, lower-margin cupcake food truck in Austin, would it make sense for you to base your employees’ pay on a two-person sushi truck operation that required skilled sushi chefs in Seattle? Probably not. You’d want to benchmark against a business — preferably multiple businesses — like yours, in your market, with similar staffing and operational needs, to feel confident you’re using the right data.

There is always a way to understand the market data for a company’s particular situation and what their competitors pay for talent. On the flip side, if you’re trying to figure out what you should be paid and what’s fair, there is market data available to help guide you. You could start by asking other people you know that do the same type of work as you what pay they’ve seen around. You could even (and should), ask your manager, recruiter or HR team for the data.

For us at Plastiq, knowing we were committed to fair pay and to formalizing that into a transparent philosophy, the next piece was to decide how competitively we wanted to pay versus the market rates. We considered three possibilities:

Lyron Foster is a Hawaii based African American Musician, Author, Actor, Blogger, Filmmaker, Philanthropist and Multinational Serial Tech Entrepreneur.

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Vivenu, a ticketing API for events, closes a $15M Series A round led by Balderton Capital

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vivenu, a ticketing platform that offers an API for venues and promoters to customize to their needs, has closed a $15 million (€12.6 million) in Series A funding led by Balderton Capital. Previous investor Redalpine also participated.

Historically-speaking, most ticketing platform startups took a direct to consumer approach, or have provided turnkey solutions to big event promoters. But in this day and age, most events require a great deal more flexibility, not least because of the pandemic. So, by offering an API and allowing promoters that flexibility, Vivenu managed to gain traction.

Venues and event owners get a full-featured ticketing, out-of-the-box platform with full real-time dynamic control over all aspects of selling tickets including configuring prices and seating plans, leveraging customer data and insights and mastering a branded look and feel across their sales channels. It has exposed APIs enabling many different custom use cases for large international ticket sellers. Since its Seed funding in March, the company says it has sold over 2 million tickets.

Simon Hennes, CEO and co-founder of vivenu said in a statement: “We created vivenu to address the need of ticket sellers for a user-centric ticketing platform. Event organizers were stuck with solutions that heavily depend on manual processes, causing high costs, dependencies, and frustration on various levels.”

Daniel Waterhouse, Partner at Balderton said: “Vivenu has built a sophisticated product and set of APIs that gives event organisers full control of their ticketing operations.”

vivenu is also the first European investment of Aurum Fund LLC, the fund associated with the San Francisco 49ers. Also investing in the round are Angels including Sascha Konietzke (Founder at Contentful), Chris Schagen (former CMO at Contentful), Sujay Tyle (Founder at Frontier Car Group) and Tiny VC.

In March 2020, vivenu secured €1.4 million in seed funding, bringing its total funding to €14 million. Previous investors include early-stage venture capital investor Redalpine, GE32 Capital and Hansel LLC (associated with the founders of Loft).

Speaking to TechCrunch Hennes said: “You have to send your seat map to Ticketmaster, and then the account manager comes back to you with a sitemap. This goes back and forth and takes ages. With us you have a seating chart designer basically integrated into the software which you can simply change yourself.”

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Nordigen introduces free European open banking API

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Latvian fintech startup Nordigen is switching to a freemium model thanks to a free open banking API. Open banking was supposed to democratize access to banking information, but the company believes banking aggregation APIs from Tink or Plaid are too expensive. Instead, Nordigen thinks it can provide a free API to access account information and paid services for analytics and insights services.

Open banking is a broad term and means different things, from account aggregation to verifying account ownership and payment initiation. The most basic layer of open banking is the ability to view data from third-party financial institutions. For instance, some banks let you connect to other bank accounts so that you can view all your bank accounts from a single interface.

There are two ways to connect to a bank. Some banks provide an application programming interface (API), which means that you can send requests to the bank’s servers and receive data in return.

While all financial institutions should have an open API due to the European PSD2 directive, many banks are still dragging their feet. That’s why open banking API companies usually rely on screen scraping. They mimic web browser interactions, which means that it’s slow, it requires a ton of server resources and it can break.

“If you’re wondering how we’d be able to afford it, our free banking data API was designed purely with PSD2 in mind, meaning it’s lightweight in strong contrast to that of incumbents. So it wouldn’t significantly increase our costs to scale free users,” Nordigen co-founder and CEO Rolands Mesters told me.

So you don’t get total coverage with Nordigen’s API. The startup currently supports 300 European banks, which covers 60 to 90% of the population in each country. But it’s hard to complain when it’s a free product anyway.

Some Nordigen customers will probably want more information. Nordigen provides financial data analytics. It can be particularly useful if you’re a lending company trying to calculate a credit score, if you’re a financial company with minimum income requirements and more.

For those additional services, you’ll have to pay. Nordigen currently has 50 clients and expects to attract more customers with its new freemium strategy.

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Databand raises $14.5M led by Accel for its data pipeline observability tools

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DevOps continues to get a lot of attention as a wave of companies develop more sophisticated tools to help developers manage increasingly complex architectures and workloads. In the latest development, Databand — an AI-based observability platform for data pipelines, specifically to detect when something is going wrong with a datasource when an engineer is using a disparate set of data management tools — has closed a round of $14.5 million.

Josh Benamram, the CEO who co-founded the company with Victor Shafran and Evgeny Shulman, said that Databand plans include more hiring; to continue adding customers for its existing product; to expand the library of tools that its providing to users to cover an ever-increasing landscape of DevOps software, where it is a big supporter of open source resources; as well as to invest in the next steps of its own commercial product. That will include more remediation once problems are identified: that is, in addition to identifying issues, engineers will be able to start automatically fixing them, too.

The Series A is being led by Accel with participation from Blumberg Capital, Lerer Hippeau, Ubiquity Ventures, Differential Ventures, and Bessemer Venture Partners. Blumberg led the company’s seed round in 2018. It has now raised around $18.5 million and is not disclosing valuation.

The problem that Databand is solving is one that is getting more urgent and problematic by the day (as evidenced by this exponential yearly rise in zettabytes of data globally). And as data workloads continue to grow in size and use, they continue to become ever more complex.

On top of that, today there are a wide range of applications and platforms that a typical organization will use to manage source material, storage, usage and so on. That means when there are glitches in any one data source, it can be a challenge to identify where and what the issue can be. Doing so manually can be time-consuming, if not impossible.

“Our users were in a constant battle with ETL (extract transform load) logic,” said Benamram, who spoke to me from New York (the company is based both there and in Tel Aviv, and also has developers and operations in Kiev). “Users didn’t know how to organize their tools and systems to produce reliable data products.”

It is really hard to focus attention on failures, he said, when engineers are balancing analytics dashboards, how machine models are performing, and other demands on their time; and that’s before considering when and if a data supplier might have changed an API at some point, which might also throw the data source completely off.

And if you’ve ever been on the receiving end of that data, you know how frustrating (and perhaps more seriously, disastrous) bad data can be. Benamram said that it’s not uncommon for engineers to completely miss anomalies and for them to only have been brought to their attention by “CEO’s looking at their dashboards and suddenly thinking something is off.” Not a great scenario.

Databand’s approach is to use big data to better handle big data: it crunches various pieces of information, including pipeline metadata like logs, runtime info, and data profiles, along with information from Airflow, Spark, Snowflake, and other sources, and puts the resulting data into a single platform, to give engineers a single view of what’s happening better see where bottlenecks or anomalies are appearing, and why.

There are a number of other companies building data observability tools — Splunk perhaps is one of the most obvious, but also smaller players like Thundra and Rivery. These companies might step further into the area that Databand has identified and is fixing, but for now Databand’s focus specifically on identifying and helping engineers fix anomalies has given it a strong profile and position.

Accel partner Seth Pierrepont said that Databand came to the VC’s attention in perhaps the best way it could: Accel needed a solution like it for its own internal work.

“Data pipeline observability is a challenge that our internal data team at Accel was struggling with. Even at our relatively small scale, we were having issues with the reliability of our data outputs on a weekly basis, and our team found Databand as a solution,” he said. “As companies in all industries seek to become more data driven, Databand delivers an essential product that ensures the reliable delivery of high quality data for businesses. Josh, Victor and Evgeny have a wealth of experience in this area, and we’ve been impressed with their thoughtful and open approach to helping data engineers better manage their data pipelines with Databand.”

The company is also used by data teams from both large Fortune 500 enterprises to smaller startups.

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