The Preql blog
Processing financial data takes too much time — how can we fix this?
According to a study involving finance leaders, finance teams spend half of their week on transaction processing. This means from Monday morning to Wednesday lunchtime, they ensure that customers receive accurate invoices, bills are paid, and fixed assets are accounted for.
Preql and Brooklyn Data: Joining Forces to Unleash the Power of Data
We’re thrilled to announce a collaborative partnership between Preql, the no-code data access platform, and Brooklyn Data Co. (a Velir company), a leading expert in the modern data stack. This alliance unites our shared vision of a data-driven world where individuals of all technical backgrounds can access, analyze, and leverage data to make informed decisions.
Breaking down silos for a unified data vision in digital transformation
In the journey of digital transformation, the spotlight often falls on adopting cutting-edge technologies. However, the real catalyst for change lies in dismantling organizational silos to establish a unified data vision. This integration is crucial in transforming how businesses understand and utilize their data to drive decisions.
The challenges of meaningful data in an evolving digital landscape
In today’s rapidly evolving digital world, the true challenge for businesses and technology leaders is not merely collecting vast amounts of data but transforming it into meaningful and actionable insights.
Preql’s 2023 data-driven leaders
2023 was an exciting year for Preql. Our team doubled in size, our product officially hit the market and we launched new AI features. But above all, the true highlight was spending time with remarkable individuals shaping the future of data.
Preql joins Snowflake Partner Network
New York, December, 2023 — Preql announced today that it has joined the Snowflake’s Partner Network. As part of the Snowflake Partner Network, Preql enables data access to joint customers to manage their own metrics and KPIs in a flexible, no-code-UI, to get the most out of the Snowflake Data Cloud.
Magic Mind and Preql: Simplifying complex data into an easy-to-use source of truth
Magic Mind is the world’s first productivity shot: it’s scientifically designed to improve focus and decrease stress. The brand has grown exponentially and is committed to making data-driven decisions, which is why they partnered with Preql.
Why creating metrics is so hard: the human and technical challenges
In the modern landscape of business, business metrics are no longer optional for success. The combination of increased competition and a rapidly changing world make measurements critical for preventing missteps and driving growth. In the first part of this series, we discussed the ways and areas good metrics can help steer a company, as well as the gaps in communication that make metrics such a challenge to implement. In this second part, we want to provide an overview of the technical and human factors that complicate the adoption and use of business metrics.
What Metrics Should You Care About?
The path to creating clearly defined metrics from raw data can be a long and winding one. I’ve been through this process a few times within organizations at different stages of data maturity. I wanted to share my experiences in the hopes of making this process less painful for anyone working on it currently, or at minimum to make you feel less alone!
How to calculate Annual Recurring Revenue (ARR)
In order to be successful, companies must align around a single metric. It’s the way to ensure every team is focused on one tangible goal that everyone understands and can work towards. Revenue is almost always a component of that metric regardless of the type of company or industry you work in.
Why creating metrics is so hard
The modern world of business is increasingly complex and competitive. Companies need every edge they can get to succeed in this climate and that makes it critical that business decisions be thoughtful and well informed. But how do we inform those decisions? The easiest answer is ‘data’, but what data and how?
Let’s talk about dashboards
People have strong opinions about dashboards: are they dead? Are they here for good? Why isn’t there a better way to consume data? No matter where you land on those big questions, it’s worth confronting a basic fact: most businesses make big investments in business intelligence tools, most data teams produce a lot of dashboards, and most dashboards end up stale and unused.
Fivetran x Preql
We’re thrilled to announce Preql’s official technology partnership with Fivetran! Fivetran is the premier automated data movement platform, offering organizations the ability to effortlessly extract and load data between a wide range of sources and destinations. Once Fivetran has loaded data from your source systems and into your cloud warehouse, Preql generates reporting-ready metrics in minutes.
The post-modern data stack
The modern data stack has made the lives of data teams infinitely easier. Even one generation of tooling ago, data teams were tasked with managing on-premise data storage and building their own pipelines. Today, tools like Fivetran and Snowflake have made moving data into a cloud warehouse and storing it in a scalable way possible within days. Even companies without a full data team can reliably move data into a warehouse.
What kind of data person are you?
Data titles are notoriously confusing and inconsistent! Personally, I’ve taken to using the term “data person,” as opposed to data analyst, scientist, or engineer because by definition, the skill sets of data people are broad and we tend to put on different hats as needed. That said, data people come in different flavors, and you can usually map personas to a few key goals and the tools and workflows they prefer to spend time in.
Introducing Preql: The Future of Data Transformation
We’re thrilled to announce our new venture, Preql! Preql is a no-code data transformation solution built for business users. Preql is the culmination of our mission to bridge the gap between business users and data teams, and to make data and analytics accessible to more people.