We’re happy to announce that we’ve strengthened our legal and PR & Communications side with Dara Gill (former VP General Counsel at Truecaller) and Kim Fai Kok (former founding team member and Director of Communications at Truecaller) — both highly appreciated startup veterans and early-stage investors in the Nordic tech scene.
Dara will step in as our external General Counsel and support us with all things legal, and Kim Fai will come in as Communications Advisor to help lay the foundations for our PR & Communications strategy as we’re accelerating towards our next phase of growth. …
Product-led growth (PLG) has emerged as the buzzword of 2021 in the startup and venture capital scene. As go-to-market strategies continue to transform, we are seeing a movement from sales-led to PLG motions. According to OpenView, PLG is “an end user-focused growth model that relies on the product itself as the primary driver of customer acquisition, conversion and expansion.”
LogMeIn went public in 2012, in what many perceive as the first B2B SaaS IPO with a true product-led growth model. This triggered a mindset change in B2B SaaS — changing the perception of the old-school “Oracle” top-down enterprise sales playbook (a dark time in tech history where almost all…
If you work in the world of data, you have at this point heard a lot of talk about the Modern Data Stack. It has gained a lot of buzz and attention as companies have begun a fundamental shift in how they think about analytics and machine learning. The Modern Data Stack is built on the new cloud-native technologies that have emerged in the last decade that are fast, reliable, scalable, and, most importantly, accessible everywhere. Some of the technologies that have made this possible are massively parallel processing (MPP) cloud data warehouses like Redshift, Snowflake, and BigQuery; ingestion tools like Stitch, Airbyte and Fivetran that have improved reliability and connector coverage; and analytics platforms like ThoughtSpot that enhance users’ experience when finding and sharing data insights, and make that data accessible to everyone, everywhere.
By this point we at Validio have spoken with over 100 data teams — from early-stage startups to fast-growing scaleups and large corporations revamping their data strategies to reflect the new reality they operate in. Most of the companies that we’ve interacted with have modern data teams with engineering-led decision-making…
Having worked with tech startups who have a strong academic/ research background and at startups that even had specific research teams at a very early stage, I started reflecting on the difference between research culture and startup culture. There’s no idea to beat around the bush: they’re very different. Based on my experience, there are more differences between the two than common denominators for a harmonious coexistence.
There are some startups, such as Deepmind and OpenAI, that have had research as an absolute core part of their organization from the getgo and have become successful in their own ways (e.g. getting acquired by Google or pivoting towards a more commercial framework by offering massive AI models such…
In our ML & Data trends post from February we discussed whether one believes MLOps has crossed the chasm or not, the rise of MLOps (i.e. DevOps for ML) signals an industry shift from PoC’s (how to build models) to operations (how to run models). Even though this shift is something that we’re extremely excited about, there’s a recurrent bottleneck that keeps haunting us year after year: data quality.
In a survey by O’Reilly from 2019, 26% of the respondents with a mature machine learning practice stated poor data quality as the nr 1 bottleneck holding them back from further AI/ML adoption.
2020 brought a digitalization explosion across the world. Microsoft estimates that the first two months of the pandemic (March & April) drove two years’ worth of digitalization. Throughout the rest of the year, the pandemic accelerated a wake-up call to the markets, which had been a long time coming: every successful modern company will need to be not only a software company, but also a data company.
The accelerated digitalization and our ever-increasing appetite for and generation of data fuelled a lot of development in the Data + ML landscape in 2020. As companies have started to reap the benefits of the last few years’ predictive analytics and ML initiatives, they clearly show a healthy appetite for…
