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The Work-Bench Snapshot Series explores the top people, blogs, videos, and more, shaping the enterprise on a particular topic we’re looking at from an investment standpoint.

Due to the rising demand for large-scale data processing, today’s data systems have undergone significant changes to effectively handle transactional data models as well as support a larger variety of sources, including log and metrics from web servers, sensor data from IoT systems and more. …


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A core area that we track at Work-Bench is the adoption of DevOps and what the transition looks like in practice for large enterprises. As businesses are increasingly tasked with delivering applications and services at higher velocity, DevOps has established itself as the right guiding principles for faster and safer development and releases of software.

Continuous Integration and Continuous Delivery (CI/CD) has emerged as a best practice within the DevOps space — it solves for quicker testing and deployment of applications, and provides a workflow pipeline that integrates and automates different stages of the delivery process and gets releases ready to be shipped. …


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The Work-Bench Snapshot Series explores the top people, blogs, videos, and more, shaping the enterprise on a particular topic we’re looking at from an investment standpoint.

Defining the Last Mile Problem in Analytics

Here at Work-Bench, as enterprise software investors, we’ve been thinking through the last mile problem in analytics first hand and have already explored similar topics in previous Snapshots (The Evolution of Data Discovery & Catalog and The Rise of Data Engineering.)

The last mile problem in analytics broadly refers to the challenges around data generation and consumption. While data generation is a process that is traditionally owned by the engineers and revolves around cleaning, validating and transforming raw data formats, data consumption on the other hand is the point at which clean data from data pipelines is consumed by product teams and business users who then leverage BI tools to query and convert data into meaningful business outcomes. …


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As enterprise tech investors at Work-Bench, we’ve seen firsthand how hard it is for early stage startups to close early customers and build a repeatable sales process. That’s why we’re all about helping our founders successfully launch and scale their enterprise go-to-market efforts through making customer introductions, navigating complex accounts and back-channeling, and helping them build out their sales team.

Recently, Amplify Partners, Data Council, and Great Expectations published a comprehensive post, “25 Hot New Data Tools and What They DON’T Do,” highlighting the top 25 data tools in the market today. …


Measuring and Communicating Security for SMBs and Large Enterprises

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The glut of data breaches over the past few years has called for stricter rules and regulations that implement the right frameworks to protect data access and ensure privacy and confidentiality. For enterprises, large and small, the consequence of a data breach not only amounts to the cost of a lost or compromised asset, but often comes at the expense of losing customers. …


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The Work-Bench Snapshot Series explores the top people, blogs, videos, and more, shaping the enterprise on a particular topic we’re looking at from an investment standpoint.

As enterprise tech investors in the infrastructure and developer tooling space, we are seeing an explosive growth in the number of data infrastructure startups. On one hand, this trend reflects the need for data tooling that promotes data-informed decision making and the massive investment in the broader space. On the other hand, data often lives in disparate sources across systems making it hard for data users to have visibility into their data pipelines, discover relevant assets, and derive value from them.


And Why Data Scientists Are Getting Eclipsed

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The Work-Bench Snapshot Series explores the top people, blogs, videos, and more, shaping the enterprise on a particular topic we’re looking at from an investment standpoint.

We are on the cusp of a new era in data and are excited to see the rise of data engineering. With over 35,600 and 119,950 results in new job openings on Glassdoor and LinkedIn respectively, there are more data engineers now than ever. From an organizational standpoint, data is totally broken — data science and engineering teams use different, siloed workflows. And the worst part is that they aren’t even efficient.

Most data scientists still spend most of their time completing tasks that are not essential parts of their jobs — such as rolling out their own infrastructure and maintaining unreliable processes. Data engineers face the same dilemma: To quote Jeff Magnusson, data engineers still “deploy platforms, services, abstractions, and frameworks that allow the data scientists to conceive of, develop, and deploy their ideas with autonomy,” most of which don’t pertain to core engineering work. In an ideal world, all data scientists and analysts want to do is build machine learning models and derive insights from the data they collect; and all data engineers want to do is be able to build things at scale. …


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One of the things we know most about as investors at Work-Bench is just how many things startup founders and CEOs have on their to-do list. At the stage we invest — Seed II — most startups don’t have an in-house CFO yet and founders are still running their finances themselves. While hiring a CFO may not be in the cards for an early stage startup, it’s still critical for founders to have a strong grasp of FP&A drivers to best determine and plan for top-line financial needs.

We recently sat down with Scott Buxton, VP of Finance at Datadog, Sarah Alper, VP of FP&A at UiPath and Peter Di Iorio, Director of Finance at Electric AI, who come from companies across multiple stages of growth to hear how they approached 2020 planning and budgeting as well as the biggest challenges they face in this department as a SaaS company. Here are the two biggest takeaways that will help you operate more like a CFO and ensure the financial health of your…


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This past month, I attended my first Declare Summit in NYC and AllRaise VC Summit in Oakland where hundreds of women founders and funders from across the globe gathered to discuss the future of technology. I was heartened to see such a strong showing of women at different stages of their careers coming together and sharing their personal journeys as well as tactical tips in navigating the VC business. I previously wrote a post after joining Work-Bench and now after spending 6 months in the role as a VC Analyst, these are some of my biggest learnings on the job. …


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Source: https://github.com/kelseyhightower/nocode

The low-code and no-code market continues to heat up as more and more VCs pour money into the space. This trend is not showing any signs of slowing down either. Just a few weeks ago, Instabase, a low-code platform that enables both technical and non-technical users to build customizable apps, achieved “unicorn” status after raising a massive $105M in its most recent round. As enterprise tech investors looking closely at this space at Work-Bench, we are constantly thinking about what the next generation of development, automation and productivity tools will look like. …

About

Priyanka Somrah 🦋

VC Analyst @Work-Bench, investing in #nextgenterprise

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