Analyse Why Integrated Marketing is Effective in 2021

It’s no secret that data is the source of competitive advantage in the enterprise market. Companies big and small are investing at record levels to collect, transform, analyze and act upon ever-growing volumes of data.

The Rise of the Cloud Data Warehouses has created massive change in the way that companies work with data. For the first time, Trollishly data has become easily and quickly accessible for everybody in the enterprise. In this new world, data teams (data analysts, engineers, and scientists) are taking center stage, and they are throwing out their legacy on-premise software and infrastructure in favor of cloud-native tooling.

The founders of Fishtown — Tristan, Conor, and Drew — have spent their careers in data analytics, and they care deeply about empowering data practitioners. They recognized the market opening created by the Cloud Data Warehouses and realized their potential to change how data folks work with data — for the better. The team founded Fishtown Analytics with a mission to empower data people to create and disseminate organizational knowledge through better tools and workflows.

Data integration/ETL software is one of the most important — and hated — tools in the data arsenal. Most companies teeter on a fragile patchwork of ad-hoc data pipelines and data transformations across siloed on-premise tools. Changing production data transformation pipelines is a stressful process that elicits a sense of anxiety (if not dread) among data teams — add a new field or change the dependencies of a single model, and the entire house of cards may fall over. If you ask any company what is holding them back from making better use of data, the answer is always the same — data engineering, and specifically data transformations.

The Fishtown team came up with a better way to handle data transformations for the Cloud Data Warehouse era. They created dbt, a system for data transformations as code, which has brought the software engineering best practices of modularity, testing, deployment, and monitoring to the field of data analytics. They call this analytics engineering. Using dbt results in a higher pace of iteration and a higher level of confidence in the quality of data across the enterprise. Within a matter of years, dbt has become the central repository for data transformation code, analogous to a GitHub for data.

We first heard about dbt from our portfolio companies and quickly called no fewer than a dozen users. We were taken aback by the results: dbt’s Net Promoter Score averaged 10.2 out of 10. This level of effusiveness over a product is exceedingly rare, especially in the enterprise. We were amazed to hear that the always-hated data transformation market had a new tool that was bringing actual *joy* to its users.

dbt users raved not only about the product, but also about the community. We have heard time and time again that the dbt Community is a one of a kind place. It is the central gathering hub for thousands of data analytics professionals to meet, help and learn from one another. Our references called the dbt community “special” and “warm and fuzzy”. They also noted that dbt had given them a deep sense of community, belonging and empowerment.

All of this suggests that dbt has solidified its position as an integral part of the modern cloud data stack. For companies looking to invest in the cloud data warehouse transition, we have published the modern data stack below. Enterprises ingest data using an ELT tool like Fivetran or Stitch; they store that data in their Cloud Data Warehouse like Snowflake or Redshift; they transform that raw data into pristine, analytics-ready data in-warehouse using a transformation tool like dbt; and finally, they send the transformed data out to BI tools and applications.

Just as Atlassian and GitHub created the tools for software developers to work better, Fishtown is creating the tools for data people to thrive in their work. Tristan and his team have fostered an incredible amount of loyalty and goodwill in the data community. The rise of the data-driven enterprise is one of the most important trends of the next decade, and Fishtown has captured the hearts of the data practitioners who will drive that lasting change.

Luckily, event organizers are not left out in the cold. Just as before pre-COVID, there are several organizations within the industry that specialize in data analytics and have a long, proven record of serving up event organizers with just what they were seeking – actionable insights to drive strategies. For the organizers out there, I might offer the following recommendations.

When shopping for a virtual event platform, after a few initial high-level questions to slim down the choices just a bit, start with an exploration of their data-insights output. Thoroughly dig into what kinds of insights you should expect to pull from their system first, and then take the time to align that internally to develop a set of measures and objectives for your events based on these KPIs. Use that as your primary selection criteria and then move into other functional comparisons afterwards. In this way, regardless of the pros and cons of any given virtual experience, you will already know that you’ll have insights to drive future actions. You should know the questions and the answers that will be generated.

What if you have already done the work to define the insights you believe you need, but can’t find a platform to support those out of the box? Then my recommendation would be to engage your data analytics vendor early and include them in the process, letting them help you define the insight expectations you should have and then ultimately deliver those to you through the analysis. I know there are budgetary considerations and all the other very real business considerations that we are all challenged with today, but in the chaos of our circumstance, I urge us to not forget the primary objective of meetings as a whole – to add value to the engagements of market participants. That value cannot be assumed or surmised, it should be measured and quantified. As it turns out, regardless of the noise and chaos generated by this environmental melee, it really is still “all about the data.”

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Christophe Rude
Christophe Rude
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