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2023: A Critical Year for ML’s Rapid Growth

Scribble Data's plans and predictions for 2023

Scribble Data's plans and predictions for 2023

Venkata Pingali

December 27, 2022

As 2022 draws to a close, it is time to reflect on the year gone by and welcome 2023!

I’d like to take this opportunity to talk about some of the highs, the lows, the opportunities and learnings in 2022, how we’ve seen the market evolving, how it’s impacted some of the choices we’ve made at Scribble Data, and some of our predictions for the coming year.

 The last three years haven’t exactly been easy, but 2022 proved to be exceptionally difficult for many – with the impending recession, the funding winter, and the mass layoffs across companies of all shapes and sizes the world over bringing a sense of disorder and uncertainty. At Scribble Data, we weren’t immune to this cycle of ups and downs either, but 2022 also provided us with moments worth celebrating – our funding round, clarity on product development, and the addition of new customers across geographies.

The State of the Market: Navigating Rapid Growth in ML with Caution and Realism

While we saw initial signs of resistance against the Modern Data Stack (MDS), this year also marked the end of the most recent phase of the Machine Learning (ML) hype cycle that started around 2016-17. Over the past 5 years, we saw many organizations try, with little success, to implement ML or MDS for critical business processes and decision-making. We saw a whole wave of over-engineered products for the narrowest of scope where vendors tried to transfer the integration costs to their customers.

 The market corrections that began early in the year led to a new sense of realism setting in the market. Data Science is no longer an R&D function that works in isolation but is now being considered a team sport that requires not just participation from all the functions across the organization, but also discipline. Discipline applies across various facets of the data journey, from quality, to exploratory code, to robust productionization, but more on this later.

 The end of the hype cycle also signifies ML becoming more mainstream – ML is here to stay and will percolate across the organization. Just like learning Microsoft Excel is a critical business skill required across every function within the organization, we can expect ML to be in the same class, transcending functional boundaries. Think of this as the early days of the internet – what started off as a faster means of sending messages is bigger than what we could have ever envisioned. Similarly, we’re barely scraping the surface when it comes to the applications of ML and the companies that will truly be ahead of the competition are the ones that can build complex solutions faster and more efficiently.

 Adoption of ML is fundamentally about transforming decision making in organizations – making them more efficient, intelligent, and forward-looking.

There are several reasons why the ML ecosystem is expected to experience significant growth in the near future. One reason is that the necessary technologies and tools, such as Scribble’s rapid Analytical Data Products platform, Enrich, better match the reality of organizations and data. Additionally, customers have gone through these hype cycles in the past, and are now becoming more strategic in their approach to ML– placing a greater emphasis on data quality, governance, and process, all while becoming more realistic about what complexity of ML their organizations can pull off. The recent success of applications like GPT3 has also helped to raise awareness and interest in ML at all levels within organizations. Furthermore, even departments that were traditionally outside the purview of ML, such as finance, are beginning to leverage ML to solve a range of problems. Finally, analytics teams with strong use cases are showing more active interest in ML than in the past.

Organization Focus: Increased Effort on Processes

Post our seed round, we began to move from being a boutique organization, with processes organically evolving, into becoming a streamlined company that is much more process driven. This evolution spanned better go-to-market processes to large strides in our product development, where we focused on developing our App Store (the layer that allows users to consume the rich datasets they build on our platform) to make it easier for customers to quickly build analytical data products. We also streamlined our overall messaging, communication and positioning of the organization.

As we begin to see more interest among large enterprises, it’s become increasingly important to showcase the dependability and trust of our product, which deals with sensitive data. To this end, we went through a formal process to ensure we are not only GDPR compliant, but also received our SOC 2 Type II certification.

However, it wasn’t all smooth sailing – the SMB market we were going after faced their own pressures of getting their business models right, especially in this new cost constrained model. As a consequence, we had a couple of customers churn. The silver lining was that it helped us narrow our ideal customer profile further, as well as to tailor our product better. We invested in improving the App Store experience to make it more user-friendly and able to deliver full data products quickly, which is going to be important in a cost-constrained, RoI-focused market.

Product Focus: Building an Aspirational Tool for Analytics Teams

We made a few course corrections in 2022. We realized there was a gap in the tooling that analytics teams wanted. A lot of analytics tooling is very SQL and reporting-centric. The problems most customers came to us with looked like lightweight ML problems. As a result, we switched our focus from catering to data science and engineering teams to analytics teams. We started making the product more full-stack to address the gaps that exist in analytics tooling today – and to help these teams focus more on data product building, from dataset creation to modeling and serving context-aware datasets through apps to these teams. 

One of our challenges has been effectively communicating this value to a market that’s seen much promise of ML but has struggled to realize returns on their investment. We’re getting better though, and we expect this to improve with more logos in the portfolio and a stronger GTM team in the first half of 2023.

People Focus: Doubling Our Headcount and Learning Valuable Lessons in Hiring

We doubled in size during the year and added several core functions to the team, including marketing, go-to-market, product, and engineering.

However, we also made some mistakes along the way in evaluating the fit, and learned from these mistakes, improving our hiring process. 

As our team grows, with both senior and mid-level hires, we see potential leaders in all functions. We are also working on creating a culture that allows our team to perform at their best and we plan to scale each of these functions in 2023.

What to expect from Scribble Data in 2023

  • Increased focus on the app store, and a focus on how analytics teams use the product will allow us to offer full solutions using our product. We’re most excited about the launch of our App store 2.0, which will greatly improve the user experience, as it will provide a more streamlined and intuitive way for analytics teams to access and utilize the full range of features and capabilities offered by our product.
  • The platform’s reliability was of the utmost importance, and a formal compliance process was implemented to ensure this. In addition, a number of under-the-hood capabilities, including dataset signatures, easy anonymization, and automated data classification are being rolled out to allow customers to use the system with greater confidence.
  • There have been a number of new applications in the field of natural language processing (NLP) that have been developed based on large language models in production. GPT-3 is a significant milestone in NLP and we anticipate seeing a range of applications beyond this. Many of these applications have the potential to transform decision-making and communication-intensive activities.
  • Expanded range of use cases available to our customers.  The many use cases that have been built on Scribble’s platform, across domains like E-commerce, Fintech, Retail and Healthcare allow us to make a number of accelerators available to new customers out of the box

In closing, I’d like to thank our well wishers – team members, customers, partners, prospects, and investors. We’re grateful we had you by our side and appreciate the trust you’ve placed in us, and the valuable feedback you’ve shared with us. We’ve got an uphill battle ahead of us, but are confident that our team, backed with your support, can achieve everything we’re planning for in 2023, and more!

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