As we look back on 2022, it's been exciting to see the rapid advancements in data, analytics and AI that have helped shape the way organizations operate. It was a turning point for many businesses as they began to realize the true potential of data-driven insights and the power of AI to drive innovation. As we head into 2023, we can expect even more breakthroughs and developments that will change the way companies leverage data and analytics to gain a competitive edge. Let's take a closer look at my predictions for the coming year and explore how organizations can prepare for the future of data, analytics and AI. Expect to see more M&A activity in 2023 as vendors look to tell more unified, end-to-end and comprehensive stories.

This is a collaborative post between Monte Carlo and Databricks. We thank Matt Sulkis, Head of Partnerships, Monte Carlo, for his contributions. As companies increasingly leverage data-driven insights to innovate and maintain their competitive edge, it's essential that this data is accurate and reliable. With Monte Carlo and Databricks' partnership, teams can trust their data through end-to-end data observability across their lakehouse environments. Has your CTO ever told you that the numbers in a report you showed her looked way off?

Big Blue's been on a buying spree lately with Databand.ai, If you do, you might miss another huge IBM buyout. Up front: Big data is a big deal. Less than a decade ago, many businesses were manually entering data into spreadsheets to meet their insight needs. Today, even the most modest startups can benefit from deep analytics.

The previous blog post in this series considered the current state of the ML tooling ecosystem and how this was reflected in ML adoption in industry. The main takeaway was the widespread use of propriety tooling amongst companies in this field, with a correspondingly diverse and splintered ML tooling market. The post ended by looking at some emerging near-term trends, highlighting the predominance of data observability and related tools, as well as the emergence of MLOps startups. This blog post will pick up from this previous thread to discuss some of the key trends in ML tooling that are likely to dominate in the near future -- or at least ones I want to talk about! As indicated in the previous blog post, I want to focus on MLOps, AutoML, and data-centric AI.

In my blog "The Importance of Determining Intent", I discussed the importance of determining user intent to create an "intelligent" user or stakeholder experience. Analytics-centric organizations specialize in determining and codifying a user's intent in order to provide a more engaging, relevant, hyper-personalized experience (Figure 1). Figure 1: Using "Intent Determination" to Create an Intelligent Customer Experience To create an "intelligent" user experience requires leveraging AI/ML to analyze a deep history of the user's interactions to determine the user's intentions, and then coupling those intentions with current trends, patterns, and relationships to match those intentions with a deep understanding of the available content to recommend the most relevant action. We reviewed how digital marketing companies, such as those featured in Figure 1, determine user intent. These companies accumulate a deep history of each individual user's interactions including what sites or content they visited or viewed, how long they spent with each site or piece of content, what they clicked on, what they did not click on, and their contextual search requests. They analyze the user's interaction history, and match that with current trends and behaviors of similar cohorts, to determine and codify (think propensity scores) the user's intentions (areas of interest) that drives real-time recommendation decisions.

We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - August 3. Join AI and data leaders for insightful talks and exciting networking opportunities. Imagine, for a moment, that you lead a customer success operations team that is responsible for compiling a weekly report for the CEO outlining data on customer churn and analytics. Over and over, you deliver the report only to be notified minutes later about problems with the data. It doesn't matter how strong the ETL pipelines are or how many times the team reviews the SQL queries -- the data are just not reliable. This puts you in the awkward position of repeatedly coming back to leadership telling them that the information you just provided was wrong.

In my blog "The Importance of Determining Intent", I discussed the importance of determining user intent to create an "intelligent" user or stakeholder experience. Analytics-centric organizations specialize in determining and codifying a user's intent in order to provide a more engaging, relevant, hyper-personalized experience (Figure 1). Figure 1: Using "Intent Determination" to Create an Intelligent Customer Experience To create an "intelligent" user experience requires leveraging AI/ML to analyze a deep history of the user's interactions to determine the user's intentions, and then coupling those intentions with current trends, patterns, and relationships to match those intentions with a deep understanding of the available content to recommend the most relevant action. We reviewed how digital marketing companies, such as those featured in Figure 1, determine user intent. These companies accumulate a deep history of each individual user's interactions including what sites or content they visited or viewed, how long they spent with each site or piece of content, what they clicked on, what they did not click on, and their contextual search requests. They analyze the user's interaction history, and match that with current trends and behaviors of similar cohorts, to determine and codify (think propensity scores) the user's intentions (areas of interest) that drives real-time recommendation decisions.