The Rise of Data Science & Machine Learning Platforms
While Data Science and Machine Learning have great potential, it is anything but simple. There are a number of hurdles to cross in the process of collecting, cleaning, organizing, and implementing data, to begin with, and then training high-quality Data Science and Machine Learning models. Many issues result from using disparate solutions and having a siloed process for organizing data within an enterprise. The key to establishing effective enterprise Data Science and Machine Learning is unifying data tools and processes into a singular UI experience to help your data scientists handle data with ease and efficiency. A data-centric Machine Learning/Data Science platform brings models and features alongside data for business metrics, monitoring and compliance. It unifies them, and in doing so, is fundamentally simpler.
Data Science Platform Market Size, By Deployment Type, 2020–2026
In many companies today, data is created and stored in information silos. Each team has become responsible for their own fraction of data, which makes it difficult for them to make data-driven decisions at an enterprise level. Linking this data within the company will help them become more effective and successful. A recent study commissioned by Forrester Consulting found that organizations that adopt a Unified Data Analytics Platform experience revenue acceleration, improved data team productivity and infrastructure savings resulting in a 417% ROI on their data analytics and AI projects. Choosing the right vendor and solution can be a complicated process — one that requires in-depth research and often comes down to more than just the solution and its technical capabilities. Many organizations have increasingly been realizing the importance of such platforms that offer unified Data Science and Machine Learning platforms and are pouring investments into their partners. This has led major unified Data Science and Machine Learning platform leaders like Databricks, H2O.Ai, Datarobot to up their game and enhance their platforms. Similarly, partners within each of these data platform ecosystems are witnessing a significant surge in demand for their services and entering into numerous strategic partnerships with varied enterprises and garnering investments.
Databricks : Databricks is the solution that combines data science, engineering, and business to use the power of AI within a genuinely unified approach to data analytics. The solution is powered by Apache Spark, which is a completely open-source platform, hosted at the vendor-independent Apache Foundation. Main users of Databricks are mostly data scientists and engineers in medium-sized and large enterprises, belonging to a variety of industries including energy and utilities, financial services, advertising, and marketing. With this service, users can unify their analytics operations, streamline workflows, increase the productivity of data teams, reduce risk, optimize I/O performance, and many more. It also offers a collaborative workspace, so data teams are able to create data pipelines, test machine learning models, and provide insights to the business via the same platform.
NTT Data Services acquired Databricks partner Hashmap to expand the company’s data analytics, artificial intelligence and machine learning expertise. This partnership, together with NTT DATA’s existing relationships with companies like Snowflake and Dataiku, is expected to greatly expand its ability to accelerate clients’ digital transformation journeys and establish a trusted data foundation to operationalize and scale AI.
H2O.Ai: H2O is an open-source, distributed in-memory machine learning platform with linear scalability. H2O supports the most widely used statistical & machine learning algorithms and also has an AutoML functionality. H2O’s core code is written in Java and its REST API allows access to all the capabilities of H2O from an external program or script. H2O.ai’s platform, the H2O AI Cloud, aims to simplify much of the work involved in machine learning projects as building an enterprise AI model involves numerous highly technical tasks that require specialized know-how and take up a great deal of time when done manually.
H2O.ai recently closed a $100 million funding round led by one of its customers, the Commonwealth Bank of Australia, with participation from Goldman Sachs, Pivot Investment Partners and several others.
Datarobot: DataRobot offers an enterprise AI platform that automates the end-to-end process for building, deploying, and maintaining AI. Its platform runs on cloud platforms, on-premise datacenters, or as a fully managed service. Once it’s deployed, customers can use it to monitor models from a dashboard and test, run, and maintain the models to optimize outcomes. Depending on a customer’s needs, DataRobot can automatically run a “competition” by testing hundreds or even thousands of solutions to a problem and delivering models to provide predictions. The platform also allows data scientists to explore, combine, and shape a range of data types and content — from traditional tabular data in rows and columns to free-form text, images, and geospatial data — into assets ready for AI models.
DataRobot, a startup creating an enterprise AI development platform, recently closed a $300 million series G funding round led by Altimeter Capital and Tiger Global, with participation from Morgan Stanley’s Counterpoint Global, Franklin Templeton, ServiceNow Ventures, and Sutter Hill Ventures.
Dataiku: Dataiku is used by data scientists, but also designed for business analysts and other people with less technical backgrounds. The platform lets companies design and deploy AI and analytics apps, turn raw data into advanced analytics and design machine learning models. It’s been used for a wide array of use cases, including fraud detection, customer churn prevention and supply chain optimization.
Dataiku recently raised a $400 million Series E, bringing its valuation to $4.6 billion. The round was led by Tiger Global, with participation from returning investors like ICONIQ Growth, CapitalG, FirstMark Capital, Battery Ventures, Snowflake Ventures and Dawn Capital.
Alteryx : Alteryx is used to answer business questions that are time-consuming, manual, error-prone, risky, and maybe impossible with the alternative tools being used. Alteryx can be used to speed up processes (accounting close, tax filings, regulatory reporting, forecast creation), automate processes (reconciliations, consolidations, marketing workflows, system integrations, continuous audits), and enable predictive and geospatial solutions. The unique innovation that converges analytics, data science and process automation into one easy-to-use platform, empowers everyone and every organization to make business-altering breakthroughs the new status quo.
Alteryx recently announced its intent to acquire Trifacta for $400 million, a provider of data wrangling solutions, to add cloud-native capabilities to Alteryx’s platform and is aimed at accelerating its move to the cloud.
Data Science and Machine Learning platforms are profoundly changing the way that companies think about, interact with, and create value with data. Early adopters are not only solving age-old data problems that continue to plague the vast majority of companies today in a matter of days or weeks — but they are also discovering entirely new products and services. We, at SA, continue to see significant investor interest in partners of the above companies, as we fully expect the range of opportunities within the segment to grow significantly.
To share your feedback on this blog or insights on this ecosystem or if you’d like to explore potential transaction opportunities for your firm with us, please write to us at info@saglobaladvisors.com.
