Machine learning is no longer about experiments. Most industry-leading enterprises have already seen dramatic successes from their investments in machine learning (ML), and there is near-universal agreement among business executives that building data science capabilities is vital to maintaining and extending their competitive advantage. The bullish outlook is evident in the U.S. Bureau of Labor Statistics' predictions regarding growth of the data science career field: Employment of data scientists is projected to grow 36% from 2021 to 2031, much faster than the average for all occupations. The aim now is to grow these initial successes beyond the specific parts of the business where they had initially emerged. Companies are looking to scale their data science capabilities to support their entire suite of business goals and embed ML-based processes and solutions everywhere the company does business.

Polaris has announced a new partnership with data science Insurtech, Incited, to broaden its machine learning capabilities offered to its clients. The new enrichment service is available to Polaris clients using their flagship rating solution, ProductWriter's Run-Time Environment (RTE), to bring real-time predictive models into its dynamic pricing capabilities. Incited's founder and CEO Nick Turner said: "The integration of our machine learning service with ProductWriter is an industry first, demonstrating our joint commitment to enabling advanced features in the Polaris ecosystem. Our long-standing partnership with Polaris continues to deliver value to insurers and brokers. Adding machine learning capabilities to the RTE signals our ongoing commitment to develop and train an increasing number of high-value models for the industry."

AIoT, the confluence of AI and Industrial IoT technological forces, gives rise to a new digital solution category – the Artificial Intelligence of Things (AIoT). AIoT is built for industrial companies looking for better ways to connect their evolving workforce to data-driven decision tools and digitally augment work and business processes and making better use of industrial data already collected. ARC Advisory Group has observed that the convergence and overlap of IT and OT groups, driven largely by the digital transformation of industry in recent years has created organizational confusion and a significant "gray-space" of common technologies between each area, one area being AI. However, leveraging AI requires data science capability, which adds additional complexity to an already complex environment. While engineering roles are skilled in analyzing large amounts of data, setting up and creating production grade machine learning environments is not easily accomplished.

The pursuit of data-driven decision-making can make business leaders starry-eyed about data science, believing that artificial intelligence in particular can instantly transform their business. What's needed is a healthy tension between data scientists and business leaders around what's possible and workable for using data to drive key decisions. The ideal scenario is all parties in complete alignment. This can be envisioned as a perfect rectangle, with business leaders' expectations at the top, fully supported by a foundation of data science capabilities -- for example, when data science and AI can achieve management's goal of reducing customer retention costs by automating identification and outreach to at-risk customers. Consider Target, which in the mid-2010s had flat in-store sales and a growing digital presence.

The ongoing shortage of data scientists has been well documented. Even as the business world grows increasingly digitized and reliant on big data modelling and analytics to drive value and profit, those possessing the requisite education and expertise in mathematics/statistics, data prep, programming, and distributed computing to meet data science challenges are rare birds. The ability to make sense of the enormous troves of transaction, customer, and equipment data across digitized industries has become a premium skillset, and the recent explosion in machine learning (ML) and artificial intelligence (AI) capabilities has compounded the problem. Now that we can access the compute power and data volumes necessary to operationalize tasks such as pattern recognition, anomaly detection/diagnosis, customer analytics, pricing and predictive planning, we want ML systems that can learn to automatically prepare and perform data science functions with minimal programming. Thus, the irony: Machine learning is often deployed as a kind of digital surrogate for the data scientist, but one that requires the skills of a data scientist to be brought into existence.

Thirty years ago, the idea of a machine learning on its own would have stoked the worst kind of sci-fi nightmares about robots taking over the planet. These days, machine learning is so commonplace, we barely notice it. Computers routinely learn what we watch on TV, what we buy, how we talk, and even how we feel--and use that to make predictions about how we'll act next. As the field of machine learning (ML) has become increasingly mainstream, says Harvard Business School doctoral student Mike Teodorescu, it has evolved into the reach of everyday companies, who are increasingly using ML to manage many aspects of their business operations. "There's been an explosion," Teodorescu says.

Over the past year we've refined this simple model to help map, evaluate and improve our clients' data science capabilities, it might work for you too. At Applied AI, most of our client projects lean very technical - we're a very technical team with experience in insurance, machine learning, quant finance, software development and more - and data science is still a fairly new field with high demands on mathematical and engineering capability. That said, we always encourage our clients to undertake projects as part of a larger, more holistic approach to improving their data science maturity: using a statistical approach when deciding strategy and embedding'data products' within their day-to-day operations. So we cooked up the following Data Science Maturity Model. It's purposefully very simple - a familiar 2x2 matrix - and describes a clear path for organisations to improve their capability in terms of the Analytical Complexity and Operational Implementation of new data sources, statistical modelling, products, processes and teams.

Salesforce has joined hands with the artificial intelligence startup, MetaMind. In a wise move, Salesforce has managed to integrate deep learning with its data science capabilities, beating other leading companies in their pursuit of machine learning and artificial intelligence. Salesforce acquires Palo-Alto-based deep learning company, MetaMind, the companies announced on April 4. Launched in July 2014, MetaMind specializes in artificial intelligence (AI) techniques of data crunching to help businesses arrive at better decisions. While the terms of the deal still remain undisclosed, the AI startup will shut down services on May 4 for their unpaid users, and on June 4 for the monthly recurring users. "[R]eal AI solutions with breakthrough capabilities that further automate and personalize customer support, marketing automation, and many other business processes," says MetaMind CEO Richard Socher, who added that he is "thrilled" with the integration.