Straumann Group\u2019s Sridhar Iyengar has a bold mission: To transform the nearly 70-year-old company\u2019s data and technology organization into a data-as-a-service provider for the global manufacturer and supplier of dental implants, prosthetics, orthodontics, and digital dentistry \u2014 and to provide business stakeholders machine learning (ML) as a service as well.\n\n\u201cMy vision is that I can give the keys to my businesses to manage their data and run their data on their own, as opposed to the Data & Tech team being at the center and helping them out,\u201d says Iyengar, director of Data & Tech at Straumann Group North America.\n\nDoing so will be no small feat. The Basel, Switzerland-based company, which operates in more than 100 countries, has petabytes of data, including highly structured customer data, data about treatments and lab requests, operational data, and a massive, growing volume of unstructured data, particularly imaging data. The company\u2019s orthodontics business, for instance, makes heavy use of image processing to the point that unstructured data is growing at a pace of roughly 20% to 25% per month.\n\nAdvances in imaging technology present Straumann Group with the opportunity to provide its customers with new capabilities to offer their clients. For example, imaging data can be used to show patients how an aligner will change their appearance over time.\n\n\u201cIt gives a lot of power to our providers in selling their services and at the same time gets more NPS [net promoter score] for us from the patient,\u201d says Iyengar, who believes AI will play a critical role in Straumann\u2019s image processing and lab treatments businesses. Hence the drive to provide ML as a service to the Data & Tech team\u2019s internal customers.\n\n\u201cAll they would have to do is just build their model and run with it,\u201d he says.\n\nBut to augment its various businesses with ML and AI, Iyengar\u2019s team first had to break down data silos within the organization and transform the company\u2019s data operations.\n\n\u201cDigitizing was our first stake at the table in our data journey,\u201d he says.\n\nSelling the value of data transformation\n\nIyengar and his team are 18 months into a three- to five-year journey that started by building out the data layer \u2014 corralling data sources such as ERP, CRM, and legacy databases into data warehouses for structured data and data lakes for unstructured data.\n\nThat step, primarily undertaken by developers and data architects, established data governance and data integration. Now, the team\u2019s information architects, in conjunction with business analysts, are working on the semantic layer, which feeds data from data warehouses and data lakes into data marts, including a finance mart, sales mart, supply chain mart, and market mart. The next goal, with the aid of partner Findability Sciences, will be to build out ML and AI pipelines into an information delivery layer that can support predictive and prescriptive analytics.\n\n\u201cAs the information layer gets mature, that\u2019s where the ML and the AI will start seeing some green shoots,\u201d he says, adding that although data transformation was a pressing need when he signed on in 2021, he wanted a more compelling vision to sell the board and business leaders on tackling it.\n\nFor that, he relied on a defensive and offensive metaphor for his data strategy. The defensive side includes traditional elements of data management, such as data governance and data quality. The offensive side? That is the domain of AI and advanced analytics that serve a role beyond just insight and business optimization.\n\n\u201cThe offensive side is how to generate revenue, all of the insights from the historical data that we have collected and, in fact, forecast the trends that are coming,\u201d Iyengar says. \u201cMost of the data that we get on the offensive side are unstructured, and we want to make sure that it makes sense to the business leaders and help them harmonize and enrich it in such a manner that they can serve their customers more efficiently and that the customers get served and leverage Straumann\u2019s services in a much more robust, frictionless manner.\u201d\n\nNot surprisingly, it was this offensive side that got Straumann\u2019s board invested in Iyengar\u2019s plan for transformation.\n\n\u201cWhen the customer-centricity and the digital transformation piece was proposed \u2014 along with data transformation \u2014 I think that resonated with them,\u201d Iyengar says.\n\nSkilling up for the future\n\nIyengar\u2019s team found success by adopting a use-case approach, not unlike that of one of Strauman\u2019s core businesses. \u201cWe pretty much took the same principle of the pre-treatment and the post-treatment images that we show to our patients,\u201d Iyengar says.\n\nThe team asked company leaders to pick a number of customer-centric vectors to illustrate how data innovations could be used to drive business outcomes. One of the targets was driving down customer churn. The team started by splitting churn propensity into two values: one for retention of existing customers and one for new customer acquisition. It used typical customer lifetime values and analyzed buying patterns to provide the marketing team and sales team with insights they could use to drive their strategies.\n\nIyengar says adopting this approach to selling digital transformation internally has made the job much easier. \u201cWe are seeing a lot of investments being approved from all the businesses in order to support that initiative,\u201d he says.\n\nIn the meantime, as the team begins to build out ML and AI capabilities, it is also imperative to transform the Data & Tech team itself.\n\n\u201cThe skill set that we have inherently from our traditional school point of view doesn\u2019t suit the ML and AI part of it,\u201d Iyengar says. \u201cWhat you need there is statisticians and mathematicians, not programmers and coders, right? So, we have been transforming ourselves as well, culturally and from a skill point of view. That takes its own time. We have a learning curve at our end to build the right skill set within us.\u201d\n\nIyengar is supplementing his team\u2019s skill set with help from enterprise AI specialist Findability Sciences. The company\u2019s Findability.ai platform combines machine learning, computer vision, and natural language processing (NLP) to aid customers in their AI journey.\n\n\u201cI have a lot of traditional ETL skills in my team,\u201d he says. \u201cWhat I don\u2019t have is the ML\/AI skill set right now. Partners are helping us in that space.\u201d\n\nUltimately, Iyengar says, these changes will transform how the Data & Tech team interfaces with the business. For now, it operates under a centralized \u201chub and spokes\u201d model. But he says hiring statisticians and mathematicians in his team won\u2019t be scalable. Instead, what he really wants within three to five years is to embed them in teams closer to the lines of business, so the businesses can run models by themselves.\n\n\u201cRight now, we\u2019re driving the bus at 100 miles and hour and changing the tires at the same time, which is not going to be scalable by any means, though I\u2019m proud of my team that we are doing it,\u201d he says.