Japan is home to millions of vending machines installed on streets and in buildings, sports stadiums and other facilities. Vending machine owners and operators, including beverage manufacturers, stock these machines with different product combinations depending on location and demand. For example, they primarily display coffee and energy drinks in machines placed in offices and sports drinks and mineral water in machines at sports facilities. The combinations also vary by season: for example, owners and operators may display cold beverages in summer and hot beverages in winter. 

Traditionally, vending machine operators have relied on the intuition and experience of sales managers to determine the optimum product mix for each vending machine. However, in recent years, manufacturers such as Coca-Cola Bottlers Japan (CCBJ) have turned to data to analyze and make strategic decisions about when and where to locate products in machines.

CCBJ is the number one Coca-Cola bottler in Asia and vending machines comprise the bulk of its business. The organization operates about 700,000 machines across Tokyo, Osaka, Kyoto, and 35 prefectures. Minori Matsuda, Google Developer Expert and also Data Science Manager at CCBJ, says “The billions of data records collected from 700,000 physical devices are a great asset and a treasure trove we can take advantage of.”

Minori points out that when considering the mix of products in vending machines in sporting facilities, the managers naturally assume sports drinks would generally sell well. However, analysis of purchase data – including hot drinks and hot drinks plus sports drinks – found many parents purchased sweet drinks such as milk tea when they attended games or sessions involving their children.  “Analyzing data gives us new discoveries and, by using catchy storytelling techniques from exploratory data analysis, we are instilling a data culture within our company,” he says. “It’s worth creating by looking at facts rather than making assumptions!”

Minori believes that to analyze the vast amount of data collected from more than 700,000 vending machines, the business needs a powerful analytical platform. However, until recently, CCBJ had to extract data for analysis from its core systems, load this data into a warehouse it created and perform the required analyses.  The billions of records of data generated across the fleet – including transaction data – exposed some challenges for traditional analysis platforms. They could not efficiently process data at a considerable scale: it could take a day to return results and required extensive maintenance due to the size.

CCBJ considered building a machine learning (ML) platform as a layer on top of existing systems in August 2020 and opted for Google Cloud the following month.  “I feel that Google Cloud has an edge in all products and is very well thought out,“ says Minori, noting the scalability and cost of the platform allow the business to take a ‘trial and error’ approach to achieve the best outcomes from ML. Google Cloud also delivered the required visibility and flexibility to help the business deliver change every day against key performance indicators. 

MLOps platform streamlines ML pipeline development

CCBJ built its analysis platform using Vertex AI (formerly AI Platform) centered on a BigQuery analytics data warehouse, and partly using AutoML for tabular data. “We have created a prediction model of where to place vending machines, what products are lined up in the machines and at what price, how much they will sell, and implemented a mechanism that can be analyzed on a map,” says Minori, adding that building the platform with Google Cloud was not difficult. “We were able to realize it in a short period of time with a sense of speed, from platform examination to introduction, prediction model training, on-site proof of concept to rollout.”

The new data analytics platform of CCBJ consists of the following parts:

Data Sources

• The data collected from the vending machines are all stored on BigQuery.

Data Discovery and Feature Engineering

• Minori and other data scientists at CCBJ are using Vertex Notebooks, where they access the data on BigQuery by executing SQL queries directly from the Notebooks. This environment is used for the data discovery process and feature engineering. 

ML Training

• For ML training, CCBJ uses AutoML for Tabular data, Custom model training on Vertex AI, and BigQuery ML. AutoML gives model performance with AUC curves and also feature importance graphs.

ML Prediction and Serving

• For ML prediction, CCBJ uses Online Prediction for AutoML models and Online Prediction for custom models for real-time prediction when the salesperson finds the interesting point
• Batch Prediction is used for generating a large prediction map that covers the whole country
• The prediction results are distributed to sales managers’ tablets

CCBJ started constructing the platform in September 2020, and completed it within a month. The business has conducted proofs of concept at its base in Kyoto since February 2021, and since April, has rolled out the platform to sales managers in 35 prefectures in one metropolitan area. “Data analysis is built into the day-to-day routines of sales managers with 100% utilization,” says Minori. “They can utilize the prediction results on tablets that were able to achieve pretty high accuracy from the start.”

The hardest part was the education of sales managers in the field; having them understand the reasoning behind the ML prediction results for particular outcomes, so they could be convinced to make use of the results. “For example, regarding a new installation location predicted by the model, it seemed that there was no effective information for installation from the map information, but when I actually went there, there was a motorcycle shop and it was a place where young people who like motorcycles gathered,” says Minori. “Or there is a small meeting place where the elderly in the neighborhood are active. 

“In many cases, new discoveries that cannot be understood from map information alone can be derived from the data.”

Minori also points to a phenomenon whereby humans pursued and confirmed factors inferred by the model – meaning that once they experienced analysis and it worked effectively, they asked why the same type of analysis or prediction could not be undertaken next time. The resulting cycle of more inquiries generated, more information gathered and more data captured for analysis meant the accuracy of results was improved.

Minori describes Vertex AI as having a number of strengths in helping CCBJ build a ML data analysis platform. “One of the major merits of Vertex AI was that we were able to realize MLOps that streamlines the entire development life cycle from construction of the ML pipeline to its execution,” he says.

With near real-time data analysis through Google Cloud, CCBJ teams can spend time developing strategies rather than waiting for data requested from the IT systems department. Exploratory data analysis is also considerably easier as repeated trial and error has greatly improved the accuracy of analyses. Before we used Machine Learning, most machine placement processes were done by human senses, by looking at a map to find the suggestion points. By using Machine Learning to generate a massive number of placement point suggestions, the efficiency of routing of salespeople has been dramatically improved. 

In the future, CCBJ aims to automate the continuous training pipeline with Vertex AI. “CCBJ is a tech company that operates in the food industry,” says Minori. With the organization operating a vending machine network of 700,000 units, it would like to create new businesses based on utilization and analyzing data. Some of these businesses may be based on Sustainable Development Goals (SDGs) initiatives such as the utilization of recycled PET bottles, measures to prevent food loss and ways of using vending machines to contribute to local communities, which we have been working on for some time. It would be interesting if we could collaborate with Google Cloud on these in the future.”

With the rise of digital assistants and conversational interfaces, people have grown accustomed to hearing and speaking to synthetic voices. But what do those voices sound like? Often, pretty repetitive. We’re all familiar with the Google Assistant voice, for example.

That’s why we are excited to announce the general availability of Custom Voice in our Cloud Text-to-Speech (TTS) API, a new feature that lets you train custom voice models with your own audio recordings to create unique experiences.

For businesses looking to build a strong brand identity, establishing a unique voice can help turn mobile app interactions or customer service based on interactive voice responses (IVR) into differentiated customer experiences. Our TTS API has included a speech synthesis service with a static list of voices for some time, but now, with Custom Voice, moving beyond these predefined options is easier than ever.

Custom Voice lets you simply submit your audio recordings to get access to the new voice directly in the TTS API. Custom Voice TTS includes guidance on the audio requirements to help make sure you generate a high quality custom TTS voice model. Once this new model is trained, all you have to do to start using the newly trained voice is reference the model ID in your calls to the Cloud TTS API.

At Google, we are committed to building safe and accountable AI products, not only because it’s the right thing to do, but because it is a critical step in ensuring successful use in production. As part of Google Cloud’s Responsible AI governance process, we conducted a deep ethical evaluation of Custom Voice TTS, and its relation to synthetic media, in order to surface and mitigate potential harms that it may create. If you are interested in Custom Voice TTS, there is a review process to help ensure each use case is aligned with our AI Principles and adequate voice actor consent is given.

Additionally, to verify that voice actors are actually the ones producing the audio, you will need to submit an audio file producing a sentence that Google Cloud chooses (for example: “I agree that my voice will be used to create a synthetic custom Text-to-Speech voice).

We’re looking forward to seeing this API help businesses solve problems in an easy, fast, and scalable way. TTS Custom Voice is now GA in these languages:

English (US)

English (AU)

English (UK)

Spanish (US)

Spanish (Spain)

French (France)

French (Canada)

Italian (Italy)

German (Germany)

Portugues (Brazil)

Japanese (Japan)

We plan to continue expanding this lineup in order to meet your needs. Ready to try for yourself? Contact your seller to get started on your use case evaluation today!

When ATB Financial decided to migrate its vast SAP landscape to the cloud, the primary goal was to focus on things that matter to customers as opposed to IT infrastructure. Based in Alberta, Canada, ATB Financial serves over 800,000 customers through hundreds of branches as well as digital banking options. To keep pace with competition from large banks and FinTech startups and to meet the increasing 24/7 demands of customers, digital transformation was a must. To support this new mandate, in 2019, ATB migrated its extensive SAP backbone to Google Cloud. In addition to SAP S/4 HANA, ATB runs SAP financial services, core banking, payment engine, CRM and business warehouse on Google Cloud. 

In parallel, changes were needed to ATB’s legacy data platform. The platform had stability and reliability issues and also suffered from a lack of historical data governance. Analytics processes were ad hoc and manual. The legacy data environment was also not set up to tackle future business requirements that come with a high dependency on real-time data analysis and insights.

After evaluating several potential solutions, ATB chose BigQuery as a serverless data warehouse and data lake for its next-generation, cloud-native architecture. “BigQuery is a core component of what we call our data exposure enablement platform, or DEEP,” explains Dan Semmens, Head of Data and AI at ATB Financial. According to Semmens, DEEP consists of four pillars, all of which depend on Google Cloud and BigQuery to be successful:

1. Real-time data acquisition: ATB uses BigQuery throughout its data pipeline, starting with sourcing, processing, and preparation, moving along to storage and organization, then discovery and access, and finally consumption and servicing. So far, ATB has ingested and classified 80% of its core SAP banking data as well as data from a number of its third-party partners, such as its treasury and cash management platform provider, its credit card provider, and its call center software. 
2. Data enrichment: Before migrating to Google Cloud, ATB managed a number of disconnected technologies that made data consolidation difficult. The legacy environment could handle only structured data, whereas Google Cloud and BigQuery lets the bank incorporate unstructured data sets, including sensor data, social network activity, voice, text, and images. ATB’s data enrichment program has enabled more than 160 of the bank’s top-priority insights running on BigQuery, including credit health decision models, financial reporting, and forecasting, as well as operational reporting for departments across the organization. Jobs such as marketing campaigns and month-end processes that used to take five to eight hours now run in seconds, saving over CA$2.24 million in productivity. 
3. Self-service analytics: Data for self-service reporting, dashboarding, and visualization is now available for ATB’s 400+ business users and data analysts. Previously, bringing data and analytics to the business users who needed it while ensuring security was burdensome for IT, fraught with recurrent data preparation and other highly manual elements. Now, ATB automates much of its data protection and governance controls through the entire data lifecycle management process. Data access is not only open to more team members but it is faster and easier to acquire without compromising security. And it’s not just raw data that users can access. ATB uses BigQuery to define its enterprise data models and create what it calls its data service layer to make it easier for team members to visualize their data.
4. AI-assisted analytics and automation: Through Google Cloud and BigQuery, ATB has been able to publish data and ML models that provide alerts and notifications via APIs to customer service agents. These real-time recommendations allow customer service agents to provide more tailored service with contextualized advice and suggested new services. So far, the company has deployed more than 40 ML models to generate over 20,000 AI-assisted conversations per month. Thanks to improved customer advocacy and less churn, the bank has realized more than CA$4 million in operating revenue. During the ongoing COVID crisis, the system was also able to predict when business and personal banking customers were experiencing financial distress so that a relationship manager could proactively reach out to offer support, such as payment deferral or loan restructuring. The AI tools provided by BigQuery are also helping ATB detect fraud that previously evaded rules-based fraud detection by using broader sets of timely and accurate data. 

Thanks to the speed and ease of moving data from SAP to BigQuery, ATB is using artificial intelligence (AI) and machine learning (ML) to do things it previously hadn’t thought possible, including sophisticated fraud prevention models, product recommendations, and enriched CRM data that improves the customer experience. 

Using the power of Google Cloud and BigQuery, ATB Financial has been able to draw more value from its SAP data while lowering cost and improving security and reliability. Speed to provide data sets and insights to internal team members has improved 30%. The bank also has seen a 15x reduction in performance incidents while improving data governance and security. Dan Semmens projects that the digital transformation strategy built on Google Cloud and BigQuery has both saved millions compared to its on-premises environment and has also realized millions in new business opportunities. 

Semmens is looking toward the future that includes initiatives like Open Banking and greater ability to provide real time personalized advice for customers to drive revenue growth. “We see our data platform as foundational to ATB’s 10-year strategy,” he says. “The work we’ve undertaken over the past 18 months has enabled critical functionality for that future.” 

Learn more about how ATB Financial is leveraging BigQuery to gain more from SAP data. Visit us here to explore how Google Cloud, BigQuery, and other tools can unlock the full value of your SAP enterprise data.

With the rise of technologies like smartphones, retailers have felt the pressure to meet evolving consumer needs and expectations. Seven-Eleven Japan(“SEJ”) has long been on the forefront of this thanks to the way they develop and invest in IT. However, in recent years, Japan’s leading convenience store chain has struggled to maintain its complex legacy systems at the rate needed to keep up with today’s rapid digitization, spurred on by the increasing proliferation of smartphones and an IT vendor-dependent structure.  

Legacy systems limiting real-time responsiveness and innovation 

Since its early days, SEJ has been proactive in adopting information technology, mainly relying on technology solutions from Japan’s leading vendors. But as the systems have grown, key business issues have been resolved using a vendor-dependent structure rather than being driven by SEJ’s own needs.  

Datasets and business logic were combined and built into legacy environments, gradually leading to data silos. As a result, data was distributed across multiple systems, causing a variety of problems, including the inability to efficiently retrieve data when needed, delays in accessing data collected in individual stores, and difficulties taking measurements at the right time in business operations that require real-time responsiveness.

Connecting different systems also takes time and money, and the lead time for introducing new services—from planning to development and launch—has been longer than expected. 

To solve these problems, SEJ’s IT department built “Seven Central”—a new platform for practical data use launched in 2020 to support the company’s future IT strategies and digital transformation initiatives.

At its core, Seven Central’s ultimate purpose is to allow real-time data views. Versatile, real-time datasets—such as point-of-sale (POS) data from 7-Eleven stores—are consolidated into a centralized location in the cloud. They created a simple data mart that provides data via an API to enable them to respond more quickly to requests from individual departments. 

“In such uncertain times, it’s vital to use data to make quick decisions,” says Izuru Nishimura, Executive Officer and Head of ICT Department. “Each department across the entire company will be able to gain an immediate understanding of the situation based on the most up-to-date data and respond accordingly. This is why we built Seven Central.”

Google Cloud selected to help SEJ build and grow their data cloud

Today’s rapidly changing business environment has also highlighted the risk of IT support becoming a bottleneck. The long-term strategy is to gradually expand the datasets managed and collected in Seven Central according to business needs. 

In the first phase, SEJ collected POS data from all 21,000+ stores to enable real-time analysis. Moving forward, they would like to collect other relevant data—for example, unstructured data, such as images and videos, or master datasets that are currently stored externally. 

Google Cloud was already a top contender when SEJ started developing Seven Central in 2019. They compared various public cloud services besides Google Cloud, focusing on three main capabilities.

“We placed particular emphasis on service scalability to drive future digital transformation; security when handling data, which is the lifeline of our company; and finally, openness,” says Nishimura. He emphasizes that openness was perhaps the most important factor for choosing Google Cloud. Breaking away from the negative aspects of an entirely vendor-dependent system enabled them to build an agile development system with multiple vendors. 

Google Cloud technologies including BigQuery and API management platform, Apigee, play a vital role in Seven Central. BigQuery’s high-speed processing at petabyte scale and fully managed infrastructure helped keep costs low during development and verification.   

“Data is stored in a way that allows you to share it easily across organizations, which helps solve the issue of data silos from the perspective of scalability. I also like the fact there are some interesting features that could be used in the future—like BigQuery ML, which enables machine learning on BigQuery,” says Nishimura. 

Apigee allows SEJ to separate datasets and business logic, which is one of the key points of Seven Central. While the trend these days is to standardize interfaces using an API, the reality tends to involve many different APIs rather than the introduction of one unified API. With Apigee, SEJ provides a single unified API for all of its data cloud, and they can now understand what data is used thanks to Apigee’s API usage visualizations.  

“Right now, we collect data from all 21,000+ stores,” says Nishimura. “But in anticipation of a future expansion in business operations, we have designed a system that can scale up and run without issue, even if we were to have 30,000 stores, with 1,000 customers per store per day, purchasing five items per person.”

Real-time insights with BigQuery and Cloud Spanner

Google Cloud partner Cloud Ace came on board early in the planning phases. Based on their recommendations, SEJ decided to continue making full use of BigQuery to analyze data collected from all 21,000+ stores throughout Japan, while also using Cloud Spanner’s availability, near-unlimited scalability and transactional consistency to help achieve the real-time results needed for the project.

“Given that both the data and the regularity with which it is accessed are expected to steadily increase in the future, we chose Cloud Spanner as backend storage for data delivery via API. We consider it a good choice,” says Shota Kikuchi, General Manager, Consulting Department, Technology Division, Cloud Ace Co., Ltd.

Finally, they chose to use Google Cloud’s Stream Analytics Solutions messaging service for collecting POS data in real time, which can then be put to immediate use with Cloud Spanner and BigQuery. 

High-speed responses exceed targets and create new value 

Seven Central went live in September 2020 with surprising results. 

They initially set a target time of one hour from when a customer makes a purchase to the point when Seven Central can use that data. But when the final system was first tried—it took barely a minute. Moving forward they estimate that the latest inventory data from the service side will become available within a few minutes of being added to the system.  

“This is real innovation, and I must admit that I am quite surprised. As well as being able to solve existing issues, we also hope it will lead to new improvements and services that have been unimaginable up until now,” says Nishimura.

The team hopes to roll out the Seven Central platform in all companies affiliated with Seven & i Holdings—not just SEJ. They also plan to explore Google Cloud AI and machine learning technologies to take on challenges in new areas. For example, they are investigating the idea of clustering individual stores using BigQuery ML.

Seven Central has already attracted attention from many departments and received a lot of requests. Nishimura and his team say they hope to continue to grow Seven Central while still observing their fundamental principles—not including business logic, maintaining real-time results, and staying true to the uniqueness of SEJ.

Learn more about Google Cloud smart analytics solutions.

For a business to succeed in the long term, it needs to learn not just to adapt to inevitable change, but to harness it. South Africa-based PPS has been an insurance company since 1941 and today is the biggest mutual insurance provider in the country.

As a mutual company, PPS is owned by more than 200,000 members, making them shareholders. In recent years, PPS and other companies like it have been affected by a number of external factors.

“For one thing, technology platforms have brought in a new gig economy that has all kinds of implications for insurance,” says Avsharn Bachoo, CTO at PPS. “What we’ve been seeing is basically a disruption of the South African insurance industry. We chose to see that as an opportunity.”

“Our servers were at the end of their life cycle and we had to decide whether to refresh them or switch completely. To embrace the world of AI and machine learning effectively, we knew we needed a cloud-based infrastructure. We’ve found the answer in Google Cloud Platform.”

—Avsharn Bachoo, CTO, PPS

In early 2018, faced with an uncertain economic environment that was squeezing growth and profitability, PPS decided to transform itself from a traditional broker-based business into a digital insurance provider. A key pillar of this new strategy was to overhaul the company’s technology infrastructure. To turn the strategy into reality, Avsharn and his team chose Google Cloud Platform (GCP).

“Our servers were at the end of their life cycle and we had to decide whether to refresh them or switch completely,” says Avsharn. “To embrace the world of AI and machine learning (ML) effectively, we knew we needed a cloud-based infrastructure. We’ve found the answer in Google Cloud Platform.”

Power, speed, flexibility with Google Cloud Platform

Previously, PPS maintained an on-premises IT infrastructure, which worked for its traditional business but was unsuited for its new way of working. In early 2018, the company started working on new products for its members but this required large amounts of compute power that proved prohibitively expensive with on-premises servers. Even existing products were starting to require more than the infrastructure could deliver. Aging equipment meant that it’s testing and quality assurance environments bore little resemblance to the actual production environment.

“We had no pre-production environments at all,” says Avsharn, resulting in more work for developers after products had been released. Meanwhile, the capital required to buy and configure more servers for new projects meant fewer resources available for innovation, and left the company less able to react to changes in the market. PPS knew it had to find a cloud-based alternative.

Shortly after devising a new digital strategy, PPS engineers attended a training session on cloud infrastructure given by leading South African Google Cloud Partner Siatik. Impressed with the presentation, PPS engaged Siatik to help run a proof of concept for a cloud-based infrastructure, running on GCP. With on-site engineers and constant communication, Siatik formed a very close working relationship with PPS. “The team at Siatik was exemplary,” recalls Avsharn. “They were well-organized, with cutting-edge technical acumen and very creative solutions to our problems. They were real game-changers.”

“We wanted the platform to retrain its models in response to new data and improve its recommendations with more information. Normally this would be a manual process but Google Cloud ML Engine lets the models do this automatically.”

—Kimoon Kim, Lead Solution Architect and Data Engineer, Siatik

The proof of concept was successful, with GCP outperforming the existing infrastructure in terms of how it handled compute demands, databases, and storage.

“It’s the speed of GCP that really impresses us,” says Avsharn. PPS saw that GCP wasn’t just an opportunity to migrate its existing infrastructure to the cloud. With Siatik’s help, it redesigned its monolithic core architecture to one based around microservices using Google Kubernetes Engine (GKE). For data processing and storage, Cloud Dataflow and Cloud Datastore proved invaluable, while Stackdriver helped the IT team stay on top of logging and monitoring the system.

“Google Cloud makes migrations very easy,” says Brett St. Clair, CEO at Siatik. “It takes care of all the hard work with configurations and replications, so when we switch the machines on, everything is ready and working.”

The ease with which PPS migrated to GCP means that it can now tackle strategic goals much more quickly than before. The most ambitious of these is an AI-powered product recommendation platform. Information is collected from customers who opt in at a defined point in their journey, this database is queried using BigQuery, and the information is fed into the platform. The AI model then calculates the most appropriate products for each member, according to their personal history.

“Most of the product recommendation engines out there are based on clustering, where you’re offered products based on your peer groups,” explains Avsharn. “For the first time, we can make recommendations to members based on their individual preferences and historical behavior. That’s really powerful for us.”

Siatik helped PPS use TensorFlow and Cloud Machine Learning Engine to build the AI platform. For the engineers, these easy-to-use tools helped speed up the process considerably, allowing them to host the models locally without any fuss. Previously, it took one to three months to manually build the model and match an offer to a customer. With the AI platform, a match takes just a few minutes. Cloud ML Engine, in particular, helped the platform adapt to new information on the fly and easily make adjustments to its hyperparameters, that is, preset variables which define the model-training process.

“We wanted the platform to retrain its models in response to new data and improve its recommendations with more information,” says Kimoon Kim, Lead Solution Architect and Data Engineer at Siatik. “Normally this would be a manual process but Google Cloud ML Engine lets the models do this automatically.”

“Google Cloud helped us cancel out a lot of the noise around machine learning and AI. We don’t have to build new complicated algorithms or hire huge teams of data scientists to benefit. We just bring our data and use the right tools to focus on what’s really important.”

—Avsharn Bachoo, CTO, PPS

Harnessing artificial intelligence for real-world results

PPS deployed its new AI recommendation platform in December, 2018. Just a couple of months later, its impact was clear. “In around eight weeks, we saw a 5 percent growth in sales,” says Avsharn. “It’s been a direct result of building our recommendation platform with Google Cloud. We can offer the right products to the right members.”

For developers and engineers at PPS, working with Google Cloud gives them access to high performance technology and automation options with GKE. As a result, the infrastructure runs 70 percent faster than before with fewer cores and less memory. Developers can also work in mature testing environments, and for the first time, are able to build pre-production environments, leading to better quality products. More strategically, moving to a serverless, cloud-based infrastructure has helped PPS take control of its budget, moving away from intermittent, large capital spends to more manageable, project-to-project flows of operational expenditure. The company expects to see savings of around 50 percent, or $695,000.

“We have a lot more flexibility with our resources thanks to Google Cloud,” says Avsharn. “When we have a new idea, we don’t have to outlay new capital such as servers before we can even start working on it. We just spin up instances when we want and spin them back down when we’re done.”

With the AI platform deployed and working well, PPS is already looking at ways to improve it, including real-time updates and further automation. Soon, the company will integrate the platform with more sales campaigns for more effective targeting to boost sales even further. Meanwhile, it’s also experimenting with machine learning to spot patterns in data at scale for fraud analytics and risk assessment.

For PPS, working with Google Cloud has helped it transform quickly and effectively from disrupted to disruptor. The company is now looking to gain the same transformative effects by implementing G Suite for increased productivity and collaboration.

“Google Cloud helped us cancel out a lot of the noise around machine learning and AI,” says Avsharn. “We don’t have to build new complicated algorithms or hire huge teams of data scientists to benefit. We just bring our data and use the right tools to focus on what’s really important.”