Women in Technology – Meet Claudia Rostagnol

Women in Technology – Meet Claudia Rostagnol

We spoke with Technical Team Leader and Senior Software Engineer, Claudia Rostagnol about women in tech and more. Claudia is based in Uruguay and has been with our organization for three years, working exclusively for a client in the financial sector as a technical team lead. We talked about how the industry is performing for women in technology, and what trends all people in tech need to pay attention to.

Claudia Rostagnol

Worldwide, women represent 40 percent of the workforce, and only 17 percent of the tech industry workforce comprises women. We operate out of Argentina and throughout Latin America, and according to data provided by Women in Technology, only 16 percent of the people from Argentina who enroll in degrees related to the tech industry are women. And further, only 14 percent of technical roles are filled by women.

Here are highlights from our conversation with Claudia:

What work are you doing for Valence LatAm? 

I’ve been working with Valence LatAm’s client, Berxi, for almost 3 years as a technical team lead. Berxi serves the insurance industry, offering policies to small businesses and professionals.

Our goal with Berxi is to migrate a monolithic system into a microservices architecture, while we keep everything working and also adding new features or products. I work with developers to help them build software; with the business analyst and product owner to identify requirements and manage the work; with the architect to define the architecture and design of the software pieces (microservices with a well-defined API – REST and event-driven,) and with the QA team to coordinate testing in different environments and bug fixing with the development team. My role is very dynamic and interesting!

How did you get started in technology? 

I became interested in technology when I was just a small 8 year-old-girl in Uruguay, with a kid-friendly programming language called “Logo”. With Logo, I could program the movements of a turtle moving on the screen with very simple instructions. I’ve been interested in computers and programming ever since.

I studied software programming throughout elementary school, mid-school, and high school. Then I found it very natural to go to the Engineering Faculty to become a Computer Science Engineer.

When I finished my engineering degree, I met a few colleagues during an internship in France. One of them became my husband, Daniel De Vera, and another is Pablo Rodriguez-Bocca, who became my master’s degree tutor. We co-founded a small start-up called GoalBit Solutions and worked together for 6 years. I learned and grew a lot (academically and professionally) during that time!

What can you tell us about the people who paved the way for you? How did mentors factor into your success? 

I need to recognize my family, especially my parents! They always support me even if they don’t understand this technical world.

My husband helped to pave the way for me to find opportunities at a US company named Vidillion where I started as a Senior Software Engineer. Their CTO at that time, Steve Popper, was a great mentor as well as a very kind person. He taught me a lot about technology and about remote work and the US tech industry. We continue being friends, even living 10.000 km far from each other. Thanks to Steve, I became more confident in my skills and language.

Let’s talk about what’s around the corner in technology. What trends are you seeing? 

AI is used more every day and for everything. I’ve been interested in AI throughout my career. It is a very powerful tool, and we need to think about how to use it well. There is a trend toward responsible AI, which is a good thing.

Also, everything happens in the cloud now. Cloud computing powers everything, including our PCs and mobile phones, and everybody is connected and storing/publishing things on the Internet.  So, I think there’s a lot happening there: social networks, crypto, mobile apps for everything, remote education, etc.

What tech does the world need now more than ever? 

Data Management and Security – When we share our information, thoughts, pictures, videos, and interests, on the internet, we generate data that may be processed and analyzed in different ways and for different purposes like marketing, sales, and connectivity.  All this data can be helpful, and at the same time, it can be dangerous if it is not correctly managed and used. We are sharing a lot of information, which can potentially be made public if it’s not protected. I support the call for additional security and regulations.

Cradle-to-cradle hardware manufacturing – The exponential increase in the use of technology is generating technical waste and digital trash. We frequently discard devices to have the latest or more powerful model and that trash is not biodegradable or easily recyclable. The world needs a clear policy on what to do with all that trash.

Claudia is a volleyball player, seen here with a championship cup

Let’s talk about how to improve tech for women. Do you think tech is changing for women? 

Tech is changing for women in the sense that we are more accepted now, but we are far from an equitable system, and it is not changing fast enough. I see too many conferences and events about technology where most of the participants or speakers are men. Men are still accessing higher roles and salaries than women. Paternity and maternity leaves are not equal for men and women.

We need a cultural change in the tech industry, which will take time. But we are making progress. It means a lot to me when I see how our company supports women in tech with events like FemIT, and technical webinars where the speakers are women, and even interviews like this.

Several other companies also have internal initiatives to recognize women’s work and to treat us equally to men. However, I still see too many differences in the number of women being promoted to important roles, or the salary we receive for the same role, especially in LATAM.

I still hear stories about women being asked if they are planning to have children as part of their interview process with other organizations. Women are asked invasive questions that men aren’t asked, and that needs to stop. Thankfully our recruiting team and processes are invested in supporting women in tech.

One thing I like in my country (Uruguay) is that the government provides all kids attending public schools with a laptop when they start school. So boys and girls have the same access to technology at home and school. However, we still have cultural/social messages with gendered toys or games that can falsely signal to girls that boys are better than girls for some things and vice versa.

What is the one thing you wish people knew to support women in technology?

People need to know that women are equally capable if we have equal support and opportunities. We have more than technical skills to add to this technical world.   We must continue encouraging girls to get involved in tech and science through messages and experiences at home, at school, and in our communities.

Women need mentors and advocates, including men and women. I wish more people understood how much they can change a woman’s life by helping them to grow in this field.

What’s one piece of advice that you’d like to share with anyone reading?

Women are not better or worse at technical jobs. It is just a matter of learning, practicing, and being supported by other industry leaders.

We need to continue working on a more profound social change that makes the world more equitable for women who want to work in technology!

Additional resources:

Retail Technology and Innovation – a Conversation with Michael Guzzetta

Retail Technology and Innovation – A Conversation with Michael Guzzetta

We recently spent some time with Michael Guzzetta, a seasoned retail technology and innovation executive and consultant who has worked with brands such as The Walt Disney Company, Microsoft, See’s Candies, and H-E-B.

Tell me about your background. What brought you to retail?

Like many people, I launched my retail career in high school when I worked in the men’s department at Robinson’s May. I also worked for The Warehouse (music retailer) and was a CSR at Blockbuster video – strangely, I still miss the satisfaction of organizing tapes on shelves.

I ignited my tech career in 2001 when I started working in payment processing and cloud-based tech, and then I returned to retail in 2009 when I joined Disney Store North America, one of the world’s strongest retail brands.

During my tenure at Disney, I had the privilege of working at the intersection of creative, marketing, and mobile/digital innovation. And this is where the innovation bug bit me and kicked off my decades-long work on omnichannel innovation projects. I seek opportunities to test and deploy in-store technology to simplify experiences for customers and employees, increase sales, and drive demand. Since jump-starting this journey at Disney Store, I’ve also helped See’s Candies, Microsoft, and H-E-B to advance their digital transformation through retail innovation.

What are some of the retail technologies that got you started?

I’ve seen it all! I’ve re-platformed eCommerce sites, deployed beacons and push notifications, deployed in-store traffic counting, worked on warehouse efficiency, automated and integrated buyer journeys and omnichannel programs, and more. I recently built a 20k SF innovation lab space to run proofs-of-concept to validate tech, test, and deployment in live environments. Smart checkout, supply chain, inventory management, eCommerce… you name it.

What are the biggest innovation challenges in retail today?

Some questions that keep certain retailers up at night are, “How can we simplify the shopping experience for customers and make it easier for them to check out?”, “How can we optimize our supply chain and inventory operations?”, “How can we improve accuracy for customers shopping online and reduce substitutions and shorts in fulfillment?” and “How can we make it easier and more efficient for personal shoppers to shop curbside and home delivery orders?” Not to mention, “What is the future of retail, and which technologies can help us stay competitive?”

I see potential in several trends to address those challenges, but my top three are:

Artificial Intelligence/Machine Learning – AI will continue to revolutionize retail. It’s permeated most of the technology we use today, whether it’s SAAS or hardware, like smart self-checkout. You can use AI, computer vision, and machine learning to identify products and immediately put them in your basket. AI is embedded in our everyday lives – it powers the smart assistants we use daily, monitors our social media activity, helps us book our travel, and runs self-driving cars, among dozens of other applications. And as a subset of AI, Machine Learning allows models to continue learning and improving, further advancing AI capabilities. I could go on but suffice it to say that the retailer that nails AI first wins.

Computer vision. Computer vision has a sizable opportunity to solve inventory issues, especially for grocery brands. Today, there’s a gap between online inventory and what’s on the shelf since the inventory system can’t keep pace with what’s stocked and on the shelves for personal shoppers, which is frustrating for customers who don’t expect substitutions or out-of-stock deliveries. With the advent of computer vision cameras, you can combine those differences and see what is on the shelf in real-time to inform what is available online accurately. Computer vision-supported inventory management will be vital to creating a truly omnichannel experience. Computer vision also enables smart shopping carts, self-checkout kiosks, loss prevention, and theft prevention. Not to mention Amazon’s use of CV cameras with their Just Walk Out tech in Amazon Go, Amazon Fresh, and specific Whole Foods locations. It has endless applications for retail and gives you the eyes online that you can’t get in stores today.

Robotics. In the last five years, robotics has taken a seismic leap, and a shift has happened, which you can see in massive, automated fulfillment centers like those operated by Amazon, Kroger, and Walmart. A brand can deliver groceries in a region without having a physical store, thanks to robotic fulfillment centers and distribution centers. It’s a game-changer. Robotics has many functions beyond fulfillment in retail, but this application truly stands out.

What is a missed opportunity that more retail brands should take advantage of?

Data. Data is huge, and its importance can’t be understated. It’s a big, missed opportunity for retailers today. Improving data management, governance, and sanitation is a massive opportunity for retailers that want to innovate.

Key opportunity areas around data in retail include customer experience (know your customer), understanding trends related to customer buying habits, and innovation. You can’t innovate at any speed with dirty data.

There’s a massive digital transformation revolution underway among retailers, and they are trying to innovate with data, but they have so much data that it can be overwhelming. They are trying to create data lakes, a single source of truth, and sometimes they can’t work because of disparate data networks. I believe that some of the more prominent retailers will have their data act together in a few years.

“Dirty data” results from companies being around for a long time, so they’ve accrued multiple data sets and cloud providers, and their data hasn’t been merged and cleaned. If you don’t have the right data, you are making decisions based on bad or old data, which could hurt you strategically or literally.

What do you wish more people understood about retail technology and innovation?

Technology will not replace people. In my experience, technology is meant to enhance the human experience, which includes employees. If technology simplifies the process so much that the employees become idle, they are typically trained to manage the technology or cross-trained to grow their careers. Technology isn’t replacing the human experience any time soon, although it is undoubtedly changing the existing work experience – ideally for the better, both for the employees and the bottom line.

Technology doesn’t always lower costs for retailers. Hardware innovation requires significant capital expenses when it’s deployed chain-wide. Amazon’s “Just Walk Out” is impressive technology, but the infrastructure, cloud computing costs, and computer vision cameras are insanely expensive. In 5 years, that may be different, but today it is a loss leader. It’s worth it for Amazon because they can get positive press, demonstrate innovation, and show industry leadership. But Amazon has not lowered its operating costs with “Just Walk Out.” This is just one example, but there are many out there.

Online shopping will not eliminate brick-and-mortar shopping. If the pandemic has taught us anything, online shopping is here to stay – and convenience is extremely attractive to consumers. But I think people will never stop going to stores because people love shopping. The experience you get by tangibly picking something up and engaging with employees in a store location will always be around, even with the advent of the Metaverse.

Retail technology

What are some brands that excite you right now because of how they use technology?

Amazon. What they have been doing with Just Walk Out technology, dash carts, smart shelves, and other IoT technology puts Amazon at the front of the innovation pack. Let’s not forget that they’ve led the way in same or next-day delivery by innovating with their automated fulfillment centers! They have the desire, the resources, and the talent to be the frontrunner for years to come.

Alibaba. This Chinese company is another retailer that uses technology in incredible ways. Their HEMA retail grocery stores are packed with innovation and technology. They have IoT sensors across the stores, electronic shelf labels, facial recognition cameras so you can check out with your face, and robotic kitchens where your order is made and delivered on conveyor belts. They also have conveyors throughout the store, so a personal shopper can shop by zone, then hook bags to be carried to the wareroom for sortation and delivery prep – it’s impressive.

Walmart and Kroger. Both brands’ use of automated fulfillment centers (AFCs) and drone technology (among many others) are pushing the boundaries of grocery retail today. Their AFCs cast a much wider net and have expanded their existing markets, so, for example, we may see Kroger trucks in neighborhoods that don’t have a store in sight.

Home Depot. They have a smart app with 3D augmented reality and robust in-store mapping/wayfinding. Their use of machine learning is also impressive. For example, it helps them better understand what type of projects a customer might be working on based on their browsing and shopping habits.

Sephora. They use beacon technology to bring people with the Sephora app into the store and engage them. They have smart mirrors that help customers pick the right makeup for their skin tone and provide tutorials. Customers can shop directly through smart mirrors or work with an in-store makeup artist.

What advice do you have for retailers that want to invest in technology innovation?

My first piece of advice is to include change management in the project planning from the start.

There are inherent challenges in retail innovation, often due to change management issues. When a company has been around for decades or even more than a century, they operate with well-known, trusted, and often outdated infrastructure. While that infrastructure can’t uphold the company for the next several decades or centuries, there can be a fear of significant change and a deeply rooted preference for existing systems. There can be a fear of job loss because of the misconception that technology will replace people in retail.

Bring those change-resistant people into the innovation process early and often and invite them to be part of the idea generation. Any technology solution needs to be designed with the user’s needs in mind, and this audience is a core user group. Think “lean startup” approach.

My second piece of advice is to devote enough resources to innovation and give the innovation team the power to make decisions. The innovation team should still operate with lean resources, focusing on minimum viable products and proofs of concept, so failures aren’t cost-prohibitive. The innovation team performs best when it has the autonomy to test, learn, and fail as they explore innovative solutions. Then, it reports its findings and recommendations to higher-ups to calibrate and pivot where needed.

In closing, I’d say the key to innovation success is embracing the notion of failure. Failure has value! Put another way; failure is the fast track to learning. Learning what not to do and what to try next can help a retail company to accelerate faster than the competition. Think MVP, stay lean, get validated feedback quickly, and iterate until you have a breakthrough. And always maintain a growth mindset – never stop learning and growing.

Additional resources:

3 Reasons Companies Advance Their Data Journey to Combat Economic Pressure

3 Reasons Companies Advance Their Data Journey to Combat Economic Pressure

By Danny Vally

Have you updated your organization’s data journey lately? We are living in the Zettabyte Era, because the volume, velocity, and variety of data assets being managed by companies are big and getting bigger.

data journey

Data is getting more complicated and siloed. Today’s data is more complex than the data a typical business managed just twenty years ago. Even small companies deal with large data sets from disparate sources that can be complicated to process. Each data set may have its own unique structure, size, query language, and type.

The types of data are also changing quickly. What used to be managed in spreadsheets now demands automated systems, machine data, social network data, IoT data, customer data, and more.

There are real economic advantages for companies that take advantage of the data opportunity by investing in digital transformation (often starting by moving data to the cloud). Companies that take control of data outperform the competition:

  • 40% more revenue per employee
  • 50% higher average net income on revenue
  • $100M in additional operating income annually

Common data journey scenarios that motivate data-driven investments include:

  • Understand and predict customer behavior in real-time
  • Cut costs and free up resources with simplified data analysis
  • Explore new business models by finding new relationships in data
  • Eliminate surprise and unnecessary expenses
  • Gather and unify data to better understand your business

A data strategy is more than a single tool, dashboard, or report. A mature data strategy for any business includes a roadmap to plan the company’s data architecture, migration, integration, and management. Building in governance planning to ensure data security, integrity, access, quality, and protection will empower a business to scale.

That roadmap may also include incorporating artificial intelligence and machine learning, which unleashes predictive analytics, deep learning, and neural networks. While these once were understood to be tools available only to the world’s largest businesses, AI and ML are actually being deployed at even small and midsized businesses, with much success.

We work with organizations throughout their data journey by helping to establish where they are, where they want to go, and what they want to achieve.

A data journey usually starts by understanding data sources and organizing the data. Many organizations have multiple data sources, so creating a common data store is an important starting point. Once the data is organized, we can harness insights from the data using reporting and visualization, which enables a real-time understanding of key metrics.  Ensuring data governance and trust in sharing data is another important step, which is often supported by security. Lastly, advanced data can use artificial intelligence and machine learning to look for data trends or predict behaviors and extract new insights. By understanding where your organization is in its data journey, you can begin to visualize its next step. 

Additional resources:

Set Your Data Retention Policy Up for Success

Set Your Data Retention Policy Up for Success: Free Download

This free downloadable paper explores best practices in setting up a data retention policy, and then how to develop your business’s policy.

Every business needs a strategy to manage its data, and that strategy should include a plan for data retention.

data retention policy

This is a must-read for any business that collects, houses, or uses data!

Download Now to Set Your Data Policy Up for Success

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Data Mesh Architecture in Cloud-Based Data Warehouses

Data Mesh Architecture in Cloud-Based Data Warehouses

Data is the new black gold in business. In this post, we explore how shifts in technology, organization processes, and people are critical to achieving the vision for a data-driven company that deploys data mesh architecture in cloud-based warehouses like Snowflake and Azure Synapse.

The true value of data comes from the insights gained from data that is often siloed and spans across structured, semi-structured, and unstructured storage formats in terabytes and petabytes. Data mining helps companies to gather reliable information, make informed decisions, improve churn rate and increase revenue.

Every company could benefit from a data-first strategy, but without effective data architecture in place, companies fail to achieve data-first status.

For example, a company’s Sales & Marketing team needs data to optimize cross-sell and up-sell channels, while its product teams want cross-domain data exchange for analytics purposes. The entire organization wishes there was a better way to source and manage the data for its needs like real-time streaming and near-real-time analytics. To address the data needs of the various teams, the company needs a paradigm shift to fast adoption of Data Mesh Architecture, which should be scalable & elastic.

Data Mesh architecture is a shift both in technology as well as in organization, processes, and people.

Before we dive into Data Mesh Architecture, let’s understand its 4 core principles:

  1. Domain-oriented decentralized data ownership and architecture
  2. Data as a product
  3. Self-serve data infrastructure as a platform
  4. Federated computational governance

Big data is about Volume, Velocity, Variety & Veracity. The first principle of Data mesh is founded on decentralization and distribution of responsibility to the SME\Domain Experts who own the big data framework.  

This diagram articulates the 4 core principles of Data Mesh and the distribution of responsibility at a high level.

Azure: Each team is responsible for its own domain, and data is decentralized and shared with other domains for data exchange and data as a product.
Snowflake: Each team is responsible for its own domain, and data is decentralized and shared with other domains for data exchange and data as a product.

Each Domain data is decentralized in its own data warehouse cloud. This model applies to all data warehouse clouds, such as Snowflake, Azure Synapse, and AWS Redshift.  

A cloud data warehouse is built on top of a multi-cloud infrastructure like AWS, Azure, and Google Cloud Platform (GCP), which allows compute and storage to scale independently. These data warehouse products are fully managed and provide a single platform for data warehousing, data lakes, data science team and to provide data sharing for external consumers.

As shown below, data storage is backed by cloud storage from AWS S3, Azure Blob, and Google, which makes Snowflake highly scalable and reliable. Snowflake is unique in its architecture and data sharing capabilities. Like Synapse, Snowflake is elastic and can scale up or down as the need arises.

From legacy monolithic data architecture to more scalable & elastic data modeling, organizations can connect decentralized enriched and curated data to make an informed decision across departments. With Data Mesh implementation on Snowflake, Azure Synapse, AWS Redshift, etc., organizations can strike the right balance between allowing domain owners to easily define and apply their own fine-grained policies and having centrally managed governance processes.

Additional resources:

How to Develop a Data Retention Policy

How to Develop a Data Retention Policy

by Steven Fiore

We help organizations implement a unified data governance solution that helps them manage and govern their on-premises, multi-cloud, and SaaS data. The data governance solution will always include a data retention policy.

When planning a data retention policy, you must be relentless in asking the right questions that will guide your team toward actionable and measurable results. By approaching data retention policies as part of the unified data governance effort, you can easily create a holistic, up-to-date approach to data retention and disposal. 

Ideally any group that creates, uses, or disposes of data in any way will be involved in data planning. Field workers collecting data, back-office workers processing it, IT staff responsible for transmitting and destroying it, Legal, HR, Public Relations, Security (cyber and physical) and anyone in between that has a stake in the data should be involved in planning data retention and disposal.

The first step is to understand what data you have today. Thanks to decades of organizational silos, many organizations don’t understand all the data they have amassed. Conducting a data inventory or unified data discovery is a critical first step.  

Next, you need to understand the requirements of the applicable regulation or regulations in your industry and geographical region so that your data planning and retention policy addresses compliance requirements. No matter your organization’s values, compliance is required and needs to be understood.

Then, businesses should identify where data retention may be costing the business or introducing risk. Understanding the risk and inefficiencies in current data processes may help identify what should be retained and for how long, and how to dispose of the data when the retention expires.

If the goal is to increase revenue or contribute to social goals, then you must understand which data affords that possibility, and how much data you need to make the analysis worthwhile. Machine Learning requires massive amounts of data over extended periods of time to increase the accuracy of the learning, so if machine learning and artificial intelligence outcomes are key to your revenue opportunity, you will require more data than you would need to use traditional Business Intelligence for dashboards and decision making.

data retention policy

What types of data should be included in the data retention policy?

The types of data included in the data retention policy will depend on the goals of the business. Businesses need to be thoughtful about what data they don’t need to include in their policies. Retaining and managing unneeded data costs organizations time and money – so identifying the data that can be disposed of is important and too often overlooked.

Businesses should consider which innovation technologies are included in their digital roadmap. If machine learning, artificial intelligence, robotic process automation, and/or intelligent process automation are in your technology roadmap, you will want a strategy for data retention and disposal that will feed the learning models when you are ready to build them.  Machine learning could influence data retention policies, Internet of Things can impact what data is included since it tends to create enormous amounts of data. Robotic or Intelligent Process Automation is another example where understanding which data is most essential to highly repeatable processes could dictate what data is held and for how long.

One final note is considering non-traditional data sources and if they should be included. Do voice mails or meeting recordings need to be included? What about pictures that may be stored along with documents? Security camera footage? IoT or server logs? Metadata? Audit trails? The list goes on, and the earlier these types of data are considered, the easier they will be to manage.

Avoid these pitfalls

The paradox is that the two biggest mistakes organizations make when building a data retention policy are either not taking enough time to plan or taking too much time to plan. Spending too much time planning can lead to analysis paralysis letting a data catastrophe occur before a solution can be implemented. One way to mitigate this risk is to take an iterative approach so you can learn from small issues before they become big ones.

A typical misstep by organizations when building a data retention policy is that they don’t understand their objectives from the onset. Organizations need to start by clearly stating the goals of their data policy, and then build a policy that supports those goals. We talked about the link between company goals and data policies here.

One other major pitfall organizations fall into when building a data retention policy is that they don’t understand their data, where it lives, and how its interrelated. Keeping data unnecessarily is as bad as disposing of data you need – and in highly silo-ed organizations, data interdependencies might not surface until needed data is suddenly missing or data that should have been disposed of surfaces in a legal discovery. This is partially mitigated by bringing the right people to the planning process so that you can understand the full picture of data implications in your organization.

In closing

The future of enterprise effectiveness is driven by advanced data analytics and insights. Businesses of all sizes are including data strategies in their digital transformation roadmap, which must include data governance, data management, business planning and analysis, and intelligent forecasting. Understand your business goals and values, and then build the data retention policies that are right for you.

We are here to help.

Additional Resources:

Using Data to Improve Patient Outcomes

Using Data to Improve Patient Outcomes

Can predictive analytics in healthcare change patient outcomes?

It’s no secret that technology is making its mark in the healthcare industry. From surgery rooms to at-home care, technology is being applied in ways that only push healthcare forward. Within the past year, companies such as Google and Microsoft have begun stepping into the healthcare field. And it doesn’t stop there, hospitals such as Johns Hopkins have also joined the movement. But why now?

At Valence we’ve seen first-hand what technology can bring to the table for a patient’s care. Whether it’s pain management through Virtual Reality, training for medical professionals, or quicker EMR workflows, technology has solved many pain points for the healthcare industry and there are no signs of slowing down. Over the years, healthcare has shifted to a more predictive approach. With this perspective doctors can focus on preventive measures with a goal of fewer hospital trips and better long-term care for the patient. This new approach has only been made possible by the large amount of data available at our fingertips and the birth of predictive analytics.

Let’s talk about predictive analytics in healthcare.

Predictive analytics in healthcare uses data to help predicate outcomes. Whether it’s for healthcare or environmental purposes there is one common goal: to prevent negative outcomes. This approach is extremely powerful, but there is an existing technology that can take it further, Artificial Intelligence. By merging the two we can truly harness the power of data to improve people’s health.

Today, artificial intelligence is being used to help doctors diagnose patients. Drawing from a patient’s family history or medical images, AI can be applied in different scenarios. For example, an artificial intelligence diagnostic device is helping doctor’s diagnosis patients with a specific eye disease. Just by uploading a high-resolution picture, this device can take the image and interpret results on its own. While artificial intelligence can assist with individual patients, the biggest advantage is its ability to operate with machine learning in which it can analyze a large amount of data, learn, and adapt. It can take data from thousands of patients, analyze their medical history, and make predictions on a much larger scale.

The integration of artificial intelligence and predictive analytics is transforming patient care on a small and large scale. It’s making value-based care attainable while keeping the patient at the heart of it all. At Valence we understand the technology of Machine Learning and the potential it will bring to your organization. Whether you are involved in healthcare, retail, manufacturing, or more, Artificial Intelligence can be applied to many industries. The time for artificial intelligence is now, so what will you do with it? Contact us, and we’ll start you off with a demo to show how remarkable this technology can be!