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Month: June 2023

esg

Powering up ESG through digital transformation

Written by Cynthia Hoza on . Posted in Data Ethics, Data Insights, Data Strategy, Glossary. No Comments on Powering up ESG through digital transformation

The term ‘ESG’ (Environmental, Social and Governance) is everywhere. In its own right, the potential impact is important enough, but it can so often be viewed as a standalone initiative. At its worst it becomes a tick box exercise, when in fact its real benefit is in informing and driving fundamental changes in your organization’s wider actions and endeavors.

ESG – good for the planet, good for business

In January 2023, the EU’s Corporate Sustainability Reporting Directive came into effect. Under its terms, all large companies and all listed companies (except micro-enterprises) must disclose information on the risks and opportunities arising from social and environmental issues, and their impact on people and the environment.

Set against this we have an AI revolution taking place – witness the activity on LinkedIn, with almost every other post lauding the benefits of some ChatGPT derivative or similar, leading to something of an AI feeding frenzy.

Looking through an ESG lens, the environmental impact of AI is huge. According to calculations by the specialist in sustainable data science, Kasper Groes Albin Ludvigsen, published in Medium at the end of 2022, ChatGPT could have consumed as much electricity as 175,000 people in the month of January 2023 alone. Equally, there are numerous articles that reference AI’s huge water impact.

One thing is clear. Whilst there can be many positive outcomes and by products from AI on ESG, the true end-to-end cost of this next wave of Digital Transformation is not yet well understood.

Given we are still trying to get to grips with the effects of the Industrial Revolution from an environmental perspective, how good is humankind’s track record of not repeating the mistakes of the past? How can we exploit opportunity without understanding the true cost and impact?

Wider business benefits of ESG

Developing an ESG strategy that is in harmony with your Digital Transformation yields multiple advantages. And whilst ESG reporting is now mandatory for corporations in the EU, doing so helps quantify the benefits that exist for every party:

  • Investors. Many investors place great importance on ESG reporting and an overall strategy
  • Customers. Consumers are increasingly concerned about the companies they place business with, and ESG is becoming far more important in their decision making
  • Suppliers / Supply Chain. Companies are receiving more requests for information on their ESG credentials, capabilities and response. They must be able to demonstrate their end-to-end position when reporting, driving positive change throughout the supply chain
  • Employees. Recruiting and retaining talent can be difficult, expensive and disruptive when there are issues with ESG policies. Research indicates that as many as 47% of employees would look for new roles if their organization is not proactive here
  • Market reputation. Creating a strong reputation and a positive view of a company takes time and effort. Negative disclosures around ESG will quickly damage reputations, whereas positive ones will confer competitive advantage

Balancing potential conflicts between digital transformation and ESG

Detractors of ESG will point to the irony that a robust ESG process itself has an environmental impact: data centers in the EU consume more than 2.7% of the bloc’s electricity. And the Ukraine war has highlighted that the geopolitics of power supply will increasingly affect decisions on data processes and sovereignty – when Cloud storage and transference requires so many terawatts of electricity, securing a good price must be balanced against political and geographic risk.

Digital transformation is, by its very definition, a process of huge change. Done right it unlocks competitive advantage, delivers cost savings, drives productivity, opens up new opportunities and delivers compliance with ESG obligations. But done half-heartedly or implemented sporadically it will almost certainly be a huge waste of time, effort and resources.

Deloitte calculates that digital transformation could unlock as much as US$1.25 trillion in additional market capitalization across all Fortune 500 companies. However, done incorrectly, market value could actually be eroded, putting more than US$1.5 trillion at risk.

Prior preparation prevents poor performance

When it comes down to it, successful digital transformation requires only three things:

  • An agreed plan
  • The right tech platforms
  • A joined-up approach

And whilst that sounds simple, it involves significant planning and project management resources. It’s not possible to retro-fix a digital solution onto your existing processes – a successful digital transformation requires a center-out approach, incorporating data privacy and protection and considering ESG objectives at the very heart of policy and technology.

When digital transformation is done correctly, “it’s like a caterpillar turning into a butterfly,” but when done wrong, “all you have is a really fast caterpillar.”

MIT Sloan Professor George Westerman

ESG at the heart of the digital transformation process

The comprehensive and insightful data analysis and management required to power your digital transformation needs a huge team of business experts, platform designers and technology specialists, all following a clear process:

  • Develop an agreed, business-wide strategy
  • Create and share a roadmap
  • Define the metrics of success, and measure them
  • Build user-friendly dashboards and data analytics
  • Use optimal data platforms and cloud services
  • Ensure data privacy and protection
  • Set and track ESG targets. Not only does ESG need to be considered, it needs to sit right at the heart of digital transformation, informing and guiding the entire organization


Simply ‘ESG washing’ operations with fancy reports is both ineffective and expensive. That’s why Calligo ensures that every digital transformation we drive is engineered with careful attention to its environmental impact. Future-proofing your data use in a way that protects everyone’s future.

To help you navigate the expansive topic of digital transformation, we’ve put together a comprehensive eBook, outlining all the key considerations for your organization. And if all this sounds daunting, don’t worry –  we’ve seen plenty of similar challenges. Data privacy, for example. Once seen as a vague afterthought or something for someone else, today it takes center stage – the concept of Privacy by Design even has its own ISO standard (31700). Understanding the end-to-end ESG impact of Digital Transformation is heading the same way.

If you want to learn some more, or if you want specific advice, consultancy support or technical implementation, why not talk to our experts, who can get your digital transformation journey underway?

Cloud and data separately

Security SOS: It’s dangerous to view cloud and data separately

Written by Cynthia Hoza on . Posted in Cloud, Cloud Migration, Cloud Strategy, Data Insights, Glossary. No Comments on Security SOS: It’s dangerous to view cloud and data separately

Security risks within the IT infrastructure of global businesses are increasingly prevalent – and damaging. When swathes of data are separated in the hybrid or multi cloud, it can leave big open doorways for malware to walk right in.

The message I want businesses to hear is that cloud and data are not separate. IT only exists to service the needs of a business’ data. Securing cloud services – and therefore your data – is a business-critical issue.

Read on to understand:

  1. The limitations of AV
  2. The dangers of remote networks
  3. The cost of getting security wrong

1. Blind faith in AV

Businesses are too often putting their faith in antivirus (AV) software. This is unintentional blind faith, in my opinion. The problem with AV software alone is that it does not go far enough to protect businesses data assets; it only detects known threats and is not reliable against new variants. We speak to a lot of businesses that assume their security box is ticked, thanks to AV software alone.

But what about zero-day attacks that make up most data breaches these days? A zero-day vulnerability is a computer security vulnerability unknown by anti-virus software creators; they’ve had ‘0’ days to work on a security patch or an update to fix the issue. Zero-day attacks leverage innovative multi-layered approaches – like BitLocker encryption – that haven’t been seen before; anomalies that business software can’t easily detect and protect against without human intervention.

The need to have human and AI based security operations centers (SOC) is increasing, but the cost to implement internally is high and the skills are in short supply. This can cause complications when trying to get pay-outs from cyber security insurers – because businesses haven’t invested in a higher level of threat protection.

Against this backdrop, AV is like wearing chain mail with a gaping hole in the front.

2. Leaving doors open in our remote working world

Unsurprisingly, zero-day vulnerability is greater in our remote working world. Weaker control systems, attacks on remote working infrastructure, sensitive data accessed through unsecured Wi-Fi networks, expanded attack surfaces, the use of personal devices…The list goes on. SaaS in one corner, Office 365 and Dynamic CRM in the other. Servers, software and data – here, there and everywhere. Not to mention outdated legacy operating systems.

Businesses have previously relied on remote access virtual private networks (VPN) for users – but this creates a tunnel between devices and company networks that’s hard to secure adequately. It also means a laptop or personal device can easily become a conduit. A virus or malware can scan for open communication channels – and find its way easily into a corporate environment. If your business IT environment has modern applications, your security must also be modernised. And fast.

This is where Zero Trust Network Access can come into play to secure access to internal applications for remote users. ZTNA gives remote users connectivity to private apps without placing them on external network tunnels or exposing the apps directly to the internet.

It’s about changing the architecture to be as secure as possible for the modern way we work.

3. The financial – and reputational – costs

Under British data protection laws, for example, a company could also face a fine of up to 4% of its global turnover if it is found to have failed to have met its data protection duties by the Information Commissioner’s Office (ICO). This is not new news. But despite the serious risk this poses to a business, many organisations still have an ‘it won’t happen to me’ attitude.

Zero-day attacks – or any type of data breach – can be hugely costly for a company. We know, because we we’ve had big business customers who’ve been in this predicament (not on our watch, I hasten to add!). Add into the mix GDPR – and uninformed reliance on AV and cyber insurance and a lack of control over remote networks has landed many in trouble with the regulators. Hefty fines – and reputational damage.

Businesses that value their data need to value security, first and foremost. And that starts in the cloud.

Machine Learning in Financial Services

Top 8 Use Cases of Machine Learning in Financial Services

Written by Cynthia Hoza on . Posted in Glossary, Machine Learning, Machine Learning. No Comments on Top 8 Use Cases of Machine Learning in Financial Services

The financial services industry is undergoing a rapid transformation with the adoption of machine learning technologies. Machine learning enables financial institutions to leverage data and gain valuable insights to improve operational efficiency, enhance risk management, and deliver personalized experiences to customers. In this blog post, we will explore the top use cases of machine learning in the financial services industry, highlighting how Calligo’s Machine Learning as a Service capability can empower financial institutions to harness the power of data and drive positive outcomes. 

1. Fraud Detection

Detecting and preventing fraudulent activities is crucial for financial institutions to protect their customers and minimize revenue loss. Calligo’s machine learning solution leverages clustering, anomaly detection, and time-series analysis to quickly identify fraudulent transactions and loan applications. By stopping illegal activities in real-time, financial institutions can safeguard their customers’ data and maintain trust while reducing losses. 

2. Automated Data Extraction

Automating data collection from various sources and storing it in a structured manner enhances operational efficiency in financial services. Calligo’s machine learning solution utilizes optical character recognition (OCR), clustering, and natural language processing to automate data entry tasks. This not only reduces labor costs but also improves data accuracy by minimizing human errors. Financial institutions can access customer information faster, make informed decisions, and streamline processes. 

3. Customer Segmentation

Understanding customer behavior and preferences is crucial for financial institutions to offer personalized services and maximize profitability. Calligo’s machine learning solution applies clustering and collaborative filtering techniques to segment customers based on their characteristics. This enables institutions to tailor marketing efforts, select profitable product offerings, manage risks, and identify expansion opportunities effectively. 

4. Risk Assessment and Underwriting

Accurate risk assessment is fundamental for financial institutions to make informed decisions regarding financial products and economic scenarios. Calligo’s machine learning solution leverages predictive models and time-series analysis to assess risks associated with consumer credit quality, complex financial instruments, and market conditions. This helps institutions reduce the likelihood of financial loss, enhance revenue streams, and ensure more reliable outcomes. 

5. Return on Investments

Measuring the return on marketing investments and identifying successful marketing methods is essential for financial institutions to allocate resources effectively. Calligo’s machine learning solution utilizes predictive models, clustering, A/B testing, and customer segmentation to capture the best return from marketing efforts. This data-driven approach informs business strategies, focuses future marketing efforts, and maximizes revenue per customer. 

6. Product Recommendations

Providing personalized product recommendations to existing customers enhances customer satisfaction and generates additional revenue for financial institutions. Calligo’s machine learning solution combines predictive models, collaborative filtering, and time-series analysis to recommend products based on customer purchase history and behavior. This enables institutions to maximize the value and satisfaction of each customer, increasing cross-selling opportunities. 

7. Targeted Marketing

Targeting the right customers with new financial products is crucial for acquiring low-risk customers and minimizing marketing costs. Calligo’s machine learning solution leverages predictive models, collaborative filtering, and time-series analysis to identify the most suitable customers for new product offerings. This data-driven approach ensures efficient marketing efforts, increased revenue, and reduced potential losses. 

8. Data Anonymization

Protecting customer data while making full use of it is a top priority for financial institutions. Calligo’s machine learning solution implements data masking and aggregation techniques to anonymize data, enabling organizations to utilize or sell customer data while maintaining privacy and mitigating legal risks. Financial institutions can maximize the value of data, increase profitability, and ensure compliance with data privacy regulations. 

Machine learning is revolutionizing the financial services industry by enabling financial institutions to leverage data and make data-driven decisions across various areas of operation. With Calligo’s Machine Learning as a Service capability, financial institutions can harness the power of machine learning to detect fraud, automate data processes, understand customers, assess risks, optimize marketing efforts, and protect customer data. By embracing these use cases, financial institutions can stay ahead in a competitive landscape, enhance customer experiences, and drive positive outcomes. 

Machine Learning in Telecom

Top 5 Use Cases of Machine Learning in the Telecom Industry

Written by Cynthia Hoza on . Posted in Glossary, Machine Learning, Machine Learning. No Comments on Top 5 Use Cases of Machine Learning in the Telecom Industry

Machine learning is revolutionizing the telecom industry by enabling data-driven decision-making, enhancing customer experiences, and optimizing operations. In this blog post, we will explore the top use cases of machine learning in telecom, highlighting how Calligo’s Machine Learning as a Service capability empowers telecom companies to leverage predictive models, optimization techniques, time-series analysis, and customer segmentation.

1. Optimize Call Center Staff

Efficient scheduling of call center staff is crucial for customer satisfaction and cost reduction. Calligo’s predictive models and optimization algorithms help telecom companies optimize call center staff scheduling based on call volumes and customer needs. By dynamically adjusting staff schedules, telecom companies can ensure efficient resource allocation, enhance customer experiences, and capture sales opportunities.

2. Market Penetration

Understanding market penetration and identifying high-potential markets are essential for telecom companies looking to expand their customer base. Calligo’s predictive models and time-series analysis help telecom companies assess market penetration and identify markets that offer the best return on investment. By leveraging data on customers, sales, and local market trends, telecom companies can focus their efforts on markets with high growth potential. 

3. Store Location Optimization

Selecting optimal locations for new retail stores is critical for maximizing revenue potential and minimizing building costs. Calligo’s machine learning solutions analyze data on network capacity, finance, customer demographics, and market trends to identify the best locations for new telecom stores. By optimizing store locations, telecom companies can capture new customers, increase market share, and ensure the best network coverage for their customers.

4. Service Interruption Detection

Predicting and quickly responding to network problems is vital for maintaining revenue, customer retention, and satisfaction. Calligo’s predictive models, time-series analysis, and anomaly detection techniques enable telecom companies to detect and respond to service interruptions proactively. By identifying network anomalies and implementing efficient troubleshooting and repair strategies, telecom companies can minimize downtime and ensure uninterrupted service for their customers. 

5. Customer Segmentation

Understanding current and potential customers is crucial for targeted marketing and sales decisions. Calligo’s clustering and collaborative filtering techniques help telecom companies segment their customer base based on various attributes such as usage patterns, demographics, and preferences. By leveraging machine learning algorithms, telecom companies gain insights into customer behavior and preferences, enabling them to tailor marketing efforts, offer personalized services, and drive revenue growth.

Machine learning is transforming the telecom industry, enabling telecom companies to leverage data-driven insights and make informed decisions. Calligo’s Machine Learning as a Service capability empowers telecom companies to optimize call center operations, improve market penetration, optimize store locations, detect service interruptions, and understand customer segments. By embracing machine learning, telecom companies can enhance customer experiences, drive revenue growth, and stay ahead in a competitive market.

lie machines

Lie Machines – The global fight against misinformation

Written by Cynthia Hoza on . Posted in Beyond Data Podcast, Data Ethics, Data Governance, Data Insights, Glossary, Machine Learning, Machine Learning. No Comments on Lie Machines – The global fight against misinformation

Listen on Spotify

Exorcizing the ghost in the machine

In this latest podcast in our ‘Beyond Data’ series, Tessa Jones (Calligo’s Chief Data Scientist) and Peter Matson (Data Science Practice Lead) talk with Oxford University’s Professor Philip Howard about the threats posed to democracy by technology, specifically in the shape of Lie Machines.

Fact or fiction? Microtargeting with lie machines

In this age of social media, chatbots and AI it’s never been easier for individuals to share their opinions.  Instant communication to, and engagement with, a global audience is now commonplace, and it seems there’s no need to let facts get in the way of a good angle. As Mark Twain, or maybe Winston Churchill, or most probably Jonathan Swift famously said, “a lie can travel halfway around the world whilst the truth is still putting on its shoes.” A great example in itself of the ease in which misunderstandings and misappropriations can become canon.

In this vein, Professor Howard has spent years studying the mechanisms in which opinion, behavior and values can be manipulated and misdirected by lie machines:

“Lie machines are large, complex mechanisms made up of people, organizations, and social media algorithms that generate theories to fit a few facts, while leaving you with a crazy

conclusion easily undermined by accurate information. By manipulating data and algorithms in the service of a political agenda, the best lie machines generate false explanations that

seem to fit the facts.”

Lie Machines: How to Save Democracy from Troll Armies, Deceitful Robots, Junk News Operations, and Political Operatives

We find lie machines in all types of countries and governing structures. They share common elements – political actors produce the lies, social media firms distribute them, and paid consultants market them. High profile examples of the effectiveness of the lie machine include the UK’s Brexit campaign, and Trump’s electioneering – in both cases patently untrue ‘facts’ and arguments were targeted at key voters by disinformation networks, troll farms and lie machines. Algorithms direct individuals towards ever-more insular sources and extreme content:

 “A healthy, public-facing algorithm might occasionally introduce another credible source…  we know the platforms play around with this stuff, especially during elections in the US”

Controlled by bad actors and forming a global ecosystem of lie development and propagation, these lie machines spread their tendrils across every social media platform, moving out from Facebook as new outlets develop.

Computational propaganda

Lie machines have evolved and finessed themselves as technology advances. Instead of stealing the photos, social media handles and biographies of real people, AI now generates new pictures and personas and thus evades technology platforms’ troll-spotting software.

Spreading propaganda far and wide, with a convincing voice, the lie machine

  • Has a profound effect on society, with a scale that is difficult to quantify
  • Is perfectly engineered to target human vulnerabilities, reducing critical thinking
  • Deliberately misrepresents and appeals to emotions and prejudices, using our cognitive biases to bypass rational thought and create echo chambers
  • Is vague and unknowable – what training data was used for large language models? (Professor Howard postulates that every Gmail sent over the last 25 years may have been scraped, along with content from junk news sites)

Doing better – where does the onus sit? User or developer?

When it comes to developing processes to combat the lie machine, there’s no one legislation or guiding principle that works. We must always consider the regional and cultural context of both data and users. Research can’t necessarily be amalgamated or directly compared from different regions and countries – for example, we know that the placebo effect is always greater in US medical studies. To date, technology has not always built in cultural nuances in how people use words, with intent and meaning lost in translation – the majority of network takedown orders are for sites that are not in English.

Wherever there is human input, there are behavioral differences that make it much more difficult to apply common rules:

“People who manage cookies are above average in terms of their knowledge of technology, so these people are generally more purposeful in terms of how they set up their news feeds and where they go for information”

The huge amount of disinformation spread around Covid and the resulting vaccination campaign demonstrates how potent the lie machine is. It doesn’t need to convince people its argument is right, all that is required is to introduce enough doubt, to highlight there is a chance of harm. After all:

“If everybody really understood probability, nobody would ever buy a lottery ticket”

Balance the field – breaking the lie machines

Professor Howard believes that whilst we are justified in our concern about the threats to democracy, the principles behind the lie machine can be harnessed for good – promoting topics that are in the public interest and generating democratic discourse:

“I am cynical, but not fatalistic”

He describes the steps we can take to break the lie machines:

  • Public policy oversight, founded in ongoing public data capture and analysis
  • Designing social media to highlight emerging consensus, rather than heated conflict – machine learning can amplify common ground
  • Setting election guidelines to create more opportunities for civic expression
  • Giving journalists, civic groups and researchers access to all the public opinion data that is currently in the hands of the technology firms
  • Ensuring that the big data collected by technology platforms is added to public archives

The answer is more social media, not less. But it needs to serve society much better.

IPIE – bringing down the lie machine

Professor Howard has recently launched a new program, creating an independent scientific body to foster global cooperation in safeguarding the online information environment. The International Panel for the Information Environment (IPIE) will assess the scope of the misinformation crisis, analyze its effects on our societies and the planet itself, and propose solutions. Featuring data scientists and engineers alongside neuroscientists and sociologists, IPIE hopes to be the beginning of a global effort to save our common information environment.

Watch the podcast for yourself below to hear more from Professor Philip Howard about the power of the lie machine, and crucially, to learn how we can use it for the collective good.

Professor Philip Howard is a social scientist with expertise in technology, public policy and international affairs. He is Director of Oxford University’s Programme on Democracy and Technology, a Statutory Professor at Balliol College, and he is affiliated with the Departments of Politics and Sociology. Currently, he is also a Visiting Fellow at the Carr Center for Human Rights at Harvard University’s Kennedy School.

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Machine learning in healthcare

Top 10 Use Cases of Machine Learning in the Healthcare Industry

Written by Cynthia Hoza on . Posted in Data Visualization, Glossary, Machine Learning. No Comments on Top 10 Use Cases of Machine Learning in the Healthcare Industry

Machine learning is revolutionizing the healthcare industry by leveraging the power of data to improve patient outcomes, enhance operational efficiency, and drive cost savings. In this blog post, we will explore the top use cases of machine learning in healthcare, highlighting how Calligo’s Machine Learning as a Service capability can empower healthcare providers to transform their operations and deliver better care. 

1. Improve STAR Rating

The STAR rating system is crucial for healthcare providers as it determines their quality of care and impacts financial incentives. Calligo’s predictive models can identify the key variables that influence STAR ratings and provide prescriptive solutions to improve them. By optimizing patient experience, lowering costs, and enhancing patient satisfaction, providers can achieve higher STAR ratings and increase their bonus payments. 

2. Health Crisis Preparedness

Health crises, such as the COVID-19 pandemic, require proactive preparation to ensure the safety of workers and mitigate financial risks. Calligo’s predictive models and time-series analysis help healthcare organizations simulate and forecast the impact of unexpected economic shocks. By making data-driven decisions around layoffs, resource allocation, and innovation, providers can navigate health crises effectively and minimize long-term financial consequences. 

3. Optimize Staff Scheduling

Efficient staff scheduling is essential to meet patient needs while minimizing unnecessary labor costs. Calligo’s predictive models enable healthcare leaders to optimize physician and facility resources based on patient demand. By aligning staffing levels with patient access expectations, providers can enhance patient experiences and remain competitive in the evolving healthcare landscape. 

4. Medical Supply Logistics

Efficient supply chain management is critical for delivering timely and life-saving healthcare services. Calligo’s predictive models and time-series analysis optimize supply chain logistics by leveraging diverse data sources. By constantly monitoring and updating logistics channels, providers can ensure the availability of essential medical supplies, reduce costs, and mitigate the risk of inadequate supplies that could compromise patient safety. 

5. Patient Insights

Understanding patient preferences and identifying high-value services are essential for improving patient satisfaction and achieving higher Medicare STAR ratings. Calligo’s predictive models and Monte-Carlo simulations enable healthcare providers to measure and analyze patient feedback, identifying the services that provide the most value. By tailoring care and service offerings to meet patient preferences, providers can enhance patient satisfaction and drive higher STAR ratings. 

6. Reduce Patient Wait Time

Reducing patient wait times is crucial for delivering efficient and patient-centered care. Calligo’s predictive models and optimization techniques help healthcare organizations anticipate patient and staffing needs, enabling effective resource allocation and streamlined workflows. By reducing wait times, providers can improve patient satisfaction, increase revenue, and optimize staff utilization. 

7. Reduce Readmission Rates

Reducing readmission rates is vital for improving patient outcomes and optimizing costs in value-based care models. Calligo’s predictive models identify indicators of readmission, allowing healthcare providers to allocate resources strategically and implement interventions that reduce readmissions. By maximizing shared savings payment models and focusing on patient-centric care, providers can improve outcomes, drive revenue, and enhance STAR ratings. 

8. Improve ER Admittance

Enhancing emergency room (ER) admittance processes is crucial for managing complex patients and improving care outcomes. Calligo’s predictive models help healthcare organizations connect different health silos and optimize procedures to ensure appropriate patient-provider matches and levels of care. By leveraging machine learning algorithms, providers can target specific patients effectively, lower facility costs, and deliver better care experiences. 

9. Improve Screening Frequency

Improving the frequency of routine screenings plays a vital role in preventive healthcare and early detection of illnesses. Calligo’s predictive models and time-series analysis help healthcare providers identify patients who would benefit from screenings and predict their compliance. By targeting the right patients and promoting routine screenings, providers can reduce the risk of costly illnesses, improve patient outcomes, and optimize resource allocation. 

10. De-Identification of Data

Data de-identification is essential for expanding the usability of healthcare data while protecting patient privacy. Calligo employs advanced predictive models and time-series analysis techniques to safely de-identify data while retaining its value and richness. By leveraging anonymized data, healthcare organizations can drive additional revenue by utilizing data for research, population health management, and healthcare analytics while complying with privacy regulations. 

Machine learning is reshaping the healthcare industry, enabling providers to deliver better care, optimize operations, and improve patient outcomes. Calligo’s Machine Learning as a Service capability empowers healthcare organizations to leverage the power of predictive models, time-series analysis, and optimization techniques to drive tangible results. By embracing machine learning, healthcare providers can unlock new possibilities and create a future where data-driven decision-making revolutionizes the delivery of healthcare services.