Imagine the potential for secure data collaboration. With the boundaries between different companies, organizations, and entire industries blurring, the use cases are endless. Organizations can perform joint data analysis and train machine-learning (ML) models while ensuring that confidential information will stay protected from their sharing partners. It’s all happening in the world of data clean rooms.

Pharmaceutical companies can identify the best hospitals for clinical trials with a look-alike analysis against patient records. Insurance companies can collaborate to identify fraudulent claims. Media outlets can offer premium placement to advertisers to ensure targeted messaging. Loyalty programs can deliver truly personalized services across hotels, airlines, and other services. Telecom operators can collaborate with location data to enrich those personalized services. Emergency and social services can collaborate to help those in need.

Yet in many cases, the relevant data is personal information, and protected by privacy laws and bonds of trust. How can that data be shared?

The use cases for secure collaboration with data clean rooms are endless

Imagine the following scenario.

A crowd of spectators is watching a big game and the teams are tied. The tension mounts. The fans grow restless. He shoots. He scores! The roar of the crowd can be heard all the way down the neighborhood street. And all the consumer brands want to know who is watching and how to reach these audiences. Yet, these sports fans are watching the game in the privacy of their homes, and the network they’re watching on must legally protect their data.

How can these media outlets share their viewer data – or the insights from it – without violating data protection laws and the trust of their subscribers?

It turns out that a similar question was posed by an academic in the early 1980s. Professor Andrew Yao introduced the problem: Alice and Bob, both millionaires, want to know which of them is richer but neither wants to reveal his or her exact wealth. Through complex mathematical proofs, Yao’s Millionaires’ problem was solved, proving it is possible to share insights without showing the underlying data. Fortunately, modern methods do not require arduous manual calculations.

“Sharing without showing? You bet!”

Increased demand for data sharing

For potential advertisers or anyone who wants to collaborate with data, that’s great news. Data sharing and collaboration deliver business value. A recent Capgemini study, Data sharing masters, found that companies with collaborative data ecosystems reported better business outcomes including new revenues, reduced costs, increased productivity, and greater customer satisfaction. And that promise has spurred new data ecosystem initiatives.

Companies have long used their own data to better understand their customers or to improve operations. Increasingly, data teams turn to external data sources to enrich their internal data and enhance analytics. Budgets for external data are significant and growing. In a recent survey conducted by external data platform Explorium, 22 percent of respondents said they were spending more than $500,000 on external data, with 13 percent saying they spent more than $1 million (up from 7 percent from a similar survey in 2021).

Customer data was the number one type of data acquisition: 52 percent purchased data on companies, followed by 44 percent purchasing demographic data. And the number of sources has grown as well: 44 percent of firms acquire external data from five or more providers. That’s up from only 9 percent the previous year. However, procuring external data is not without challenges, with regulatory constraints often topping the list. Concerns about GDPR or other privacy regulations loom large, and for good reason.

Introducing modern data clean rooms

Not long ago, data sharing meant copying and sending files to a partner. That practice certainly complicated data governance. Short of a manual audit, knowing who accessed the data and for what purpose was impossible. Now, using the principles demonstrated by Yao’s millionaires, two or more parties can derive insights from data without revealing the underlying information.

With a Snowflake Global Data Clean Room, each party controls its own data, allowing governed, controlled analytics by other parties. That is to say, each party specifies who can access the data and for what purpose. Let’s take a look at how it would work with Yao’s two millionaires, Alice and Bob.

First, each party creates a table with the data to be shared. Then one party, let’s say Bob, creates a table to store allowed statements. This is where the queries that Bob will allow another party to run against his data will be maintained. He then creates an access policy granting use of these statements and applies this access policy to his data table.

Next, Bob defines the exact statement or query he will allow, and inserts it into his “allowed statements” table. The statement includes the comparison of their wealth and the answers that will be returned in each case: “Bob is richer,” “Alice is richer,” or “Neither is richer.” Finally, he grants Alice permission to access and use his data for only this specific purpose. Alice then asks the question in the form of the specified query and receives the response: Bob is richer. Sorry, Alice.

Now imagine a more realistic business scenario where two companies want to know which customers they have in common – an overlap analysis. They would put the data in tables, establish the statements to compare their customer lists, and specify the information to be returned. Or one company might be interested in finding new prospects among a partner’s customers and would perform a look-alike analysis comparing customer attributes.

Data clean rooms transform the ad world

In a real use case, commonly seen in media and advertising these days, brands want to optimize their ad spend through better targeting to specific customers or personas – like the fans watching that exciting game. Media outlets want to offer premium placements by knowing exactly which programming the brand’s customers are watching. Comparing customers is a win-win. However, neither wants to show the underlying data. The clean room allows them to share without showing. In this case, as illustrated in the diagram, the returned information would include a customer count for each of the media outlet’s programs, but not specific customer data, in order to ensure compliance with privacy regulations. All queries of the data would be monitored and logged for audit purposes.

In the past, this scenario required data to be copied and moved across the AdTech value chain from enrichment to activation to attribution. Not only were there the aforementioned governance concerns, but that data was also immediately stale. With Snowflake, live, near real-time data can be shared where it resides – no copies necessary. Data governance capabilities allow all parties to assign access and use policies that limit both who can query the data and exactly which queries are allowed. Additional capabilities add further security to the clean room. Data can be encrypted, anonymized, tokenized, or pseudonymized with built-in hashing functions, or obfuscated with data masking or by injecting differential privacy.

With today’s technology, data clean rooms allow parties across teams, companies, government agencies, and international organizations to collaborate and securely share sensitive or regulated data. As Thomas Edison said, “The value of an idea lies in the use of it.” The more data is used, the more value is created. Secure data collaboration accelerates value creation.

INNOVATION TAKEAWAYS

CROSS – INDUSTRY COLLABORATION AND DATA SHARING

A growing trend that’s here to stay.

DATA CLEAN ROOMS FACILITATE JOINT DATA ANALYSIS AND ML

While ensuring that confidential information will stay protected

from sharing partners.

DATA ECOSYSTEMS AND SECURE DATA COLLABORATION

They accelerate value creation.

Interesting read?

Capgemini’s Innovation publication, Data-powered Innovation Review | Wave 6 features 19 such fascinating articles, crafted by leading experts from Capgemini, and key technology partners like Google,  Starburst,  Microsoft, Snowflake and Databricks. Learn about generative AI, collaborative data ecosystems, and an exploration of how data an AI can enable the biodiversity of urban forests. Find all previous Waves here.

#DATACLEANROOMS

#DATACOLLABORATION

#DATAECONOMY

#DATAECOSYSTEMS

While there is a huge adoption of Generative AI across organizations and industries – our research reveals that over 95% of executives are engaged in Generative AI discussions in their boardrooms – we can observe clearly a shift in the way people perceive AI now.

I have been working in the field since many years, and the unprecedent enthusiasm around Gen AI is impressive – 74% of executives believe the benefits of generative AI outweigh the associated risks. Beyond the positive feedback around it, there is a massive need for information, education and guidance. Especially for organizations to successfully and responsibly implement Generative AI across their data value chain, considering ethics, privacy and security from the start.

However, when you leverage Generative AI in a secured and trusted environment the opportunities are immense. From tasks and workflow optimization, to content production, product innovation and R&D, it is revolutionizing the way we create, interact and collaborate, completely shifting the way organizations operate. What if you could as a CXOs leverage Gen AI, across your organization, in a safe, secured and controlled manner, to fit your business reality?

IBM Maximo application and iPhone deliver defect detection at a fraction of the cost.

Visual inspection is an opportunity. It is estimated that the market will be worth more than $25 billion worldwide by 2027. Companies are excited about the prospect of cost-savings, error reduction, better accuracy, and higher quality output. Automated quality assurance is the utopia all manufacturers want to achieve. The pandemic helped the process along, as companies tried to reduce the amount of human intervention. But finding the right skills and experience to manage a visual-inspection system can be challenging and it is an expensive investment.

Visual inspection is an application of computer vision with machine learning and artificial intelligence that helps humans make better products with fewer defects. It is much more efficient to find the defects during the process rather than fixing issues later or having to initiate recalls.

We have been working with Professor John Ward and his team at the University of South Carolina on a new way to perform visual inspections. Rather than waiting months for a system to be designed and installed, Professor Ward has created an easier answer. This leverages the IBM Maximo iPhone application as an input device to create a visual model using photos. The iOS devices are installed on the production line.

There is a beauty in the simplicity of using smartphones, but any visual-inspection system creates significant amounts of data. High-speed, 5G, millimeter wave, low-latency networks have to move high-resolution images to a system to analyze the information and communicate results in split seconds to stop a line in case of a defect. Having a system to collect information is only the first step. The power is in the data.

Our contribution is a comprehensive business-intelligence analytics suite that allows companies to drill down all the way to see the image of the defect. It means companies can look at the return on investment (ROI) per defect caught as well as all the way up to the line or plant or number of plants so the company can start aggregating quality data and use it more broadly. Instead of being a point solution, the visual inspection is now a company-wide node in a manufacturing machine.

We also bring an agile way of working and a focus on collaboration, iterative development, and continuous learning. This enables the rapid delivery of business value so ideas get to market faster. In addition, we further amplify the impact of agile methods by providing specialized expertise in the latest technologies, including cloud, automation, artificial intelligence, and high-speed private networks. From idea to launch, the advanced technology delivery team working from the development center in Columbia, South Carolina, helps visionary companies turn ideas into business value at industry-leading speed.

Dashboards make managing corrective actions easy. This can include identifying areas that need more training, since people are a huge variable in building products. The immediate feedback of real-time data makes operators more efficient. It also paves the way to a continuous improvement culture and mindset.

Visual inspection is about more than just quality; it can also be applied to sustainability initiatives that seek to decrease waste and increase accountability through managing more variables at greater levels of detail. As manufacturers trend towards Industry 4.0 with smart plant-control towers, private 5G, and edge computing, this visual-inspection application can provide the rationalization for these foundational technologies to finally present a clear business case for adoption.

Find out more about our partnership with IBM.

The hype in AI is about whether a truly intelligent AI is an existential risk to society. Are we heading for Skynet or The Culture? What will the future bring?

I’d argue that the larger and more realistic threat is from Deep Stupidity — the weaponization of Artificial General Intelligence to amplify misinformation and create distrust in society.

Social media is the platform, AI is the weapon

One of the depressing things about the internet is how its made conspiracy theories spread. Where before people were lone idiots, potentially subscribing to some bizarre magazine or conspiracy society in a given area, you really didn’t have the ability to industrial scale these things. Social media and the Internet has increased the spread of such ideas. So while some AI folks talk about the existential threat of AGI, personally I’m much more concerned about Artificial General Stupidity.

So I thought it is worth looking at why it is much easier to build an AI that is a flat earther than it is to build a High School physics teacher, let alone a Stephen Hawking.

It is easier being confidently wrong and not understanding

LLMs are confidently wrong, that inability to actually understand is a great advantage when being a conspiracy theorist. Because when you understand stuff, then conspiracy theories are dumb.

This means the training data set for our AI conspiracy theorist must be incomplete, what we need is not something that has access to a broad set of data, but actually something that has access to an incredibly small and specific set of data that repeats the same point over and over again.

To be a conspiracy theorist means denying evidence and ignoring contradictions, this is much easier to learn and code for than actually receiving new information that challenges your current model and altering it.

Small data set for a single topic

So this is a massive advantage for LLMs when trying to create a conspiracy theorist. What we need is a limited set of data that repeats a given conclusion and continually lines up all evidence to that conclusion. We can apply this to lots of conspiracy theorists out there, for instance those folks who scream “false flag” after every single mass shooting incident in the US, in other words we have a small set of data, possibly only a few hundred data points that always result in the same conclusion. This means for our custom trained conspiracy theorist the association it always knows is “what ever the data, the answer is the conspiracy”.

Now we could get fancy and have a number of conspiracies, but given very few of them are logically consistent with each other, let alone with reality, it is more effective to have a model per conspiracy and just switch between them. That a conspiracy theorist is inconsistent with what they’ve previously said isn’t a problem, but we don’t want inconsistencies between conspiracies on a single topic. What we need to add are the standard “rebuttals of reality” like “Water finds its level”, “We don’t see the curve”, “NASA is fake” or “Spurs are a top Premier League club”.

Hallucinations help

This small set of data really helps us take advantage of the largest flaw in LLMs, hallucinations, or when the LLM just makes stuff up either because it has no data on the topic, or because the actual answer is rare so the weightings bias it towards an invalid answer. This is where LLMs really can scale conspiracy theories, because the probabilities are weighted towards the conspiracy theory already (as that is the only “correct” answer within the model) then any information we are provided with is recast within that context. So if someone tells us that the Greeks proved the earth was round in the 2nd Century BC our LLM could easily reply:

Context makes hallucinations doubly annoying

Our LLMs can go beyond the average conspiracy theorist thanks to the context and hallucinations. While an average conspiracy person will only have a fixed set of talking points, and potentially be constrained at some level by reality, the hallucinations and context of the conversation enables our conspiracy LLM to keep building its conspiracy and adding elements to it. Because our LLM is unconstrained by reality and counter arguments, instead being able to reframe any counter argument by using a hallucination it will be significantly more maddening. It will also mean it will create new justifications for the conspiracy that have never been put forwards before. These will, of course, be total nonsense but new total nonsense is mana from heaven to other conspiracy theorists.

Reset and start again

The final piece that makes a conspiracy LLM much easier to create is that if the LLM goes truly bonkers and you need to reset… this is exactly what conspiracy theorists do today. So if our LLM is creating hallucinations that fail some form of basic test, or just every 20 responses, we can reset the conversation in a totally different direction. Making my generative LLM detect either a frustration or an “ah ha” moment from the person it is annoying, a trivial task, enables me to then have my conspiracy bot just jump to another topic, and to do so in a much smoother way than most conspiracy theorists do today.

This is a much smoother transition for a flat earth conspiracy than you’ll hear on TikTok or YouTube.

We have achieved AGS, that isn’t a good thing

I’ve argued that the current generation of AIs aren’t close to genuinely passing the Turing test, let alone more modern tests. Turing set the bar of intelligence as the CEO of a Fortune 50 company, and made it have awareness of what it didn’t know.

Some folks are concerned about a coming existential crisis where Artificial General Intelligence becomes a threat to humanity.

But for me that is assuming the current generation of technologies are not a threat, and that intelligence is a greater threat than weaponized stupidity. Many people in AI are in fact arguing that GPT passes the Turing test, not because it replicates an intelligent human, but because either it can pass a multiple choice or formulaic example, or because it can convince people they are speaking to a not very bright person.

We can today make an AI that is the equivalent of a conspiracy theorist, someone untethered to reality and disconnected from logic. This isn’t General Intelligence, but it is General Stupidity.

Deep fakes and deep stupidity

Where Deep Fakes can make us not trust sources, Deep Stupidity can amplify misinformation and constantly give it justification and explanation. Where Deep Fakes imitate a person or event, Deep Stupidity can imitate the response of the crowd to that event. Spinning up a million conspiracy theorists to amplify not just the Deep Fake but the creation of an alternative reality around it.

The internet and particularly social media has proven a fertile ground for human created stupidity and conspiracy theories. Entire political movements and groups have been created based on internet created nonsense. These have succeeded in gaining significant mindshare without having the capacity to really generate either convincing material or convincing narratives.

AIs today have the ability to change that.

Stupidity and misinformation are today’s existential threat

We need to stop talking about the challenge with AI being only when it becomes “intelligent”, because it is already sufficiently stupid to have massive negative consequences on society. It is madness to think that companies, and especially governments, aren’t looking at this technologies and how they can use them to achieve their ends, even if their ends are simply to sew chaos.

Stupidity is the foundation for worrying about intelligence

Worrying about an AI ‘waking up’ and threatening humanity is a philosophical problem, but addressing Artificial Stupidity would give us the framework to deal with that future challenge. Everything about controlling and managing AI in future can be mapped to controlling and avoiding AGS today.

When we talk about frameworks for Trusted AI and legislation on things like Ethical Data Sourcing these are elements that apply to General Stupidity just as much as to intelligence. So we should stop worrying simply about some amorphous future threat and instead start worrying about how we avoid, detect and control Artificial General Stupidity, because in doing that we lay the platform for controlling AI overall.

This article first appeared on Medium.

Disclaimer: This blog was NOT written by ChatGPT, but by a group of human data scientists: Shahryar Masoumi, Wouter Zirkzee, Almira Pillay, Sven Hendrikx and myself.

Whether we are ready for it or not, we are currently in the era of generative AI, with the explosion of generative models such as DALL-e, GPT-3, and, notably, ChatGPT, which racked up one million users in one day. Recently, on March 14th, 2023, OpenAI released GPT-4, which caused quite a stir and thousands of people lining up to try it.

Generative AI can be used as a powerful resource to aid us in the most complex tasks. But like with any powerful innovation, there are some important questions to be asked… Can we really trust these AI models? How do we know if the data used in model training is representative, unbiased, and copyright safe? Are the safety constraints implemented robust enough? And most importantly, will AI replace the human workforce?

These are tough questions that we need to keep in mind and address. In this blog, we will focus on generative AI models, their trustworthiness, and how we can mitigate the risks that come with using them in a business setting.

Before we lay out our trust issues, let’s take a step back and explain what this new generative AI era means. Generative models are deep learning models that create new data. Their predecessors are Chatbots, VAE, GANs, and transformer-based NLP models, they hold an architecture that can fantasize about and create new data points based on the original data that was used to train them — and today, we can do this all based on just a text prompt!

We can consider chatbots as the first generative models, but looking back we’ve come very far since then, with ChatGPT and DALL-e being easily accessible interfaces that everyone can use in their day-to-day. It is important to remember these are interfaces with generative pre-trained transformer (GPT) models under the hood.

The widespread accessibility of these two models has brought about a boom in the open-source community where we see more and more models being published, in the hopes of making the technology more user-friendly and enabling more robust implementations.

But let’s not get ahead of ourselves just yet — we will come back to this in our next blog. What’s that infamous Spiderman quote again?

With great power…

The generative AI era has so much potential in moving us closer to artificial general intelligence (AGI) because these models are trained on understanding language but can also perform on a wide variety of other tasks, that in some cases even exceed human capability. This makes them very powerful in many business applications.

Starting with the most common — text application, which is fueled by GPT and GAN models. Including everything from text generation to summarization and personalized content creation, these can be used in education, healthcare, marketing, and day-to-day life. The conversational application component of text application is used in chatbots and voice assistants.

Next, code-based applications are fueled by the same models, with GitHub’s Co-pilot as the most notable example. Here we can use generative AI to complete our code, review it, fix bugs, refactor, and write code comments and documentation.

On the topic of visual applications, we can use DALL-e, Stable Diffusion, and Midjourney. These models can be used to create new or improved visual material for marketing, education, and design. In the health sector, we can use these models for semantic translation, where semantic images are taken as input and a realistic visual output is generated. 3D shape generation with GANs is another interesting application in the video game industry. Finally, text-to-video editing with natural language is a novel and interesting application for the entertainment industry.

GANs and sequence-to-sequence automatic speech recognition (ASR) models (such as Whisper) are used in audio applications. Their text-to-speech application can be used in education and marketing. Speech-to-speech conversion and music generation have advantages for the entertainment and video game industry, such as game character voice generation.

Although powerful, such models also come with societal limitations and risks, which are crucial to address. For example, generative models are susceptible to unexplainable or faulty behavior, often because the data can have a variety of flaws, such as poor quality, bias, or just straight-up wrong information.

So, with great power indeed comes great responsibility… and a few trust issues

If we take a closer look at the risks regarding ethics and fairness in generative models, we can distinguish multiple categories of risk.

The first major risk is bias, which can occur in different settings. An example of bias is the use of stereotypes such as race, gender, or sexuality. This can lead to discrimination and unjust or oppressive answers generated from the model. Another form of bias is the model’s word choice. Its answers should be formulated without toxic or vulgar content, and slurs.

One example of a language model that learned a wrong bias is Tay, a Twitter bot developed by Microsoft in 2016. Tay was created to learn, by actively engaging with other Twitter users by answering, retweeting, or liking their posts. Through these interactions, the model swiftly learned wrong, racist, and unethical information, which it included in its own Twitter posts. This led to the shutdown of Tay, less than 24 hours after its initial release.

Large language models (LLMs) like ChatGPT generate the most relevant answer based on the constraints, but it is not always 100% correct and can contain false information. Currently, such models provide their answers written as confident statements, which can be misleading as they may not be correct. Such events where a model confidently makes inaccurate statements are also called hallucinations.

In 2023, Microsoft released a GPT-backed model to empower their Bing search engine with chat capabilities. However, there have already been multiple reports of undesirable behavior by this new service. It has threatened users with legal consequences or exposed their personal information. In another situation, it tried to convince a tech reporter he was not happily married and that he was in love with the chatbot (it also proclaimed their love for the reporter) and consequently should leave his wife (you see why we have trust issues now?!).

Generative models are trained on large corpora of data, which in many cases, is scraped from the internet. This data can contain private information, causing a privacy risk as it can unintentionally be learned and memorized by the model. This private data not only contain people, but also project documents, code bases, and works of art. When using medical models to diagnose a patient, it could also include private patient data. This also ties into copyright when this private memorized data is used in a generated output. For example, there have even been cases where image diffusion models have included slightly altered signatures or watermarks it has learned from their training set.

The public can also maliciously use generative models to harm/cheat others. This risk is linked with the other mentioned risks, except that it is intentional. Generative models can easily be used to create entirely new content with (purposefully) incorrect, private, or stolen information. Scarily, it doesn’t take much effort to flood the internet with maliciously generated content.

Building trust takes time…and tests

To mitigate these risks, we need to ensure the models are reliable and transparent through testing. Testing of AI models comes with some nuances when compared to testing of software, and they need to be addressed in an MLOps setting with data, model, and system tests.

These tests are captured in a test strategy at the very start of the project (problem formulation). In this early stage, it is important to capture key performance indicators (KPIs) to ensure a robust implementation. Next to that, assessing the impact of the model on the user and society is a crucial step in this phase. Based on the assessment, user subpopulation KPIs are collected and measured against, in addition to the performance KPIs.

An example of a subpopulation KPI is model accuracy on a specific user segment, which needs to be measured on data, model, and system levels. There are open-source packages that we can use to do this, like the AI Fairness 360 package.

Data testing can be used to address bias, privacy, and false information (consistency) trust issues. We make sure these are mitigated through exploratory data analysis (EDA), with assessments on bias, consistency, and toxicity of the data sources.

The data bias mitigation methods vary depending on the data used for training (images, text, audio, tabular), but they boil down to re-weighting the features of the minority group, oversampling the minority group, or under-sampling the majority group.

These changes need to be documented and reproducible, which is done with the help of data version control (DVC). DVC allows us to commit versions of data, parameters, and models in the same way “traditional” version control tools such as git do.

Model testing focuses on model performance metrics, which are assessed through training iterations with validated training data from previous tests. These need to be reproducible and saved with model versions. We can support this through open MLOPs packages like MLFlow.

Next, model robustness tests like metamorphic and adversarial tests should be implemented. These tests help assess if the model performs well on independent test scenarios. The usability of the model is assessed through user acceptance tests (UAT). Lags in the pipeline, false information, and interpretability of the prediction are measured on this level.

In terms of ChatGPT, a UAT could be constructed around assessing if the answer to the prompt is according to the user’s expectation. In addition, the explainability aspect is added — does the model provide sources used to generate the expected response?

System testing is extremely important to mitigate malicious use and false information risks. Malicious use needs to be assessed in the first phase and system tests are constructed based on that. Constraints in the model are then programmed.

OpenAI is aware of possible malicious uses of ChatGPT and have incorporated safety as part of their strategy. They have described how they try to mitigate some of these risks and limitations. In a system test, these constraints are validated on real-life scenarios, as opposed to controlled environments used in previous tests.

Let’s not forget about model and data drift. These are monitored, and retraining mechanisms can be set up to ensure the model stays relevant over time. Finally, the human-in-the-loop (HIL) method is also used to provide feedback to an online model.

ChatGPT and Bard (Google’s chatbot) have the possibility of human feedback through a thumbs up/down. Though simple, this feedback is used to effectively retrain and align the underlying models to users’ expectations, providing more relevant feedback in future iterations.

To trust or not to trust?

Just like the internet, truth and facts are not always given — and we’ve seen (and will continue to see) instances where ChatGPT and other generative AI models get it wrong. While it is a powerful tool, and we completely understand the hype, there will always be some risk. It should be standard practice to implement risk and quality control techniques to minimize the risks as much as possible. And we do see this happening in practice — OpenAI has been transparent about the limitations of their models, how they have tested them, and the governance that has been set up. Google also has responsible AI principles that they have abided by when developing Bard. As both organizations release new and improved models — they also advance their testing controls to continuously improve quality, safety, and user-friendliness.

Perhaps we can argue that using generative AI models like ChatGPT doesn’t necessarily leave us vulnerable to misinformation, but more familiar with how AI works and its limitations. Overall, the future of generative AI is bright and will continue to revolutionize the industry if we can trust it. And as we know, trust is an ongoing process…

In the next part of our Trustworthy Generative AI series, we will explore testing LLMs (bring your techie hat) and how quality LLM solutions lead to trust, which in turn, will increase adoption among businesses and the public.

This article first appeared on SogetiLabs blog.

How you can utilize your data to optimize your applications – and build the foundation for your future business

Digital transformation strategies that fail to recognize and apply the power that data holds can confine themselves to darkness – or at best – leave a lot of potential opportunities untapped. A recent Capgemini Research Institute (CRI) study entitled The data-powered enterprise: Why organizations must strengthen their data mastery highlights how companies can exploit data to drive real business value. The study found that companies that use data as a foundation for their operations – so called “Data Masters” – realize a significant performance advantage relative to their peers. This advantage spans customer engagement, revenue growth, operational efficiency, and cost savings – including 70% higher revenue per employee and 22% higher profitability overall.
 
However, becoming a Data Master is a journey – not a one-off project with an immediate ROI. A focus on leveraging data within application landscapes and the wider IT ecosystem enables companies to build the foundation for their evolutive journeys to becoming Data Masters.

Digital transformation that puts you in the driver’s seat – Harnessing the true potential of data-driven ADM

Building application development and maintenance (ADM) services that can fully utilize data is the first step in a company’s data modernization journey. The systems that are core to the delivery of data-enabled ADM contain a wealth of data and insights to accelerate the delivery of services. For example, the data residing in an ITSM tool can be extracted and analyzed to understand the nature of incidents that typically make up the bulk of an application maintenance team’s workload. This can help in identifying the highest-impact incidents to target automated resolution or enhance monitoring to drive down incident volumes.

Additionally, analyzing ticket data for recurring incidents targets root-cause analysis initiatives on the highest-impact problems. Extending this data analysis can also facilitate an AI-enabled capability to identify not just the root cause – but also the resolution that eliminates these incidents from even occurring.

The combination of automating the resolution of one batch of high-frequency incidents, and pre-emptively eliminating another batch of recurring incidents can bring a material reduction in application support effort.

The resources freed up through this process can then be applied to further the data modernization journey. Assessing the “as-is” and then modernizing data landscapes to eliminate data silos and redundancies enables the further exploitation of data. This newly standardized and sanitized data provides fresh insights into further transformation opportunities – particularly on the business side – that enhance the value of data to drive real change.

Capgemini’s ADMnext^Data – Bringing data to light to help you successfully navigate your digital transformation journey

Capgemini’s ADMnext^Data integrates all the assets and capabilities of our market-leading ADM services with our unique insights and data capabilities. These combined capabilities enable us to help guide you on your data modernization journey as part of a long-term relationship.

Firstly, our Enterprise Automation Fabric (EAF) offering specifically focuses on incorporating data into the heart of the ADM services we offer. EAF is the foundational automation suite that underpins the delivery of services across technology and business process operations. It works with your ITSM to extract incident data and identify the highest value transformation and automation initiatives. It also possesses the AIOps capabilities to automate the resolution of incidents and root-cause-analysis processes.

As support requirements fall and resources are freed thanks to EAF, Capgemini can then leverage assets such as our eAPMand Advantage-ROI tools to help you better understand your current maturity and implement the highest value transformation opportunities across your data estate. Value can be identified both from your modernization of data landscapes (for example, through migration to cloud or application rationalization), as well as business process transformation efforts.

Data-enabled digital transformation provides companies with an unprecedented opportunity to leverage data that separates themselves from their competition. Capgemini’s ADMnext^Data gives you the tools and expertise to guide on your data-enabled digital transformation journey.

To start your drive on the path to data-enabled digital transformation as a Data Master, drop me a line and visit us here to learn more.