Talking to marketers at this point is rather like the Distracted Boyfriend meme: they’re watching AI walk down the street, as the Metaverse looks on.

AI has been every client’s only focus since ChatGPT came noisily to our attention. It’s ubiquitous, but new enough for everyone to be still figuring out their AI strategy and how they’ll adapt. While there are endless interesting potential applications for this technology, it’s fair to say no one has fully processed the level of risk that comes with it. Like any new technology, AI presents a new frontier of brand security.

To tackle it in the relative absence of any other collective guardrails, it’s down to clients and their agency partners to show restraint and self-police how we use AI to a greater extent than we’ve done with any other technology. That process of self-policing starts with a genuinely robust scrutiny of the tools that are brought in-house, coupled with due respect for the walled garden of information and data that exists in every business.

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AI learns through data. The technology comes with many ethical and moral questions about how we use other people’s data – or any data at scale. We arguably don’t yet know enough about AI to ask the questions that will enable us to make good, brand-safe decisions. Sure, it’s tempting to be able to generate cool imagery faster using a learning model, but without knowing the source of the data used to create that model, the stakes are high.

Just how high is evident in the lawsuits we’re starting to see unfold, such as Getty’s [against London-based Stability AI], as well as embarrassing moments like the leak of sensitive information from Samsung employees via generative AI platform ChatGPT over something as seemingly benign as converting a meeting recording to notes.

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When even the father of AI, Geoffrey Hinton, quits, you worry you’ve already reached something of an Oppenheimer moment. So, how do you act responsibly using AI, while making the most of its potential?

With in-housing models like ours, the most potent element is the level of intimacy we can establish with clients. We’re sitting right next to them and that enables us to act as brand guardians to ensure that our clients aren’t on a platform that feels unsafe, or in a situation that puts them at reputational risk. This includes recommending AI-enabled tech that operates in a data-safe way; in effect, using data that belongs to the business, so that the business learns from itself.

What is incredible about AI is that a learning model can ingest everything there is to know about your brand, from how it talks about itself, what font it uses, and its visual identity, to how consumers typically react to it. That information is what makes a brand unique, and a learning model that knows that uniqueness better than you know it yourself can make a brand better, stronger, and more consistent.

Based on what we know performs within a business, we can use AI to write copy better. If we already know what makes an effective brief, we can create better briefs faster. We can even comp up assets for segmented advertising messages faster – all while using the information from inside the business.

In my company, we’ve developed an AI Council. This is made up of our legal team, our compliance team, and creatives – anyone who has a role in making sure that ours or our clients’ businesses are safe is across it. No one in the company is allowed to use any sort of technology without it going through the AI Council first, and 95% of all of our employees have already gone through training on AI compliance, just as they would go through diversity and inclusion training.

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It’s important to state the Council isn’t there to stop or facilitate great work and better efficiencies. Nobody wants to get in the way of advancement, especially not in the relatively laissez-faire North American market. So the Council is born – and is run – in the belief that to get AI right in its infancy, we need to show restraint and self-police how we use this technology in the short term.

AI Councils like ours will likely spring up industry-wide, albeit in a splintered way as we figure things out. In the U.S. we tend to fall back on state-level legislation. Take California, which was the first state to adopt any sort of really substantive privacy rules – though it’s just one out of fifty. In the meantime, as stewards of our client’s businesses, it’s up to us to make sure they don’t find themselves on the wrong side of a lawsuit.

The in-house model keeps the agency and client working closely together and operating within that safe walled garden of information – if it’s your first-party information you’re drawing from, you’re in a safe space. That approach won’t be the approach forever; but at least in these early Wild West days of AI, it’s a productive start in navigating towards an intelligent AI-rich future.

[To share your insights with us, please write to sghosh@martechseries.com]

The post In AI’s Wild West, Brand Safety Comes First appeared first on AiThority.

Marketing has a wealth of customer data it can harness to drive decisions that enable positive sales and marketing outcomes, but that data can drive decisions that do so much more, including transforming the way their companies do business and how they’ll drive innovation and outpace the competition—if you know how to capitalize on it.

Unfortunately, most marketing organizations are simply overwhelmed by the hundreds, even thousands, of precious customer data points they gather each day. Too many of us see this as a data management problem rather than a game-changing resource that positions marketing as business-critical subject matter experts who intimately understand customer needs and wants.

Look, we all know today’s marketing organizations must continually juggle competing priorities across a seemingly endless list of stakeholders (engineering, product development, senior leadership) in their companies. Focusing on where to add the greatest value across the largest segment of stakeholders is critical but challenging, and guessing won’t get you there. If you’re ready to completely rethink how customer data drives your decisions, the Edge to Core (E2C) approach will be of interest and can help your marketing organization win with more stakeholders across your company and with customers.

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The decentralized approach of E2C puts marketers in the driver’s seat with the autonomy they need to be nimble and make agile adjustments to their programs based on massive amounts of qualitative and quantitative customer data.

E2C pulls significantly higher-quality data from the edge (where it’s often lost) into the marketing “core” where it can be immediately deployed to generate the deep insights required for driving better decision-making–not only by marketing teams but also across the entire business from corporate strategy to product development, and sales. Marketing’s connection with leads and prospects gives them unique insights into customer challenges and changing needs, making them subject matter experts on the markets they serve. By sharing insights discovered at the edge and developed in the core, marketing can assist teams with everything from making product roadmap decisions to shifting corporate strategy and enabling sales.

So, how can you move from an E2C philosophy that lets you lay the groundwork for data-driven decision-making that saves you from the pitfalls of constantly shifting gears on your approach based on stakeholder hunches?

First, you need to capture a lot of data from analytics, conversations, customer conversations, sales communications, and customer support.

Then, you need to be able to evaluate what data leads to actionable insights. Finally, you’ll need to establish success metrics for how you’re moving the needle with an Edge to Core approach.

Using data we discovered at the edge, we established key insights that bring us closer to reaching our goals. While your mileage may vary, I wanted to share some of the sample metrics we use to show our progress and chart our success. Be mindful that the more metrics you put in place, the more complexity you add to measuring success.

At the edge, our customer data came from three key marketing categories: air cover, close opportunity advancement, and pipeline acceleration. The marketing activities we implemented in each category yielded several critical marketing metrics:

• Voice reach from air cover activities such as thought leadership, paid media, digital out-of-home (OOH), AR/PR, and social media.

• Key account engagement levels from pipeline acceleration tactics including digital journey, organic search, and product trials and demos.

• Sales velocity and win percentage from close-opportunity advancement programs like account-based marketing (ABM), field and proprietary events, and customer loyalty.

These metrics are fueled by continuous data analysis, competitive assessment, and insight distillation of data happening at the core. The tactics don’t change but the outcomes, over time, will prove transformational.

The agility of E2C decision-making helps nurture customer connections and leads to real long-term relationships because it facilitates engagement with customers in the moments that matter most. Marketing is no longer an intrusion but a welcome wealth of information to customers and across your organization and shows subject matter expertise that moves the needle.

Future articles will dive deeper into Edge to Core (E2C) topics and include AI-based data strategies, product roadmap development, and managing culture change in a modern marketing organization.

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[To share your insights with us, please write to sghosh@martechseries.com]

The post Edge to Core Marketing: The New Paradigm of Modern Enterprise Marketing appeared first on AiThority.

Four Inc. has been named a federal aggregator for Dun & Bradstreet, a leading global provider of business decisioning data and analytics. Under the agreement, Four Inc. will provide Dun & Bradstreet solutions to the public sector through Four Inc.’s NASA Solutions for Enterprise-Wide Procurement, Information Technology Enterprise Solutions- Software 2 (ITES-SW2) contract, and its network of channel partners as part of Four Inc.’s boutique aggregation program. This collaboration brings together the expertise and capabilities of both organizations to deliver innovative data solutions tailored specifically for government agencies.

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As a global leader in business decisioning data and analytics, Dun & Bradstreet equips government organizations with tools to make mission-critical decisions, mitigate risks, and undertake compliance processes efficiently. Among Dun & Bradstreet’s solutions included in the agreement are D&B Investigate™, D&B Protect™ and D&B Fortify™, which provide a broad range of data and analytics through a single sign-on platform that leverages the Dun & Bradstreet Data Cloud containing more than 500 million total organizations, AI-driven technology, and business decisioning data and analytics. The platform enables government agencies to address complex challenges, optimize operations, and enhance citizen services by delivering deep business insights and enabling timely decision-making inside complex business environments.

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Dun & Bradstreet’s extensive experience in data and analytics, combined with Four Inc.’s comprehensive understanding of the government sector’s unique challenges, creates a powerful synergy that will transform the way government agencies leverage data.

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“We are excited about the strategic agreement with Dun & Bradstreet,” said Chris Wilkinson, EVP of Sales at Four Inc. “By leveraging Dun & Bradstreet’s robust data and analytics, our government clients can access insights and make informed decisions that lead to enhanced service delivery and better overall outcomes for the public.”

[To share your insights with us, please write to sghosh@martechseries.com]

The post Four Inc. Announces Agreement with Dun & Bradstreet to Empower Government Agencies with Data-Driven Solutions appeared first on AiThority.

Large Language Models (LLMs) and Generative AI (Gen AI) are taking SaaS platforms to new heights. Customer Data Platforms (CDPs) in martech, for example, are currently at the center of every customer experience (CX) management discussion. In short, the AI inflection point in CDPs is here. With CX emerging as the core of any business decision today, there is an obvious need to redefine customer experiences through the lens of new-age technologies, including Gen AI and LLMs. Redefining CX means redefining CDPs. This is an important development for the CDP industry as it embraces the generative AI hype for its further growth. Speaking of growth, the CDP software market is expected to grow to $5.6 billion by 2026. Gen AI and personalization would grow by three times during the same time, according to a recent Martech Landscape trends report.

This is a huge opportunity for the CDP vendors who could completely change the way Martech users use the platforms for omnichannel CX management, contact center service automation, and building AI-powered customer journeys across new emerging channels, such as TikTok.

According to CDP expert, Rabid Raab, the use of modern cloud data warehouse technologies has led to a subtle shift in the perception of the CDP’s purpose. Raab mentions the importance of sticking to the fundamental definition of a CDP and its purpose. From the modern martech data management perspective, there is a need to redefine CDP as a “profile sharing system”, based on its identity as a packaged CDP or a warehouse CDP.

Historically, according to Google Cloud, marketing and advertising teams have relied on “packaged” CDPs to run their data management activities and then push the data into downstream martech and adtech tools for personalized customer experiences.

In 2024, this “redefinition” movement of packaged and warehouse CDPs can be achieved and consolidated in the martech stack by utilizing LLMs for CDP and generative AI.

By definition, a customer data platform is meant to be a seamless environment for integrating discrete data from multiple sources connected to the marketing technology stack. The sole objective of CDPs is to combine structured and unstructured (and anything in between) data to create a unified, singular customer profile. This profile is collected by ingesting billions of data points from multiple sources such as the company website, the internet, organizational ERPs, CRMs, DAMs, email, chat, mobile, and social media.

Here’s a graphical representation of different CDPs- traditional versus packaged versus composable CDPs. (source- Hightouch and Databricks).

The integration of CDP with different martech tools and ERPs through APIs and pre-built connectors. Using machine learning applications, it is possible to democratize data and analytics, making CDPs more agile, flexible, and scalable for multi-channel customer journey orchestration for different industries and technology landscapes such as Fintech, HR Tech, Sales tech, Martech, IT and Storage, and Cybersecurity.

The Emergence of Composable CDPs Using No-code AI Tools

Less than 60% of deployed CDPs are delivering any significant value to the martech teams. Thanks to the rise of modern data stacks and warehouses for CDPs such as Snowplow, Databricks, Hightouch, Google BigQuery, and AWS, composable architecture based on Reverse ETL would become the norm. In a recent AIThority article, Merkle’s Courtney Trudeau wrote about the emergence of Composable CDPs in the marketing technology landscape. To embrace experimentation as a culture in CDP deployment, generative AI is a big game-changer.

There’s room for the introduction of AutoML into composable CDPs so that data scientists can introduce unique AI-led instances for managing and governing data activation for big data organizations and their customers.

Generative Artificial Intelligence tools embedded into modern composable CDPs will replace traditional CDPs in 2024. These composable CDPs would emerge at the frontline of all major martech stacks that focus on customer journey mapping and audience profile segmentation. Composable CDPs can optimize existing data stacks with robust gen AI-powered no-code workflows, providing integrations with the top martech tools and SaaS applications to meet a wide range of business needs.

Popular data analytics tools provide instantaneous AI integrations with ChatGPT and other generative AI tools. Microsoft Power BI, Qlik Sense, Tableau GPT, Salesforce Einstein GPT, CoPilot AI, and Notion AI are examples that are worth naming in this domain.

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Utilizing Generative AI for Building a Real-time CDP

Redefining CDP and Generative AI applications for the martech landscape requires a flawless activation of real-time data analytics. Gen AI, powered by billions of real-time live-streaming data from multiple sources, can create interactive experiences delivered by virtual AI assistants.

According to mParticle, an AI CDP is built on these three capabilities:

1. High-quality first-party data collection across different streams;
2. Unifying this data to generate a 360-degree customer profile; and
3. Forwarding these AI-generated customer profiles to downstream Martech tools for activation.

CDP engineering teams could use Google BigQuery to build customer 360 for multi-channel engagements (including offline and metaverse). The BigQuery integration with CDP for customer 360 provides a strong foundation for real-time customer data analytics, 1:1 hyper-personalization, and compliance.

 AI Lakehouse and Google BigQuery have ensured CDPs get a unified architecture to arrive at a single true unified customer persona.

In-game advertising campaigns use CDP and generative AI tools to capture real-time customer intent.

Imagine you are watching your NFL game and suddenly feel the urge to drink your favorite cola during the match. You click on the cola ad, and voila! your smartphone app orders a cola drink for you, based on your latest click actions. Similar experiences can be found in interactive video games and live-streaming music concerts. Overall, this is possible by parsing customer data using generative AI, quickly sending a rippling stream of data analytics to create a personalized customer journey.

Martech stacks are incomplete without a real-time CDP. Adobe Real-time CDP is a great example in this space.

Adobe’s CDP is meant for full-funnel marketing, enabling martech teams to build customized journeys from acquisition through loyalty stages. Such CDPs help different customer-facing teams to prospect ever-changing digital personas and provide them with the best experiences — without the assistance of third-party cookies.

What’s behind this redefined CDP superpower?

Built on the Adobe Experience Cloud, Adobe Real-time CDP is a top-notch updated definition for CDPs and Gen AI integration.

Adobe’s proprietary AI platform, Sensei. Adobe Sensei GenAI for Real-Time CDP creates unique audience personas and launches activation campaigns using generative templates based on previous performances and profile matching. By continuously training with AI data, this CDP refines and improves audience definitions with more personalized outcomes for “in-the-moment” experiences.

Predictive Intelligence to Influence CX Journeys

With generative AI, Marketing organizations can embrace total CX transformation. This is capitalized on the composable CDPs with AI.

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Traditionally, Marketers relied on an army of CX specialists, CDP engineers, and AI programmers to scale their CDP orchestration. With the rise of LLMs in martech engineering, we are witnessing a surreal transformation of customer engagement strategies based on predictive intelligence.

Modern CDPs are required to do more than gather real-time insights from centralized, unified data. World-class AI CDPs generate predictive recommendations for automated decision-making based on unified customer data. Martech customers want their CDPs to orchestrate a bigger picture with deep data analytics and predictive intelligence. Gen AI-powered predictive intelligence can influence live customer segmentation by ingesting and integrating data via stream, batch, and interactive APIs.

CDPs such as Lemnisk use the RAMANUJAN AI ML capability to create moment-based CX via omnichannel customer touchpoints. This AI-powered CDP drives marketing automation efforts to understand ever-evolving buying intent and digital behaviors.

Sitecore’s David Schweer explains what a modern CDP looks like in the gen AI era. He defines it as a smart hub CDP that brings together data management, intelligence, and orchestration for reimagining the CX. New AI applications, including generative AI, trigger a range of predictive insights that help marketers design agile automation workflows for audience-building activities and modify marketing voice based on CX objectives. For example, Gen AI-powered customer data activation could be used to determine prescriptive churn and conversion predictions. Similarly, predictive intelligence could power other martech engines, such as AI-powered chat messengers or automated outbound emails.

Signal Discovery to Supercharge CDPs for CX

Marketers are finding new ways to capture experience signals to drive customer loyalty, enhance satisfaction, reduce churn, and maximize revenues. Signal Discovery in customer experience journeys are critical data points extracted from different martech systems. These click-stream data points could provide a complete picture through AI-driven conversation intelligence techniques for marketing, e-commerce, and contact centers. Invoca does it for Tealium’s AudienceStream CDP.

In June this year, Invoca’s generative AI capabilities were merged with its patented ML technologies to improve digital advertising and contact center performance running on CDPs. These Gen AI-led innovations promise to deliver an exceptional real-time conversation-driven CX with far better conversion rates than previously achieved with standalone martech tools. ChatGPT-based call summarization, smart alerting, and agent coaching are some of the biggest benefits of using CDP and generative AI to streamline signal discovery workflows in 2023. Different systems such as ERPs, CRMs, DAMs, ECMs, HRIS, and CDPs could work in sync to create a “playlist-looking” intelligent dashboard for compliance, auditing, and zero-party data insights.

Now that I have mentioned auditing, let’s talk about CDP security management with generative AI.

Customer Data Governance and Security Management using Gen AI

According to Tealium’s fourth annual report, titled “State of the CDP” 2023, companies with a CDP confide in their CDPs’ capabilities in meeting the current and future data privacy regulations. The same report also placed “privacy and regulatory compliance” among the top three CDP use cases, alongside customer retention, and customer acquisition.

Despite evident concerns related to the use of generative AI models for designing CDPs, business leaders feel this is a unique opportunity to push the boundaries in CX with prompt engineering and no-code LLMs. Heading into an AI-assisted CDP workflow means constantly brushing shoulders against cyber risks and data protection laws and principles. Trusted companies such as NVIDIA accelerate CDP developments using purpose-built tooling for agile experimentation, data analytics, and machine learning. Self-serving ML models can take care of the various IT requirements related to storage, network security, backup and recovery, data governance, and compliance. GPU accelerators from NVIDIA support CDP-ready configurations created for vendors who want to accelerate AI and ML training on their first-party data foundations and third-party apps.

Challenges for Generative AI Applications in the CDP Marketplace

LLMs are on a meteoric rise in the CDP landscape. Despite their immense popularity, IT leaders are averse to deploying generative AI to replace existing CDP models. There are two major reasons for this aversion in CDP and generative AI integration. The first reason is that it takes millions of dollars of computing resources to train AI models for high-end data management and activation. Generative AI can write limericks because it has been trained on trillions of data points related to limericks.

But, how many CDP engineering data points are available for training LLMs and generative AI models for this martech category? Our AIThority analysts are trying to find this out.

FAQs related to CDPs and AI

What are Customer Data Platforms (CDPs)?

Gartner defines customer data platforms (CDPs) as software applications that support marketing and customer experience use cases by unifying a company’s customer data from marketing and other channels. CDPs optimize the timing and targeting of messages, offers, and customer engagement activities, and enable the analysis of individual-level customer behavior over time.

According to Oracle, a CDP is packaged software that creates a comprehensive customer database accessible by other systems to analyze, track, and manage customer interactions.

How is a CDP different from a DMP?

There are four main differences between CDPs and DMPs. These are:

1. CDPs are used for the unification of data from marketing and non-marketing sources. DMPs unify data from advertising sources only. DMPs can’t provide insights on “personalization” and CX efforts in marketing.
2. CDPs collect all types of data. DMPs primarily collect only third-party data.
3. CDPs collect user’s personal information. DMPs collect only anonymized data.
4. CDPs store data for a very long time. DMPs retain this data for a very short time.

Adobe’s Real-time CDP is a unique platform. It provides the features of both DMP and CDP under the same roof.

Who are the biggest CDP vendors with AI offerings?

Salesforce, Tealium, Treasure Data, Adobe, Twilio Segment, and Informatica are some of the leading CDP vendors that offer AI capabilities for analytics, reporting, and predictive insights.

CDP and generative AI create a formidable combination to solve complex IT and marketing challenges. These are related to:

• Automated issue resolution
• Dynamic content creation for emails, website landing pages, surveys, product recommendations, and chat support
• Data cleanup
• Error identification and clean-up
• Co-programming for no-code self-service CX campaigns
• Customer segmentation
• Anomaly detection
• Data Enrichment
• Cybersecurity and privacy-related threat management

What are the top CDP use cases in 2023?

84% of marketers are putting significant effort into analyzing first-party data in 2023. This marks the origin of new use cases in the CDP marketplace. The top CDP use cases in 2023 are:

1. Unified Customer Data Analytics
2. Hyper-personalization
3. Customer Journey Mapping
4. Multichannel Messaging
5. Data Privacy and Governance (GDPR, CCPA, CA Delete Act, etc)
6. Generative AI / LLM Development

What are the top CDP vendor selection factors?

The top five factors while selecting a CDP vendor are:

1. Ease of use
2. History of successful CDP implementation/ user reviews
3. Single view of the customer
4. Meeting real-time data transfer requirements
5. Marketplace integrations
6. Customer service and support
7. Global locations and teams
8. Data privacy and compliance
9. Trust in CDP partnership

What kind of data does a CDP analyze for CX?

A CDP brings together first -, second – and third–party data together in one place. Modern CDPs also unify zero-party data to orchestrate hyper-personalized customer experiences in the B2B and B2C markets. A CDP gathers data from diverse data sources. These include ERPs, HRIS, e-commerce, CMS, CRMs, mobile websites, social media, apps, payments, and marketing automation platforms.

This is a useful reference related to the different types of data unified by a CDP. (source: Treasure Data)

Is a CDP the same as a Customer Engagement Platform (CEP)?

No, a CDP is different from a CEP.

A CDP enriches customer data and pushes the unique customer profiles into a CEP. The CEP uses the CDP-generated customer personas in different channels for orchestrating omnichannel CX campaigns. A CDP can also extract data from a CEP to build segmentations used for the creation of a unified customer profile. CDPs and CEPs can work in sync to enhance conversation intelligence and improve conversion rates.

What are the top CDP integrations that Martech users should have in 2024?

The top CDP vendors who offer AI-powered capabilities provide hundreds of customizable integrations to drive better CX. This is a useful directory of all CDP-related integrations that a martech team could refer to.

What are the key CDP marketplace trends for 2024?

CDP users are more likely to meet their marketing and customer experience objectives as compared to non-CDP martech users. CDP users are also 2x times more satisfied with their marketing outcomes as compared to the non-users.

Who owns the success of CDP initiatives?

IT teams are entrusted with the CDP deployments, and therefore, the CIO and the CTO share the primary responsibility for CDP’s success. With composable CDPs, the CDPs have become a shared resource across IT organizations. Therefore, the responsibility gets distributed among the CIO, CTO, CISO, CMO, and Chief Experience Officer. According to Tealium, 36% of CDP teams in 2023 consist of 6-10 people; 45% of CDP teams have 11+ people in their organization for the effective management of CDP.

So, riding on the generative AI hype, do you have a new definition for CDP in 2024?

[To share your insights with us, please write to sghosh@martechseries.com]

The post Riding on the Generative AI Hype, CDP Needs a New Definition in 2024 appeared first on AiThority.

Utilizing anonymized electronic medical record data to contribute to drug development and medical support

Fujitsu Limited and TOPPAN Holdings Inc. concluded a business alliance agreement to jointly promote the medical big data business with the aim of promoting research and development utilizing medical big data under Japan’s Next Generation Medical Infrastructure Act and realizing a healthy and long-lived society by creating new industries and businesses.

By combining TOPPAN Holdings’ analytical technology with Fujitsu’s data cleansing technology in an anonymously processed electronic medical record database, the alliance will promote the analysis of medical big data with higher precision and the provision of services that support research and development in medical care. In the future, the companies will jointly promote the use of a variety medical and health data for its customers in Japan, including anonymously processed electronic medical record data.

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Background

TOPPAN Holdings has set “Digital & Sustainable Transformation” as its key concept, and one of its new business areas is the healthcare business. Among other initiatives, TOPPAN Holdings is driving digital transformation (DX) to promote the medical big data business with an analysis service based on anonymously processed electronic medical record data held by the Japan Medical Association Medical Information Management Organization (J-MIMO), a certified producer of anonymously processed medical information under the Next Generation Medical Infrastructure Act.

Under Fujitsu Uvance, which aims to realize a sustainable world, Fujitsu promotes “Healthy Living” focusing on improving the life experience of all people. To this end, Fujitsu has been advancing the utilization of data in the medical field by leveraging the code and master management methods of electronic medical record data, data cleansing technology cultivated in the electronic medical record system business, for which it has the top market share in Japan. Fujitsu also draws on its AI technologies including medical data analysis technology, unique expressions and relation extraction.

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Partnership Overview

Through this partnership, the companies aim to increase the volume of data including anonymized electronic medical records used for data analysis, and to link the two companies’ expertise in the use of medical data with their data platforms to provide more advanced analysis services for pharmaceutical companies and medical institutions.

Development and provision of data analysis services TOPPAN Holdings will introduce electronic medical record data anonymously processed by J-MIMO into its medical information analysis and provision service, “DATuM IDEA®”. Fujitsu’s data cleansing platform helps to structure each hospital’s medical data in a different data format. This will enable highly accurate analysis, accelerate drug safety and efficacy evaluation, and contribute to more effective and efficient drug development and the realization of personalized medicine using databases. Fujitsu will develop SaaS analytics services for pharmaceutical companies and medical institutions using anonymized electronic medical record data. Aiming to improve the efficiency of the drug development process and improve the quality of medical care, Fujitsu will develop services such as medical data visualization and ad hoc analysis, promoting the use of medical AI models such as prediction and foreshadowing in research and development. Increase in electronic medical record data in data analysis Together with medical institutions and J-MIMO, the companies will collect previously unused data items, such as discharge summaries, nursing records, and surgical records, in addition to medically relevant information stored in electronic records, to increase the data used for analysis.

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[To share your insights with us, please write to sghosh@martechseries.com]

The post Fujitsu and TOPPAN Holdings Collaborate to Expand Medical Big Data Business appeared first on AiThority.

Maintaining seamless operations and delivering exceptional customer experiences are critical to any business. Yet, traditional IT incident management approaches often struggle to keep up with increasingly complex IT infrastructures. As the limitations of traditional, manual methods become more glaring, organizations need to consider how trusted AI-powered, intelligent automation can help them stay more competitive and better ensure uninterrupted business operations.

IT Operations and DevOps teams rely on incident management to respond to and resolve any number of unplanned events, from the mundane (Wi-Fi connectivity issues) to the profound (network downtime and cybersecurity attacks). Traditional IT incident management tools typically have manual processes to identify these incidents and react only after they have occurred, as well as to log and classify, contain, diagnose, remedy, and review them. As IT environments become larger and more complex, conventional approaches to incident management become untenable.

Imagine that a global business might have 10,000 servers across dozens of countries in addition to the networking devices connecting them, thousands of apps, databases, and other assets.  Manual processes are difficult to scale, often leaving teams overwhelmed and leading to delays in identification, prioritization, and resolution of incidents. What follows is significant downtime and disruptions to critical business processes, which can lead to revenue loss, customer dissatisfaction, and tarnished brand reputation.

Traditional incident management approaches are often also hindered by siloed data scattered across various systems and teams. That makes it challenging to gain comprehensive insights into the underlying causes of incidents and inhibits collaborate with other business units, slowing down incident resolution and increasing Mean Time to Repair (MTTR). Moreover, conventional approaches to managing incidents tend to rely on historical data and predefined thresholds, requiring manual inputs in order to detect and address issues. As a result, they lack the capability to anticipate and proactively prevent incidents before they impact the business. This reactive nature prevents organizations from achieving true operational resiliency.

AI Automates and Improves Incident Management

As IT organizations evolve, so too must their approach to incident management for the modern era of digital business. Adopting AI-powered incident management technology can allow IT organizations to:

Address Issues Proactively.

AI-based intelligent automation can detect anomalies, predict potential issues, and take proactive measures to prevent incidents before they impact the business. With the power of AI, the technology can provide valuable information that will help identify and solve future issues, potentially uncovering answers to questions that were never thought to be asked. Proactive maintenance or capacity adjustments also help enhance operational efficiency by reducing the likelihood of incidents occurring in the first place. This fundamental shift from reactive to proactive incident management enables organizations to maximize uptime, reduce revenue loss, enhance customer satisfaction, and safeguard their brand reputation.

See a Holistic Data View.

To combat siloed data and inefficient processes, companies can use AI and automation to correlate data from various sources and provide a comprehensive view of the entire IT landscape. When IT operations teams can identify the underlying causes of incidents quickly and collaborate effectively with other business units, they are able to expedite incident resolution. The result: a reduction in the Mean Time to Repair (MTTR) and improved overall operational efficiency.

Scale for Business Transformation.

It’s important for IT teams to have a platform that can handle the increasing volume and variety of incidents found in modern IT infrastructures. Intelligent automation makes it possible to analyze vast amounts of data in real-time. This scalability ensures that organizations can more effectively manage incidents, regardless of the size and complexity of their IT environment. By leveraging advanced analytics and machine learning algorithms, the platform can analyze vast amounts of data from disparate sources, enabling IT operations teams to gain more actionable insights and make data-driven decisions across incredibly complex environments.

Electrolux AB, for example, manages a large, global infrastructure of servers, devices, apps, and databases. The Sweden-based appliance manufacturer is turning to AI to automate a menial task that consumes 1,000 hours a year of employee time. Not only can the company recoup much of that time, but its employees’ expertise can be applied to more valuable, higher-level tasks, such as identifying new correlation criteria that can be fed to the AI system, or refining rules and actions based on local conditions. In this way, automation saves time that can be reallocated to improving the automation.

The need to better ensure uninterrupted business operations and maintain a competitive edge in the digital landscape demands a shift toward AI-based intelligent automation solutions. AI and AIOps solutions are fundamentally changing how IT professionals manage increasingly complex environments. By leveraging advanced AI and automation capabilities, organizations can better proactively detect, prevent, and resolve incidents, and position themselves for success.

Stay ahead of the competition, embrace the future of incident management and remember: If you’re reacting, that means the system is already broken.

The post AI Shifts Incident Management From Reactive to Proactive appeared first on AiThority.

Customer Data Platforms (CDPs) have been a game changer in delivering new levels of insight into consumer behavior. But with such a range of information on CDPs, it can be hard to sift through the useful and the not so useful. So, let’s cut straight to the point.

What do CDPs actually do? 

CDPs, in a nutshell, are built to serve three main purposes.

Firstly, they collect and unify all of a company’s customer data across its touchpoints — think website activity, emails, apps and social media, and even legacy systems, like CRMs, which keep data in their own silos — into one platform. This creates a unified 360 profile and single source of truth. 

Secondly, they manage this customer data in compliance with data privacy regulations, essentially controlling data consent, how it is used and who can see what in the company. And then, finally, they make this data actionable. By having data merged into a single profile for each customer, these profiles are then structured into audience segments based on behavior. They can then be used by platforms across the company.

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But the arrival of second generation CDPs (and continued advancements) are bringing real-time upgrades and benefits.

So, here we remodel and break down these purposes to offer the ABC of CDPs:

The A: Accessibility 

Whatever the sector, having easy-to-access data from a central location has become a vital tool. Yet the majority of systems employed by marketers, such as email or customer relationship management, operate independently. Crucially, this means that data is not shared across these platforms and marketers are left with only a few pieces of the jigsaw.  

If you can only access data for a specific group or touchpoint, for example, then you can strategize well for that area, but you are missing out on a much wider picture. Likewise, if you can only access data on general consumer trends, you are missing out on individual detail needed for personalization.

What if you could access both examples and more in one database? 

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A CDP acts as a link between fragmented sources and becomes a single source of truth. The platform is accessible across marketing’s various departments, such as sales, loyalty, customer service and social media. Through the CDP, it is possible to acquire consumer data from each touchpoint, merge this data and then unify it into digestible customer profiles. By doing this, it becomes a hub of data for both micro trends from specific touchpoints and macro trends across several touchpoints. 

But of course not everything can be seen by everyone; accessibility needs to be joined with compliant protocols. The CDP can be set up in line with company and data privacy regulations to ensure that only those who are allowed to view certain pieces of data are allowed to do so, safeguarding any sensitive and personal information. This is ‘compliant accessibility’.  

The B: Behavior 

The impending end of third-party cookies has sparked a new focus on different forms of data, and most notably, on zero-party data.

Zero-party data comes straight from the horse’s mouth. Making the most of devices such as surveys, pop-ups and preference centers, brands are able to hear first-hand what types of information the customer wishes to receive. This enables marketers to deliver a far more personalized and emotionally resonant customer experience. 

Then there is first-party data. While zero-party data is voluntarily shared by customers, first-party data is able to track website, apps and social media activity, showing valuable metrics such as what products customers are looking at and for how long. Powered by AI, these CDPs can collect, manage and use all of this zero and first-party data together to create customer profiles, allowing brands to understand consumer behavior on an individual and audience level. 

And it doesn’t stop there. Through AI, CDPs allow you to react in real-time to customers using your website. Taking advantage of real-time personalization and AI, for instance, you can remind customers of their ‘watched products’ and offer AI recommendations for similar items as they browse your website. 

Finally, with all this knowledge, AI is able to predict the optimum next best move that is most likely to land with a customer, increase engagement and forecast chances of conversion or churn as a result. The more you are able to deliver a personalized 1-to-1 experience for the customer, the more you can build trust and loyalty. 

The C: Connectivity 

For both A and B to work to their full effect, they need C. Connectivity takes into account the overall omnichannel experience, linking touchpoints, departments and products with customers. There is, firstly, the connectivity of customer touchpoints. Customers interact with brands through their website, emails, social media and in store. And then, secondly, as previously mentioned, there is connectivity between departments, such as sales, marketing and customer service.

This connectivity is made possible through application programming interfaces (APIs), which essentially allow information and data to be shared between different devices and apps. Using APIs, brands can connect all of their apps and platforms with each other seamlessly and process this customer data into a CDP. 

If departments can access data from other areas of the business, then new insights may emerge that had previously been missed. Rather than treating departments individually with their own goals and targets, a CDP helps you to create a more collaborative strategy, seeing a wider picture of how departments can complement each other. With this approach, customers can then enjoy a connected experience with all the various touchpoints they interact with. It’s a process that benefits both parties. 

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It’s easy as ABC 

The more Accessibility you have to data, the better you can understand the overall customer experience. And, to create a truly personal experience, you need to acquire a deep understanding of customers’ Behavior, enabling you to plan for, react to and predict trends. Finally, this all comes together through Connectivity, joining up the experience for both marketers and customers to strengthen the relationship and deliver new levels of customer intimacy. 

With a CDP, marketing really can be as easy as ABC. 

The post Understanding The ABC of Customer Data Platforms (CDPs) appeared first on AiThority.

Artificial intelligence has penetrated almost every sphere of life, from education to medicine, from art to entertainment, from customer service to streaming services suggestions. Data collection is no exception.

Today, any business, big or small, relies on data when making decisions, be it the perfect time for posting on social media or pricing strategies. Naturally, various means of gathering useful data have emerged in the last decade, web scraping is one of them. 

Web scraping is a popular method of gathering business intelligence, and now, enhanced by AI, it’s becoming more appealing to various companies.

What Is Web Scraping?

The term “web scraping” refers to the process of harvesting data from websites. In the past, it could be executed even manually, but today businesses prefer to turn to software that can do the job. 

Mostly, data extraction happens through proxies, and the results are converted into an appropriate format for data storage systems. This process has been around for years, and it helps businesses gather information on their competitors, customers, and market trends. The practice is especially popular in the e-commerce sector, but it’s widely applied within other sectors as well. 

Web scraping allows companies to figure out pricing, perfect product offerings, and many other crucial business-related questions. Also, a business can use web scraping to analyze the social media strategies of its competitors and determine their weak spots.

However, web scraping is not an easy process, bringing numerous challenges to those who use it. The internet is a vast ocean of data, and some websites are built with enormous complexity. These and other obstacles make web scraping a demanding task. That’s why it benefits from AI automation.  

How AI Improves Web Scraping

The current wave of hype around artificial intelligence is closely connected to Natural Language Processing (NLP). NLP is a rapidly growing field of AI that focuses on the interaction between human language and computers. It involves teaching machines to understand, interpret, and respond to human language in a way that is similar to how humans communicate with each other. NLP has many applications, including chatbots (e.g. ChatGPT), text-to-image processing (e.g. Midjourney), and web scraping.

Since websites are created for humans and by humans, NLP is a highly efficient tool to automate web scraping because it can perceive the web similar to humans. It means that AI is able to understand and read correctly even complex layouts that other purely technological web scraping instruments can’t efficiently decode without the interference of a human. 

AI can recognize patterns and structures in web pages, quickly identify the relevant data and extract it in a structured format. Besides, websites are constantly updated, and AI algorithms can quickly adapt to new layouts and continue to collect data accurately. 

For example, an e-commerce company that wants to monitor its competitors’ prices can use NLP to extract data from their websites more accurately. Instead of relying on predefined rules to identify the price on a product page, the company can teach its web scraping tool to understand the context of the price, such as whether it is a sale price or a regular price, and extract the information accordingly.

How It Benefits Business

At Infatica, we are constantly working with ethical proxies and web scraping. Naturally, as we are a tech company, we are also excited about AI because it is another step toward improving the quality of web scraping. The latest impressive advancements of NLP and its growing availability for commercial use make it more accessible to businesses of all sizes.

AI web scraping tools can be scaled up or down based on the business’s needs. This means that businesses can extract data from a few websites or thousands of websites, depending on their requirements. This scalability can help businesses stay flexible and adapt to changing market conditions.

AI is currently revolutionizing numerous business processes. As for data collection, it helps businesses to extract useful data from websites quickly, accurately, and reliably, which improves their decision-making capabilities. Of course, it still has its challenges, and you need to ensure that the technology is used ethically and legally. Overall, AI-powered web scraping is a powerful tool that can help your company to stay ahead of its competitors.

The post The Rise of AI in Data Collection: Implications and Opportunities for Businesses appeared first on AiThority.

AI and Big Data Expo Europe is taking place on September 26-27, 2023, at RAI, Amsterdam. The event brings together industry-leading technology companies, giving attendees the opportunity to discover the newest applications of AI technology. With over 6,000 AI enthusiasts expected, the event is sure to be one of the largest Artificial Intelligence expos in the world. Previous editions of the event were attended by the representatives of such companies as – AWS, Deloitte, Phillips, HSBC, Nestlé, Levi Strauss, Danone, just to mention a few!

AiThority.com is a media partner for this event. Check our events calendar here: https://itechseries.com/events/

Get ready to experience a game-changing moment that will ignite your passion for Artificial Intelligence. AI and Big Data Expo Europe has just announced the latest additions to their line-up of speakers, and we guarantee you won’t want to miss out on their life-changing insights!

Newly announced speakers include: 

• Irakli Beridze, Head of the Centre for Artificial Intelligence and Robotics – UNICRI, United Nations​
• Sanchit Juneja, Director – Product (Data Science & Machine Learning Platform) – Booking.com
• Marloes Pomp, Coordinator International Network ELSA labs – Netherlands AI Coalition
• Joris Krijger, AI & Ethics Specialist – de Volksbank
• Haider Alleg, General Partner – Allegory Capital
• Oliviana Bailey, Women in AI Ambassador in the Netherlands – Women in AI

The AI and Big Data Expo offers a variety of presentations and panel discussions, led by industry experts and thought leaders. The sessions will cover a range of topics, from Enterprise AI, Machine Learning, Security, Ethical AI, Deep Learning, Data Ecosystems, and NLP. Attendees will gain valuable insights and practical knowledge that they can apply to their own businesses. 

In addition to all the exciting presentations, the AI and Big Data Expo will feature an impressive line-up of exhibitors showcasing the latest advancements in technology, healthcare, finance, manufacturing, and more. Attendees will have the opportunity to explore the latest AI products and solutions, network with industry professionals, and gain valuable insights into emerging trends. 

The event’s official networking party will take place at the Strandzuid Boathouse, next to the RAI, Amsterdam! The networking party will allow Gold and Ultimate pass holders to share the experiences of the day and will provide the opportunity to meet with existing and new business partners in a more relaxed setting, with free food and drinks provided.

Register now for the AI and Big Data Expo and be inspired by these incredible speakers who are changing the game in the world of AI.  

The post AIThority Events Coverage: AI and Big Data Expo Europe Announces New Speakers appeared first on AiThority.

VCI Global Limited announced that it has entered into discussions with the Fusionex Group (“Fusionex”) to establish a mutually beneficial exclusive collaboration between both parties.

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This partnership envisages VCI Global providing AI and big data consulting services to its clients. With a primary focus on Southeast Asia, while keeping opportunities open worldwide, VCI Global has selected Fusionex as its technology partner. The global AI market, estimated to be valued between USD110 billion and USD120 billion in 2022, presents immense potential for growth. Furthermore, industry projections indicate that the Asia Pacific market is poised to grow at a compound average rate of 35% to 40% from 2022 to 2030.

Leveraging Fusionex’s Analytics GIANT solution, VCI Global aims to deliver value and strategic guidance to its clientele. With its expertise in analytics, Big Data, machine learning, and AI, the multi-award-winning data technology market leader, Fusionex, empowers its clients in managing invaluable insights from the vast amounts of data available.

“Economies are frantically bridging gaps between businesses and the fast-evolving technologies such as artificial intelligence, that are made available to markets. This collaboration is thus ideal as whilst tapping on Fusionex’s expertise, we subsequently are able to offer our consulting expertise to our vast network of members,” said Dato’ Victor Hoo, Group Executive Chairman and Chief Executive Officer of VCI Global.

“AI represents the most revolutionary and promising field of technology, playing a vital role in the development of all sectors and industries. The transformative power of AI promises to usher in a new era of efficiency, automation, productivity, strategic planning, and decision-making, unlocking unprecedented opportunities for growth and innovation in these areas. The partnership with VCI Global serves as an impetus to augment their offerings with our technology solutions and expertise,” said Jacob Isaac, Managing Director (New Technologies) of Fusionex.

VCI Global is a multi-disciplinary consulting group with key advisory practices in the areas of business and technology. The Company provides business and boardroom strategy services, investor relation services, and technology consultancy services. Its clients range from small-medium enterprises and government-linked agencies to publicly traded companies across a broad array of industries. VCI Global operates solely in Malaysia, with clients predominantly from Malaysia, but also serves some clients from China, Singapore, and the US.

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Fusionex is an established multi-award-winning data technology leader specializing in Analytics, Big Data Management, IR 4.0, Internet of Things, Machine Learning and Artificial Intelligence. Its state-of-the-art offerings are focused on helping clients unlock value and derive insights from data.

Featured on Forbes, Bloomberg, Gartner, Frost & Sullivan, IDC, Forrester, Edison, and Huffington Post, Fusionex is one of the largest Big Data Analytics company and market leader in ASEAN, bringing state-of-the-art, innovative, and breakthrough data-driven platforms to its stable of clientele (including Fortune 500, FTSE companies, large conglomerates, as well as a wide array of small and medium enterprises [SMEs]) that spans across the United States, Europe, as well as Asia Pacific. Fusionex is also a MDEC Global Acceleration and Innovation Network (GAIN) company.

Gartner’s report on Modern Analytics and Business Intelligence shortlisted and commended Fusionex’s data technology platform. In addition, Fusionex has been identified as a Major Player in IDC’s MarketScape Report for Big Data & Analytics. Fusionex is the only ASEAN-based company to be featured in both reports, cementing its credentials in the data technology market for this region.

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 [To share your insights with us, please write to sghosh@martechseries.com] 

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