#data-integration

#data-integration

A MIT study last year showed that 95 percent of generative AI implementations fail. They generate little to no business value. There can be various reasons for this. Think of poor integration, little focus on solving real problems, and unrealistic expectations. As far as Appian CEO Matt Calkins is concerned, this is an absurd percentage for something that is seen as the breakthrough technology of this generation.

Over the past decade, marketers have invested in dozens of creative data solutions to measure attention, emotion, distinctiveness, brand recall, and more. Yet these data sources remain disconnected, with brands switching between dashboards to gather creative intelligence. This means valuable insights can't be scaled, merged, or operationalised fast enough for modern marketing.

With that dubious success rate in mind, CPG advertiser Reckitt has taken a strict approach to its own generative AI marketing projects, piloting specific use cases designed to save its staffers time and stress before rolling them out to its 700-strong marketing organization. The approach - which Bastien Parizot, Reckitt's svp of IT and digital calls "functional reinvention" of the marketing workflow - has delivered, speeding up creative asset adaptation by 30%.

Salesforce has completed its acquisition of Informatica. The CRM provider paid more than $8 billion (€6.9 billion) for the data management company. The acquisition is intended to lay the foundation for reliable AI agents within the Salesforce ecosystem. The completion brings Informatica's data catalog, integration tools, and governance services to the Salesforce platform. CEO Marc Benioff emphasizes the importance of this step: "You have to get your data right to get your AI right. Data and context is the true fuel of Agentforce."

Misaligned teams. Sales and marketing often operate with different goals. Lead generation metrics don't translate easily into ABM performance. Old habits die hard. Existing processes, tech stacks, and KPIs are built around leads, not buying committees. Personalization takes work. ABM requires deeper insights, custom content, and coordinated outreach. That's resource-intensive. Data gaps. Poor data quality, disconnected systems, and lack of visibility into key accounts make targeting a guessing game. ROI anxiety. ABM doesn't always deliver quick wins.

APIs, or application programming interfaces, started out as a mechanism to let computers talk to other computers, but somewhere along the way, they've evolved into an ecosystem all their own. For virtually any development need, there is likely an API ready and waiting to deliver. Like the Lincoln Logs or Lego bricks of old, APIs are building blocks for creating applications.

Wang and his colleagues built a tool called Funding the Frontier, which integrates data on research publications, patents, policy papers and clinical trials, and presents the information in a visually intuitive way. They also combined the tool with a machine-learning-driven predictive algorithm to forecast which studies and fields are likely to lead to the most societal benefits in the future - for example, which grants are most likely to result in a patent.

The ever-evolving nature of IT operations and the growing complexity of modern technology environments have clearly spelled the need for AIOps tools. AIOps, or Artificial Intelligence for IT Operations, uses artificial intelligence (AI) and machine learning (ML) technologies to enhance and automate various IT operation tasks. AIOps platforms are designed to analyze and interpret data generated from various IT operations tools and platforms, providing service assurance and insights, automating routine tasks, and helping organizations detect and resolve issues more efficiently.

What we recognized was, buying a home is a really complex process, and there are a lot of documents and requirements, and just one of those was verification of insurance. As it exists today, that process is complicated, so we saw an opportunity to say, how can we take that piece of it and just simplify it? Tuttle shared that HOIVerify can take a process that once took several days and perform it in seven seconds.

Marketers today aren't just swimming in data - they're besieged by it. Every campaign, click and customer touchpoint continuously generates insight. But this data only matters when it's activated. That requires systems to ingest, analyze, orchestrate and act on it successfully. Far too often, data remains siloed, fragmented or trapped in disconnected tools, leaving businesses with noise instead of clarity.

Gotham is an investigative platform built for police, national security agencies, public health departments, and other state clients. Its purpose is deceptively simple: take whatever data an agency already has, break it down into its smallest components, and then connect the dots. Gotham is not simply a database. It takes fragmented data, scattered across various agencies and stored in different formats, and transforms it into a unified, searchable web.

Not long ago, building an app meant endless spreadsheets, coding, and clunky tools. Today, 70% of new apps are expected to be built with low-code or no-code tools-nearly triple the 2020 rate. Modern AI platforms allow anyone, coder or not, to create powerful automated workflows. But with so many options, finding the right fit isn't simple. You need a tool that matches your team's skills, integrates with your stack, and delivers real value.

This introduces latency, complexity and what we call 'bloat,' explains Chad Meley, SVP of Marketing at StarTree. We're collapsing that serving and query layer into one piece of the puzzle, significantly reducing the bloat and simplifying that architecture.

Blockchain technology enables companies to create a trusted architecture for sharing data across networks securely and reliably, thereby addressing master data issues more effectively.

A metadata-driven ETL framework enhances scalability and security in integrating various data sources through Azure Data Factory, reducing manual rework.

The crossover [of] what's performing at those two ends of the spectrum, paid and organic - that's where you get the clearest and most interesting behavioral insights from your customer.

DuckDB provides a powerful, seamless way to manage large datasets in Python, utilizing OLAP optimization for enhanced data handling and query capabilities.

MCP servers enhance marketing workflows by integrating external tools and data sources, automating tasks, and providing real-time insights for personalized customer experiences.

Relay42 helps businesses to turn siloed customer data into personalized customer journeys that drive business growth, enhancing Relay42 with Supermetrics' rich data integrations.

In today's marketing landscape, the challenge lies in overcoming siloed tools and fragmented data that hinder personalized and efficient campaigns while maximizing ROI.

B2B marketers face challenges when transitioning to an account-based marketing (ABM) approach, primarily due to CRM limitations, data integration issues, complex decision-making units, and measurement challenges.

The Enterprise Data Integration Platform on AWS Cloud has transformed organizations' capacity to leverage information assets despite challenges of fragmented data ecosystems.

The platform integrates data from electronic lab notebooks, allowing chemists to quickly develop test formulations for cosmetics using AI to analyze molecular interactions.

The realities of integration often involve ad hoc scripts, scheduled exports, and messy data joins, as analysts create systems that deliver visibility without waiting for perfection.

BayesPPDSurv allows the integration of historical data using both fixed and random power prior models, enhancing design efficacy in clinical trials involving survival data.