Many organizations struggle to turn their data into actionable insights. Often, the data is scattered, unorganized, or not fully leveraged to support decision-making. TXI bridges this gap by creating a cohesive, scalable system that integrates your data from multiple sources, turning it into a powerful tool for your business. Our approach empowers teams with the high-quality data and insights they need to make smarter, more impactful decisions.
Purpose
Turning data into value by amplifying human expertise
Our goal
Help 1,000,000 people harness the power of their data by 2034
The most successful digital transformations don’t just implement technology — they amplify operational expertise. We build data products that enhance human wisdom rather than replace it. Unlock the true business value of your information by using it to amplify people’s impact.
The lost knowledge problem
Data transformation failures
Digital transformations often look great as plans on paper but fail in execution. Our research reveals a consistent “knowledge-gap failure mode": when organizations don’t prioritize knowledge over data, transformational outcomes are not realized. From food manufacturers whose modernized facilities can’t match original quality, healthcare systems that disrupt doctor-patient relationships, or manufacturing upgrades that lose decades of expertise in the name of progress—the pattern is seen across every sector.
This "Lost Knowledge Problem" occurs when organizations prioritize technology over human wisdom. Our approach offers an antidote: rather than attempting to replace expertise—the data products we design—capture, enhance, and scale expertise through thoughtfully crafted software solutions.
Our approach
Turn data into a powerful tool for your business
Data strategy
We align your data initiatives with your business goals and product strategy, ensuring seamless integration to drive growth and informed decision-making.
Data governance
We establish clear rules, policies, and quality controls for data collection and storage, optimizing data quality and usage to ensure consistency and reliability across your operations.
Data visualization
We explore and uncover patterns within large data sets, turning them into actionable insights to fuel strategic decisions.
Data integration
We integrate data assets from diverse sources into a streamlined, scalable framework that supports growth and operational efficiency.
Data engineering
We design and build robust data management pipelines and infrastructure to seamlessly support and enhance your data-driven functions.
Data science
We leverage artificial intelligence and data analytics to conduct in-depth analyses and build predictive models to generate actionable insights that drive smarter decisions.
The DIKW difference
Data products through the human lens
While others chase technological sophistication, our research reveals successful data transformation isn't about technology adoption—it's about operational impact. This insight shapes the distinctive data products we create at each stage of the journey.
Data: capturing what matters
We create products and systems that capture "dark data" – information such as institutional knowledge, overlooked internal sources, and IoT and sensor data. By capturing what matters, we can uncover useful insights that standard methods often miss.
Our strategists and engineers ensure your data is captured, structured, and primed for insightful analysis. These intuitive applications centralize user-generated data, making it not just accessible, but truly actionable.
Examples:
Personalized dashboards
Customized notifications and alerts
Automated reports that regularly generate and distribute key metrics
Information: contextualizing data for operational reality
We don't just provide data. We provide information and recommendations to optimize human decision-making.
Data products that deliver information create meaningful connections between otherwise isolated information. Your teams will be able to track vital operational indicators like quality control and production efficiency in real-time. From monitoring supply chain performance to predicting maintenance needs, these tools provide the visibility and insights needed to make proactive, data-driven decisions.
Examples:
Simplified and easy-to-understand insights, with data gathered from multiple sources
Data-driven recommendations based on gathered, structured data
Clear models and rules for better decision-making.
Knowledge: preserving the human element
We create knowledge management systems and tools, deliberately integrating expertise, experiential insights, and tacit knowledge with hard data.
Knowledge products go beyond "what happened" to help answer "why it happened" and predict "what might happen next." These products use data analysis, statistical modeling, and basic machine learning to uncover correlations, find insights, or predict future trends.
Examples:
Predictive models to anticipate future customer churn and sales
Machine failure forecasting using telematics for preventative maintenance
Big data tools to analyze product feedback and sales trends
Wisdom: balancing automation with judgment
We build predictive applications and decision support tools enhancing human judgment rather than replacing it.
Our approach to automation respects the continuing role of human oversight, creating systems that make recommendations operators and managers can evaluate through the lens of their experience—from maintenance alerts to quality forecasts to resource optimization.
Examples:
Systems that enhance strategic decision-making
AI-driven insights refined by human expertise
Context-aware recommendations for complex scenarios
Automation that adapts to evolving business needs
Decision frameworks integrating intuition and data
The A-squad
Integrated teams who deliver for you
Industry transformation requires people who understand the impact of data in unique operational environments. Unlike consultancies that assign junior teams supervised by senior advisors, we deploy the “A-squad"—senior practitioners averaging 17 years of experience who engage with your experts as peers to design, build, and implement data products to solve real business problems.
Our integrated teams of strategists, designers, and software engineers bring their human perspective—ensuring your data products aren't just technically sound but culturally aligned with how your organization works—delivering measurable outcomes your business cares most about.
Industry experts × digital innovators
Getting started
Our research shows that success rates rise dramatically when industry experts who understand transformation challenges are involved. That’s why we integrate your experienced practitioners with our digital innovators, creating a unique shared perspective. This ensures the data products we deliver respect operational expertise while unlocking new technological possibilities.
With your domain expertise and our digital innovation, we deliver value in just 2–3 weeks. We assess your unique challenges and develop custom data solutions that address immediate needs while laying the foundation for long-term success—whether through integration platforms, visualization tools, predictive systems, or automation frameworks.
Understanding data value
Insights & FAQS
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Why is data value important for businesses?
Data value is crucial for businesses as it drives growth, efficiency, and competitive advantage. Leveraging data effectively enables:
New revenue streams – Identifying opportunities for monetization, optimizing pricing, and uncovering untapped markets.
Timeliness – Making faster, data-driven decisions that improve responsiveness to market changes and operational challenges.
New products & services – Innovating based on customer insights and market trends to develop offerings that meet emerging needs.
Enhanced customer experiences – Personalizing interactions, improving satisfaction, and fostering loyalty through data-driven insights.
By harnessing data strategically, businesses can drive innovation, improve efficiency, and stay ahead in their industry.
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What are some data value examples?
Data value manifests in various ways across industries, driving efficiency, innovation, and revenue growth. Some key examples include:
Customer insights & personalization – Retailers use purchase history to recommend products, increasing sales and engagement.
Predictive maintenance – Manufacturers analyze machine sensor data to prevent breakdowns, reducing downtime and costs.
Dynamic pricing – Airlines and e-commerce platforms adjust prices in real-time based on demand and competitor trends.
Fraud detection – Banks use machine learning to identify suspicious transactions and prevent financial losses.
Operational efficiency – Logistics companies optimize routes and fleet management using real-time tracking and analytics.
New product development – Consumer brands analyze social media and feedback data to identify trends and create products customers want.
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How do you find data value?
Finding data value involves identifying, analyzing, and leveraging various types of data within a business's ecosystem. Here are key approaches:
Types of data – Businesses should assess both structured (e.g., transaction records) and unstructured data (e.g., social media interactions, customer feedback) to uncover insights.
Existing data – Often, valuable data already exists within an organization (e.g., CRM systems, operational logs) but may be underutilized. Cleaning, organizing, and analyzing this data can unlock significant value.
Ecosystem – Collaborating within the broader business ecosystem—suppliers, partners, and industry networks—can provide access to valuable external data sources that enhance decision-making.
Intangible assets – Data itself is often an intangible asset. Using analytics to monetize data insights, such as creating reports or predictive models, can turn this asset into tangible business value.
Data marketplaces – Exploring third-party data marketplaces offers access to external data sets that may complement internal data, helping businesses make more informed decisions or discover new revenue streams.
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How to measure data value?
Measuring data value involves assessing its potential to drive business outcomes and generate economic benefits. Here are key methods to evaluate data's value:
Data valuation – Establishing the monetary worth of data by analyzing its contribution to business performance, either through direct revenue generation or cost savings.
Methodologies – Common approaches include the Income Method (assessing future revenue potential), the Cost Method (calculating investment costs to acquire or create the data), and the Market Method (comparing data with similar assets in the market).
Data monetization – Determining how data can be directly monetized, whether through selling data to third parties, offering data-driven services, or licensing insights to other organizations.
Economic benefits – Quantifying data’s impact on reducing operational costs, increasing efficiency, improving decision-making, or driving revenue growth (e.g., through targeted marketing or process optimization).
Market value – Comparing data against industry benchmarks or evaluating it based on its ability to create competitive advantages, such as improving customer experiences or enabling faster innovation.
Ready to bridge your ambition-to-reality gap?
Join us in helping 1,000,000 people harness the value of data by 2034.
Let's create data products that transform your organization without losing what makes it competitive and human.