Wie lautet die Website?

https://www.striped-giraffe.com/en/

Striped Giraffe Innovation & Strategy GmbH, Lenbachplatz 3, München (2026)

20/10/2026

The data your organization has forgotten about may contain exactly the information a future business initiative needs. Consider what can happen when an old dataset suddenly becomes relevant ...

A manufacturer may have years of historical product, service, or customer data from a completed project. A new pricing initiative, product analysis, regulatory requirement, or AI application may later need that history.

The challenge is knowing what is actually there — and whether it can be trusted.

This is where a structured approach to dark data becomes essential.

👉 𝗦𝘁𝗮𝗿𝘁 𝘄𝗶𝘁𝗵 𝗺𝗲𝘁𝗮𝗱𝗮𝘁𝗮.
Identify what datasets contain, where they came from, when they were created, and how they relate to business processes.

👉 𝗧𝗿𝗮𝗰𝗲 𝘁𝗵𝗲 𝗹𝗶𝗻𝗲𝗮𝗴𝗲.
Understand how information was collected, transformed, combined, and used. This helps establish whether historical data can support a new business purpose.

👉 𝗔𝘀𝘀𝗲𝘀𝘀 𝗱𝗮𝘁𝗮 𝗾𝘂𝗮𝗹𝗶𝘁𝘆 𝗮𝗻𝗱 𝗿𝗶𝘀𝗸.
Determine what is reliable, what is sensitive, what is incomplete, and what requires remediation before it can be reused.

👉 𝗥𝗲𝘀𝘁𝗼𝗿𝗲 𝗼𝘄𝗻𝗲𝗿𝘀𝗵𝗶𝗽.
Someone needs to be accountable for the data and its future use.

👉 𝗧𝗵𝗲𝗻 𝗺𝗮𝗸𝗲 𝗮 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻.
Activate valuable data. Retain what may have future value. Archive what has limited operational relevance. Delete what has no justified purpose.

This turns dark data management from a storage exercise into a business capability.

The goal is to make forgotten information discoverable, understandable, trustworthy, and usable when the business needs it.

❓ 𝘏𝘰𝘸 𝘮𝘶𝘤𝘩 𝘷𝘢𝘭𝘶𝘢𝘣𝘭𝘦 𝘣𝘶𝘴𝘪𝘯𝘦𝘴𝘴 𝘩𝘪𝘴𝘵𝘰𝘳𝘺 𝘤𝘰𝘶𝘭𝘥 𝘺𝘰𝘶𝘳 𝘰𝘳𝘨𝘢𝘯𝘪𝘻𝘢𝘵𝘪𝘰𝘯 𝘳𝘦𝘤𝘰𝘷𝘦𝘳 𝘣𝘺 𝘢𝘱𝘱𝘭𝘺𝘪𝘯𝘨 𝘵𝘩𝘪𝘴 𝘢𝘱𝘱𝘳𝘰𝘢𝘤𝘩?

15/10/2026

Every patient tells a different story. Different clinicians may each hear only part of it. 𝗔𝗜 𝗰𝗮𝗻 𝗵𝗲𝗹𝗽 𝗰𝗼𝗻𝗻𝗲𝗰𝘁 𝘁𝗵𝗲 𝗽𝗶𝗲𝗰𝗲𝘀. 🧩🤖🧩

Clinical information is spread across consultations, referrals, test results, and years of medical records. A symptom may only become meaningful alongside an earlier diagnosis, a medication change, or a specialist’s note.

AI can help bring this information together and reduce the effort required to turn it into usable clinical documentation. Four applications illustrate the opportunity:

1. 𝗔𝗺𝗯𝗶𝗲𝗻𝘁 𝗰𝗹𝗶𝗻𝗶𝗰𝗮𝗹 𝗱𝗼𝗰𝘂𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻
With patient consent, AI can turn consultation conversations into structured draft notes for clinician review.

2. 𝗟𝗼𝗻𝗴𝗶𝘁𝘂𝗱𝗶𝗻𝗮𝗹 𝗽𝗮𝘁𝗶𝗲𝗻𝘁 𝗵𝗶𝘀𝘁𝗼𝗿𝘆
AI can summarize years of records into a patient timeline and surface information relevant to the current episode of care.

3. 𝗥𝗲𝗳𝗲𝗿𝗿𝗮𝗹 𝗮𝗻𝗱 𝗶𝗻𝘁𝗮𝗸𝗲 𝗽𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴
AI can extract key information from referral letters and attachments, helping teams prepare cases for the appropriate clinical workflow.

4. 𝗗𝗶𝘀𝗰𝗵𝗮𝗿𝗴𝗲 𝗱𝗼𝗰𝘂𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻
AI can consolidate notes, procedures, medication changes, and test results into a draft discharge summary for clinical review.

The potential is significant.

🤔 A US study estimated that office-based physicians spent 125 million hours documenting outside normal office hours in 2019. More recent observational studies have reported 15% less documentation time per consultation in an outpatient setting and 28% less on-shift documentation time per encounter in an emergency department.

These findings show promise, although less documentation time does not automatically translate into shorter appointments or more capacity.

Real value depends on reliable data, traceable sources, clinical review, and integration into everyday workflows. Connecting information is only useful if clinicians can trust and act on it.

𝘞𝘩𝘪𝘤𝘩 𝘰𝘧 𝘵𝘩𝘦𝘴𝘦 𝘸𝘰𝘳𝘬𝘧𝘭𝘰𝘸𝘴 𝘤𝘳𝘦𝘢𝘵𝘦𝘴 𝘵𝘩𝘦 𝘨𝘳𝘦𝘢𝘵𝘦𝘴𝘵 𝘥𝘰𝘤𝘶𝘮𝘦𝘯𝘵𝘢𝘵𝘪𝘰𝘯 𝘣𝘶𝘳𝘥𝘦𝘯 𝘪𝘯 𝘺𝘰𝘶𝘳 𝘰𝘳𝘨𝘢𝘯𝘪𝘻𝘢𝘵𝘪𝘰𝘯?

13/10/2026

Dark data may sit quietly in your systems, but its impact rarely does.
It surfaces when organizations spend time reconstructing missing business context, resolving conflicting interpretations, or working around information they struggle to access and use.

DATAVERSITY describes this as the “𝗗𝗮𝗿𝗸 𝗗𝗮𝘁𝗮 𝗧𝗮𝘅” — the hidden operational cost of losing track of enterprise data and the knowledge needed to use it.

Consider what this can look like in practice:

🔸 During a system migration, teams must reconstruct undocumented data relationships and transformation rules.
🔸 Finance and sales reconcile conflicting figures because reports use different definitions of the same business metric.
🔸 A department maintains its own spreadsheets and calculations because the data available through enterprise systems lacks the necessary business context.
🔸 A new integration project spends additional time tracing dependencies that were never properly documented or maintained.

These problems can persist even when data processing is automated. AI can accelerate discovery, comparison, and analysis, but business definitions, dependencies, and rules still need to be established and validated.

The cost accumulates through project delays, repeated investigations, and duplicated effort. Skilled employees spend time reconstructing knowledge that should already be available.

𝗥𝗲𝗱𝘂𝗰𝗶𝗻𝗴 𝘁𝗵𝗶𝘀 𝗗𝗮𝗿𝗸 𝗗𝗮𝘁𝗮 𝗧𝗮𝘅 means identifying recurring workarounds and addressing their causes through maintained data architecture, shared business definitions, and reliable documentation.

𝘏𝘰𝘸 𝘮𝘶𝘤𝘩 𝘵𝘪𝘮𝘦 𝘥𝘰𝘦𝘴 𝘺𝘰𝘶𝘳 𝘰𝘳𝘨𝘢𝘯𝘪𝘻𝘢𝘵𝘪𝘰𝘯 𝘴𝘱𝘦𝘯𝘥 𝘳𝘦𝘤𝘰𝘯𝘴𝘵𝘳𝘶𝘤𝘵𝘪𝘯𝘨 𝘬𝘯𝘰𝘸𝘭𝘦𝘥𝘨𝘦 𝘢𝘣𝘰𝘶𝘵 𝘥𝘢𝘵𝘢 𝘪𝘵 𝘢𝘭𝘳𝘦𝘢𝘥𝘺 𝘰𝘸𝘯𝘴?

12/10/2026

Digital commerce systems have been built around identifying the customer. Agentic commerce adds another participant as AI agents begin to transact for people and companies. The question “Who is the customer?” now comes with another: “𝗪𝗵𝗼 𝗶𝘀 𝘁𝗵𝗲 𝗮𝗴𝗲𝗻𝘁 𝗮𝗰𝘁𝗶𝗻𝗴 𝗼𝗻 𝘁𝗵𝗲𝗶𝗿 𝗯𝗲𝗵𝗮𝗹𝗳?” 🤖❓

And identity is only the beginning. Businesses also need to know 𝘄𝗵𝗮𝘁 𝘁𝗵𝗮𝘁 𝗮𝗴𝗲𝗻𝘁 𝗶𝘀 𝗮𝘂𝘁𝗵𝗼𝗿𝗶𝘇𝗲𝗱 𝘁𝗼 𝗱𝗼.

This becomes particularly important in B2B commerce.

Imagine a procurement agent monitoring inventory and demand. It detects a potential shortage, searches approved suppliers, checks contract terms and pricing, places an order, and potentially initiates payment.

For the supplier, verifying the agent’s identity answers only one question. The transaction also depends on whether the agent is authorized to buy that product, from that supplier, at that price and quantity, within a defined spending limit — and whether human approval is required above a certain threshold.

This is the emerging challenge of delegated authority.

𝗧𝗵𝗲 𝗶𝗻𝗱𝘂𝘀𝘁𝗿𝘆 𝗶𝘀 𝗮𝗹𝗿𝗲𝗮𝗱𝘆 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝘁𝗵𝗲 𝗶𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 𝗮𝗿𝗼𝘂𝗻𝗱 𝗶𝘁.

✅ Work underway within the Internet Engineering Task Force (IETF) community explores fine-grained agent authorization, user consent, constrained permissions, and multi-hop delegation.

✅ Visa describes agents being embedded in procurement systems and B2B marketplaces, with transactions operating within predefined parameters and auditable decision trails.

✅ Stripe’s Shared Payment Tokens provide a practical example of enforcing such boundaries in payments: an agent’s payment credential can be limited to a specific seller, amount, and time window.

✅ Mastercard adds another layer: verifiable intent. Its work with Google is designed to provide tamper-evident proof of what a user actually authorized, while its Agent Pay Trust Framework connects identity, intent, controls, trusted ex*****on, and intelligence.

The distinction matters:

𝗜𝗱𝗲𝗻𝘁𝗶𝘁𝘆: This is agent X.
𝗔𝘂𝘁𝗵𝗼𝗿𝗶𝘇𝗮𝘁𝗶𝗼𝗻: Agent X may purchase.
𝗩𝗲𝗿𝗶𝗳𝗶𝗮𝗯𝗹𝗲 𝗶𝗻𝘁𝗲𝗻𝘁: Agent X is authorized to make THIS purchase under THESE conditions.

As software gains the ability to transact on behalf of companies, B2B commerce needs to establish more than who is acting. It needs to establish the scope, conditions, and provenance of that authority — and preserve the evidence behind every transaction.

08/10/2026

Some of the biggest data risks in an organization have nothing to do with cyberattacks, system failures, or regulatory changes. They come from data that nobody actively owns. 𝗗𝗮𝗿𝗸 𝗱𝗮𝘁𝗮. 😬

Enterprise data often outlives the projects, teams, and business priorities that originally gave it purpose.

A project ends. A team is reorganized. An application is replaced. Responsibilities move elsewhere.

The data stays within the organization, while accountability and context can gradually disappear.

That creates an 𝗼𝘄𝗻𝗲𝗿𝘀𝗵𝗶𝗽 𝗴𝗮𝗽.

When nobody is clearly accountable for a dataset, data issues can remain unresolved. Business and technical teams may work with the same information while making different assumptions about its meaning, quality, or intended use.

Over time, the organization remembers less about why the data exists, how it was created, and whether it can still be trusted.

This is one of the ways 𝗱𝗮𝘁𝗮 𝗯𝗲𝗰𝗼𝗺𝗲𝘀 𝗱𝗮𝗿𝗸:
Information remains stored, while its context, ownership, and business value become increasingly difficult to establish.

The underlying challenge is organizational as much as technical. Data ownership needs to survive changes in projects, systems, teams, and business structures.

For organizations dealing with growing data estates, this creates a simple principle:
📌 Data ownership should outlive the project that created the data.

𝘏𝘰𝘸 𝘤𝘭𝘦𝘢𝘳𝘭𝘺 𝘪𝘴 𝘢𝘤𝘤𝘰𝘶𝘯𝘵𝘢𝘣𝘪𝘭𝘪𝘵𝘺 𝘧𝘰𝘳 𝘤𝘳𝘪𝘵𝘪𝘤𝘢𝘭 𝘥𝘢𝘵𝘢 𝘥𝘦𝘧𝘪𝘯𝘦𝘥 𝘢𝘤𝘳𝘰𝘴𝘴 𝘺𝘰𝘶𝘳 𝘰𝘳𝘨𝘢𝘯𝘪𝘻𝘢𝘵𝘪𝘰𝘯?

05/10/2026

Getting enterprise data ready for AI starts with understanding how AI will actually consume it.

A RAG-based copilot retrieving authoritative documents has different requirements from a forecasting model, recommendation engine, or agentic workflow. Each depends on different sources, semantics, quality levels, permissions, latency, and controls.

This is where AI data readiness moves from identifying problems to designing the environment around real AI workloads.

In the second article of our AI Data Readiness series, “Building an AI-Ready Data Environment,” we outline five capabilities that help enterprises supply AI systems with reliable context, governed knowledge, and production-grade data flows at scale:

1. Defining AI consumption patterns before designing the architecture
2. Building dedicated AI-serving layers
3. Operationalizing shared semantics and ownership
4. Governing unstructured knowledge as a production asset
5. Operating with continuous data quality and AI observability

The goal is an environment where data quality, semantics, governance, access, and observability work together across AI applications and use cases.

In our article, we explore what it takes to create such an environment and move enterprise AI from isolated use cases toward reliable, governed production at scale.

https://striped-giraffe.com/en/blog/ai-data-readiness-part-2-building-an-ai-ready-data-environment/

01/10/2026

🔗 Connecting customer signals in B2B is only the beginning. The next challenge is finding the patterns. 🔍

A single customer issue may look trivial. Repeated across hundreds of accounts, similar signals can reveal a broader problem.

Consider a B2B manufacturer receiving recurring questions about the same product. Over time, similar signals appear across customers, channels, and regions.

AI can uncover patterns that are difficult to detect manually. It may reveal that customers buying the product repeatedly search for the same information, ask similar pre-sales questions, and generate more support requests after implementation.

That pattern can point to gaps in product information or onboarding.

The response can be concrete: enrich product content, improve documentation, update onboarding, and surface information across digital channels. This can reduce support requests, speed up buying decisions, and prevent post-purchase issues.

AI can then check whether the pattern appears across other customer segments, regions, or product categories, helping teams address root causes before they spread.

For this to work, AI needs connected customer, product, and operational data, plus business context to separate meaningful patterns from noise.

The opportunity is discovering questions the organization did not know it should be asking.

❓ 𝙒𝙝𝙖𝙩 𝙥𝙖𝙩𝙩𝙚𝙧𝙣𝙨 𝙢𝙞𝙜𝙝𝙩 𝙮𝙤𝙪𝙧 𝙘𝙪𝙨𝙩𝙤𝙢𝙚𝙧 𝙙𝙖𝙩𝙖 𝙧𝙚𝙫𝙚𝙖𝙡?

30/09/2026

In B2B, customer insight rarely comes from one conversation. It emerges when signals from across the customer relationship are connected.

A contract concern from procurement, a recurring technical issue, delivery friction, or a change in payment behavior may each seem like an isolated event. Together, they can reveal how the customer relationship is evolving.

Consider a customer reporting repeated technical problems with the same product.

𝗖𝘂𝘀𝘁𝗼𝗺𝗲𝗿 𝘀𝗲𝗿𝘃𝗶𝗰𝗲 sees the incidents.
𝗦𝗮𝗹𝗲𝘀 team sees growing frustration during account reviews.
𝗘-𝗰𝗼𝗺𝗺𝗲𝗿𝗰𝗲 data shows that several people from the same company are repeatedly searching for product documentation.

𝗜𝗻𝗱𝗶𝘃𝗶𝗱𝘂𝗮𝗹𝗹𝘆, these signals belong to different processes. 𝗖𝗼𝗻𝗻𝗲𝗰𝘁𝗲𝗱 to the same customer account, they may point to a deeper issue: customers are struggling to find the information they need to use the product successfully.

That changes the response. Instead of treating each service case separately, the organization can investigate the product content, update the relevant documentation, give service teams better information, and check whether similar patterns appear across other accounts.

This is where customer data architecture becomes a business capability. CRM, e-commerce, service and product data need to be connected through shared customer and product identities, with enough context to interpret the signals together.

The outcome is more valuable than a consolidated customer record. The organization can learn from individual interactions, recognize patterns across accounts, and turn those patterns into actions that improve the customer experience and the underlying process.

𝙃𝙤𝙬 𝙘𝙡𝙤𝙨𝙚 𝙞𝙨 𝙮𝙤𝙪𝙧 𝙤𝙧𝙜𝙖𝙣𝙞𝙯𝙖𝙩𝙞𝙤𝙣 𝙩𝙤 𝙩𝙪𝙧𝙣𝙞𝙣𝙜 𝙘𝙤𝙣𝙣𝙚𝙘𝙩𝙚𝙙 𝙘𝙪𝙨𝙩𝙤𝙢𝙚𝙧 𝙨𝙞𝙜𝙣𝙖𝙡𝙨 𝙞𝙣𝙩𝙤 𝙩𝙝𝙞𝙨 𝙠𝙞𝙣𝙙 𝙤𝙛 𝙞𝙣𝙨𝙞𝙜𝙝𝙩?

28/09/2026

On paper, many organizations look AI-ready. Modern cloud platforms. Integrated systems. Governance frameworks. Years of accumulated data.

The gaps only become visible when copilots, RAG systems, recommendation engines, or forecasting models start using enterprise data directly: fragmentation, inconsistent definitions, missing context, unmanaged documents, and unclear ownership.

Our latest article, 𝗪𝗵𝘆 𝗠𝗼𝘀𝘁 𝗘𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗗𝗮𝘁𝗮 𝗦𝘁𝗶𝗹𝗹 𝗙𝗮𝗶𝗹𝘀 𝗔𝗜, explores why.

In this first article of our 𝗔𝗜 𝗗𝗮𝘁𝗮 𝗥𝗲𝗮𝗱𝗶𝗻𝗲𝘀𝘀 series, we examine five data challenges that frequently separate successful AI adoption from disappointing results:

1. Data fragmentation across systems
2. Data quality issues
3. Data environments not designed for AI data consumption
4. Unmanaged unstructured data
5. Limited access to data

If AI is becoming part of your business strategy, data readiness deserves the same attention as models, platforms, and infrastructure.

That is why, before taking AI further, take a closer look at the data behind it. Our latest article shows you where to start.

🔗 https://striped-giraffe.com/en/blog/ai-data-readiness-part-1-why-most-enterprise-data-still-fails-ai/

23/09/2026

🎾 Ein Match dauert oft nur wenige Minuten. Gute Geschäftskontakte deutlich länger. 🤝😊

Gemeinsam mit tennis2business bringen wir am 17. Oktober 2026 in München Unternehmer:innen und Entscheidungsträger:innen zusammen, die ihre Leidenschaft für Tennis mit wertvollen persönlichen Begegnungen verbinden möchten.

Tennis, Networking und ein gemeinsames Get-together – in entspannter Atmosphäre und mit viel Raum für echte Gespräche.

Alle Details finden Sie in den Slides in diesem Beitrag. 📄

Zur Anmeldung wenden Sie sich bitte an:

ℹ️ Daniel Brands
📧 [email protected]
📞 +49 170 100 7104

Mehr Informationen: www.tennis2business.com

Striped Giraffe Innovation & Strategy GmbH

20/10/2026

15/10/2026

13/10/2026

12/10/2026

08/10/2026

05/10/2026

01/10/2026

30/09/2026

28/09/2026

23/09/2026

Adresse

Telefon

Website

Benachrichtigungen

Service Kontaktieren

Verknüpfungen

Teilen

Kategorie

Antrag auf Entfernung des Eintrags