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1Scan for outdated or missing drivers - takes under a minute2Repair Windows errors before they cause bigger problems3Fix the driver behind crashes, sound loss and screen glitchesData teams in AI-driven organizations need to do more than answer one-off requests: they must make organizational data secure, trustworthy, easy to find, and usable by business teams building AI applications. Five priorities help make that shift: establish strong governance, prepare unstructured documents for AI, simplify access, improve discovery through marketplaces, and manage reusable data and AI capabilities as products.
1. Make security and governance foundational
AI increases the number of ways data can be accessed and reused. Before expanding access, data leaders need a clear view of what information exists, where it is stored, how sensitive it is, and who should be allowed to use it.
- Inventory and catalog data assets, including unstructured sources.
- Classify sensitive information and understand where it resides.
- Apply least-privilege access so users and systems receive only the permissions they need.
- Assess third-party risks and define governance policies for AI use.
Governance should make appropriate use possible, not simply block access. Rules need to cover which data may be used for AI, how access is approved, and how teams remain accountable for the systems they build. Isaac Sacolick’s InfoWorld analysis published January 7, 2025 cites a third-party risk figure of 61% of companies reporting a breach or security incident, described as a 49% increase over the prior year. The article does not identify the underlying study, so that figure should not be treated as independently verified or assigned a specific study year.
2. Prepare documents before using them with LLMs
Reliable AI answers depend on the quality and suitability of the information supplied to the model. For retrieval-augmented generation (RAG) and other LLM applications, teams should clean, prepare, and catalog unstructured content rather than assuming that documents are ready to use as-is.
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Depending on the source and use case, preparation pipelines may include entity extraction, sentiment analysis, and bias detection. These steps help teams assess what a document contains, whether it is an appropriate input, and whether the resulting AI application remains aligned with organizational goals.
Document preparation also connects AI work to data lifecycle management. InfoWorld reports that Deloitte’s Q3 2024 State of Generative AI in the Enterprise report found 75% of organizations had increased technology investment in data lifecycle management to support generative AI. This figure is reported secondhand through the InfoWorld article.
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3. Make governed data easier for business teams to use
Centralized access can help business users work with data without making them navigate a different silo for every question. The architectural choice—warehouse, lake or lakehouse, or data fabric—matters less than whether people can get timely, trustworthy information through an experience they can use.
For some users, that may mean asking questions in natural language rather than writing SQL, using an LLM alongside or instead of a dashboard, or receiving model-generated recommendations instead of manually exploring data. Data leaders should treat these interfaces as ways to reach governed data, not as substitutes for quality controls.
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APIs that connect to source systems and knowledge repositories can also give teams a consistent way to build RAG applications and agents. The aim is to reduce friction while preserving access controls and dependable data.
4. Use marketplaces to improve discovery
A data marketplace can give employees a clearer route to discover and request approved data. It can build on catalogs and data dictionaries, making available information easier to understand and helping self-service programs scale without abandoning governance or security.
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This is especially useful where multiple departments need to combine large volumes of information or access it in real time. InfoWorld points to manufacturing, construction, and energy as examples of industries with such cross-department needs. An internal marketplace can help users see what is available and how to access it under established controls.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.5. Manage data and AI capabilities as products
Dashboards, machine-learning models, LLM capabilities, and agents are more useful when treated as products with defined users and outcomes—not as isolated technical deliverables. For each capability, identify:
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- ✅【Adjustable & Ergonomic】：This laptop stand can be adjusted to a comfortable height and angle according to your actual needs, letting you fix posture and reduce your neck fatigue, back pain and eye strain. Very comfortable for working in home, office and outdoor.
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- ✅【Heat Dissipation】 ：The top of the laptop stand is designed with multiple ventilation holes. The open design offers greater ventilation and more airflow to cool your laptop during operation other than it just lays flat on the table.
- ✅【Portable & Foldable】：The foldable design allows you to easily slip it in your backpack. Ideal for people who travel for business a lot.
- ✅【Broad Compatibility】：Our laptop holder is compatible with all laptops from 10-17.3 inches, such as MacBook Air/ Pro, Google Pixelbook, Dell XPS, HP, ASUS, Lenovo ThinkPad, Acer, Chromebook and Microsoft Surface, etc.Be your ideal companion in Home, Office & Outdoor.
- Customer segment: who will use it and what work they need to accomplish.
- Value proposition: what problem it solves or decision it improves.
- Strategic objective: how it supports the organization’s priorities.
A vision statement and product roadmap help teams coordinate development, clarify what belongs in the product, and keep the work connected to a business need. This product mindset also gives data teams a way to prioritize among requests rather than treating every new dashboard or AI feature as a separate project.
How the five practices fit together
These priorities are interdependent. Governance establishes safe boundaries; document preparation improves the material AI systems rely on; accessible services and APIs let business teams use that material; marketplaces make governed assets discoverable; and product management ties reusable capabilities to users and outcomes. Organizations can choose different architectures, but should evaluate them against security, data quality and freshness, discoverability, usability, integration support, and connection to customer needs.
The urgency is reflected in figures reported by InfoWorld from named sources: its article attributes weekly generative AI use by 72% of respondents, and medium-to-high work impact from generative AI among more than 80% of respondents across IT, business intelligence, customer service, marketing, operations, and product development, to Wharton’s 2024 AI report. These numbers are relayed by InfoWorld rather than verified here against the original report. They indicate broad reported use and impact, not proof that any particular architecture or practice produces better results.
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