Services

Six ways to work with Insight Sphere.

Every engagement is built around the data you actually run on, not a generic framework dropped in from somewhere else.

Source-Risk Diagnostic
For: CDOs · CROs · Heads of Data Governance · Risk Leaders
"Which of our critical data domains are carrying the highest source-control risk, and are we governing them like it?"

A short, sharp engagement that maps your critical data domains by source, control, processing state and business reliance. It's normally the right starting point for organisations that want a clear read on their source-risk profile before they commit to a bigger governance or remediation programme. We've designed it to move quickly and to produce findings senior risk and data leaders can actually use the same week.

Typical Outputs
Source-risk map of critical data Risk exposure profile Hidden external-data risk indicators Critical source dependency register Prioritised recommendations
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Data Quality Risk Review
For: Data Quality Leaders · Internal Audit · Regulatory Affairs · Risk Committees
"How are we currently identifying, governing and controlling data quality risk, and what does the source-control lens show us that we've been missing?"

A broader advisory review of how data quality risk gets spotted, governed, measured and fixed in your business, across both data you produce and data you receive. It goes a step deeper than the diagnostic, into existing controls, accountability and remediation paths. You end up with a clear taxonomy of risks, a gap analysis and a prioritised set of recommendations that match the source-control reality, not a generic playbook.

Typical Outputs
Current-state assessment Data quality risk taxonomy Governance gap analysis Control recommendations Executive findings pack
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Governance Readiness Assessment
For: CDOs · Transformation Leaders · Governance Committees · Model Risk Teams
"Is our governance capability actually fit for the data risks we're carrying right now, and if not, how do we close the gap?"

A structured readiness review of your data governance capability relative to the data you actually depend on. We look at where governance is calibrated to the right risks, where it isn't, and what a defensible target state looks like for your business. The output is a clear, sequenced roadmap of where to invest, prioritised by risk rather than by data category or department.

Typical Outputs
Readiness assessment Governance capability review Gap analysis Target-state roadmap Risk-aligned investment priorities
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Insurance Data Risk Advisory
For: Insurance Executives · Claims · Underwriting · Pricing · Fraud · Compliance Leaders
"Where in our operating model are broker submissions, customer declarations and third-party feeds quietly creating source-control risk?"

Sector-specific work for insurers dealing with the full complexity of external data. Motor, claims, underwriting, pricing and fraud all run on data that originated outside the business. This advisory takes apart the source-control profile of broker, customer, third-party and telematics data flows, and helps you build governance and assurance that reflect that profile, rather than treating every data source the same way.

Typical Outputs
Source-control operating model review Claims and underwriting data flow assessment Broker and third-party dependency analysis Data assurance recommendations
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AI Input & Assurance Advisory
For: AI Governance Leaders · Model Risk · CDOs · Regulatory Affairs · AI Teams
"Given the data our AI systems actually run on, can we say they'll work reliably, and can we show that to regulators?"

A specialised piece of work for organisations where AI systems lean heavily on data they didn't produce. Unlike most AI governance approaches, we care about whether the system can do its job in the real operating environment, given the conditions of the data it actually consumes. We also help you put together evidence-based assurance narratives for regulators and audit.

Typical Outputs
AI input assurance review Risk and effectiveness review AI input dependency mapping Business-facing effectiveness evidence Regulator-facing assurance narrative
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Executive Workshops
For: Leadership Teams · Boards · Risk Committees · Strategy and Transformation Leaders
"How do we get our leadership team thinking about data, AI and governance risk in the way the business actually needs them to?"

Short, focused sessions for leadership teams who need to get their heads around source-aware risk thinking and apply it to a real problem they're already wrestling with. Each one is scoped to a specific question or domain. They work well as a way in before a formal engagement, as part of a governance programme, or as a standalone input to strategy and transformation work. Built for senior people who want something they can use on Monday, not a lecture.

Workshop Topics
Where Does Your Data Quality Risk Actually Enter the Business?For risk and data leaders who want a practical first pass at source-aware risk thinking, applied to their own environment.
Hidden Risk in Polished External DataFor governance teams working on data assurance, audit readiness or model risk where inherited data is doing more work than it should.
Source-Aware Thinking for AI GovernanceFor AI, model risk and technology leaders putting assurance around AI processes that depend on data they didn't produce.
Data Quality Risk in Insurance Operating ModelsFor insurance executives looking hard at broker, customer and third-party data dependencies across the value chain.
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