Digital Transformation & Data Advisory FAQs

Start with the decision you want to change, not the platform. Name one operational decision made late or wrongly because two systems disagree, and who owns it. Scope a first slice to that, deliver it in weeks, and let it earn the case for the next. A transformation that starts with a platform purchase usually stalls; one that starts with a decision compounds.

A maturity assessment looks honestly at data foundations, governance, tooling, skills and — most tellingly — whether decisions actually change on the current data. Our Digital Maturity Diagnostic scores this in about 30 minutes. The useful output is not a radar chart but a ranked list of what to fix first, by operational risk.

Whether the sources are accessible or gated, whether key metrics are disputed between departments, who will own the platform after go-live, and which single decision the investment is meant to change. If those are unclear, the platform becomes an expensive reporting layer. We diagnose them before recommending any spend.

A data strategy worth funding names the operational decisions it will improve, the metrics that prove it, and the sequence to get there — tied to the P&L, not to a technology wish-list. It works backwards from the business outcome to the data needed, not forwards from the data you happen to have.

Modernise one subject area at a time — the strangler pattern — standing the new platform alongside the old and moving workloads behind a stable reporting layer, with a decommission date per workload. A big-bang rebuild usually dies in month four; a slice-by-slice migration keeps the business on trusted numbers throughout.

A single source of truth is a governance achievement — one definition per metric, with an owner — served from one governed model, not a piece of software. Fragmentation ends when the sales measure is defined once and every team reads the same figure. The technology enables it; the definitions and ownership deliver it.

Sequence it as Unify, then Predict, then Act — get the data trusted before forecasting on it, and forecast before automating decisions from it. AI on a broken data foundation produces confidently wrong answers faster. The roadmap prioritises use cases by value and readiness, not by which is most fashionable.

The right platform follows your team, estate and serving needs — not the other way round. A Microsoft-centric operations business with business-user reporting is usually best on Fabric; a heavy data-science, multi-cloud estate may justify Databricks. We choose on cost, skills and requirements, and say when the answer is "not yet".

For a Microsoft-centric operations business where business users need proximity to the data and there is no dedicated data-engineering team, Fabric is usually the answer. Databricks suits production machine learning and very large-scale engineering with Spark engineers on staff; Snowflake suits certain multi-cloud warehousing needs. Most mid-market industrial estates land on Fabric — but we assess before asserting.

Build it bottom-up from your own records: labour recovered from manual reporting, working capital released through better decisions, and cost avoided by retiring licences and legacy infrastructure. Any ROI percentage quoted before that work is invented. A defensible case survives with the largest, least-attributable benefit modelled at zero.

Rank use cases by the value of the decision they change and the readiness of the data behind them. Fund the ones where a better signal would change an action this month and the data exists. Prioritising by decision value stops you building impressive analytics nobody acts on — the most common way these programmes disappoint.

Governance means a named owner per certified metric and dataset, agreed definitions, access control, and a change process — applied across the estate, not bolted on after go-live. The single most useful step is assigning an owner to every number that matters; an unowned metric drifts and the platform loses trust within two quarters.

A lakehouse sits alongside your ERP, MES and WMS as the analytical layer — it reads from them, it does not replace them. Shortcuts and mirroring reference existing data in place, so modernisation is additive. Your systems of record stay; what changes is that their data finally becomes comparable in one governed model.

A current-state assessment, the priority decisions and use cases, the target architecture, a phased delivery sequence with a six-week first slice, the governance and ownership model, and a benefits case tied to the P&L. It is a decision document with owners and dates — not a three-horizon diagram with no costings.

MyData Insights runs data and digital transformation advisory for operations-led industrial businesses — maturity assessment, strategy, platform selection and governance — practitioner-led and directly accountable to Amit, on a Fractional Chief Data Officer or fixed-scope basis. We diagnose before we propose, and will tell you when the honest answer is to wait.