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What this category actually covers

Three related things sit under this heading. Data warehousing is where your information gets pulled together from different systems into one reliable place. Analytics engineering is the work of building and maintaining the pipelines that keep that data accurate and up to date. Business intelligence dashboards are the visible layer on top, the charts and numbers a manager actually looks at. Most businesses only need the third once the first two are sorted, a beautiful dashboard fed by messy, disconnected data will quietly mislead you.

Choosing a provider: what to look for

Ask what they would connect first and why, a provider who understands your business will point at your most important numbers, not just whatever is technically easiest to pull. Ask how they handle data that is currently messy or incomplete, because most businesses existing records are not clean, and a provider who pretends otherwise is setting you up for disappointment. Ask to see an example dashboard they have built for someone else in a similar business, not just a generic template screenshot.

What affects the cost

How many separate systems need connecting is usually the biggest driver, pulling data from one system into a dashboard is a much smaller job than reconciling data across five. Whether the data needs significant cleaning before it is usable also matters a great deal. Ongoing maintenance is worth asking about separately, a dashboard that breaks quietly when an underlying system changes is worse than no dashboard at all. Compare quotes from a couple of providers on BusinessLinq against your actual systems rather than guessing at a price.

What to expect: the process

A provider will usually want to see what systems you currently use and how clean the underlying data is before quoting anything firm. A basic dashboard on data that is already reasonably organised can be running within a couple of weeks. Pulling together several messy sources takes considerably longer, and any provider who promises otherwise before actually looking at your data is worth being cautious of.

Common mistakes to avoid

Building a dashboard before the underlying data is trustworthy is the most common trap, it produces confident-looking numbers that are quietly wrong, and wrong numbers presented cleanly are more dangerous than no numbers at all. Trying to track everything at once instead of the two or three figures that actually drive decisions usually produces a dashboard nobody opens after the first week. And assuming a system will stay accurate forever without anyone checking it, a provider or a member of staff needs to own it once it is live, or it drifts quietly out of date.

Analytics Infrastructure: common questions