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Managed Services

What is a retainer in managed services?

A retainer reserves fixed specialist time for your data environment. What it covers, how it differs from ad-hoc support and when it pays off.

Robin du Maine ·
#retainers #managed services #SLA #Snowflake #Qlik

A retainer is reserved specialist capacity: agreed in advance and available on a recurring basis, whether or not there is a concrete incident or project at that moment. It pays off as soon as an organisation needs specialist knowledge regularly, but not often enough to justify hiring that knowledge full-time. Filling that need purely ad-hoc costs more in the end: in waiting time, in reactive choices, and in knowledge that has to be rebuilt every time.

I often see the retainer go first when budgets come under pressure, treated as something that can wait as long as no project is running. That misreads what a retainer is for: structural access to specialist knowledge and support, built into the rhythm of the organisation.

What a retainer covers

At cimt Managed Services we fill in a retainer with four recurring activities.

  1. Architectural reviews: periodically checking whether the design of a Snowflake data warehouse, a Qlik environment or an ETL pipeline still fits the growth of the organisation.
  2. Code reviews: checking whether new work, from dashboards to data integrations, meets the agreed quality standards before it is production-ready.
  3. In-depth questions: specialists answer the questions that stay open inside a team because nobody has the time or the depth to work them out.
  4. Follow-up advice: the findings from the reviews lead to a concrete recommendation for the next step.

The difference from ad-hoc support is predictability. Ad-hoc support responds to what has already happened. With a retainer, the specialist reviewing your environment already knows it before anything goes wrong, and can therefore see what still might go wrong.

Why this delivers more than one-off support

A specialist who reviews the environment every month builds up knowledge of that environment. With occasional work, it has to be explained from scratch every time. That accumulated knowledge is exactly why an architectural review after a year on retainer delivers something different from a review carried out for the first time: the specialist knows the history, the earlier choices and the reasoning behind them.

A retainer also makes costs predictable. Instead of fluctuating invoices for one-off work, the capacity for a fixed period is already planned and budgeted, which makes budgeting and internal planning simpler.

At cimt Managed Services, a retainer is not a separate product alongside the SLA but an addition to it. An SLA for a data environment covers management and availability. A retainer covers further development: the part where the data environment grows with the organisation.

Not sure whether a retainer fits your situation? Get in touch and we will look at the capacity your data environment needs.

About the author

Robin du Maine

Robin du Maine

Managing Consultant

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Frequently asked

About retainers

What is the difference between a retainer and an SLA?

An SLA defines which management services are delivered and to which standards. A retainer reserves specialist capacity for further development, reviews and advice. The two are often combined.

When does a retainer not pay off?

If the need for specialist knowledge is one-off or very occasional, ad-hoc work is usually cheaper. A retainer pays off when the need is recurring.

How much capacity does a retainer reserve?

That differs per organisation and is matched to the service tier of the SLA (Basic, Professional or Enterprise). The right size follows from the complexity and the growth rate of the data environment.

Can a retainer grow with the organisation?

Yes. The scope of the retainer is reviewed periodically, so the reserved capacity keeps matching the actual need.

Is a retainer only relevant for technical teams?

No. The advice coming out of architectural reviews and code reviews also lands with decision-makers, because it bears directly on the stability and the further development of the data environment.

Further reading

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