About That Brand / Industry guides
Branding agencies for data and analytics companies
A dashboard is an output, not a positioning strategy. Data businesses need to explain which decisions they improve, whose work changes and what has to happen before the promised value is possible.
Compare the work and the fit
This guide looks at two different data-related assignments: infrastructure analytics and the data used to train AI. They offer useful comparison points for a brief, but neither should be treated as proof that an agency has worked across every type of business intelligence or data platform.
Infrastructure analytics and enterprise communication
A distinctive position for a data-dependent AI business
Selected examples, not a numerical ranking. Sources checked 15 September 2026. The fit guidance is our editorial assessment.
Infrastructure analytics and enterprise communication
Phable
Phable reports identity, digital experience, messaging and campaign work for Byanat, whose proposition spans AI, telecom infrastructure and data analytics.
Source: Byanat portfolio account ↗This is relevant when several technical capabilities need a clear commercial explanation. Ask how the team would distinguish the platform, its individual applications and the outcomes buyers care about. For a different data category, request an example closer to your own product and sales process.
A distinctive position for a data-dependent AI business
Ragged Edge
Ragged Edge documents Kili’s strategy, messaging and identity around human expertise in AI training data. Its case study credits ON with the website and Oh Studio with mascot animation.
Source: Kili project ↗Consider this example when the challenge is making a technical data proposition meaningful beyond a specialist audience. Ask how the agency would express your actual differentiation, and whether website delivery and animation involve additional partners. A memorable device should support a clear product explanation.
What to put in your brief
- Describe the decision your product supports, the people making it and their current alternative. Avoid starting with a list of platform features.
- Map the data journey from ingestion to action. Identify which steps your company owns and which depend on customers or partners.
- Provide usable examples with permission: a before-and-after workflow, a realistic demonstration and limitations that need to stay visible.
- Separate brand illustration from explanatory data visualisation. Agree who checks accuracy, accessibility and the meaning of charts or diagrams.
Questions worth asking
- What would you need to learn about our data before proposing a narrative?
- How will you distinguish our platform from the category’s generic claims?
- Which assets will help a sales team explain implementation and value?
Common questions
Why is this shortlist shorter?
The guide prioritises a clear account of relevant work. Two distinct, documented assignments are more useful than padding the list with agencies whose role in the category is unclear.
Is AI branding the same as data branding?
The briefs can overlap, but the questions differ. A data platform may need to explain quality, governance, integration or analysis without presenting itself as an AI company. Use the category that matches the buyer’s problem.
Explore related guides
Project accounts describe published work, not guarantees of future outcomes. Confirm the proposed team and scope directly. Read our methodology.