What Managed Analytics Actually Means (And Why Agencies Offer It)

The term "managed analytics" gets used loosely. Some companies use it to mean a BI tool with onboarding support. Others mean a full outsourced data team. The ambiguity makes it hard to evaluate.

Here is a clear breakdown of what managed analytics actually is, how it compares to the alternatives, and when it makes sense.

The four ways companies get analytics done

### Option 1: DIY with software

You buy Tableau, Power BI, or Metabase. Someone on your team — usually a finance person or ops manager — learns the tool and builds reports.

Pros: Low monthly cost ($50-$500/month for the tool). Cons: That person has a day job. The dashboards get built when they have time, which is rarely. When they leave, the dashboards break and nobody knows how to fix them.

Best for: Companies with less than $2M in revenue and simple data needs.

### Option 2: Consulting engagement

You hire a consulting firm to assess your data needs, recommend a strategy, and maybe build some initial infrastructure. Engagements typically run $20,000-$100,000 and last 2-6 months.

Pros: You get experienced people and a clear roadmap. Cons: When the engagement ends, you own the output but not the expertise. Maintenance, updates, and new requests require another engagement — or a hire.

Best for: Companies that know they have a data problem but do not know what the solution looks like.

### Option 3: Full-time hire

You hire a data analyst or data engineer. Salary: $90K-$180K plus benefits. Ramp time: 3-6 months.

Pros: Dedicated resource, deeply embedded in your business. Cons: Expensive. One person cannot cover analytics, engineering, and visualization. You often end up needing two hires. And if they leave, you are back to square one.

Best for: Companies above $20M with complex, ongoing data needs that require daily attention.

### Option 4: Managed analytics

A team handles your data infrastructure, dashboards, and reporting on an ongoing basis for a fixed monthly fee. They build the pipelines, maintain the dashboards, respond to ad hoc questions, and evolve the system as your business changes.

Pros: Faster to start (days, not months). Lower cost than hiring ($1,500-$5,000/month vs. $10K-$20K/month for a full-time hire). No single point of failure. You get a team, not a person. Cons: Less embedded than a full-time hire. Not the right fit if you need someone in every standup and deeply involved in product decisions.

Best for: Companies doing $2M-$50M that need real analytics infrastructure but are not ready (or do not need) to build an internal data team.

How to evaluate a managed analytics provider

Ask these five questions:

1. What does the first 30 days look like? A good provider has a structured onboarding process. They should connect your data sources, build your first dashboards, and deliver value within 2-4 weeks. If the answer is "we will start with a 6-week discovery phase," keep looking.

2. Who owns the data and infrastructure? You should own everything. If you part ways, the dashboards, the data warehouse, and the pipelines should stay with you. Ask this explicitly.

3. How do you handle ad hoc requests? Business questions do not follow a schedule. A managed analytics provider should be able to handle 5-10 ad hoc data questions per month without a separate SOW for each one.

4. What is included in the monthly fee and what costs extra? Get specifics. How many data sources? How many dashboards? How many users? What happens when you need to add a new data source?

5. Can you show me a sample dashboard or deliverable? If they cannot show you what their output looks like before you sign, that is a red flag.

The managed analytics sweet spot

The model works best for companies that have outgrown spreadsheets but are not ready to invest $200K+ per year in a data team. You get the infrastructure, the expertise, and the ongoing support — without the overhead, the hiring risk, or the six-month ramp time.