Fractional CDO & Analytics Strategy

Your data should drive decisions — not just reports.

Mid-market operators in CPG, manufacturing, and DTC carry expensive analytics infrastructure that rarely connects to the decisions that drive margin. Ankeny Advisory embeds senior data leadership where it matters — without the cost or commitment of a full-time hire.

Talk to Steve See who we work with
$15M
First-year margin improvement identified through predictive analytics at a $3.5B CPG company
$3.5M
External reporting costs eliminated by consolidating 1,800+ fragmented reports into 30 governed BI products
16×
Growth in key product segments driven by analytics-led lifecycle management at a $100M+ manufacturer
CPG & Consumer Products Manufacturing Direct-to-Consumer Microsoft Fabric Power BI & DAX PE-Backed Portfolio Companies BI Modernization AI Governance Vendor Strategy AWS Bedrock & SageMaker Marketing Measurement CPG & Consumer Products Manufacturing Direct-to-Consumer Microsoft Fabric Power BI & DAX PE-Backed Portfolio Companies BI Modernization AI Governance Vendor Strategy AWS Bedrock & SageMaker Marketing Measurement
The problem

Most analytics investments don't pay off. Here's why.

Whether a company is founder-led, family-owned, or PE-backed, the data situation at $20M–$200M in revenue is almost always the same: a patchwork of reports nobody trusts, an analytics team buried in ad-hoc requests, and no clear line between the data and the decisions that drive margin.

The instinct is to hire a full-time CDO, buy new tools, or commission a strategy engagement. These are expensive bets with long runways and uncertain returns — especially when the problem isn't the technology, it's the operating model.

There is a faster path. A seasoned analytics executive — embedded part-time, focused on operating decisions, and accountable to outcomes — can close that gap in months. That is what Ankeny Advisory does.

Symptom 01
Executives make decisions from gut or conflicting spreadsheets. There is no single trusted number for revenue, margin, or customer behavior.
Symptom 02
Marketing spend is optimized for bottom-of-funnel metrics that look good in dashboards but don't connect to lifetime value or margin contribution.
Symptom 03
The analytics team spends 80% of their time on reports and 20% on insight. The operating model itself is the problem — not the people or the tools.
Symptom 04
Vendors defined your data strategy. Internal teams maintain what was built but don't own the roadmap, the outcomes, or the business case.
Who we work with

Built for mid-market operators

Ankeny Advisory works best with companies in CPG, manufacturing, food and beverage, and direct-to-consumer — typically $20M to $200M in revenue — where analytics leadership would materially change operating decisions but a full-time executive hire isn't the right move yet.

This includes founder-led businesses scaling through complexity, PE-backed portfolio companies on a value creation timeline, and growth-stage operators preparing for a liquidity event. The common thread is not ownership structure — it's a leadership team that is serious about connecting data to margin.

CPG & Consumer Products

Companies with complex channel data, promotional analytics needs, and margin pressure from retailer relationships. Deep experience in demand forecasting, pricing analytics, and category management.

Manufacturing & Distribution

Operations-heavy businesses where supply chain, logistics, and procurement analytics drive most of the cost structure. Built AI-enabled logistics suites covering 70% of total cost at scale.

Direct-to-Consumer

DTC and subscription businesses where customer analytics, LTV modeling, and marketing measurement determine whether growth is profitable. Experience across global DTC operations at $1B+ scale.

PE-Backed Portfolio Companies

Portfolio companies where analytics infrastructure must support a value creation plan and eventual exit. Understands hold-period constraints, EBITDA bridge conversations, and board-level reporting needs.

Not sure if there's a fit? The best starting point is a direct conversation — no pitch deck, no proposal. If your team is making significant operating decisions without trusted data, there is probably a fit worth exploring.

Services

What Ankeny Advisory delivers

Every engagement starts with a diagnostic conversation. We map your current data infrastructure against your operating priorities, identify the highest-leverage gaps, and scope a focused engagement with clear deliverables and measurable outcomes.

01

Fractional CDO

Embedded analytics leadership 2–3 days per week. Own the roadmap, manage the team, report to the CEO or board. Full executive accountability without the full-time cost or commitment.

Data strategyTeam leadershipBoard reportingOKR alignment
02

BI Modernization

Migrate from legacy tools to Microsoft Fabric and Power BI. Consolidate fragmented reports into governed semantic models. Enable self-service analytics for operating leaders — with guardrails that keep data trusted.

Microsoft FabricPower BISemantic modelsGovernance
03

Marketing Measurement

Build the measurement infrastructure your agencies don't want you to have. Connect spend to margin contribution. Stop optimizing for metrics that look good in dashboards but don't move the bottom line.

AttributionFunnel analyticsSpend optimizationLTV modeling
Methodology

Analytics as a product, not a reporting function

Most analytics teams are organized as service desks: requests come in, reports go out, and the strategic layer never gets built. The operating model itself is the problem.

Ankeny Advisory brings a product discipline to analytics — with a prioritized roadmap, defined business value for every initiative, adoption metrics, and governance frameworks that internal teams can own long after the engagement ends. The goal is never dependency. It is capability transfer.

Value-First Prioritization

Every analytics initiative is evaluated against a business value hypothesis before a single line of code is written. ROI gates prevent low-impact work from consuming capacity.

Product-Minded Delivery

Analytics deliverables are built with adoption in mind from day one — clear user stories, acceptance criteria, and feedback loops, not just technical specifications.

Internal Ownership Design

Every platform and workflow is designed for internal teams to own, not maintain. Vendors extend capacity — they do not define strategy or hold institutional knowledge hostage.

Decision-Outcome Linkage

Analytics is embedded into operating rhythms — OKRs, planning cycles, executive forums — so insights translate into accountable decisions, not slide decks that get filed.

The reporting factory model

How most analytics teams operate

  • Requests flow in via ticket queue — FIFO, no prioritization by value
  • Success measured by throughput and on-time delivery
  • Analytics is a cost center evaluated on cost reduction
  • Vendors provide solutions; internal teams maintain them
  • Insights delivered in reports; action is someone else's job
  • Data quality issues discovered in meetings, not before them
The analytics-as-product model

What Ankeny Advisory builds

  • Initiatives prioritized by business value, adoption potential, and strategic fit
  • Success measured by decision quality and business outcome
  • Analytics is a revenue and margin driver with a clear ROI case
  • Internal teams own the roadmap; vendors extend capacity selectively
  • Insights embedded into operating rhythms with explicit decision owners
  • Governance ensures data is trusted before it reaches leadership
Perspective

How Ankeny Advisory thinks about data

All articles
BI Modernization

Why your $500K analytics stack isn't being used — and what to do about it

Most mid-market companies invest heavily in data platforms and lightly in adoption. The problem isn't the technology. It's that nobody treated analytics as a product with customers, adoption metrics, and a roadmap tied to operating decisions.

10 min read  ·  Analytics Operations
CPG & Manufacturing

The margin you're leaving on the table: pricing and routing analytics in distribution businesses

7 min read
Marketing Measurement

Bottom-of-funnel optimization is eating your brand — and you can't see it in your dashboard

8 min read
PE & Value Creation

Three questions every operating partner should ask before approving an analytics budget

6 min read
Operating Model

The hidden cost of the reporting factory: why your analytics team is always busy and never impactful

9 min read
Steve Rogers, Founder of Ankeny Advisory
Steve Rogers
Founder  ·  Ankeny Advisory
  • 20+ years in analytics & product leadership
  • MBA (Marketing), University of Iowa — 3.78 GPA
  • Built two analytics orgs from scratch: 25–26 people each
  • CPG, manufacturing, DTC, and SaaS experience
  • Microsoft Fabric, AWS Bedrock, Power BI, Snowflake
  • Former VP Product Management — full P&L ownership
About

A product owner's instincts. An enterprise operator's track record.

My career began not in analytics, but in product management — running P&L for consumer brands at Meredith Corporation, launching nationally distributed products, and learning early that the only data that matters is the data that changes a decision. That instinct has shaped everything since.

Over the past decade I have led enterprise analytics transformations across CPG, manufacturing, and direct-to-consumer — at organizations ranging from a $100M design manufacturer to a $3.5B beverage bottler to a $1B global DTC wellness company. In each case, the work was the same: build a team that owns outcomes; modernize the platform so it is trusted and actually used; and connect analytics investment directly to the operating decisions that drive margin.

I treat analytics as a product portfolio — with a roadmap, adoption metrics, ROI measurement, and executive accountability. Vendors extend capacity; internal teams own the strategy. That is the operating model I build, and the one I bring to every engagement through Ankeny Advisory.

What makes this unusual is the product management foundation. Most analytics leaders come up through data engineering or data science. I came up through market research, voice-of-customer work, and P&L ownership — which changes what gets built, how it gets adopted, and whether it actually drives decisions rather than just informing them.

$15M Margin improvement identified, year one
1,800+ Reports consolidated into 30 governed BI products
16× Product segment growth through analytics-led lifecycle management
Get in touch

Start with a diagnostic conversation

No pitch deck. No proposal template. A direct conversation about where your analytics infrastructure is today, where the gaps are costing you margin, and whether there is a fit worth pursuing.

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