Analytics & AI Strategy

You Have Enough Data.
What's Missing Is Control.

We turn your analytics and AI investments into genuine management tools, with a solid plan for how data becomes decisions, processes, and competitive advantage. Compact, with clear ownership and ROI metrics, ready to feed into your next budget cycle.

Book a Strategy Session Learn more

01 · Starting point

The Problem With Analytics Strategies

Over the years we have seen dozens of analytics strategies delivered by major consultancies. Elaborate slide decks with maturity models and target pictures. Often solid in content, yet missing the bridge to execution. The strategy document was there, but nobody knew what it concretely meant for Monday morning in the project.

We deliberately take a different approach. Our strategies are as compact as possible, with each recommendation tied to an owner, a budget, and a deadline. We see strategy as the concrete starting point for a project, not a finished document.

For every recommendation

An owner
A budget
A deadline

02 · Maturity

Maturity Assessment & Current State Analysis

Before defining a target picture, we need an unvarnished view of the current situation. That includes data quality, the tool landscape, and the actual capabilities within the team.

01 · Data

Data Maturity: Where Does Your Data Actually Stand?

Most enterprises rate their data maturity significantly higher than it actually is. Master data is often unmaintained, a data catalog is missing, and business definitions diverge between departments. Even the term revenue illustrates this: controlling calculates net, sales counts gross, marketing tracks leads. We assess maturity through concrete questions: Does a data dictionary exist, and who maintains it? Are there data owners per domain? How many reports are in circulation, and which of them are actually used?

Data DictionaryData OwnershipReport-InventurStammdaten-Audit

02 · Technology

Technology Inventory

Most enterprises run a mix of SAP BW, Power BI, Tableau, some Python, and various Excel files that have long since become “the system.” The key question is less about which tools are in use and more about how much runs redundantly, what is actually utilized, and where critical knowledge depends on individual people. We map the entire tool landscape including data flows and interfaces, creating a reliable inventory as the basis for all subsequent decisions.

Tool-MappingData FlowsRedundanz-AnalyseKey Person Dependencies

03 · Organization

Organization & Skills Assessment

Often there is no central analytics team. Instead, data engineers sit in IT operations while business analysts report to controlling, with little regular exchange between them. We assess which capabilities exist, who knows SQL, who can deploy ML models to production, and where gaps need to be filled internally or externally. A reliable overall picture only emerges when organization, technology, and data quality are evaluated together.

Skill-MatrixTeam-StrukturBuild vs. BuyKompetenz-Gaps

03 · Operating model

Target Operating Model (TOM)

More important than the choice of tool or cloud platform is the question of how analytics is organizationally anchored in the enterprise. The target operating model defines who is responsible for what and how collaboration is structured.

Central, Decentralized, or Hub & Spoke?

A central model means one analytics team, one platform, and uniform standards. This works well in smaller organizations but becomes a bottleneck past a certain size, because every request has to go through the central team and time-to-insight increases.

In a decentralized model, each business unit manages its own team. This accelerates delivery but over time leads to departments developing their own definitions, their own tools, and their own truths. The board then receives multiple conflicting revenue figures.

The Hub & Spoke model combines the advantages of both approaches: a central competence center is responsible for standards, platform, and governance, while decentralized spokes in the business units handle fast execution. For most larger enterprises, this model has proven effective.

Operating models compared

Central: every request goes through the central team. Past a certain size, it becomes a bottleneck.

Decentralized: each business unit has its own team. Fast, but with its own definitions, tools and truths.

Hub & Spoke: the competence center owns standards, platform and governance, while the spokes deliver quickly in the business units.

Central unitTeam in the business unitIllustrative

What We Define in the TOM

Reporting line: Who does the CDAO report to? CFO, CTO, CEO?
Roles & job profiles: Which profiles (Data Engineer, Analytics Engineer, Data Scientist, BI Developer) are actually needed?
Demand management: How do requests come in? Ticket system, product owner, self-service?
Governance board: Who decides on platform questions, budget conflicts, prioritization?
Operating model: Allocation of capacity between ongoing operations (Run) and new projects (Change)

04 · AI strategy

AI Strategy: From Hype to Value

The question “What are we doing with AI?” is now on the agenda of nearly every board. The answer depends less on the technology and more on the state of the organization’s own data and its readiness to adopt.

Use Case Identification & Prioritization

For many questions, a good report remains a better solution than an AI model. We evaluate use cases along three dimensions: business impact (quantifiable benefit in euros), feasibility (availability and quality of data), and organizational readiness (the department’s willingness to work with the results). This produces a structured prioritization rather than an unsorted list of ideas.

GenAI & LLMs: What’s Actually Worth It?

In our experience, the use cases with the best cost-benefit ratio are internal chatbots on company knowledge, automated document analysis, and code assistants for development teams. Beyond use case selection, we also support the platform decision: fine-tune your own model or connect via API? Azure OpenAI, AWS Bedrock, or SAP AI Core? And which hosting option is compatible with data privacy requirements? We work through these decisions together and document the rationale.

BI Meets AI: Where the Synergies Are

AI models only deliver value when their results are embedded in existing processes and systems. A churn prediction, for example, is worthless unless it reaches the account manager’s dashboard. Anomaly detection in the supply chain requires a reliable underlying data pipeline. That is why we always plan AI as an extension of the existing analytics platform, not as an isolated initiative.

AI Governance & Compliance

The EU AI Act, GDPR, and internal company policies require AI projects to have a governance framework from the outset. This includes questions such as: Which models may access which data? Who checks for bias? How are model decisions documented? We integrate this framework into the strategy phase so that it is in place before the first pilot goes live.

05 · Business case

Business Cases, Roadmaps & Action Plans

For a strategy to survive the next steering committee, it must be backed by numbers and translatable into concrete projects.

01 · Why

Business Cases That Convince CFOs

A solid analytics business case goes beyond vague promises of savings. The CFO expects answers to concrete questions: What does the setup cost including licenses, personnel, and external consulting? What are the ongoing costs for platform, maintenance, and support? When is break-even reached, and what is the cost of inaction? We build business cases based on conservative assumptions, with sensitivity analysis and a clear separation between hard savings and soft benefits.

TCO-BerechnungSensitivity AnalysisHard vs. Soft SavingsBreak-even

02 · What & when

Roadmap With Real Milestones

With a multitude of ideas, multiple systems, and limited time, the question of sequencing always arises. We prioritize based on three criteria: quantifiable business value, technical feasibility (data availability, platform readiness), and quick-win potential for short-term visible results. The outcome is a roadmap with concrete quarterly milestones, assigned teams, and documented dependencies that feeds directly into the PMO.

Priorisierungs-MatrixQuick WinsDependenciesQuartals-Meilensteine

03 · Who & how

Action Catalog & KPIs

While the roadmap establishes what and when, the action catalog clarifies who, how, and with what budget. Each action is assigned to a specific owner (by name, not department), given a defined budget and deadline, and tied to measurable success criteria. In addition, we define KPIs for the overall analytics program, such as adoption rate, time-to-insight, data quality score, and self-service ratio, so that progress becomes traceable.

Adoption RateTime-to-InsightData Quality KPISelf-Service-Quote

06 · Target picture

Vision & Target Picture

The question of where a company wants to be with analytics and AI in a few years sounds straightforward but proves surprisingly multifaceted in practice. In large organizations, sales, controlling, IT, and the business units often hold quite different views of what data-driven work should actually look like. Without a structured process, these divergent perspectives lead to parallel initiatives lacking a shared direction.

We facilitate this alignment process and condense the outcomes into a compact target picture. At its core, it answers the essential questions: which decisions should be data-driven going forward, which capabilities need to be built internally, and where the line between in-house analytics and external sourcing should be drawn. The result is a guiding framework that is equally comprehensible for the executive board and the business units.

Integral part

Data Strategy as Foundation

An analytics strategy requires a solid data foundation. That is why data strategy is an integral part of our approach. We clarify which data is truly strategically relevant, where it currently resides (whether in ERP, CRM, data lakes, or personal files), and who bears responsibility for it. We also assess existing data quality based on concrete measurements and evaluate whether a data mesh approach or a traditional data warehouse architecture is better suited to the respective organizational structure and team maturity.

Why Barling Consulting for Analytics & AI Strategy

07 · Differentiation

We Stay After the Strategy

Many consultancies deliver a strategy document and end their engagement there. We continue to support the first initiatives on the roadmap, because it is only during implementation that it becomes clear which assumptions hold and where adjustments are needed. This approach ensures that the strategy does not end up in a drawer but actually translates into running projects.

Board Room and Engine Room

Strategic recommendations need to be comprehensible for the executive board and actionable for the technical teams. We operate confidently on both levels and can explain the benefits of a data mesh architecture to a managing director just as clearly as the differences between Snowflake and Databricks to a data engineer. This ability to translate between business and technical language reduces misunderstandings and accelerates decision-making.

Extensive Corporate Experience

Matrix organizations, works councils, budget cycles, and IT governance boards are part of everyday life in large enterprises. Our strategies take this organizational reality into account from the outset. This includes, for example, factoring procurement processes into the timeline or considering approval paths when prioritizing initiatives. The result is plans that are genuinely feasible within a corporate environment.

Contact

Start a Strategy Project?

We bring structure to your analytics and AI initiative, whether you are starting from scratch or evolving an existing approach.

Book a Strategy Session

[email protected]+49 176 609 402 67

You Have Enough Data.What's Missing Is Control.