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Articles on data strategy, analytics, and making smarter decisions with your business data.

22 articles · Updated April 2026

Latest Articles

No LookML needed
Multi-warehouse
Governed definitions
Natural language
Own execution layer

Best Looker Alternative in 2026

Compare the top Looker alternatives across governance, self-serve access, warehouse dependency, and cost predictability.

Christian Futschik
June 2, 2026 15 min read Data Strategy
Semantic layer
No self-hosting
Multi-source (DB + SaaS)
Row-level security
Flat pricing

Best Metabase Alternative in 2026

Compare 6 alternatives across governance, multi-source connectivity, self-hosting, and cost predictability.

Christian Futschik
May 28, 2026 15 min read Data Strategy
ERP Analytics Cross-module
Supplier costs +12% 3m
Vendor-7 pricing above threshold
Scrap rate 1.8% 18m
Product line B beating quality target
Procurement review flagged 2h
3 product lines affected by cost correlation

ERP analytics: getting insights from your operational data

Your ERP contains years of operational data. Learn how to extract insights from SAP, Monitor, and NetSuite without a data engineer or custom BI project.

Adam Lewenhaupt
May 26, 2026 15 min read Manufacturing Analytics
Revenue Operations Governed
Pipeline coverage below 3x 4m
Enterprise MQLs from LinkedIn down 40%
Q2 revenue on track 18m
Closed-won $1.4M vs $1.2M target
Board report generated 3h
Pipeline and revenue metrics auditable to source

Revenue operations platform: complete guide

A revenue operations platform unifies marketing, sales, and finance data into one governed view of the revenue engine.

Anton Melander
May 21, 2026 15 min read Marketing Analytics
Platform Eval Comparing
Conversational Manual
User initiates, no autonomous monitoring
Agentic Auto
Continuous monitoring, root cause traced
Execution layer 10-20x
Query volume vs human users

Agentic Analytics Platform: What to Look For

What makes an analytics platform truly agentic? Compare capabilities, architecture, and evaluation criteria.

Adam Lewenhaupt
May 19, 2026 15 min read Data Strategy
FP&A Comparison 6 tools
Anaplan
$100K+
3–6mo
Pigment
$50K+
4–8wk
AI-native
€200
Days
BudgetForecastVarianceBoard

FP&A tools comparison 2026: complete guide

Six FP&A tools compared across pricing, implementation time, Excel dependency, and governance. From Anaplan to AI-native platforms.

Anton Melander
May 14, 2026 15 min read Finance Analytics
Data connectors
Semantic governance
Own execution layer
Row-level access
Flat pricing

Best Funnel.io alternative in 2026: 6 tools compared

Compare the best Funnel.io alternatives for marketing data aggregation. Side-by-side evaluation on cost, governance, and accuracy.

Christian Futschik
May 12, 2026 15 min read Marketing Analytics
Platform Comparison
Traditional AI-native
Self-serve Low High
Formula lang. DAX/SQL None
Warehouse Required Optional
Governance Manual Built-in
Time to insight Weeks Hours

Best Power BI alternative in 2026: full comparison

Compare the top Power BI alternatives for 2026. Find platforms that deliver self-serve analytics without DAX or data modeling.

Christian Futschik
May 7, 2026 15 min read Data Strategy
Adoption by Generation
Gen 1: Visual BI
25%
Gen 2: Governed BI
35%
Gen 3: AI-native
80%

Self-service analytics tools: the 2026 buyer's guide

What separates real self-serve analytics from the marketing label, and how to evaluate tools that actually deliver.

Adam Lewenhaupt
May 5, 2026 15 min read Data Strategy
Analytics Maturity
L4 Agentic
L3 Natural Language
L2 Guided
L1 Drag & Drop

No code analytics: the complete guide to data without SQL

How no code analytics platforms connect to your systems and deliver governed answers anyone can trust.

Anton Melander
April 30, 2026 15 min read Data Strategy
Self-serve access
No warehouse needed
Governed definitions
Natural language
Fixed pricing

Best Tableau alternative in 2026: a hands-on guide

Compare the top Tableau alternatives across self-serve access, cost, governance, and natural language capabilities.

Christian Futschik
April 28, 2026 15 min read Data Strategy
Production Line Status Live
CNC-01 87%
CNC-02 62%
Press-A 91%
Weld-03 41%
Running Idle Down

OEE dashboard: build one without code in 2026

Build an OEE dashboard without code. Connect your ERP, define availability, performance, and quality metrics, and monitor production in real time.

Adam Lewenhaupt
April 22, 2026 15 min read Manufacturing Analytics
Marketing Analytics Cross-channel
Pipeline +34% vs Q4 2m
LinkedIn and Google Ads drive enterprise growth
CPL up 28% in DACH 14m
New audience segment underperforming
Attribution reconciled 1h
CRM + ad touchpoints aligned across 340 MQLs

B2B marketing analytics: the complete guide for 2026

How B2B marketing analytics connects campaign data across channels to pipeline and revenue, fully governed.

Anton Melander
April 16, 2026 15 min read Marketing Analytics
Financial Report Live
Revenue
4.2M
+12%
Margin
68%
+3pp
Burn
280K
–5%
P&LBalanceCash FlowBoard

Automated financial reporting: the complete guide

How software collects, transforms, and presents financial data without manual intervention. From ERP to board deck, fully governed.

Anton Melander
April 15, 2026 15 min read Finance Analytics
Self-Serve Obstacles 3 found
cost obstacle 1
Ad-hoc queries hit the warehouse; agents make it nonlinear
accuracy obstacle 2
Definitions diverge; semantic layers go stale
governance obstacle 3
Policy in prompts is not enforcement
control plane solution
Own execution, federated context, structural governance

The three obstacles to self-serve analytics at scale

Three structural obstacles explain why most enterprises never get past 25% adoption, and what an architecture that solves all three looks like.

Anton Melander
April 9, 2026 12 min read Data Strategy
AI Agent Monitoring
Conversion rate dropped 18% in DE correlates with pricing change Mar 3
EMEA revenue +12% vs forecast driven by enterprise upsells
!
OEE anomaly detected on Line 4 post-maintenance window pattern

What is agentic analytics? The future of business intelligence

AI agents that proactively monitor your data, detect anomalies, and surface insights before you ask. How agentic analytics differs from conversational BI, and why the semantic foundation matters.

Christian Futschik
April 7, 2026 12 min read Data Strategy
Data connectors
AI exploration
Built-in analytics
No warehouse needed
Flat per-seat pricing

Best Supermetrics alternative in 2026: 7 tools compared

Moving data is the easy part. A no-nonsense comparison of 7 alternatives across pricing, connectors, AI capabilities, and ease of use.

Anton Melander
March 24, 2026 15 min read Marketing Analytics
OEE 78%
Availability92%
Performance88%
Quality96%

Manufacturing analytics: from ERP data to decisions

Your ERP has everything you need. The problem is getting it out. How manufacturers use production analytics to cut downtime, track OEE, and reduce scrap.

Adam Lewenhaupt
March 24, 2026 15 min read Manufacturing Analytics
Context Layer Activity Live
revenue reconciled 2m ago
dbt + Confluence definitions aligned automatically
customer_id conflict 14m ago
Entity mismatch: HubSpot vs Stripe. Flagged for review.
CLV rule learned 1h ago
24-month lookback window captured from Slack correction
CAC definition suggested 3h ago
Asked 18x with no formal definition. Proposed for review.

Beyond the semantic layer: the federated context layer

The semantic layer solved metric consistency for dashboards. AI agents need something broader. Here is what comes next.

Adam Lewenhaupt
March 19, 2026 18 min read Data Team
txn_amt_usd_net Net Revenue cust_id_fk Customer dt_created Order Date mrr_calc_excl_trial MRR

The broken promise of the semantic layer – and how AI fixes it

Semantic layers promised a single source of truth but required months of committee meetings. Continuous semantic mining offers a better path.

Anton Melander
March 19, 2026 12 min read Data Strategy
customers documented
24 tables · 535K rows
revenue documented
12 tables · 890K rows

What is a data discovery platform? The complete guide (2026)

Where traditional data discovery stops short, what the three missing layers are, and what modern data discovery needs for AI analytics and self-serve access.

Anton Melander
March 18, 2026 15 min read Data Strategy

Ronja Platform

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Frequently asked questions

What does Ronja actually do?

Ronja is the control plane through which AI agents access enterprise data – securely, accurately, and without driving up warehouse costs. It connects to your data warehouse, data lake, and 100+ SaaS apps, brings all of it under one governed layer, and lets anyone in the organization get answers through Slack, ChatGPT, Claude, or Ronja directly. Every number traces to its source: real code, real rows, real lineage. No hallucinations.

How is Ronja different from a traditional BI tool?

Traditional BI tools like Tableau or Power BI require analysts to build dashboards that business users then consume. Ronja removes the middle step: business users ask questions in plain language and get governed, accurate answers instantly. Ronja also layers on top of your existing stack – the warehouse stays, dbt stays, existing dashboards stay. It handles the ad-hoc questions that currently sit in the data team's backlog.

Does Ronja replace our data team?

No. Ronja is the data team's first line of support. It handles routine ad-hoc questions so the data team can focus on the hard problems that require human judgment. As more users interact with Ronja, it surfaces what the business actually needs: data gaps, divergent definitions, and common patterns. The data team goes from reactive firefighting to proactive, strategic work.

What about warehouse costs when everyone starts querying data?

This is one of the three core obstacles Ronja solves. Ad-hoc queries run on Ronja's own execution layer – they never hit the warehouse. Adding more users does not increase your warehouse bill. Your warehouse continues to handle what it was designed for: heavy transformations, scheduled pipelines, and system-of-record storage.

How does Ronja ensure answers are accurate and consistent?

Ronja maintains a federated context layer that aggregates definitions from wherever they already live: dbt models, Cube, Confluence, warehouse metadata, and user conversations. It detects where definitions diverge, learns from every correction, and ensures the same question always gets the same answer, regardless of who asks or which channel they use. Every answer traces to source – real code, real rows, real lineage.

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