Data Insights: How to Turn Raw Data Into Better Business Decisions

Every business collects data. Far fewer can turn it into decision-ready insight.

Your CRM, marketing systems, and revenue teams generate signals across accounts, campaigns,  pipeline, and customer conversations.

Yet many organisations still struggle to answer the questions that matter:

Which markets to prioritise, which accounts to focus on, where pipeline quality is changing and which segments deserve more investment.

Data insights aren’t dashboards, charts, or isolated observations. They’re conclusions that help businesses make better decisions.

For revenue teams, that might mean refining segmentation, improving CRM quality, reallocating budget, changing territory design or identifying where market conditions are shifting.

As Andy Mowat, Founder at Whispered, notes in the video below, today’s winning companies don’t just collect data; they operationalise the insights across sales, marketing, and product teams in real time.

Click to watch the full episode of The RevOps Review or read the blog to find Andy’s data-based insight examples throughout. 

What are data insights?

Data insights are actionable findings derived from analysing raw data that help you understand patterns, predict outcomes, and make informed business decisions.

Think of it this way:

Data is the ingredient, insights are the recipe. You might have flour, eggs, and sugar (your raw data), but without understanding how to combine them properly, you won’t create anything valuable (your insights).

Insights provide the knowledge and context needed to leverage the raw data into action.

For example:

A CRM report might show that enterprise opportunities in Germany have longer sales cycles than enterprise opportunities in the US. That’s data.

A deeper analysis might show that German enterprise opportunities involve more members of the buying committee, longer procurement processes, and stricter compliance reviews. That’s analytics.

The insight is the decision-ready conclusion:

Your German enterprise motion needs different qualification criteria, longer forecast assumptions and more senior stakeholder mapping than your US motion.

Data vs analytics vs insights

Data,  analysis, and insights are often used interchangeably. They shouldn’t be. 

Here’s a breakdown:

The important point is that insight is not the end of analysis. It is the bridge between analysis and action.

Why data insights matter for revenue teams

Revenue teams are under pressure to grow more efficiently.

Which means they need to make better data-driven decisions. Decisions that help them decide where to focus, which accounts to prioritise and how to execute consistently across markets.

Insights data help revenue leaders improve:

• Market prioritisation
• Account segmentation
• Territory planning
• Pipeline forecasting
• CRM hygiene
• Marketing investment
• Sales and marketing alignment
• AI-assisted workflows
• Compliance oversight

But the value of an insight depends on the quality of the data behind it.

A dashboard built on stale, incomplete or non-compliant data can create false confidence. It may look precise, but it won’t be decision-grade.

Poor data quality can distort market analysis, inflate TAM assumptions, misdirect B2B sales capacity and weaken forecast reliability.

What Cognism’s data shows about insight-led GTM teams

B2B data insights become more valuable when they connect market signals to commercial decisions.

Cognism knows this, which is why we’ve created a reports hub that provides revenue leaders with insights into changes in buyer behaviour, channel performance, market conditions, and sales execution, and how they can influence a GTM strategy.

The Report Hub brings together proprietary Cognism data and external research across go-to-market strategy, market trends, sales execution, marketing performance and revenue operations.

Our pillar reports use proprietary data and external sources, while our market insight reports use Cognism’s B2B data asset to identify trends in hiring, job creation, economic growth, technology usage and other commercial signals.

Here are a few examples of data insights from these reports:

1. Market insight: business conditions are shifting

In Fluent in data: What Cognism’s data reveals about the shifting business economy, we analysed signals such as leadership churn, shrinking tech stacks and rising AI-driven buyer intent.

These signals are real-time data insights for revenue leaders.

For example, we’ve learned that over the past 12 months, 5.22% of VP-level and above leaders across Europe have changed roles, highlighting sustained movement at the top of organisations.

These are important market data insights for anyone focused on account prioritisation, territory planning, and expansion.  

They tell us that market conditions are not static, and your strategies should be shaped by these live commercial signals rather than historical assumptions.

2. Inbound insight: traffic is no longer the whole story

Our Inside Inbound 2026 report highlights a 33.6% year-on-year decline in organic traffic, alongside growth in in-platform research.

That matters because many B2B marketing teams still treat website traffic as a primary indicator of demand.

The stronger insight is that buyer research is becoming more distributed.

Revenue teams need to understand where high-intent buyers are researching, comparing and validating solutions, not just how many visitors reach the website.

3. Outbound insight: precision matters more than volume

Our outbound research shows that SDR answered rates are close to AE warm-calling rates, with SDRs at 13.3% and AEs warm-calling at 14.4%.

Our cold-calling research also reports a 11.3% cold-calling success rate with verified contact data.

This data-driven insight shows that outbound performance depends on the quality of the data layer supporting it. 

Verified, current and relevant contact data gives revenue teams a stronger basis for prioritisation, coverage and execution.

4. Buyer insight: enterprise decisions require trust

Our buyer research at Cognism includes a report titled “How Mid-Market & Enterprise Buyers Buy Revenue Data Software in 2026.” It examines the business data insights of how buying behaviour changes across company sizes and GTM roles.

For larger organisations, the implication is that B2B data buying is not only about access. It is about confidence.

Enterprise teams need evidence of accuracy, compliance, coverage and governance before they can rely on data for CRM quality, forecasting and AI-driven workflows.

5. AI insight: visibility depends on data quality and content structure

When it comes to AI data insights, we’ve got that covered too, with a study on how to create content LLMs actually surface: 800+ links audited.

For GTM teams, this reinforces a broader point.

AI performance depends on the information it can access and trust.

Whether the workflow involves content visibility, account prioritisation or CRM automation, the quality and structure of the underlying data affects the quality of the output.

Together, these reports show why data insights are not just an analytics function. They’re a GTM discipline.

The strongest revenue organisations use trusted data to understand market movements, interpret buyer behaviour, improve execution, and make better decisions across sales, marketing, and operations.

Keep in mind that market conditions, compliance requirements, buyer expectations and data availability vary by country.

Reliable insights depend on a data foundation built for that complexity - a complexity Cognism can help you navigate with accurate, compliant and current European B2B data.  

“Many complaints about Salesforce are that it’s only as good as the sales data you put into it, and that’s true. There are some things you cannot automate for your reps.” “But Cognism takes away many of those worries about whether the data is incorrect because we know we can trust it if it’s from Cognism.”

Read the full case study

72-77%

opportunities influenced

Director of Revenue Operations @Openprise

How to turn data into insights: 8 key steps

Ready to get insights from your data? Here are the key steps to follow. 

1. Start with clear business questions

Don’t start with: “What does the data show?”

Start with questions such as:

• Which markets should we prioritise next year?
• Which account segments produce the strongest pipeline quality?
• Why is conversion falling in one region but improving in another?
• Where is poor CRM quality affecting forecasting?
• Which buyer signals indicate expansion readiness?

The sharper the question, the more useful the insight.

This is also a point practitioners often make in analytics communities.

In one Reddit discussion about becoming better at deriving insights, several contributors emphasised that analysis should start with the business problem and stakeholder decision, not simply with exploring a dataset.

2. Check whether the data is fit for the decision

Before analysing the data, assess whether it is reliable enough to support the decision.

Ask:

• Is the data current?
• Is it complete?
• Is it consistent across markets?
• Is it compliant for the intended use?
• Are key CRM fields standardised?
• Are duplicates distorting the analysis?
• Are account hierarchies accurate?
• Are contacts verified and reachable?

This step is often skipped because it feels operational. It isn’t. It determines whether the customer data insight can be trusted.

For example, a market expansion analysis built on incomplete company coverage will underestimate the opportunity. A territory plan built on stale account records will misallocate sales capacity. An AI workflow built on poor CRM data will automate the wrong actions.

3. Bring relevant data together

If you’re using multiple systems or contaminated data for your insights, then your findings will be unreliable.

Centralise your data by doing the following: 

• Aggregate data from all relevant sources. To prevent siloed data, pull information from your CRM, web analytics, advertising platforms, product usage databases, and customer support systems into a single platform. 
• Ensure data is clean and consistent. Remove duplicates, standardise formats, and fill gaps where possible. 
• Establish B2B data governance protocols. Create standards for how data enters your systems and consistently enforce them. This prevents future quality issues. 

As an example, when Andy Mowat was at Culture Amp as their VP of RevOps, they integrated product usage data with CRM records to enable objective and subjective risk scoring. This gave their customer success leaders early warning of churn risks that would otherwise vanish in Salesforce.

4. Apply data analysis techniques

Choose analytical methods that match your business questions and data types. Different insights require different approaches.

• Use trend analysis to understand patterns over time. This helps identify seasonal variations, growth trajectories, or performance changes that correlate with business events.
• Apply cohort analysis to understand customer behaviour. Group customers by shared characteristics, such as acquisition channel or company size, and compare KPIs over time.
• Implement attribution modelling to understand what drives conversions. This is particularly valuable for marketing teams optimising budget allocation across channels.
• Don’t overcomplicate the analysis. Start with simple techniques and add complexity only when it provides additional value. 

When Andy was at Box as the Sr. Director: Customer Success Operations, RevOps found that sales needed 2.5-4.5x pipeline coverage (depending on segment) at the start of each quarter to reliably hit their goals. Tracking these ratios weekly enabled proactive course correction, something that Salesforce alone couldn’t provide at scale.

5. Visualise for clarity

Choose visual formats that impress viewers and help stakeholders get the point quickly.

The goal is immediate comprehension, not artistic beauty, and these tips can help: 

• Match chart types to insight types. Use line charts for trends, bar charts for comparisons, funnels for conversion processes, and heatmaps for correlation analysis.
• Highlight one key takeaway per visual. Cluttered charts can confuse viewers rather than provide clarity. If you have multiple insights, create multiple focused visuals rather than one complex diagram.
• Use colour strategically. Highlight the most important data points with distinct colours whilst keeping supporting information neutral. 
• Include context and benchmarks. Show current performance and how it compares to previous periods, targets, or industry standards. 

For example, a marketing team might increase their reporting impact by 50% after switching from complex multi-metric dashboards to simple, focused visuals that highlight one key insight each.

6. Interpret with context

Raw analysis results aren’t insights until you understand what they mean and why they matter for your business.

• Ask “What does this mean?” and “Why is this happening?” Connect your findings to real business events, customer behaviours, or market conditions to drive more effective actions. 
• Collaborate with cross-functional teams to validate your interpretations. Sales teams can explain customer behaviour patterns, marketing teams understand campaign impacts, and product teams know feature release timelines that might influence your data.
• Consider external factors that might influence your results. Economic conditions, competitor actions, or industry changes can all impact your metrics in ways that pure data analysis might miss.
• Challenge your assumptions. Look for alternative explanations. The most obvious interpretation isn’t always correct, especially when dealing with complex business systems.

7. Turn insights into action

Insights only create value when they can influence decisions and drive measurable changes in business performance. Here are a few tips to turn your B2B data insights into actions: 

• Leverage learnings into specific initiatives. Don’t just present insights, recommend concrete actions. If the analysis shows enterprise customers have a higher lifetime value, propose adjusting your sales qualification criteria and marketing messaging.
• Create implementation playbooks. Translate insights into repeatable processes. This ensures insights influence ongoing operations, not just one-time decisions.
• Use automation tools and deploy changes quickly where possible. If insights reveal optimal email send times, automate your marketing platform to use those times. If certain lead sources perform better, automatically adjust the scoring algorithms.
• Track the impact of actioned insights over time. Measure whether implementing your recommendations improved performance to win stakeholder confidence. 

Andy suggests:

"Don’t just say yes to every request. Force trade‑offs so the business focuses on the most impactful insights first.’ This prevents teams from being spread too thin and ensures every insight leads to measurable ROI.”

8. Monitor whether the insight remains true

As markets, tools, and customer behaviours evolve, yesterday’s insight may no longer hold true.  Revenue teams should revisit insights regularly, especially when they inform:

• Market entry

• Budget allocation

• Territory design

• Pipeline forecasting

• Account scoring

• AI workflow rules

• Compliance processes

• Establish feedback loops with sales, marketing, and product teams.

• Audit your data sources regularly - Andy notes that CRM snapshots aren’t enough without a data warehouse.

• Revisit prioritisation: Force trade‑offs to keep focus on business impact.

• Set automated alerts for anomalies (e.g., sales pipeline dips).

• Re‑evaluate tools annually to ensure governance, enrichment, and AI readiness.

Tools for generating and operationalising data insights 

The right data insights tool depends on what kind of insight you need.

Some tools help visualise data. Others prepare, clean, enrich or operationalise it.

1. Power BI

Best for: Business intelligence and reporting in Microsoft environments.

Power BI is a business intelligence tool that integrates data from multiple Microsoft applications (including Excel, Dynamics, and SharePoint) while offering robust visualisation capabilities.

It’s a powerful option for organisations already using Microsoft Office 365, offering natural language queries, automated insights, and collaborative reporting.

This data insights platform supports both simple dashboards and complex analytical models, making it suitable for teams with varying levels of technical expertise.

2. Tableau

Best for: Advanced visualisation and analytics.

Tableau (by Salesforce) offers unparalleled flexibility in creating complex visualisations and supports advanced statistical analysis.

It’s ideal for organisations with dedicated data teams who must create highly customised reports and interactive dashboards.

The platform excels at handling large datasets and offers extensive integration options for diverse data sources, helping prevent silos and data decay.

3. Looker Studio

Best for: Accessible marketing and web reporting.

Google Looker Studio integrates seamlessly with Google Insights data (including Google Analytics, Google Ads, and YouTube) and connects to external data sources.

The free version of the tool is relatively robust, making it a good option for budget-conscious teams, though a paid version is available.

It’s perfect for teams that need professional-looking reports,  though it has limitations compared to enterprise-grade solutions.

4. Salesforce CRM Analytics

Best for: Pipeline, opportunity and sales performance analysis.

Salesforce CRM analytics provides sophisticated forecasting, opportunity analysis, and insights into sales teams’ performance.

It’s designed specifically for Salesforce users who need to understand sales patterns, pipeline health, and revenue forecasting.

The platform offers AI-powered insights and predictive analytics that help sales teams focus on the most promising opportunities.

5. Talend

Best for: Data integration and preparation.

Talend handles the complex work of connecting separate data sources, identifying inconsistencies, and preparing datasets for analysis.

It’s helpful for organisations with data quality issues or multiple systems that need to be combined for comprehensive insights.

This data insights software supports both batch and real-time data processing.

6. Validity

Best for: CRM and email data quality.

Validity focuses specifically on ensuring contact data accuracy, which is crucial for sales and marketing insights.

It provides email verification, data cleansing, and ongoing monitoring to maintain database quality. This foundation work is essential for generating trustworthy insights about customer engagement and campaign performance.

7. Cognism

Best for: Building the trusted B2B data foundation behind revenue insights.

Cognism provides the accurate, compliant European B2B data layer that revenue teams need to trust their CRM, segmentation, enrichment and AI-assisted workflows.

For organisations expanding across the UK and Europe, this matters because market coverage, contact accuracy and regulatory readiness determine whether insights can be acted on with confidence.

Cognism supports GTM revenue teams by improving the quality of the data used for planning, targeting, prioritisation, forecasting and execution.

It is especially valuable for organisations that need decision-grade data across complex European markets.

“Would I recommend Cognism? Yeah, I absolutely would! I’ve already referred Cognism to another company in Europe that lacks data. They’re particularly interested in Cognism’s European coverage.”

Read the full case study

Helped generate

7-figure opportunities

Stevie Howlett
Director of Business Development @SUB1

8. HubSpot Reports

Best for: Marketing and sales performance insights.

HubSpot Reports has pre-built reports and dashboards specifically designed for inbound marketing, sales pipeline analysis, and customer lifecycle tracking.

It’s particularly valuable for teams already using HubSpot’s CRM and marketing automation tools, offering seamless data flow and context-aware insights that don’t require technical setup.

9. Snowflake

Best for: Cloud data warehouse that powers business intelligence tools with scalable data storage and processing.

Snowflake provides the underlying infrastructure for enterprise-scale analytics, handling massive datasets while maintaining query performance.

It’s particularly valuable for organisations with complex data requirements or those planning to implement advanced analytics and machine learning capabilities.

FAQs about data insights

Build data insights on a trusted European data foundation

Revenue teams need more than dashboards. They need accurate, compliant and current data they can use to plan, prioritise and execute with confidence.

Cognism provides the premium European B2B data layer for modern revenue organisations, supporting CRM quality, market coverage, compliant execution and AI-ready GTM workflows.

See how Cognism helps revenue teams turn trusted data into better decisions.