AI Intelligence & Personalization · Data and Decision Intelligence
From Data Swamp to Revenue Lake:
AI-Powered Decision Intelligence for B2B Revenue Teams
Disconnected systems and dirty data kill marketing performance — not strategy failures and not team failures. When customer data lives in the MAP, campaign data lives in the ad platforms, pipeline data lives in Salesforce, and none of it talks to each other under a consistent customer identifier, no analysis produces reliable answers and no AI model produces reliable predictions. TPG transforms this situation: unifying your data infrastructure, embedding AI decision support into the workflows your team uses every day, and building the revenue intelligence layer that replaces dashboard debates with clear signals that drive action.
Three Core Capabilities
Data, decisions, and revenue — unified
TPG's Data and Decision Intelligence practice delivers across three interconnected capabilities. Each depends on the others: you cannot make AI-powered decisions without unified data, and you cannot produce revenue intelligence without the AI decision layer on top of the unified foundation.
Data Unification
AI-powered data lakes that consolidate, clean, and enrich your customer, campaign, and performance data into one revenue-ready environment. The foundation everything else requires.
- Cross-system entity resolution and identity matching
- Field standardization and taxonomy alignment
- Data quality remediation and governance
- Real-time data pipeline architecture
- CRM, MAP, web analytics, and ad platform integration
AI Decision Support
Real-time recommendations that empower your team to act faster, not just report later. Next-best actions, forecast accuracy improvements, and AI decision support embedded into the systems your team already uses.
- Next-best-action recommendations in CRM and MAP
- AI-powered lead and account prioritization
- Decision augmentation with projected outcomes
- Automated decision workflows within governed parameters
- Model explainability for sales and marketing trust
Revenue Intelligence
360-degree view of pipeline, performance, and attribution. No more debating dashboards. Clear signals that drive smarter investments and stronger alignment across marketing, sales, and finance.
- Multi-touch attribution modeling and configuration
- Pipeline contribution reporting by channel and program
- Predictive pipeline and forecast modeling
- Executive revenue dashboards that connect to decisions
- Scenario modeling for marketing investment optimization
Section 01
The Problem: Disconnected Systems and Dirty Data Kill Performance
Why most B2B marketing organizations cannot answer the revenue question — and why the root cause is infrastructure, not strategy.
The data swamp problem and why it cannot be solved with better reporting tools
The data swamp is not a reporting problem. It is an infrastructure problem. A marketing organization with a data swamp has customer data in the MAP database, behavioral data in web analytics, campaign spend data in the advertising platforms, lead and contact data in Salesforce, product usage data in a separate product analytics tool, and customer support data in a service platform — and none of these data sources share a consistent customer identifier that would allow them to be joined. When the CMO asks "which marketing programs produced pipeline this quarter?" the analyst cannot answer because there is no technical path from a marketing program in HubSpot to an opportunity in Salesforce to a revenue line in the finance system that is reliable and complete. The instinctive response to this problem is to buy a better reporting tool or hire a better data analyst. Neither works, because the reporting tool and the analyst both face the same underlying problem: the data is not connected, and no amount of analytical sophistication produces reliable answers from disconnected inputs.
The practical manifestation of the data swamp in B2B marketing organizations is the dashboard debate: the marketing dashboard shows that the Q3 ABM program influenced 47 opportunities, the Salesforce pipeline report shows 12 opportunities with marketing source, and the finance forecast shows pipeline from 3 different sources depending on which report the CFO opened this morning. Every stakeholder has different numbers because every report draws from a different slice of the data, and the slices have never been reconciled. The solution is not a new dashboard. It is a unified data model where every record — contact, lead, account, campaign, opportunity, and revenue event — is connected under a consistent customer identifier, and every analytical and reporting system draws from the same unified foundation. TPG calls this the revenue lake: the infrastructure investment that makes every downstream analysis, prediction, and decision reliable.
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Section 02
Data Unification: From Data Swamp to Revenue Lake
How TPG designs and implements the unified data infrastructure that makes every downstream analysis, prediction, and decision reliable.
What data unification for revenue marketing actually involves
Data unification for revenue marketing is a four-step infrastructure build. Step one is entity resolution: establishing consistent customer identifiers across every system in the stack. A contact in HubSpot, a lead in Salesforce, a visitor in web analytics, and a user in the product platform are all the same person, but each system has a different internal ID for that person. Entity resolution builds the matching logic that reconciles these disparate identifiers into a single customer identity, and applies that logic retroactively to historical records so that historical analysis can connect campaign touches to pipeline outcomes that occurred years before the unification was built. Step two is field standardization: every system uses slightly different field names, values, and formats for the same information. The company size field in HubSpot uses revenue ranges. The company size field in Salesforce uses employee count. The company size field in the data warehouse uses NAICS codes. Field standardization maps every system's field definitions to a common taxonomy before any analysis is performed, so that "enterprise account" means the same thing in every report.
Step three is data quality remediation: the unified data model is only as reliable as the data feeding it, so the remediation step addresses the specific quality problems in each source system before unification. This includes duplicate record detection and merge logic (a CRM with 40% duplicate rate produces attribution reports that inflate marketing influence by 40%), invalid field value correction (lifecycle stage fields with inconsistent values produce segmentation that assigns contacts to the wrong nurture programs), and incomplete record enrichment (ICP and segmentation criteria that are blank on 60% of records cannot drive reliable scoring or targeting). Step four is attribution architecture: configuring the campaign influence and deal touch recording in each platform so that marketing touches create the trackable records that connect them to opportunities. Without attribution architecture, data can be unified but the revenue contribution question still cannot be answered because the data does not contain the links between marketing activity and pipeline outcomes.
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Section 03
AI Decision Support: Act Faster, Not Just Report Later
How TPG embeds AI decision support into the day-to-day systems your marketing and sales teams use — delivering next-best actions and forecast accuracy improvements in context, not in a separate analytics tool.
The gap between having data and making better decisions faster
Most B2B marketing organizations have more data than they act on. The data exists in the MAP, the CRM, the web analytics tool, and the attribution report. The problem is not that the data is absent. The problem is that the decision-making workflow does not incorporate the data at the moment the decision is made. A sales representative opening a lead in Salesforce does not have time to switch to the attribution report, filter by the lead's company, review their engagement history, and determine the best approach before calling. A marketing manager planning next week's campaign sends does not have time to run a propensity analysis on each segment, identify which segments have the highest likelihood of converting this week, and adjust the send priority before the campaign deadline. The data needed to make these decisions better exists. It is not embedded in the workflow at the moment the decision happens. AI decision support closes this gap by delivering the relevant insight at the point of decision — in the Salesforce record, in the HubSpot deal, in the campaign planning interface — rather than in a separate analytics tool that requires context-switching to use.
TPG implements AI decision support at three levels of operational depth: decision prompts (AI-generated next-best-action recommendations surfaced in the CRM and MAP at the moment of user interaction, requiring human confirmation before action), automated decisions within guardrails (AI models that execute defined, low-risk decisions autonomously within policy parameters, logging every action for audit and review), and decision augmentation (AI models that analyze patterns from historical decisions, identify which scenarios produce which outcomes, and recommend specific options with projected outcome ranges for high-stakes decisions that require human judgment). The model explainability layer is a prerequisite for adoption: a sales representative who receives an AI-generated recommendation that says "call this account today" without any explanation of why will ignore it. A recommendation that says "call this account today: they visited the pricing page 3 times in the last 48 hours, downloaded the competitive comparison guide, and have not responded to the last two nurture emails — our model shows a 71% probability of converting to a meeting within 7 days if outreach happens today" will be acted on.
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Section 04
Revenue Intelligence: 360-Degree Pipeline Visibility
How TPG builds the revenue intelligence infrastructure that connects every marketing investment to pipeline and closed revenue — ending dashboard debates with a single shared source of truth.
What 360-degree pipeline visibility actually means and what it requires to build
The phrase "360-degree view of pipeline" has been in marketing technology marketing materials for a decade. Most organizations that believe they have pipeline visibility discover, when asked to explain which specific marketing programs produced specific pipeline this quarter, that the answer requires a multi-step manual process involving data exports from the MAP, VLOOKUP joins in Excel, and a conversation with the data analyst about which opportunities should be attributed to which campaigns. This is not pipeline visibility. It is pipeline approximation, produced on demand by skilled analysts who have learned to work around the data infrastructure gaps. True pipeline visibility produces the answer immediately, from a live dashboard that all stakeholders trust, because the underlying data model connects marketing program to campaign member record to opportunity to closed revenue without manual joins. Building this requires the unified data model, the attribution architecture, and the reporting configuration to all be in place simultaneously — which is why it is an infrastructure project, not a reporting project.
TPG's revenue intelligence program produces four specific outputs: a unified pipeline report that shows marketing-sourced and marketing-influenced pipeline by program type, channel, segment, and time period, drawing from the same underlying data that sales and finance use; a campaign attribution dashboard that shows first-touch, last-touch, and multi-touch credit assignments for every closed opportunity in the period, with enough data for stakeholders to validate the model against their own deal knowledge; a program ROI dashboard that shows cost-per-opportunity, cost-per-SQL, and marketing-influenced revenue by program, enabling investment reallocation decisions based on return rather than intuition; and a forward-looking pipeline forecast that combines current pipeline data with historical conversion rates and seasonal patterns to project marketing-attributed revenue for the next 30, 60, and 90 days. These four outputs together constitute the revenue intelligence infrastructure that moves marketing from monthly reporting cycles to real-time visibility, and from budget defense conversations to investment optimization discussions.
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Section 05
Predictive Modeling: See What Is Coming Before It Arrives
How TPG builds and deploys predictive models that forecast pipeline, identify at-risk accounts, surface high-propensity opportunities, and enable proactive action before problems or opportunities become visible in standard reporting.
The four predictive models with the highest direct pipeline impact
Predictive modeling in B2B revenue marketing encompasses several model types, but four have consistently demonstrable pipeline impact. Lead propensity models score each contact or account's probability of converting to an opportunity within a defined window, based on behavioral signals, firmographic attributes, and historical conversion patterns specific to the organization's ICP. These models replace static scoring thresholds with dynamic probability estimates that adjust as new signals arrive, producing a prioritization queue that reflects actual buying intent rather than accumulated engagement points. Account health models predict churn risk, expansion readiness, and advocacy potential for existing customers, enabling the customer success and expansion teams to focus on the accounts most likely to renew, expand, or refer rather than distributing attention uniformly. Revenue forecast models apply historical stage conversion rates and velocity patterns to current pipeline to generate the statistically grounded pipeline forecast that replaces manager-submitted probability estimates with model-derived ranges that the CFO can trust.
Campaign response models are the fourth category with direct pipeline impact: they predict which content offers, messaging variants, and delivery channels will produce the highest response rate for each audience segment at each stage of the buyer journey, enabling content investment to be directed toward the combinations with the highest predicted pipeline contribution rather than the combinations the marketing team finds most interesting. All four model types share a common prerequisite: the unified data model and data quality infrastructure described in the earlier sections. A propensity model trained on a database with 40% duplicate records produces scores inflated by duplicate engagement. A forecast model built on pipeline data where stage definitions are inconsistently applied produces range estimates that do not reflect actual deal risk. TPG builds predictive models as part of the full data and decision intelligence engagement, not as standalone analytics projects, because the model quality depends entirely on the infrastructure quality it runs on.
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Section 06
Attribution Architecture: Connecting Every Touch to Revenue
How TPG designs and configures the multi-touch attribution infrastructure that connects marketing program investments to pipeline creation and closed revenue — in the systems that sales and finance already use.
Why attribution fails in most B2B organizations and what the fix requires
Attribution fails in most B2B marketing organizations not because of disagreement about the right attribution model (first touch, last touch, linear, time decay, or algorithmic) but because the technical infrastructure required to run any attribution model is never built. Attribution requires three things: that every marketing touch creates a record in the system that can be linked to the contact or account that experienced the touch; that the contact or account that experienced the touch is connected to the opportunity that eventually opened in the CRM; and that the opportunity is connected to the revenue that was recorded when the deal closed. In practice, most B2B organizations have the last two connections (contact to opportunity is the CRM's job, opportunity to revenue is the CRM's job) but not the first (MAP touches creating records that link to CRM contacts). The campaign member record in Salesforce is the standard mechanism for this connection, but it requires Connected Campaigns or campaign sync configuration in the MAP that most organizations have not completed, or have completed incorrectly.
TPG's attribution architecture work covers the full technical implementation required to make multi-touch attribution operational: MAP-to-CRM campaign influence configuration (Connected Campaigns for Pardot, campaign sync for Marketo and Eloqua, deal attribution for HubSpot), campaign hierarchy design (structuring campaign records in Salesforce or HubSpot to support the attribution model the organization needs), influence window configuration (defining the lookback period for which marketing touches count against opportunities), model selection (choosing the attribution model that best reflects the organization's sales cycle length and marketing investment mix), and reporting layer configuration (building the attribution dashboards in the CRM or BI tool that surfaces the model's outputs in a format stakeholders can act on). Attribution is not a philosophical question about which model is theoretically correct. It is a technical configuration question about which model the organization can implement given its current system setup and data quality, and then a governance question about how to maintain that implementation as systems and processes evolve.
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Section 07
The Decision Intelligence Framework: From Data to Action
How TPG's decision intelligence framework sequences the three capability layers — data unification, AI decision support, and revenue intelligence — and ensures each layer is built in the right order.
Why the sequence matters and what happens when it is skipped
The most common decision intelligence implementation failure is building the analytical or AI layer before the data infrastructure is ready. Organizations that invest in predictive lead scoring before their MAP-CRM integration is stable build models that produce inaccurate scores. Organizations that invest in revenue attribution dashboards before their campaign influence configuration is complete build dashboards that show zero marketing attribution because the data does not exist. Organizations that invest in AI decision support before their data quality remediation is done build recommendation engines that confidently recommend wrong actions because the data they are trained on contains systematic errors. The sequence matters: data unification before analytics, data quality before modeling, attribution infrastructure before attribution reporting. TPG's decision intelligence framework enforces this sequence through the RM6 diagnostic, which identifies which data and infrastructure capabilities are in place before any analytics or AI investment is designed.
The framework operates in three phases: Foundation (data unification, entity resolution, field standardization, and data quality remediation — the infrastructure that makes every subsequent investment reliable), Intelligence (attribution architecture, predictive model development, decision support configuration, and revenue intelligence dashboard build — the analytics and AI layer on top of the unified foundation), and Action (decision workflow embedding, next-best-action delivery in operational systems, automated decision implementation within governance parameters, and the continuous improvement loop that updates models as new conversion data accumulates). Organizations at different RM6 maturity stages enter the framework at different phases: Stage 1 Traditional organizations typically need full Foundation work before Intelligence investment makes sense. Stage 3 Demand Generation organizations often have partial Foundation work done and can begin selective Intelligence work in the areas where data quality is strongest. Stage 4 Revenue Marketing organizations are typically in the Action phase, continuously improving models and expanding the scope of AI-assisted decision-making.
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Section 08
Data Quality and Governance as Intelligence Prerequisites
Why data quality is not a data engineering problem but a revenue problem — and how TPG builds the ongoing governance program that keeps the intelligence infrastructure reliable over time.
The specific data quality problems that corrupt marketing intelligence and how each is addressed
Data quality problems in marketing systems corrupt intelligence outputs in predictable ways. Duplicate records inflate engagement metrics and produce attribution overcounting: a contact who exists in Salesforce as three records (one from each lead submission over three years) appears to have 3x the engagement of a contact with one clean record, which inflates their lead score above threshold and produces false MQL qualification. Incomplete records produce unreliable scoring and segmentation: a lead scoring model cannot correctly qualify a contact whose industry, company size, and job title fields are blank, so the model defaults to behavioral signals alone and systematically misqualifies contacts who are strongly behaviorally engaged but outside the ICP. Stale data produces incorrect prioritization: a contact whose lifecycle stage has not been updated since their initial form submission two years ago may be enrolled in an early-stage nurture program while they are actually in an active evaluation, receiving irrelevant content that suppresses the engagement signals the scoring model needs to advance them.
TPG's data quality governance program addresses these problems with four operational components: deduplication (automated detection and merge logic on a rolling basis, with governance standards for which system owns the authoritative record when a merge is performed), field completion programs (structured data enrichment campaigns and progressive profiling configurations that systematically collect the ICP and segmentation criteria that scoring and personalization require), lifecycle stage governance (automation rules and MAP configurations that maintain consistent stage progression from first touch through customer, aligned with the CRM stage definitions the sales team uses), and data health monitoring (monthly data quality reports showing duplicate rate, invalid field rate, and lifecycle stage distribution, with defined thresholds that trigger remediation cycles when quality falls below acceptable levels). The governance program is not a one-time project. It is an ongoing operational function that maintains the intelligence infrastructure's reliability as new contacts enter the database, as contacts change jobs and companies, and as the ICP and product offering evolve.
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Section 09
Revenue Dashboards That Drive Decisions, Not Debates
How TPG designs revenue dashboards that produce action rather than discussion — organized around the questions that drive investment decisions, not the metrics that are easiest to measure.
The difference between a dashboard that gets opened in meetings and one that gets acted on between meetings
Most marketing dashboards are opened in meetings, reviewed, and closed without producing a specific decision or action. The data is present. The charts are well-designed. But the metrics on the dashboard do not connect directly to the decisions the team needs to make this week. A marketing dashboard showing email open rates, LinkedIn impressions, and webinar attendance produces no specific action because these metrics do not answer the question of where to allocate next week's marketing investment for maximum pipeline impact. A revenue dashboard showing which three ABM target accounts have accelerated intent signals in the last seven days, which two nurture programs are producing below-average MQL-to-opportunity conversion, and which one content cluster is producing above-average meeting conversion for the CFO persona produces three immediate action decisions without requiring any additional analysis. The design principle for revenue dashboards is to organize every visualization around a question that produces an action when answered, not around a metric that is available and easy to display.
TPG's revenue dashboard design framework covers four dashboard types: the executive pipeline dashboard (pipeline coverage, forecast accuracy, marketing-influenced revenue, cost-per-opportunity, and MoM pipeline trajectory — the view the CMO and CRO need for weekly alignment), the program performance dashboard (campaign-level attribution, program ROI, segment conversion rates, and content-to-pipeline contribution — the view the demand gen team needs for weekly optimization), the account intelligence dashboard (account engagement scores, intent signal changes, buying committee coverage, and next-best-action recommendations — the view the ABM team and sales need for account prioritization), and the data health dashboard (duplicate rate, field completion rate, lifecycle stage distribution, and attribution coverage rate — the operational view that ensures the first three dashboards remain reliable over time). All dashboards are built in the tools the relevant stakeholders already use — Salesforce, HubSpot, Tableau, Looker, or Power BI — rather than in a standalone analytics platform that requires context-switching to access.
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Section 10
How to Engage TPG for Data and Decision Intelligence
The entry points, engagement models, and first steps for organizations at different data maturity stages.
Starting with where you are, not where you want to be
Organizations that engage TPG for data and decision intelligence typically arrive at one of three starting points. The first is the infrastructure gap: the organization has been investing in analytics tools and dashboards but cannot produce reliable attribution reporting because the MAP-CRM integration was never fully configured, entity resolution was never done, and every report produces different numbers depending on who runs it and which system they use. For these organizations, the engagement starts with a data audit that maps the specific infrastructure gaps and a phased remediation plan that prioritizes the gaps by their impact on revenue reporting reliability. The second starting point is the analytics gap: the organization has reasonable data quality but is not getting actionable intelligence from it because the dashboards show activity metrics rather than revenue contribution, predictive models have not been built, and the data that exists is not embedded in the operational workflows where decisions are made. For these organizations, the engagement starts with the attribution architecture build, the predictive model development, and the dashboard redesign. The third starting point is the AI gap: the organization has unified data and revenue attribution but wants to move from descriptive and predictive analytics to prescriptive decision support and automated decision workflows.
For all three starting points, the process begins with an RM6 diagnostic to confirm which capabilities are in place and which are gaps, and a data audit that assesses quality across the specific dimensions that matter for the desired intelligence outputs. The RM6 diagnostic is available as a free self-assessment at pedowitzgroup.com/revenue-marketing-maturity-assessment. The data audit is conducted as the first phase of a TPG engagement. From these two inputs, TPG produces a prioritized data and decision intelligence roadmap that sequences the infrastructure, analytics, and AI investments in the order that produces the fastest reliable improvement in revenue reporting and decision quality — starting with the foundation rather than building analytical sophistication on top of data that is not ready to support it.
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Data and Decision Intelligence: Frequently Asked Questions
Direct answers to the most common questions about decision intelligence, data unification, revenue attribution, and what TPG delivers.
What is decision intelligence in marketing?
Decision intelligence in marketing is the application of AI, data science, and behavioral science to improve how marketing and revenue teams make decisions. It operates across three layers: decision support (AI surfaces insights that help humans choose better), decision augmentation (AI analyzes patterns from past decisions and recommends specific options with projected outcomes), and decision automation (AI makes defined decision types autonomously within governed workflows).
The result is a marketing organization that acts on real-time signals rather than monthly reports — moving from after-the-fact analysis to proactive, AI-guided execution.
What is a revenue lake and how is it different from a data warehouse?
A revenue lake is a unified data environment specifically designed to answer revenue questions: which marketing programs produced pipeline, which pipeline closed, at what cost, and what the model predicts for next quarter. Unlike a general-purpose data warehouse, every record in a revenue lake is connected to a customer identity, every marketing touch is linked to an opportunity, and every data source is mapped to a unified model before analysis begins.
The transformation from data swamp (data in ten disconnected systems) to revenue lake (data unified under consistent identifiers with attribution architecture built in) is the foundational infrastructure investment that makes decision intelligence possible.
Why do marketing dashboards fail to produce better decisions?
Most marketing dashboards fail to produce decisions because they report on activity rather than revenue contribution, because the data feeding them has never been unified across systems, or because they present information at an aggregation level too high to drive specific action. A dashboard showing email open rates declined 8% does not tell the CMO what to do. A decision intelligence system that identifies the specific segment where rates declined, surfaces the reason, and recommends the correction produces actionable direction.
TPG builds revenue dashboards organized around the questions that produce decisions: what is driving pipeline this quarter, where are the conversion gaps, and what does the model predict we will close.
How does TPG approach data unification for marketing organizations?
TPG approaches data unification starting with the revenue question the organization needs to answer, then identifying which data sources need to be unified. The process covers: entity resolution (consistent customer identifiers across CRM, MAP, web analytics, and other systems), field standardization (mapping inconsistent field definitions to a common taxonomy), data quality remediation (deduplication, invalid field correction, incomplete record enrichment), and attribution architecture (campaign influence and deal touch recording that connects marketing activity to pipeline).
Data unification is always a prerequisite to analytics investment. Building dashboards or predictive models on disconnected, uncleaned data produces unreliable outputs regardless of the sophistication of the analytics layer.
What is the difference between predictive analytics and decision augmentation?
Predictive analytics uses historical data to forecast future outcomes. A lead scoring model predicts which contacts are most likely to convert. A pipeline model predicts close probability. Decision augmentation goes further: it analyzes patterns from past decisions, the scenarios that produced those decisions, and the consequences of each decision, then recommends specific options to the decision-maker with projected outcomes for each option.
A predictive tool tells a sales manager that three accounts are at risk. A decision augmentation system tells the manager which specific intervention has the highest historical retention rate for accounts showing that attrition pattern, and what the projected retention lift is for each option.
What data sources does TPG integrate in a revenue intelligence program?
A TPG revenue intelligence program typically integrates: CRM pipeline data (Salesforce, HubSpot) as the primary revenue record, marketing automation engagement data (Marketo, Eloqua, HubSpot, Pardot) as the marketing touch record, web analytics as behavioral and intent signal, advertising platforms (Google, LinkedIn) as paid channel investment records, and BI tools (Tableau, Looker, Power BI, Datorama) as the reporting layer. Product usage data is added for organizations with SaaS or subscription products as a customer health and expansion signal.
The architecture principle is consistent regardless of stack: every record connects to a customer identifier, every marketing touch connects to an opportunity, and every data source is mapped before any analytical or predictive model is built on top.
Transform Your Data Swamp into a Revenue Lake
Most B2B marketing organizations have more data than they act on. The problem is not the data. It is the infrastructure that connects it, the AI layer that extracts intelligence from it, and the dashboards that surface that intelligence at the moment decisions are made. TPG builds all three.