ZTABS delivers data analytics solutions in Atlanta — custom analytics platforms for payment processors analysing transaction patterns across billions of card swipes, public health organisations leveraging Atlanta's CDC proximity for epidemiological data intelligence, logistics companies optimising freight routes through Atlanta's intermodal hub, and Fortune 500 enterprises transforming operational data into competitive advantage.
Data analytics in Atlanta converts raw business data into decision-making intelligence for industries where the volume, complexity, and specificity of data exceed what standard business intelligence tools can handle. Atlanta's data analytics landscape is shaped by: payment transaction volume (Atlanta's payment processing companies generating billions of transaction records monthly — authorisations, settlements, chargebacks, and fraud alerts creating data volumes that require purpose-built analytics infrastructure. Analytics: extracting merchant performance patterns, fraud signals, and market intelligence from transaction streams), public health data concentration (Atlanta as home to the CDC, Emory University's Rollins School of Public Health, and multiple healthcare systems — creating an ecosystem where epidemiological data analysis, clinical outcome research, and population health management drive analytics demand. Analytics: translating clinical and population data into actionable health intelligence), logistics data complexity (freight movement through Atlanta's intermodal hub generating tracking, routing, pricing, and performance data across rail, truck, and air modes. Analytics: optimising network performance and predicting capacity requirements from operational data that spans multiple transportation modes and hundreds of trading partners), and enterprise operational scale (Atlanta's Fortune 500 companies generating operational data across national and global footprints — supply chain, customer, financial, and workforce data requiring analytics that span organisational silos. Analytics: providing the cross-functional visibility that enables enterprise-level decision-making)).
Atlanta's data analytics demand spans payment intelligence, health analytics, logistics optimisation, and enterprise operations. Payment analytics: transaction intelligence platforms. Analytics requirements: merchant analytics (analytics platforms segmenting merchants by industry, geography, transaction volume, average ticket, and growth trajectory — enabling ISOs and processors to identify portfolio opportunities, predict attrition, and optimise pricing. The analytics: transforming raw transaction data into merchant intelligence that drives portfolio management), fraud analytics (real-time and historical fraud analysis — identifying fraud patterns across merchant categories, geographies, and transaction types. The analytics: supporting fraud rule development, false positive reduction, and investigation prioritisation), interchange optimisation (analytics identifying transactions that could qualify for lower interchange rates through different authorisation methods or data inclusion — helping merchants and processors reduce the largest variable cost in payment processing), and market intelligence (transaction data aggregated and anonymised to reveal market trends — consumer spending patterns by category, geographic economic indicators, and seasonal demand shifts. The analytics: providing the market visibility that payment companies sell as data products). Health and public health analytics: clinical and population intelligence. Analytics requirements: epidemiological analysis (analytics platforms for disease surveillance, outbreak detection, and public health trend analysis — connecting clinical data, laboratory results, and environmental factors. Atlanta's CDC proximity: creating a talent pool and institutional knowledge base for public health analytics that few cities match), clinical outcome analytics (analytics measuring treatment effectiveness across patient populations — risk-adjusted outcomes, readmission predictors, and care pathway analysis. The analytics: supporting quality improvement at Atlanta's major health systems), and population health management (analytics identifying at-risk populations and predicting health events — claims data, social determinants, and clinical data combined to stratify risk and target interventions. The analytics: enabling payer and provider organisations to manage population health proactively). Logistics analytics: network and operational intelligence. Analytics requirements: network optimisation (analytics modelling freight flow through Atlanta's hub — lane volume analysis, mode selection optimisation, carrier performance comparison, and capacity planning. The analytics: enabling logistics companies to optimise the network decisions that determine transportation cost and service quality), shipment analytics (analytics tracking on-time performance, transit time variability, damage rates, and cost per shipment across carriers, lanes, and service levels. The analytics: providing the operational visibility that drives carrier selection and contract negotiation), and demand forecasting (analytics predicting freight volume by lane, mode, and time period — enabling capacity positioning and pricing that anticipates demand rather than reacting to it). Enterprise analytics: cross-functional operational intelligence. Analytics requirements: supply chain analytics (analytics providing end-to-end supply chain visibility — supplier performance, inventory optimisation, demand forecasting, and logistics cost analysis. The analytics: enabling the supply chain decisions that Fortune 500 companies make daily), customer analytics (analytics segmenting customers, predicting behaviour, and measuring lifetime value — enabling the customer strategy decisions that drive growth and retention), and financial analytics (analytics providing financial planning, forecasting, and performance analysis — budget variance, profitability by product and segment, and working capital optimisation).
Our Atlanta data analytics methodology addresses data engineering, analytical model development, and the organisational adoption that determines analytics impact. Data engineering: building the analytical foundation. Data consolidation: collecting data from disparate sources — transaction systems, operational databases, third-party data, and streaming sources — into a unified analytical data store. The consolidation: handling the schema differences, data quality issues, and volume challenges that make multi-source analytics difficult. Data quality: automated data quality validation — completeness checks, consistency validation, outlier detection, and freshness monitoring. The quality: ensuring that analytics produce trustworthy insights rather than garbage-in-garbage-out conclusions. Data modelling: dimensional models designed for analytical queries — fact tables capturing business events, dimension tables providing context, and aggregation layers enabling fast queries across large datasets. The models: optimised for the access patterns that analysts and dashboards actually need. Analytics development: models and dashboards that answer business questions. Exploratory analysis: initial investigation of data to understand patterns, relationships, and anomalies — working with stakeholders to identify the highest-value analytical questions before building production analytics. The exploration: ensuring we build analytics that answer the right questions. Dashboard design: interactive dashboards designed for specific roles — executive dashboards providing strategic KPIs, operational dashboards providing real-time monitoring, and analytical dashboards providing deep-dive investigation capability. The design: matching information presentation to how each role makes decisions. Organisational adoption: analytics that people actually use. Training and documentation: stakeholder training on dashboards and self-service analytics tools — not just tool mechanics but analytical thinking and data interpretation. The training: enabling analytics consumers to use tools effectively and ask better questions. Feedback loops: regular review of analytics usage and value — identifying which dashboards are used, which questions remain unanswered, and where analytics could create additional value. The loops: ensuring that analytics evolve with business needs.
Payment Processing / Financial Services
A mid-tier Atlanta payment processor (processing $38B annual card volume across 94,000 merchant accounts, 680 employees, serving ISOs and direct merchants in 42 states) was making portfolio and risk decisions using fragmented data and basic reporting. The processor's data situation: 2.1 billion annual transaction records stored across 4 separate systems (authorisation, settlement, chargeback, and merchant management) — each with its own data model, retention policy, and reporting capability. Analysis problems: (1) merchant performance blind spots — the processor unable to answer basic portfolio questions without significant manual effort: "Which merchant segments are growing fastest?" requiring a multi-day analysis combining data from authorisation and settlement systems. "Which merchants are likely to churn?" unanswerable because no system tracked the leading indicators. "What is the true profitability of each merchant?" requiring manual combination of revenue (from settlement), cost (from interchange), and risk cost (from chargebacks), (2) fraud analysis limitations — fraud analysts investigating individual alerts using case-level tools but unable to analyse fraud patterns across the portfolio. Pattern questions: "Is BIN range X showing elevated fraud across our merchant base?" requiring manual querying across systems. "Which merchant categories have increasing fraud rates?" not answerable without custom analysis. Fraud rules: created based on analyst intuition rather than data-driven pattern analysis, (3) ISO and sales analytics gap — ISO and sales performance measured primarily on new merchant count and total processing volume. Missing dimensions: merchant quality (attrition, profitability, risk), portfolio mix, and growth trajectory. Sales compensation: based on volume metrics that did not account for the churn and risk problems that low-quality merchants created, and (4) reporting latency — most reports generated from overnight batch processes and available the next business day. Monthly reports: often not available until the 5th business day of the following month. Ad hoc analysis: requiring data team involvement (2-5 day turnaround) because self-service tools could not access the relevant data. Board and management reporting: PowerPoint presentations manually assembled from disparate data sources.
We built a transaction intelligence platform over 16 weeks. Data unification layer (weeks 1-6). Unified transaction data store: all 4 source systems (authorisation, settlement, chargeback, merchant management) consolidated into a cloud-based analytical data warehouse — transaction records enriched with derived metrics (interchange cost, net revenue, risk score) and linked across systems by merchant and transaction identifiers. The unification: creating the single source of truth that enabled cross-system analytics. Data pipeline: near-real-time data ingestion from authorisation system (streaming), settlement system (hourly batch), chargeback system (real-time), and merchant management (event-driven). The pipeline: ensuring analytical data was current within 1-4 hours depending on source system. The freshness: enabling same-day decision-making rather than next-day reporting. Historical backfill: 36 months of historical transaction data migrated and reconciled — enabling trend analysis, seasonal pattern detection, and model training against historical data. The backfill: essential for the predictive models that required historical patterns. Data quality framework: automated validation checking transaction counts, volume totals, and derived metrics against source system reports — discrepancies flagged and investigated before data was made available for analysis. The framework: ensuring that the analytical platform was trusted as accurate. Merchant intelligence (weeks 4-10). Merchant 360 analytics: comprehensive merchant analytics — processing volume trends, revenue and profitability, chargeback rates, transaction patterns, and peer comparisons. Each merchant: profiled against merchants in the same industry, geography, and volume tier. The profiling: enabling portfolio managers to understand each merchant in context rather than isolation. Portfolio segmentation: automated merchant segmentation by industry, volume tier, growth trajectory, risk level, and profitability — segments updated monthly and tracked over time. The segmentation: revealing portfolio composition and trends that were previously invisible. Churn prediction model: ML model predicting merchant attrition risk based on processing volume trends, chargeback rate changes, transaction count patterns, and seasonal comparisons. The model: scoring each merchant monthly with a 0-100 attrition risk score. Validation: the model identifying 73% of merchants that churned within 90 days, enabling proactive retention outreach for the highest-risk accounts. Profitability analytics: true merchant profitability calculated by combining revenue (processing margin), cost (interchange, network fees, operational cost allocation), and risk cost (chargeback losses, fraud losses, reserve requirements). Profitability: calculated per merchant, per segment, and per ISO — revealing that 22% of merchants were unprofitable when all costs were included. Fraud intelligence (weeks 6-12). Fraud pattern analytics: transaction data analysed across the entire portfolio to identify fraud patterns — BIN ranges with elevated fraud, merchant categories experiencing new attack vectors, geographic clusters, and time-of-day patterns. The analytics: enabling fraud rules to be data-driven rather than intuition-based. Rule effectiveness analysis: each fraud rule measured for detection rate, false positive rate, and financial impact — rules ranked by net value (fraud prevented minus false positive revenue impact). The analysis: identifying rules that caught fraud efficiently and rules that blocked legitimate transactions without proportionate benefit. Fraud dashboard: real-time fraud monitoring dashboard — fraud rate by merchant category, alert volume trends, analyst productivity, and financial impact. The dashboard: giving fraud management the operational visibility to allocate analyst resources effectively. Sales and ISO analytics (weeks 8-14). ISO performance analytics: ISO and agent performance measured across multiple dimensions — new merchant boarding, processing volume growth, portfolio quality (attrition rate, chargeback rate), and profitability contribution. The analytics: providing the complete picture that volume-only metrics missed. Sales pipeline analytics: opportunity tracking with conversion rate analysis by lead source, merchant category, and sales representative — identifying which lead sources and sales approaches produced the highest-quality merchants. Revenue forecasting: ML model predicting processing volume and revenue by merchant, segment, and portfolio — providing the 30/60/90 day revenue forecast that the CFO needed for financial planning. Self-service analytics layer (weeks 10-16). Executive dashboards: role-based dashboards — CEO dashboard showing portfolio health, growth trends, and strategic KPIs. CFO dashboard showing revenue, profitability, and financial forecasts. CRO dashboard showing sales pipeline, ISO performance, and growth opportunities. VP Fraud dashboard showing fraud rates, rule performance, and analyst productivity. Analyst self-service: self-service analytics tool enabling analysts to explore transaction data without requiring data team involvement — pre-built data models, drag-and-drop query building, and scheduled report delivery. The self-service: enabling the ad hoc analysis that previously required 2-5 day data team turnaround. Automated reporting: monthly board report, weekly management report, and daily operational report generated automatically from the analytical platform — replacing the manual PowerPoint assembly process.
The analytics platform deployed across the organisation within 18 weeks. Merchant retention impact: churn prediction enabled proactive retention outreach — contacting at-risk merchants 30-60 days before predicted churn. Retention team: retaining 41% of contacted high-risk merchants. Annual merchants retained: approximately 2,150 merchants representing $620M in processing volume. Revenue impact: $4.34M annually in retained processing margin. The prediction accuracy: 73% of merchants that actually churned had been flagged as high-risk, validating the model. Portfolio profitability: profitability analytics revealing that 22% of merchants were unprofitable — enabling pricing adjustments, cost reduction, and selective attrition of the highest-loss accounts. Portfolio margin improvement: 0.8 basis points across the entire processing volume — translating to $3.04M annual revenue increase from informed pricing and portfolio decisions. Fraud detection: data-driven fraud rules replacing intuition-based rules — detection rate improved 34% while false positive rate decreased 28%. Financial impact: $2.8M annual fraud loss reduction. Rule effectiveness analysis: identifying 14 legacy rules that generated high false positive rates with minimal fraud detection — removing or modifying those rules recovering $1.2M in previously blocked legitimate transactions. Operational efficiency: ad hoc analysis: from 2-5 day turnaround (data team involvement) to self-service (minutes). Monthly reporting: from 3-5 day manual assembly to automated same-day delivery. Data team reallocation: 60% of data team time freed from report generation to analytical model development. Sales and ISO management: ISO performance visibility driving recruitment and management decisions — low-quality ISOs identified and counselled, high-quality ISOs given incentives and resources to grow. New merchant quality: 23% improvement in first-year merchant retention for newly boarded merchants — better lead source analytics and quality metrics improving acquisition targeting. Financial results: retained merchant revenue: $4.34M/year. Portfolio margin improvement: $3.04M/year. Fraud loss reduction: $2.8M/year. Recovered legitimate transactions: $1.2M/year. Operational efficiency: $480K/year. Total annual impact: $11.86M/year. Analytics platform development: $520,000. Annual operations: $96,000/year. First-year ROI: 2,181%.
Interactive dashboards with real-time metrics, drill-downs, and exportable reports.
Data ingestion, transformation, and loading from multiple sources into a central warehouse.
Scheduled reports, ad-hoc analysis, and KPI tracking aligned with your business.
ML models for forecasting, churn prediction, and trend analysis when needed.
Connect CRMs, databases, APIs, and spreadsheets into a unified analytics layer.
Atlanta's data analytics market includes enterprise BI platforms (Tableau, Power BI, Looker — offering strong visualisation but requiring clean, consolidated data that most organisations do not have without significant data engineering), consulting firms (McKinsey, Deloitte, Accenture — offering analytical strategy and implementation but at price points that include large teams and extended engagements), data engineering firms (offering data pipeline and warehouse development but without the analytical modelling and business understanding that produces actionable insights), and analytics product companies (offering industry-specific analytics products with limited customisation for unique business requirements). ZTABS differentiates through full-stack analytics (data engineering, analytical modelling, and visualisation delivered as an integrated solution — not just dashboards on messy data), Atlanta industry expertise (payment processing, healthcare, logistics, and enterprise operations — analytics designed for the specific data patterns and business questions each industry generates), predictive modelling (ML models for churn prediction, fraud detection, demand forecasting, and financial projection — analytics that predict outcomes rather than just report history), and adoption-driven delivery (analytics designed for how people actually make decisions — role-specific dashboards, self-service tools, and automated reporting that ensure analytics get used).
Data analytics in Atlanta ranges by data complexity and analytical depth. Analytics dashboards (existing data, visualisation focus, 4-8 weeks): $40,000-$120,000. Analytical platform (data engineering + analytics, 8-14 weeks): $150,000-$400,000. Enterprise analytics platform (multi-source, ML models, self-service, 14-22 weeks): $400,000-$1,000,000. Ongoing analytics operations and model refinement: $8,000-$25,000/month.
Verified reviews from real client engagements — sourced from our public testimonial archive and Clutch profile.
My experience is throughout positive. Communication, service, the short response times and the flawless execution of a challenging topic was absolutely great. ZTABS is definitely my first choice again.
Christian Neff
Bank Software Advisory · Bank Software Advisory
Fintech
Fantastic Agency! I couldn't fault them even if I tried. They always go above and beyond to meet your expectations and always produces quality work. Thank you ZTABS.
Stephanie Kal
CEO · Beauty Finder Australia
Marketplace
It has been great working with ZTABS. They bounce off the ideas along the way. Amazing Experience.
Joel Rowe
CEO · Drill Quoter
Marketplace
We don't just contract. We ship and operate our own software. 23 products in production.
Common questions about data analytics & bi for Atlanta businesses
We build automated data quality validation into the pipeline — completeness checks, consistency validation, outlier detection, and cross-source reconciliation. Issues are flagged, classified, and routed for correction before affecting analytics. Quality monitoring dashboards track data health continuously.
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