Data integration

Gain insight and speed by rationalizing data across diverse cloud and legacy apps

To find insights and make decisions to move business forward, you need lots of cohesive, coherent data. But your data is fractured and split across many applications, and the move to cloud computing has only made the problem worse. Before, all the data was under your control, but now it's spread across many cloud platforms that charge real money to move data around. Torry Harris has the data architecture, data integration, and cloud platform expertise to bring your data together when, where, and how it's needed to drive transformation decision-making and build streamlined, efficient business processes.

Explore our approach and tools

What we do for data integration

We help in...

Identifying your organization's critical needs for cohesive data to drive business insights and process efficiency.
Assessing the costs and value of potential data integration scenarios to identify alternatives for where and how to synchronize, connect, or virtualize data across cloud platforms and applications.
Prioritizing and sequencing implementation of data integration solutions.
Building data models and metadata management processes as your foundation for continual data optimization of data integration success.
Applying diverse data integration strategies and technologies to drive your business forward, including big data integration, master data management, data virtualization, and more.

What our clients and analysts are saying…


48 disrupts market with unique digital experience for its young customers

The video features Paddy Leahy, Digital Product Lead at 48, Three Ireland. In a first for the Irish market, 48 offers its subscribers a choice of how they use their data allowance via “Flexi Data” in the my48 application. In this video, Paddy discusses how 48’s flexible, digital-only proposition was made possible by migrating legacy systems and infrastructure to the cloud, working with partner Torry Harris Integration Solutions (THIS). A complete overhaul of customer portals and user experience further accelerated 48’s digital transformation.


Torry Harris helps Schneider Electric to integrate over 21 ERPs across 40 countries

Jean-Christophe Pharose, Global Finance Program Manager at Schneider Electric, talks about the “Concur” program, why it is useful and how Torry Harris played a crucial role in its success.

Our approach and tools

Data integration is difficult not only because it is becoming fractured across so many cloud platforms and SaaS apps, but also because data tends to be the most latency-sensitive layer of solution architectures. To ensure your enterprise data architecture and integration strategy drives your business forward, we ensure a strong understanding of:

  • The major structures of your enterprise data model - long-duration, fully-detailed modeling efforts waste time and resources, but a broad understanding provides a foundation for focusing on the details needed to solve current business problems.
  • What sources of data add value to your business, whether it's applications in your data center or SaaS, internet-of-things events, transaction streams, external data sources, a competitor's website, structured, semi-structured, unstructured, or some new data source.
  • Who needs and uses which data - and will need it in the future - and whether the need is for reference data, transaction processing, automated decisions, finding business patterns and trends, or something else, since how data is used drives availability and quality of service characteristics of your data architecture.
  • How to get the right data, whether raw or preprocessed for quality or quantity, to the right place and connected to other relevant data - especially considering the costs of moving data in and out of SaaS and cloud platforms.
  • How to assemble a variety of data infrastructure, data integration tools, data residency regulations, analytics tools (e.g., AI-ML, semantic, geo, et al), data architectures, and delivery mechanisms into a cohesive strategy for high-value data-driven business.
What data capabilities, data-driven value and insight do you need? What is our expertise? What tools do we use?
Customer 360, Partner 360, Anything 360
  • Enterprise data modeling
  • Metadata management
  • Data quality
  • Operational data stores
  • API enablement of large data stores
  • ETL, ELT, and change data capture (e.g., Qlik, SnapLogic, Informatica, Ab Initio, Talend, StreamSets)
  • Master data management (e.g., Reltio)
  • Data virtualization (e.g., Denodo)
  • Denodo Accelerator built by THIS – Helm charts for deploying Denodo workloads on Kubernetes cluster
  • Cloud data platforms (e.g., GCP, AWS, Azure)
  • OData, GraphQL, REST APIs
  • ElasticSearch
Customer value assessment Customer churn prediction
  • Big data management
  • Cloud-based data lakes (BigQuery, CloudSQL, AWS RedShift)
  • Data streaming (e.g., Kafka)
  • AI-ML tools (4Sight, accelerator built by THIS, Google Cloud AutoML, TensorFlow, Amazon Lex, Amazon SageMaker
Automated decision-making
  • Stream processing
  • Event management
  • Application integration
  • Streaming analytics (e.g., KSQL)
  • Event processing (e.g., Kafka)
  • Rules engines (e.g., Ab Initio BRE)
  • 4Sight, AI-ML tool built by THIS
Competitive threat analysis
  • Internet data mining
  • Pattern detection
  • Social monitoring
  • AI-ML (e.g., Google Cloud AutoML, TensorFlow, Amazon Lex, Amazon SageMaker)
  • Traditional business intelligence
  • IoT event filtering
  • THIS IoT Glue®
  • Kafka, Kafka Streams, CoAP
  • DigitMarketTM Microgateway
High-end application scaling
  • Data performance patterns (e.g., Sharding)
  • Secure API enablement and exposure of data
  • Cloud-native architecture
  • Auto-scaling
  • Data design for microservices
  • Distributed transactions
  • In-memory data grids (e.g., Pivotal GemFire, Oracle Coherence, IBM WebSphere Application Server)
  • Gigaspaces, Smart Cache
  • Microservices using Kafka Streams
  • Docker and Kubernetes
  • Logstash, Kibana, Grafana
Cloud to Cloud data integration
  • Data synchronization across Cloud services
  • Multi-cloud data management
  • Cloud to On-Premise, On-premise to Cloud and Cloud-to-Cloud Integration
  • AWS-Azure data bridge case study
  • iPaaS
  • Deplomatic
  • Automaton®

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