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.
What we do for data integration
Identifying your organization's critical needs for cohesive data to drive business insights and process efficiency.
Prioritizing and sequencing implementation of data integration solutions.
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.
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.
Success stories
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.
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