November 7, 2024

How to Achieve Seamless System Integration

From sharing account log-in information with family members to integrating systems in a company, moving data from one place to the next is a complex task.

Data in the workplace often doesn’t flow effortlessly between different systems or across different departments. Because of this, different parts of the same company may experience frustration and confusion when sharing or using information. The process of getting these systems to “talk to one another” in a pinch can be costly and time-consuming, which is why a foundation for seamless system integration is one of the best things you can do for your business. Read on for insight into achieving seamless data integration.  

Why System Integration Matters

System integration is necessary when information from one context needs to be transferred and used in another context. Multiple systems are required for many organizations to operate well, and the more complex system integrations are, the higher the cost will be to your company.

Most companies recognize that as the marginal cost of compute moves to zero, the effective digitization of workflows will yield market winners. As Marc Andreessen, the progenitor of the modern browser, explains in his article “Why Software is Eating the World,” this mega-trend started developing back in 2011.

A lot has changed since 2011, and manual processes such as spreadsheets have been fully replaced by the cloud ecosystem. Now the growing data and analytics space includes major players like Snowflake, Microsoft Azure, AWS, DataBricks, and Google Cloud. 

According to Gartner, 89% of board directors say digital business is now embedded in all business growth strategies. Still, a mere 35% of board directors report that they have achieved or are on track to achieving digital transformation goals. This means that while complex systems and data are used daily in business, the management of this data puts an expensive, clunky strain on business owners.

The good news is that infrastructure constantly evolves in the cloud, which means mistakes can be contained and remediated rather quickly. Instead of brushing over this information integration challenge and getting more and more overwhelmed, it's time to approach it head-on and create proper integrations. 

Types of System Integration

It can be overwhelming to know where to begin with system integration. Let’s narrow down your information architecture choices a bit by walking through the four main types of system integration.

  1. Enterprise application integration (EAI) 

This is a high-level type of integration made to accommodate and connect different types of software used by large enterprises, such as POS, CRM, and HR systems. 

  1. Horizontal integration

This type of integration occurs when systems from across the company that are used at the same stage in the business’s order of operations are made compatible with one another. 

  1. Vertical integration

Vertical integration is used to make systems compatible that are located in the same location within a company but are needed at different steps in the business process.

  1. Point-to-point integration 

Point-to-point integration uses an API to take away all barriers between two systems, so they can directly communicate. 

Integration of Operational and Analytical Data

Two main types of data are valuable to an organization:

  • Operational data: This data powers your company’s day-to-day operations. 

It is largely transactional and event-based data, produced by internal business processes such as purchase orders in the ERP, accounting data from the financial systems, and events in the CRM. Integrating operational data requires maintaining consistent data points in multiple systems or triggering downstream workflows in a coordinated way.

  • Analytical data: Analytical data can be analyzed for decision support and intelligence when consolidated from different sources – for example, integrating Salesforce CRM data with Google site traffic to get a holistic view of customers and their behavior. 

Bringing data into the analytical plane and integrating data from different sources helps organizations extract insights and model data for applied use cases like predictive analysis.

How Operational Data Integration Streamlines Processes

Activities supported by operational data may include scheduling services or ordering products and shipping materials. For example, integrated operational data allows e-commerce orders to make it to the ERP system for fulfillment, where tracking numbers can be accessed by the customer through Amazon.

These operational systems are important for customers and your company’s back-end data, but this data can be difficult to work with if it doesn’t integrate well between systems. 

CRM systems don’t care about bank account numbers, and financial systems don’t care about how many candidates the HR team might be recruiting. The more disparate these systems become, the more important seamless system integration is, because while these systems “don’t care” about one another, there is a good chance that they will eventually need to be used in conversation with one another. When that day comes, your organization needs to be prepared with proper integration. 

Finding the right tools for operational data integration depends on your data architecture, cloud footprint, and application landscape. 

Here are some technologies & patterns to consider:

  • Custom development: Micro-services, events, and messaging queues.
  • Platform features: Webhooks and APIs (ERPs, CRMs, and other SaaS solutions).
  • iPaaS solutions: MuleSoft, Jitterbit, Azure Services, and Informatica for integrating multiple systems with a variety of patterns and transformation logic.

How Analytical Data Integration Powers Business Decisions

Analytical data provides an understanding of operational processes. Generally represented and accessed in schemas, models, and views using database technology, it requires high-quality data applied in the correct context to accurately represent the state of any business. 

Analytical data helps companies “map the terrain” of their business with up-to-date analytical frameworks. 

Effective analytical integration tools implemented at Kenway are: 

  • Key concepts: Data products, data contracts, and metadata management. 
  • Key toolsets: Synapse, Snowflake, Google Big Query, Redshift. 
  • Concepts to be aware of: Data sharing and data clean rooms.

How Kenway Helps With System Integration

Kenway offers a flexible and tailored approach to system and data integration by guiding clients with a data strategy that aligns with corporate objectives and drives long-term value. Based on our experience with a wide array of system integration projects, we keep the following in mind when handling data in the analytical plane:

  • Develop a metadata standard and implement metadata-driven ingestion: Every database technology has a metadata model specific to its unique way of executing compute operations. Likewise, data teams within organizations develop their own unique metadata management methods. Developing a data-contract-driven ADLC for public and private data is key for standardizing data-management standards across your organization. 
  • Conceptualize data products for storage vs reporting: A product framework should be used to decouple reports and use cases from stored data. Data products can be leveraged in both operational integration and analytical modeling without impacting other applications using the same data. Data product consumption patterns should be considered for different business needs, such as data science operations and downstream applications.
  • Scope teams for technical skills and throughput: Business stakeholders and subject matter experts (SMEs) in data engineering, data modeling, cloud architecture, and infrastructure are often the constricting factors. When making any organizational shift, build a task force of the best people to put insights into action to avoid hangups.
  • Rationalize required in-house vs. outsourced skills: Pipeline development should be easily repeatable and able to remove main dependencies. Data modeling with SME involvement should be cultivated in-house where domain knowledge about your data is high, but bring in outside experts when necessary.
  • Encourage cross-functional requirements across the business: Build product roadmaps before project plans and empower development teams and project managers to collaborate from the start. 

If you’re looking for data integration solutions for your organization, connect with us to discover how to complement your business objectives and maximize return on investment while minimizing operational overhead.

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