Managing and Prioritizing Technology Integrations

Managing and Prioritizing Technology Integrations

Our IT Executive Roundtables are invite-only events hosted by peers for peers that bring together a select group of senior IT leaders from across industries for topic-driven, intimate dialog on current trends and topics. The group met remotely to discuss managing and prioritizing technology integrations led by the CIO of a leading North American natural gas producer. This Session was sponsored by Insightly.

September 22, 2022

In today’s digitally connected world, integrations play a vital role. They allow you to build dynamic, scalable architectures, integrate your on-premises applications with cloud services, and deliver omnichannel experiences to your customers. APIs and standardized communication technologies have made integration more manageable than ever before, but do organizations still face any challenges while integrating? How does data fit into the equation?  

Pain points of software integration

Our attendees broke down the most significant challenges they face while integrating software.  

  • Integrating legacy applications with SaaS platforms has been a major problem for them. — Director of Information Services
  • Building a unified data model is a big headache when integrating disjoint systems. — Senior Executive
  • Employee turnover is another deterrent to seamless integration. When people with domain knowledge of critical applications leave, it becomes difficult to integrate those applications with other software. — VP
  • Resistance to change can also be a pain point while integrating things. — CIO

The key principles of strategic IT alignment

A participant outlined the four key principles of strategic IT alignment. The first principle is to be cloud-first, which allows you to build and deploy fast. The second is to treat data as an asset and transition to a data-first company. The third piece is integrating as much as possible to increase data consistency. The last principle is to automate wherever possible, which increases overall efficiency and resilience. Another participant added three more principles:  

  1. Don’t reinvent the wheel.  
  2. Don’t over-architect a solution that can be much simpler, and don’t under-architect a solution that needs to be more complex.  
  3. Have a transparent culture of accepting and incorporating change.  

Implementing a master data management model

A speaker mentioned that they implemented a master data management (MDM) model while integrating with different SaaS products. Using an MDM platform allowed them to streamline the aggregation, processing, and flow of data across legacy and SaaS applications. This data consolidation also gave them visibility into how they could drive their business forward. They were able to forecast pricing and identify strategic avenues for improvement.

Dealing with legacy applications

A big headache while integrating with legacy applications is getting security right. Legacy applications often use outdated protocols and aren’t interoperable with contemporary security controls. Moreover, it can be hard to achieve advanced business outcomes, like reporting, AI, and ML, when dealing with legacy applications. Instead of using hacks (and adding more technical debt) to make a legacy application work, a more viable approach may be to replace it altogether. People who use or depend on the legacy system may not be receptive to the idea of change, making it hard to move forward. However, if you get the executive buy-in early on, it becomes easier to convince people to move on from the traditional ways of doing things.  

How to clean data?

Cleaning multi-source data is one of the most critical steps in building a reliable data analytics solution. A speaker told the audience that MDM platforms act as enablers and catalysts for data cleaning. They have data quality dashboards, which allow you to filter out and analyze mismatches and outliers. It’s also crucial to identify the key data elements for your company and decide who owns what. Develop and promote an organizational culture that encourages data ownership and cleaning.  

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