To succeed with their digital transformation initiatives, companies need to use data to the full extent of their capability. Having the right data and applying it to the right problems can help companies gain new insights, create new models and disrupt stagnant processes. No argument there.
The problem is, not everyone is putting their data in a position to succeed. According to analyst reports, many IT departments are behind the curve in developing processes to integrate their data, leaving them at a disadvantage when it comes to supporting their organization’s digital transformation efforts.
Why is this happening? How big is the issue? And what can companies do about it?
An Explosion of Data
The why is easy to answer. Data is running wild, and society hasn’t fully figured out how to rein it in. There have been shifts in the way data is being created and consumed, forcing companies to rethink the way they integrate it and manage it on a broad scale.
On the creation side, business data volumes are exploding, doubling nearly every year, according to some estimates. Data integration has evolved from an environment where traditional endpoints remained static to one of modern endpoints that are dynamic and constantly changing. Plus, the technology landscape itself is undergoing continuous transformation, with the regular emergence of new applications and business models.
Data consumption is changing, too. Companies are reacting by trying to adapt integrations quickly, make changes and fixes as fast as possible, keep data secure and stable, and give more users access to the technology to avoid IT bottlenecks.
To manage these more complex, data-driven processes, many organizations are still trying to handle integration projects by writing custom code. This is typically done to meet development efforts triggered by an immediate business need. The benefits can seem enticing: The staff can get started right away, there are no new development tools to bring on, and deployment is simple.
But there are hidden costs to doing custom data integration. The inability to reuse code forces developers to rewrite integrations over and over. Brittle code can create a maintenance overload. When developers leave, the organization’s knowledge base breaks down. And custom work tends to address one set of business parameters, creating a lack of standardization and further duplication of work and planning.
The Benefits of Hybrid Integration
Using a hybrid integration model, organizations can leverage an assortment of tools to support today’s level of pervasive integration, spanning deployment models, diverse endpoints, integration domains and constituent users of integration technology. These hybrid platforms tend to include more enterprise-focused competencies ranging from prebuilt workflow templates, connectors, and automated management and maintenance capabilities.
How equipped are today’s organizations in data integration? According to a Gartner report released earlier this year, “IT organizations struggle to meet the challenge of pervasive integration for digital business transformation.”
In its report, “Use the Integration Maturity Model to Assess and Improve Your Integration Competency,” Gartner outlined a “five-level integration maturity model based on more than 20 years of analysis of the integration technology market and organizations’ approaches to integration. Each level is characterized by the degree of mastery of integration challenges in terms of awareness, organizational settings, technology platform use, methodology, approach and sourcing policy.” The firm estimated that 55 percent of Gartner clients (including SMBs and large enterprises) are in the “Getting Started” phase at either level 1 (ad hoc) or level 2 (enlightened) and less than 5 percent of Gartner clients (including SMBs and large enterprises) are in the “Staying-on-Top” phase, at level 5 (plug and play).
How can your organization evaluate and improve its integration maturity? How can you help move the needle?
Here are a few tips to position your company better to handle data-related challenges.
- Traditional point-to-point integration solutions typically fail due to integration complexity, error handling, divergent practices and change management. Take the time to evaluate and understand the total cost of managing and maintaining custom coded data and application integrations before you commit your highly specialized resources to the projects.
- Evaluate the benefits of implementing a unified approach for converged data and application integration using a truly hybrid data integration platform that provides reusable integration templates that can be configured and deployed for ad-hoc and enterprise data hub use cases alike.
- Look to the future and choose a platform that supports a variety of integration management and deployment options, and avoid cloud lock-in with iPaaS-only providers.
Conclusion
Integrating data isn’t easy. Data is coming from more places and used in more ways by more people than ever before. Organizations that develop strong data integration capabilities will do a better job leveraging the data they need to drive transformation projects forward in a positive way.
