Over the past year, we saw unprecedented rates of change across the digital landscape as COVID-19 caused new ways of working and living. With the shift to more remote and distanced work, organizations put data management practices to the test across industries. Companies modernized their digital infrastructure and moved away from legacy data systems to keep up with new demands effectively, efficiently, and responsibly.
According to Concord’s Data Engineering Director, Jeff Rogers, the digital transformation trend will continue into the foreseeable future. This year will bring a more holistic focus on data management which considers data integration, security, warehousing, operations, and more. Before the shift towards digital transformation, each of these components was addressed and optimized independently within siloed teams. This shift led to a need for technical and structural reworking each time an issue occurred. In addition, the pandemic drove faster transformation than ever before, forcing companies to consider each component of data management in relation to one another.
The pandemic accelerated the need to modernize the IT back office and develop self-service schemes to eliminate repeatable work and distill information, but many firms have been slow to adapt their practices accordingly. Having the capacity to integrate and manage data helps ensure that companies can keep pace with a digital-first landscape during a global health crisis. Both Gartner and MuleSoft see this as one of the pivotal data questions facing companies in the upcoming year.
A subset of this challenge and an essential part of managing data is harnessing automation for speed and accuracy.
Cloud vendors demonstrate the benefits of automating repetitive work such as administration, monitoring, and reviews. In addition to improving auditability, automation can free up talent to focus on higher-value tasks. CIOs are leveraging artificial intelligence (AI) and machine learning (ML) to standardize operations – gains in efficiency and reductions in cost are already evident. A Deloitte survey revealed that 74% of respondents, comprising of IT and technology leaders, found that automation yields a more effective workforce, 59% reported cost reductions of up to 30% on teams that have embraced automation, although only 21% have said that it remains a high priority.
The transition from managing things to managing code that manages things is not a simple task. It can involve overcoming resistance from the C-suite or functionality challenges from legacy systems. Given this, tackling automation using a dedicated team participating in all aspects of the process is essential. Accomplishing this creates a stable groundwork for outcome-oriented data management efforts.
The healthcare industry is an example of how cloud services can utilize back-office data management. The demands of compliance with the Health Insurance Portability and Accountability Act of 1996 (HIPAA) stimulated the need to manage patient data in the cloud and explore using cloud models to improve treatment. Cloud-based capacities can reshape internal activity and enable companies or providers to spend more energy on the areas that make them distinguished. But effective data management must go beyond technical considerations like data architecture and dedicated IT teams to incorporate holistic business strategy and alignment between business and technology practitioners.
The challenges that emerged over the last two years have exacerbated the need to consider data within broader organization contexts rather than as a standalone department. Once thought to be on the bleeding edge, the concept of cloud migration is falling out of favor. Interest is now shifting to how to best leverage cloud platforms and more rapidly alter business outcomes. Customers historically sought out Concord’s expertise specifically on the data integration front, but data integration alone is not an especially valuable service. Instead, Jeff recommends approaching data management with a wider lens. This shift entails appreciating the whole life cycle: how the data is produced, sourced, stored, and how it drives decision-making.
Data integration is the process that facilitates this journey, but it is not synonymous with data management: a more holistic notion which covers the capacity to evaluate data critically. Data management provides a foundation for business analysis and governance using appropriate models, interfaces, and personnel. Jeff emphasizes that these components strengthen data architecture capable of endurance and flexibility. This approach is better-suited to harnessing usable data, and ensuring that business objectives are woven into how data is consumed and the processes that generate it.
Jeff’s depiction of integration moving towards more holistic management of data is in lockstep with wider trends in the industry. MuleSoft, attempting to anticipate emerging trends in data integration for 2022, identifies establishing a single source of truth as a critical goal. This trend requires organizations to remove barriers across an enterprise. Business acumen is instrumental in connecting data architectures to overall organizational goals and effective decisions.
While technical capabilities are a core component of optimizing company data, alignment between technical teams and business strategy is paramount. Data management synchronizes all relevant parts of an organization to align with business objectives and optimize data for informed growth-oriented decisions. Companies would be remiss to focus on data integration alone without considering the broader context of data management.