Simplifying the Multisiloed Landscape to Achieve Data-First Modernization

One of many complexity challenges when it comes to the modern IT landscape is that different functional areas and IT domains are heavily invested in their own systems and data silos. This leaves IT leaders stuck navigating a wave of transparency, security, and governance roadblocks that are impeding the imperative for data-first modernization.

Silos have been built up over the years, some the result of organizational structure, others related to data and systems that are managed and maintained as separate entities. For example, traditional IT domains such as operations, networking, and enterprise applications are typically managed and maintained by different IT groups, resulting in wholly separate systems and data management workflows. More recently, groups associated with cloud, big data, or other technology specialties have entered the mix, fostering additional silos and adding to overall complexity.

The silo problem expands even further when you consider that different functional areas gravitate to using their own data and systems. For example, Finance relies on one set of systems and data whereas Marketing or HR is dependent on a wholly different set of solutions. Manufacturing is creating large volumes of data at the edge, which is yet another silo not easily available to the greater organization to inform business insights and initiate data-first decision-making.

Digital transformation efforts, accelerated during the global pandemic, have created even more silo sprawl, adding to the complexity of the current data landscape. In a survey conducted by industry analyst and consulting firm BARC (Business Application Research Center), 65% of the respondents reported that they have not been able to significantly reduce their number of data silos. Moreover, the complexity of finding, managing, and maintaining data for a specific purpose across these burgeoning silos takes time away from innovation and data-driven business activities.

The BARC survey found that 41% of the respondents are busy dealing with existing data problems instead of working on digital transformation initiatives; 55% reported that they don’t have enough resources to improve their current data landscape.

The exponential growth of data, coupled with a worldview in which individual domains produce and consume their own data, has made it difficult to create a shared data lens. That leaves organizations struggling to quickly identify and access the right data, impeding the ability to derive valuable insights at enterprise scale and to achieve optimal business outcomes.

“The idea of data gravity as well as issues around governance, compliance, and security are the key symptoms and risks around these silos, and that’s probably not going to go away for a long time,” says Brian Ott, vice president of HPE GreenLake Hybrid Cloud Managed Services. “Companies now recognize data as a true asset, but we’re trying to force the silos to work together without having the foundation to do so in place. For a company to get to the desired outcomes, they need quicker access, not just to the data but to the insights from that data.”

Building the right foundation

Creating the optimal foundation for a shared data lens and data-first business starts with defining a sound data strategy. This starts at the highest levels by establishing standard data definitions, identifying who needs access to what data, and then continuing all the way through the creation of a governance model for how data is managed holistically across the enterprise.

“Most organizations don’t have mature data management practices that are holistic across the enterprise,” Ott says. “Security and compliance controls also need to be factored into an enterprise data management approach, but the reality is that most organizations don’t orchestrate such controls in a consistent and coordinated manner. Instead, they pursue varied approaches across silos,” he adds.

Beyond a shared governance model and data strategy, the right partner and platform can significantly reduce inefficiencies, simplify the siloed landscape, and ensure that business users throughout the enterprise are empowered with insights on demand, safely and at scale.

The HPE GreenLake edge-to-cloud platform ensures that data is universally accessible no matter where it resides, whether on-premises, at a colocation site, or across multiple clouds. The HPE GreenLake platform’s support of frictionless data movement means that data is processed and analyzed from every location and data silo, including at the edge, using enterprise-grade controls that ensure that data is consistently safeguarded and in compliance, mitigating corporate risk and exposure.

HPE GreenLake also delivers a unified modern platform for analytics, from edge to hybrid cloud, capable of handling diverse data types in one consistent platform. This helps eliminate data silos and simplifies data engineering. Moreover, a central console and common experience provide clear visibility into how, when, and where data, applications, and resources are being consumed across the entire IT estate.

Built on a next-generation architecture including Kubernetes, the HPE GreenLake platform and related services deliver the combined performance and elasticity required for advanced analytics. Artificial intelligence (AI), machine learning, and MLOps capabilities can be leveraged to automate aspects across the data life cycle, including security and compliance. This allows for consistency across data science, data engineering, and analytics teams while accelerating data-first business insights across the greater enterprise.

At the same time, organizations need to evolve their thinking about the risks and value associated with data to break free of silos and to fully capitalize on data-first business. Historically, companies have operated from a mindset of restricting data access to only those that need it; with data-first modernization, organizations need to shift the culture so that data is seen and handled as a universal asset.

“One key means of doing that is to establish a governance body that transcends functional domains and data silos,” Ott says. “It’s not about adding governance for governance’s sake but adding governance to accelerate the value and usage of data as well as automation of those processes. That gets key stakeholders involved and moves them beyond the blinders of specific silos.”

For more on how HPE GreenLake promotes a holistic data strategy and simplifies a siloed landscape, click here.