As businesses worldwide adopt new go-to-market models, heightened supply chain expectations and flexible workforce policies, their enterprise data architectures should also be examined and updated.
Leveraging a modern data architecture helps enterprises remain competitive in the new economy. Some mandates aren't new. Accurate data has long been a component of budgeting and planning. But, today, strategic planning is not a once-a-year ritual involving reams of print-outs and hand-plotted charts.
Modern analytics must be in real-time, based on a company-wide version of the truth. It must be highly consumable in easy-to-read formats, available on-demand and in a user-friendly format.
The data must be unified, fully integrated to company-wide systems and rely on cloud-based solutions for elastic storage space. If this is achieved, the analytics will be highly accurate and reliable, with clear definitions and sound, well-documented logic.
These qualities are essential for building a company culture, which can react with great speed and proactively address changing issues. Markets are changing at an unprecedented pace.
New challenges are emerging every day that strain our already over-taxed resources and expose weaknesses in manual or cumbersome processes. Often this pushes current infrastructure and technologies to their limits, and sometimes beyond.
This doesn't have to be where it ends. Enterprises can take advantage of analytics that are highly flexible and support important, rapid decision-making processes, at multiple layers in the organisation, not just the top tier.
Resilience is just as important. Resilience is how quickly businesses can adjust to change both negative or positive. Organisations strive to be future-proof. That means they need the ability to be elastic and bounce back after changes — returning to a 'routine' that is in a better position than before the change.
Resilience means we need to be able to move fast, make decisions quickly and then pivot to a new normal rapidly and with confidence.
Three common challenges typically impede organisations with outdated data architectures. The first is the inability to handle the multiple data sources and use cases that are involved in delivering analytics at scale.
The second challenge is an inability to rapidly scale, coupled with high maintenance costs. The third common challenge is an inability to discover new insights rapidly, due to bottlenecks in reporting, usually as a result of legacy technologies that can't meet the current demand.
This slow-moving analytics structure wasn't adopted intentionally but was a product of evolution. For many companies, the original goal was to build a repository with all the information necessary to make decisions in critical business categories.
However, processes and tools were often bolted on, creating a haphazard structure of multiple tools, extracts, data stores and teams working on multiple data silos. Projects were often stalled as stakeholders argue over who has the right data. With long lead times needed to obtain relevant data, decision making was slow.
Some detours in the journey to modern analytics have plagued some companies as they experimented with short-term solutions. For example, first-generation data lake technology offered the ability to mash-up multiple forms of data by standing up massive farms of commodity hardware servers. This, too, had limitations.
User adoption was a challenge because the toolsets were primarily designed for developers and data scientist. As such, users had to be highly skilled to generate new reports.
The IT team became the gatekeepers (or bottlenecks). Also, some early data solutions weren't built for dynamic needs, frustrating front-line users. A lack of governance or semantic layer also added complexity.
Every new use case required starting at step one, leading to mistrust and teams resorting to their own data tracking, often using spreadsheets. Thankfully, enterprises now have more options.
The key is now to distinguish a modern solution from an outdated one, which will hinder agility. Modern solutions are results-driven, automated, flexible, elastic, adaptable, smart, collaborative and secure.
A modern data architecture is focused on enabling any user to take their analysis in any direction. It allows for multiple tools, with various use cases, to easily and securely access any data object.
It supports users through guided self-service to a single, semantic definition of all the enterprise assets, while providing a version of the truth that is simple to access and trusted company-wide.
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