I love home improvement, DIY blogs, and a good ‘rags to riches’ renovation. This blog isn’t about that. This is about my other love, data, and the ways in which organizations can continue to use data structures to save time, money, and resources while elevating their employees to higher levels and more rewarding tasks. This is about scaling your data solutions to not just be about a single outcome but endless outcomes through the years.

The last 15 years have seen the data industry grow from a niche business to a multi-billion dollar cash cow. Businesses large and small are striving to be data-driven in hopes of servicing end-users better, increasing efficiencies, and of edging out competitors on the next new thing. However, while 48% of businesses in the US and worldwide claim to use data to drive innovation, only 30% have a well-articulated data strategy. This means those 12% of organizations are attempting to create outcomes without the structural support of a full-scale data solution. While doable, these outcomes are at risk of being unable to maintain scalability, security, transparency, audit, governance, storage, and a host of other needs a data fabric provides. Those initial outcomes will almost always exist in a vacuum that is difficult to maintain and difficult to iterate upon.

Best practices in data implementation have been nearly exclusively a use case-based approach. Identifying low-hanging fruit and highly visible projects can show quick success at the outset of a larger data solution implementation. This can garner favor with hesitant stakeholders, help identify technical issues, and attract funding early on. Use cases also provide a common language with which to communicate a comprehensive data solution to a less technical audience. Especially as the data fabric becomes more complex, use cases will be key in driving home the need for data solutions overall. After all, data is just the tool by which we are able to solve the problems these use cases present.

Too often though these use cases, the drive to create quick outcomes, and the desire to then move quickly to additional use cases is detrimental to the larger data solution and long-term scalability. Quick outcomes become favored over comprehensive strategy and buildout. Without a designed architecture and foundation from the outset, use cases are instead built disparately in siloed sources on an ad-hoc basis. Initially well-intentioned, short timelines create an expectation of similar timelines down the road with little time or resources to pause for the buildout of a cloud solution, security protocols, or consolidation to a data lake. Finally, perhaps the most detrimental outcome of this path is the outward appearance of success. Because end-users often are only interacting with the outputs of use cases, they interpret the solution to be working as expected. It is only when the layers of the output are traced back that they would be able to see the underlying issues.

Many of these issues relate to and can be solved by a designed and built data foundation. Data foundation is what supports any analytics, BI, or data science use case including ingestion, access, integration, storage, processing, warehousing, networking, security, quality, and many other services. If a use case is the data solution’s curb appeal, the data foundation is its framing. While less visible than use cases and outcomes, a comprehensive data foundation is necessary for building a long-term sustainable data solution.

With traditional IT solutions, infrastructure has built a reputation for being expensive, time-consuming, and difficult to implement. A modern infrastructure supporting a solution’s foundation, particularly within the realm of data solutions, has become streamlined and modular, allowing organizations to strategize a data roadmap while moving forward in parallel on use cases that drive business outcomes. Attacking data foundation like this in an Agile way, particularly when working in a cloud-based system, stands up quickly with the ability to grow with your organization’s needs.

As the data landscape evolves, businesses will need to pair initial use cases with their data foundation to ensure they can receive benefits not just for today but for years ahead. Ultimately combining the short-term benefits of use case development and implementation with long-term roadmap planning for underlying foundation, the overall data solution will prove to be more stable and scalable with greater return on investment. In practice, this involves expanding technical resources, significant change management, and patience as the underlying solution is architected and built. The timeline on this will be longer than addressing use cases on an ad-hoc basis. Over time though, ease of access to and understandability of the usable data will decrease any timelines on future use cases. The quality of the data will also improve as transparency and consolidation allow for broad, automated validation. End-users of the data will not need to be highly technical because the data will have gone through cleansing and manipulation processes before reaching them. Security will be centralized and standardized, decreasing the likelihood of a data breach. And finally, consolidated management analytics will allow for easier and more creative ROI calculation, ensuring organizations maintain focus on the projects that best serve their goals while also reducing redundancy.  

Identifying and executing on use cases will always be the element of focus in any data fabric buildout. They are the driving factor for any data solution because if there is no problem to solve, there is no need to build the solution in the first place. As the landscape of information becomes increasingly complex, though, the need for strong data infrastructure has grown to the point where it is no longer optional.

At CTI, we focus on the comprehensive building and sustaining of scalable solutions to ensure attention is paid to all aspects of the implemented system. Implementing early and assessing often alongside use case rollout creates more robust and scalable solutions for organizations, enabling them to focus on innovation, effectiveness, and margin instead of technical difficulties.

 Amanda Darcangelo is a Senior Consultant, Data & Analytics Practice at CTI.

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