Data Architecture is the structural design and functionality of large data sets, and the discipline of storing, organizing, and integrating that data. An acute awareness and purposeful use of market data is becoming increasingly vital for business leaders who value sound decision making and maintaining any competitive advantage.
There is no shortage of data – as a society, we are immersed in it. What is in short supply, however, are the leaders who recognize and value the ability of others to serve business goals by discerning, organizing, and synthesizing meaning from large data sets.
Imagine the following scenario within the medical industry: any diagnostician worth their weight, whether a surgeon or otherwise, would not present a treatment plan without considering the comprehensive volume of information at their disposal. The same principle applies to the business world – the ability to manage high volumes of data is critical for businesses to anticipate market trends, much less, stay relevant within their field. Data Architects uniquely meet this need for corporations hoping to elevate their position within the market.
Where designers and engineers prototype, code, and iterate – the architect synthesizes and identifies the minimal variables, inputs, materials, and operations needed to communicate the desired result of the values-driven effort. What has changed that the popularity of this role has only recently emerged?
The standard volume and complexity of information are ever-increasing, as is the corresponding pressure for businesses to achieve optimal outcomes. This changing landscape is a beacon to business leaders to reframe the way they think of information. By overlooking or undervaluing information, business leaders are opening themselves up to the risks and vulnerability of reactive decision making, when calculated decisions are called for. As signals and noise inevitably affect the marketplace, it is in the best interest of a business to be equipped with a team that can design a data environment, enabling them to cut through the noise.
Adopting the term “architect,” Big Data looked to the design and construction world for a blueprint of this role. Whereas a building comprises of bricks, glass, concrete, and wiring, it is also the result of a use-purpose that was designed to meet the needs and wants of the tenants who will live and work in that space. Sure, it’s possible to begin building without much planning – designers could use familiar core materials regardless of use-design foresight. However, the selection and use of these components would be far more calculated and ultimately efficient with a functional outcome developed during the planning stage. This process allows future iteration by providing opportunities to find out which materials do and do not add value to the overall stricture, or which components are missing that would allow for more efficient use of space, and perhaps how to anticipate these needs in the future.
Success has already been found by several companies who chose to embrace large data sets to focus on sales opportunities – this, as a result of smart inference and the incorporation of machine learning to strategic industrial data. One such example is Chorus.ai – this company boasts rapid sales growth by revealing observations from call center audio data, which revealed trends and anomalies, which translate into sales representative behaviors.
This proactive use of information inevitably creates an advantage over one’s competition, versus the team that would draw generalizations or assumptions of human analysis from such data.
In the financial world, data is critical to matters of compliance and regulatory standards, which provides a promising opportunity to develop systems that highlight the value of hierarchical contextually for countless businesses in the sector. The need to safely store financial records over long periods is universally acknowledged. However, this need often results in many companies defaulting to processes that archive data by siloing in ways that promote security over accessibility. By doing so, companies are robbed of information that could provide a nice advantage for their businesses through the use of Artificial Intelligence to former patterns and behaviors to help inform strategy and identify future opportunities.
At VATBox, for example, where tax regulation and corporate compliance inform the method of automated VAT & GST reclaims;
we are elevating our beliefs of what we believe data can do when it is studied, analyzed, and selected intelligently towards a values-based goal. Consider the following – the ability to analyze travel trends over several periods and destinations can result in compelling takeaways that equip our customers to plan and negotiate their expense budgets more efficiently, while also developing early signs of credit card fraud, or other anomaly-based alerts to underpin their regulatory and tax compliance.
Most importantly, the relational values afforded to a company by a Lead Data Architect, allow for non-linear results that can give a company counter-intuitive influence in their decision making. As data sets build upon one another, the relationships and the granularity of this information is as important as the process and timing of other operational procedures in developing a strategy for success. Layers of data reveal value, which then motivates and elevates shared efforts. Information defines our narrative and reason for existing as a living, breathing company.
Architected Data Becomes our Narrative Worth Sharing
Data Architecture spearheads this process, so that data scientists, engineers, and analysts can have adequate material to hand in a sequence that connects to vital short and long-term business goals. Considering tomorrow’s narrative began crafting itself from the utilization and intuitive takeaways that results from incorporating this business discipline today.