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Since our company's inception, we have had the privilege of engaging with a diverse range of customers, all grappling with a common challenge-the management of their data. Whether a small business or a large enterprise, the issue of technical debt, a result of short-term thinking about data, is a consistent theme. Technical debt, in this context, refers to the cost that accumulates when short-term solutions are implemented to address immediate needs, leading to a complex, inefficient, and duplicate data infrastructure over time.
This technical debt spawns from a patchwork of applying technologies one by one, reacting to a current need. A prime example is when companies opt for a do-it-yourself plan because they don't have to engage procurement or IT and currently have resources available. Aside from the expanding technical debt, this strategy ignores the ongoing service support issues that cost 10x what an off-the-shelf product when looking at the total cost of ownership. I have observed extreme examples of this behavior at companies that have been around for many years. The decision to patch things and solve a short-term pain is very tempting. Leaders convince themselves they can do it cheaper and better because it is custom-built for them versus the pain of trying to convince stakeholders to procure a product.
It's clear that addressing and reducing technical debt is not a simple task. It requires a structured methodology for identifying, justifying, and funding new projects. This funding is not just about acquiring technology; it's about generating the emotional momentum needed to overcome the inertia that has built up over many years in replacing outdated technology.
In today's world, it's all about money and budgets. Showing how new technology can increase profit and revenue or reduce operating costs is more important than ever. Data should be at the heart of unleashing value. Machine learning and AI are at the highest level of productivity metrics. But to get to that value, you need a solid data foundation and a method to reduce technical data debt while increasing productivity. The potential for profit and cost reduction through these means is immense and within our reach.
Genuinely great companies have developed methodologies to identify high-value data. This could be customer behavior data that inform marketing strategies or product performance data that guide product development. They have also developed strategies to migrate from the current state to the future while keeping one foot in the current.
As the diagram below illustrates, data creation by itself has little to no value. However, the value increases as the data is incorporated into an application and begins to create insights. The highest levels of automation can drive immense value for corporations. This value creation is true in designing, building, marketing, and selling a product. Data is required every step of the way.
In the end, understanding the data journey, which encompasses the creation, storage, analysis, and application of data, removes obstacles and speeds the transformation to drive meaningful action.
Leading-edge companies have figured out the value of their data and put their precious resources to work, mining it and turning it into automated outcomes.