Traditionally, data governance has been disregarded and its consequences left unquantified. As a result, enterprises have accumulated huge amounts of data debt as they prioritized ever-growing data demands and technological advancements. Today, data debt is widely recognized as a major barrier to effective, rightful use of enterprise data as businesses expend much more time, effort, and money into tackling volume, variety, velocity, and veracity issues. Furthermore, data debt is now better defined and appreciated as a significant challenge due to its impact and long-term risks if left unattended. However, the current marketplace lacks a comprehensive approach towards addressing data debt, leading to competing problem definitions and unresolved issues.
What is Data Debt?
Data debt manifests in many forms, and as a result, hinders businesses’ ability to effectively utilize and apply their data in many ways:
- Inability to present contextual data, resulting in misrepresented outcomes.
- Lack of ownership and business stewardship, leading to poorly defined data and its purpose
- Poor data quality which lacks completeness and accuracy, resulting in unreliable decisions and misinformation.
- Redundancy due to duplicate or siloed data, which increases storage and processing costs.
- Failure to update or refresh data regularly, leading to insights based on irrelevant or obsolete information.
- Poorly administered or nonexistent data governance policies, causing security, availability, and access issues.
Today’s enterprise technology landscape continues to evolve. Notably, new multi-tiered, complex systems centered around the enterprise data have further highlighted the importance of staying on top of data debt. Today’s enterprise data features include:
- Continuously growing, expanding
- Always in motion - a moving target
- Exists in variety of avatars
- Spans across organizational boundaries
- Grown into large volumes
- Must cater to rapidly changing business needs
- Distributed across many different types of repositories
- Deploys various technology tools
The Cueris Approach to Remedying Data Debt
Cueris recognizes how a strong data foundation drives the resolution of data debt. Paying down data debt is a journey: a continuous, iterative undertaking that requires a holistic, business-centered approach consisting of the following major steps. We have a four-step process in place to help our clients make the most of their data:
Identifying data gaps
We start by Reviewing the existing data landscape to assess how well it meets business and operational needs. This involves cataloging existing data challenges related to storage, availability, quality, access, and security. We also document known unfulfilled and future business data needs to define a baseline ‘as is’ state.
Measuring the impact of data debt
Next, we examine the costs of two types of data debt: direct and indirect. Direct data refers to storage, compute, and network costs. In other words, the financial investment being made directly towards maintaining data-related infrastructure; such direct costs can be easily quantified. On the other hand, indirect data includes lack of governance practices, time lost to poorly created systems and organizational inflexibility towards data transformations. This cost is trickier to quantify, but often betrays which management practices can be improved and initiatives prioritized.
Setting up a data governance framework
With the impact and sources of data debt understood, we can devise a governance framework to address the underlying problems in a holistic manner. The governance framework prescribes the roles and responsibilities, policies and standards, and data management capabilities necessary to meet business needs.
Building solid data architecture
Finally, formulating a solid data architecture is key in proactively mitigating data debt. Cueris typically proposes a technology agnostic data model based on business requirements such as content, usage and presentation. The goal is to evolve towards a sustainable data model; we want to establish a technology agnostic enterprise view that enables a context rich understanding of the data used by the enterprise.
Conclusion
Our approach is akin to teaching a man how to fish rather than giving him a fish. In providing an iterative process for making data a business priority, we help our clients create much more sustainable and valuable data infrastructure. The key to that success is our process; its resulting data model can drive high-quality and robust implementations.
Everyone knows that data debt is a significant challenge unlocking the full potential of business data assets; Cueris’ approach offers a path forward towards efficiently handling data debt through a commitment to data quality, governance. In doing so, Cueris can help your organization achieve long-term success in the digital age. Please reach out to us at info@cueris.com to learn more.