In the world of data governance, companies often feel trapped between two seemingly stark choices: the expensive, high-end solutions that can start at a staggering $100,000 or more or the DIY approach that promises cost savings but often ends in frustration and failure. The allure of going the do-it-yourself route is undeniable, but what many fail to realize is that it comes with its own set of hidden costs and challenges that can cripple even the most well-intentioned efforts. The good news is that DIY is not the only cost-effective option available. Hidden beneath this dilemma lies a powerful alternative - self-service data governance. This blog delves into the reasons why DIY data governance frequently falls short of expectations, leaving organizations in a state of confusion and vulnerability. Moreover, it highlights the transformative potential of low-code/no-code self-service data governance solutions.
The Temptation of DIY
The decision to pursue DIY data governance often stems from a desire to avoid the hefty price tags and long implementation times associated with enterprise-level solutions. Companies believe they can cut costs by employing in-house programmers to write custom code and allocating existing resources to manage data governance. At first glance, it seems like a cost-effective solution, but this path is fraught with pitfalls.
- The Cost of Expertise
One of the most significant hidden costs of DIY data governance is the expense of hiring and retaining experienced data engineers. Customizing data governance solutions requires skilled professionals who understand the intricacies of data management, security, and compliance. These experts demand competitive salaries and can be challenging to find and retain.
- Time-Consuming Commitments
DIY data governance projects often monopolize valuable time and resources that could be better allocated to core data-related tasks. Teams become bogged down with the intricacies of developing and maintaining custom solutions, diverting attention from strategic data initiatives.
- Scaling Challenges
As organizations grow and accumulate more data and users, the limitations of DIY solutions become painfully apparent. Custom-built systems struggle to scale efficiently, leading to bottlenecks, performance issues, and a growing sense of frustration.
- Governance Changes and Enforcement
Enforcing and changing data governance policies becomes increasingly complex when dealing with a DIY approach. Who is responsible for setting and editing policies? How can changes be made without disrupting the entire system? What initially appears as a straightforward change to a data governance policy can spiral into a protracted development, testing, and promotion process. These issues lead to policy enforcement gaps and an added layer of complexity that can grind data governance to a halt.
- Forgoing Critical Decision Making
Another common pitfall is the rush to migrate data to the cloud without considering critical decisions such as security standards. Neglecting issues like adhering to NIST standards or establishing a clear permissions hierarchy can lead to security breaches and compliance violations down the line.
- The Spectrum of Gray in Data Governance
Data governance isn't a one-size-fits-all concept, and this lack of clarity contributes to the failure of many DIY initiatives. The absence of a unified strategy can lead to confusion within organizations, with individuals not understanding the boundaries of their roles or how different teams will consume data. The result is a fragmented approach that hinders effective data governance.
Unlock the Power of Self-Service Data Governance
At the crossroads where DIY data governance meets the realm of costly enterprise solutions, a game-changing alternative emerges: self-service data governance. Within this paradigm, two formidable allies take center stage to address the challenges that DIY data governance presents—automation and low-code/no-code software. Together, these dynamic features provide transformative benefits, fundamentally reshaping how organizations approach data access and data security and alleviate the intricacies that frequently hinder DIY initiatives.
Automation takes center stage, revolutionizing the scalability of data governance. It empowers organizations to effortlessly navigate the intricate landscape of data access management, regardless of their size. As data pools expand and user demands grow, automation becomes a resource-efficient solution, preventing accidental data breaches and ensuring regulatory compliance. It supplants the labour-intensive processes associated with DIY, providing a systematic, agile approach that adapts seamlessly as an organization's data requirements evolve.
Low-code/no-code solutions further augment the arsenal against DIY challenges. These interfaces simplify the implementation of data governance by eliminating the need for extensive custom coding. No one needs to know SQL, Apache Ranger or YAML.
Low-code/no-code software expedites the automation of data security, enabling organizations to effortlessly apply granular access policies to multiple users simultaneously. This approach accelerates the deployment of governance measures and makes them accessible to a broader range of users, reducing the reliance on specialized technical expertise.
While DIY data governance may seem like an appealing alternative to costly enterprise solutions, it often leads organizations down a treacherous path filled with hidden expenses, scalability issues, and governance challenges. Luckily, self-service data governance solutions like ALTR offer a transformative path for organizations seeking to transcend the limitations and pitfalls of the DIY approach. With the ability to reduce time-value from 6 months to 60 minutes, ALTR provides an efficient and cost-effective means to streamline data governance processes, enhance data security, and maximize the value of data assets. And the best part, organizations can get started for free. Embracing tools like ALTR heralds a future where data governance becomes agile, accessible, and highly effective, ensuring that organizations thrive in an increasingly data-driven landscape.