In today's data-driven world, where vast quantities of data are collected, processed, and shared to various sources and technologies, data governance has become a mission-critical concern for businesses across the globe.
A data expert from Fiskars said, “Governance and control could be a value center but it’s a little far fetched today. By not having it [governance], it impacts the brand, but having it doesn’t really help accelerate the brand. You could argue that with a strong data foundation you could have better analysis and experiences for customers. This would be a long narrative to get there, and hard to quantify the financial value.”
The state of data governance has evolved from being a back-office issue to one that influences the very core of an organization's operations, risk management, and competitive advantage. Traditional methods of collecting data first and then implementing governance measures downstream are too slow and inefficient. Either you delay processing and usage to accommodate governance and end up missing out on real-time use cases, or you ignore governance for real-time use cases and risk violations or breaches.
In today's world governance practices are done at rest, post collection. This data can then make its way into other tools before a governance process is applied. Imagine having your cake and eating it too! You get to apply governance controls in flight while collecting in real time!
Think of data governance at the point of collection like the water supply in your own home. Imagine if you had a dozen taps, and each one poured out unfiltered water. To make that water safe to drink, you'd have to clean and boil it at every single tap, a process that's not just time-consuming but also terribly inefficient.
Now, picture an alternative scenario. What if, right at the source, you could filter and purify the water once? This way, you'd ensure that clean and safe water flows from every tap in your house. It's all about preventing data pollution and ensuring that your data is clean, compliant, and top-notch quality right from the very beginning. Instead of trying to clean it up at multiple downstream locations, you take care of it right at the source.
We hear this all the time– that when data gets where it needs to go (i.e. straight into Facebook for conversion and measurement), then it's too late to flag it for potential compliance concerns. An MDM may identify a risk and alert you that “some identity data got collected that violates policies,” but if it's already sitting inside Facebook, you’ve already had a violation, you now have to report that, and now you’re in triage.
Let’s talk about how to avoid this and the following data governance challenges by taking a different approach.
Top Challenges in Data Governance
Data governance is the set of processes, roles, policies, standards, and metrics that ensure that an organization's data is compliant, accurate and secure. To achieve this companies of all sizes need to have scalable data quality, data security, privacy, and compliance practices. Not to mention stay up to date on latest regulations being stood up around the globe.
The need for robust data governance at the point of collection is critical for several reasons:
Building A Privacy-First Data Foundation
Building a strong privacy-first data foundation is complex and challenging to implement. This is only exacerbated by rising data volumes and the need to keep up with evolving privacy regulations. Today, data infrastructure and governance operate as separate workflows.
Infrastructure collects data, undergoes consent filtering, establishes data stores, and enforces governance policies, each varying between companies. The disjointed nature of data pipelines and internal siloes, coupled with inconsistent controls, poses a significant challenge in maintaining data quality. In turn, data consumers may encounter low-quality and non-compliant data, leading to substantial issues during utilization.
Connecting the infrastructure layer and governance controls into a single unified structure streamlines the entire stack for businesses. With this structure in place, organizations can:
- Apply the data controls needed prior to it even making its way into disparate data stores
- Future proof their protection
- Ensure their data foundation is constructed with high integrity and respects privacy and compliance requirements.
- educe the time burden on data teams, saving them from monitoring multiple controls across multiple data sources and pipelines
With all of the above in mind, it’s apparent that governance and infrastructure should be intertwined. But, where exactly should the intersection of governance and infrastructure live?
Multiple sources, and most prominently a Principal Analyst with Forrester said, “Governance controls should live as close to collection as possible.” She relayed a story of how she tried to do this at her last company where she served as CIO. In fact, she went a step further to say that putting governance at the point of collection is the “holy grail” of “edge” data architecture, which is a common way to describe the kind of server-side infrastructure MetaRouter offers by companies like Adobe and others.
Many organizations find this kind of edge data architecture to be a difficult thing to do, particularly when they’re dealing with a high volume of data. They are wary of filtering out too much data that might be useful to downstream use cases. This might seem like an idealistic future state, but many organizations are implementing edge governance today as the standard best practice. This push forward is driven by the necessity to build a solid yet flexible data foundation that allows them to accommodate safety, privacy and compliance now and in the future. The Need for High Quality Data
Without a high quality and compliant data foundation, you have nothing- Najeeb Uddin, VP of Technology & Transformation, AARP
Data accuracy, precision and quality are paramount for achieving effective use case execution. As data takes center stage in ever-increasing use cases and downstream workflows, its value will skyrocket proportional to the expanded use cases. Precision and accuracy are no longer mere ideals; they're the bedrock upon which data's worth and effectiveness is measured.
Let’s think about data as the “new oil.” If the oil is converted into the gasoline that you put into your car, watered down, sediment-filled, or otherwise degraded oil will mean your car’s engine will not run efficiently and, eventually, break down. The same thing will happen to businesses with data. Garbage in/garbage out could not be more true here. If the data is of low quality, then the decisions coming from the Analytics team will be subpar. Additionally, if the right users are not tied to the right behaviors, businesses open themselves up to miscommunication that will lead to inconsistent customer experiences- potentially ruining relationships with those customers in the process.
At present, this is being pushed into hyperdrive by AI/ML accessibility and awareness. Once again, an organization’s data foundation- built on quality and compliance- comes into the mix. The AI/ML models the organizations create will reflect the quality of the underlying data, as well as the quality and speed of new data coming in. To build effective models that have truly transformational effects on organizations, they need to first focus on transforming what powers their models first. Namely, they need a new real-time data infrastructure that is constructed to handle privacy and compliance in the context of significant AI/Ml usage.
This must be driven by specific business use cases. When the data structure is driven by real use cases, accuracy and time to value are enhanced greatly. Specifically, driving data quality and connectivity initiatives with real-world use cases prevents the needs for constant recalibration of data collection, transformation and storage layers.
The AI/ML models the organizations create will reflect the quality of the underlying data, as well as the quality and speed of new data coming in. To build effective models that have truly transformational effects, they need to first focus on transforming what powers their models first.
A private-cloud based marketing and customer data infrastructure that you can fully and transparently control is absolutely crucial to your success in efforts for high quality data.
The Expanding Ecosystem
The past five years have witnessed substantial advancements across various facets of the data stack. Every organization now has a complex web of vendors, media channels, Customer Data Platforms (CDPs), cloud services, data warehouses (DWHs), clean-rooms, system integrators (SIs), consultants, and more. Each of these components represents a potential point of exposure where data can be compromised. Whether it's bad data, risky data, or data that might lead to legal trouble, it's a risky game.
At present, this is being pushed into hyperdrive by AI/ML accessibility and awareness. This is another critical point where an organization's data foundation - built on quality and compliance - comes into the mix. This data must be scrubbed of liability and exposure risk, as well as highly standardized and quality controlled, in order to be viable for AI use-cases. But most critically, AI is not just another consumer, it is a problem multiplier, if fed poor quality or risky data. The automation and further action taken by AI dramatically enhances the risk profile, and your investment in the data normalization, quality enrichment, and liability removal efforts as a business needs to be at or near your investment in the tooling itself. Further, the liability risk is not only bad insights and legal/privacy risk, but competitive exposure as well. You need your AI to be fed safe, quality data, and for that AI to operate in walled-off, safe environments.
This expanding ecosystem demands a proactive approach to data governance – one that safeguards your data right from the start and ensures that it remains clean, compliant, and secure throughout its entire journey and to wherever that data is routed.
Data Engineering Bandwidth
Without the right data governance foundation in place, teams are not able to be as efficient as they need. As a result, they end up missing their projects’ timelines, reduce the overall quality of their output, and most critically, erode the trust across the organization. Adding data engineers isn’t the solution, in fact, this often adds more strain to an already stressed data governance solution.
Data governance at the point of collection helps this exchange by
- Decreasing the amount of work data engineers have to do once the data gets into the data warehouse
- Giving data engineers and their multiple stakeholders a common source of truth
- Aligning all teams in the organization around a process that starts from inception
- Providing more opportunities for data to be processed and used more quickly
Key considerations when looking for a technology partner that helps data engineers increase their bandwidth are transparency and ease of use. This solution needs to allow everyone in the organization with the transparency they require while being easy enough to use for everyone to adopt and use in their day-to-day workflow.
The Path Forward: Building A Foundation of Trust that Aligns Governance and Infrastructure
Data governance has previously been performed at various points along the customer data lifecycle. While consistent data governance will always be a critical part of the process, a notable improvement in modern data governance is establishing data governance at the point of collection in order to improve data quality, compliance, and privacy upfront and ease the burden downstream.
There is no doubt in our minds that the data governance space is more critical than ever. This is partially driven by an AI catalyst, but also a wave of government regulations and public data breaches that is forcing brands to start to prioritize more data governance and infrastructure investments.
At MetaRouter, we're here to guide you through the world of data governance and control where real-time event collection and data controls converge. We help organizations take charge of their data right from the moment it's collected, providing complete control, down to every detail, and ensuring that only the data you want to share with third parties actually gets shared. First and foremost, we give users a fine degree of control over how data collected is manipulated and mapped to both your first-party tools like data warehouses and third-party tools that normally rely on tags to collect data on your page.
Plus, our single-tenant, private cloud setup allows you to have full control. You can enforce your compliance rules and use your own event schema or analytics stream within our platform, making it a breeze to work with your data your way.
MetaRouter empowers businesses to take control of their data, from the point of collection to its transformative potential - to streamline data governance and maximize the value of data for every downstream destination and activation.