The goals of Enterprise Digital Governance
Any organization undergoing a digital transformation embarks on a unique journey based on its business needs and priorities. However, every organization will eventually run into challenges managing the growth of their modern data environment and ensuring data usage meets various requirements. For example, modernizing data, adopting multiple data sources, and developing cloud infrastructure without a clear roadmap and controls can rapidly lead to immense tech debt and exploding costs. Adding in the complexities of leveraging cloud-enabled technologies like IoT and machine learning, effectively scaling modern IT environments and avoiding tech debt can rapidly become any organization’s biggest challenge.
Implementing data, cloud, and ML governance can help mitigate these challenges, but establishing these types of governance without a clear roadmap for integrating them can create friction or roadblocks. Organizations should implement governance so that different types of governance support each other and enable growth, not hinder it due to conflicting policies and rules. This is where Enterprise Digital Governance comes into play.
What is Enterprise Digital Governance?
Enterprise Digital Governance is a holistic approach to implementing cloud, data, and ML governance. Enterprise Digital Governance aims to roll out governance using best practices to ensure the different forms of governance work together to supporting developing and scaling out a modern IT environment rather than impairing it.
Neal’s approach to Enterprise Digital Governance
At Neal Analytics, our approach to Enterprise Digital Governance includes implementing cloud governance, data governance, and ML governance either separately or concurrently. To help decide which is best, our consultants work with our customers to assess the organization’s existing governance maturity, identify unique challenges and requirements, build a roadmap to address those challenges, and implement the roadmap. The goal is to enable comprehensive digital governance that facilitates the organization’s ability to modernize at scale.
What is the best way to implement Enterprise Digital Governance?
Neal’s flexible approach to implementing Enterprise Digital Governance is designed to help support our customers in whatever way works best for them. Still, many are curious about the best way to implement the individual components: cloud, data, and ML governance. More specifically, many are curious whether it makes more sense to implement these 3 types of governance individually or simultaneously.
The short answer is we typically recommend concurrently, but the longer answer is that it depends on the customer’s unique goals, priorities, and existing governance maturity. After conducting a governance maturity assessment, if we find that the existing governance maturity is lacking for cloud, data, and ML, we will typically recommend approaching all 3 at the same time to ensure the organization implements each in a way that facilitates and simplifies governance for the other areas. On the other hand, if we determine the organization already has mature governance policies in one or more areas, we would typically recommend an approach where we focus on implementing governance for whichever areas are weaker or has a more urgent business priority, starting with data governance, then cloud, then ML.
We recommend the order of data, cloud, then ML governance because each lays the foundation for the next: for the customer to have a quality cloud environment, they need to have adequate governance guiding data modernization to ensure data is usable. To have successful ML models, the organization needs to have implemented a well-thought-out cloud environment leveraging cloud governance policies.
What are the goals of Data Governance?
The primary goal of data governance is to establish trust in your data. Trusting data is one of the most critical factors in ensuring data creates the value intended. To this point, organizations use data governance to provide the consistency, transparency, traceability, and understanding required to enable data-based decision-making and foster strategic changes to business practices and operations.
- Establishing a single source of truth for data
- Ensuring data accuracy
- Enabling quicker and more accurate data analysis
What are the goals of Cloud Governance?
Organizations leverage cloud governance to enable efficient development processes. Development processes can include scaling out infrastructure methodically to avoid tech debt, reduce costs, manage change, establish disaster recovery, implement proper security and controls, and achieve more efficient IT spending.
Cloud governance goals also include implementing and managing:
- Resource provisioning, automation, and orchestration
- Monitoring, logging, and metering
- Governance policy definition
- A cloud business office
- Service desk management
- Cloud assets
- Change management policies
- Cost transparency & optimization
- Service level agreements
- Capacity & resource optimization
What are the goals of ML Governance?
Organizations looking to mature their use of machine learning will want to leverage ML governance policies to improve the effectiveness of their ML models and deliver a greater number of them to production. The goal of ML governance should be to shorten ML dev cycles, manage and maintain models in production and improve overall model quality.
Additionally, ML governance should:
- Allow the organization to derive more value from ML models
- Enable the use of CI/CD practices in ML development
- Result in ML driving business success
If you would like to learn more about Enterprise Digital Governance and how it can benefit your organization, complete our contact us form, and one of our data experts will quickly reach out to help answer your question.