What is Data Enablement?
Data enablement is a broad term that encompasses storing, managing, sharing, analyzing, and maintaining data with integrity and quality – and then using it. It encompasses the ideas of:
- Data accessibility – If people don’t know the data exists, or if they can’t easily access it, they won’t use it. Data self-service is an important step toward data enablement.
- Data management – You must have policies and practices in place to ensure data quality, security, and governance.
- Data use – All the clean, easily accessible data in the world won’t help you if you’re not using it to generate insights, make decisions, and inspire innovation.
Data enablement starts with having a clear strategy. Government entities need a plan for acquiring, storing, securing, and using data.
“Data Enablement means getting your data in shape and then using it well.”
Another aspect of data enablement is data engineering, which is the process ofdesigning, constructing, and managing systems that transform raw data into usable formats for analysis. Data engineering projects include creating data pipelines, handling data storage, and ensuring data quality and availability.
Accurate analysis is impossible without clean and organized data. Clean data leads to correct conclusions and evidence-based decision-making.
How Does Data Enablement Impact AI and ML?
AI and ML tools use data. They don’t give you the results you want if the data isn’t available, organized, and clean. Clean data is free from duplication, errors, and outliers, and it is necessary if you want to accurately train machine learning models and artificial intelligence programs. You can’t trust improperly managed data for making predictions, automating tasks, or enhancing decision-making processes—all the tasks we turn to AI and ML tools to help us with.
Think about Generative AI, and how it creates new content such as text, images, or code. If your Generative AI application is using a corrupt or unmanaged dataset, your results will not be accurate. Your code will not work as needed. Your images might feature people with too many limbs. Your content could also contain hallucinations—false information that is presented as correct and credible. These are issues common to AI solutions anyway; drawing from unreliable data makes them even more likely.
How Do You Get Your Data Ready?
Full data enablement is a significant, multi-phase process for most organizations. So how do you get started?
One way to begin is to look at what you have and where you want to go. Assessment and prioritization activities don’t have to be formal projects, but they are important for understanding what data is being stored, who has access to it, who is using it, and what you want to tackle first.
Once you have that baseline, consider adopting a DataOps (Data Operations) pipeline. DataOps is a way to handle the ingestion, cleansing, and transformation of large datasets. The goal is to enable rapid data processing and ensure high-quality, refined data output. It provides a basic structure to support your data enablement initiatives.
The next step is MLOps (Machine Learning Operations), which is a term for the CI/CD automation practices that support DevOps culture. MLOps practices simplify machine learning by training, validating, packaging, deploying, and monitoring ML models in production. MLOps reduces bias by automating fairness checks in your model development process. It improves model performance and aligns your models with any regulatory compliance requirements your agency or department is governed by.
By taking these steps, you’ll be poised for success when your data is ready and your AI/ML projects are in place.
The Benefits of Data Enablement
There is power in your data. It can provide insights, enable better planning, and, increasingly, it can power the AI and ML tools that will take your operations to the next level. Data enablement is the cornerstone of successful AI and ML initiatives. Now is the time to put robust data practices in place—for enhanced decision-making and operational efficiency, and to pave the way for your AI/ML success.