Enterprise Data Management
Enterprise Data Management
To support a variety of drivers, particularly agility and cyber security, the government is embracing new software architectural styles such as:
These new architecture styles dramatically affect how software is organized and how the data is organized. Axiologic Solutions has SMEs that can assist with the emerging architecture/design data patterns to properly architect/design the data for these architecture styles.
As data becomes increasingly critical to a variety of business domains including data science, business intelligence, cybersecurity, data mining, data migration, data analysis and business reporting (including auditing), the need for well-thought out and sound data solutions has emerged; this is commonly referred to as “data engineering”.
Though the term “data engineering” is relatively new, this function also has deep roots in systems engineering, and software engineering, particularly in ETL data processing. Simply put, data engineering is focused on helping an organization (and its many functions) locate, access, move, process, and analyze data. If data is at the center of the capability/requirement, then “data engineering” has an important role; if data is secondary, then “software engineering” or “systems engineering” disciplines take the lead.
Axiologic Solutions provides the following data engineering services:
Data ingestion is an important area of EDM where significant resources are spent. Axiologic Solutions offers a mature set of data ingestion services, including:
Many EDM functions are historically performed using rules-based, hardcoded software-based logic. This logic is very brittle and requires constant change as the data changes. This leads to a perpetual EDM software maintenance “treadmill.”
What is needed is a way to automatically derive EDM logic directly from the data itself using “augmented data management.” Augmented data management uses ML, RPA, and AI techniques to mature, optimize, improve, and automate multiple parts of the data management lifecycle, reducing the overall software development /maintenance costs/effort.
Axiologic Solutions has expertise in various EDM areas – many of which can benefit greatly from greater automation via ML/AI/RPA, including:
As data is fuel to ML model creation (e.g., training data) any latency in the availability of good quality data adds to the delays of having new models updated to better reflect operational realities. DataOps (or MLDataOps or data-as-code) is a spin-off of business application software centric DevOps (or DevSecOps) and is concerned with optimizing the development, testing, deployment, execution, and monitoring of data processing workflows/pipelines (or more precisely feature pipelines) for ML model creation. DataOps has some overlap with:
The focus of DataOps is how the output from the data pipeline will feed the ML model pipeline. The data pipeline is concerned with creating the feature datasets (training, validation, testing) that are used by the ML model pipeline. Since data has a completely different lifecycle from ML models (e.g., data can be continuously collected during a day, but ML models are only refreshed once per day; data can be used by multiple ML model pipelines; a ML model pipeline may use data from multiple data pipeline), we explicitly decouple DataOps from MLDevSecOps. This decoupling is a critical element to achieving machine learning at enterprise scale.
Axiologic Solutions has the expertise to assist the government’s adoption of ML at enterprise scale, focusing on the training data, using DataOps and MLDevSecOps.