The Data Analytics division of ADA serves as the functional lead for establishing an Analytic Framework to support "Data Driven Decisions" within ASD(A). The division gathers strategic questions from the organization, integrates acquisition data from disparate DoD and Federal data stores, and conducts appropriate analytics to answer those questions. This informs acquisition strategic decision making and policy changes in OUSD (A&S) and the DoD at large, at the speed of relevance, improving acquisition outcomes. The Data Analytics Division also conducts analyses to help the Department inform the public and other parts of the U.S. Government, including the Legislative Branch, on the performance of the defense acquisition system.
Data Analytics provides cross cutting analyses of DoD acquisition by commodity type, contract, contractor, contractor incentive, portfolio, and individual program using data mining principles, analytic tools, data scientists, and functional subject matter experts.
Report by Data Analytics assessing DoD implementation of the AoA process: time required to complete, recommendations to improve, etc.
IDA study exploring specific examples of how AI can help Acquisition analysts be more effective in their predictions and efficient in their processes.
Study by IDA assessing the feasibility of predicting program performance using text analytics and machine learning techniques on contract text data.
Using Natural Language Processing, Sentiment Analysis, and Text Mining to test correlation between SAR executive summaries and unit costs.
Using ANOVA and Multinomial Logistic Regression to analyze DAES and APB milestone estimates to determine contributing factors to schedule slips.
Annual report by A&S (formerly AT&L) which uses quantitative analysis of broad data to measure the effectiveness of the defense acquisition system.
Research study conducted by RAND on the use of data analysis, measurement, and other evaluation-related methods in DoD acquisition programs.
Report to Congress on the use of data analysis, measurement, and other evaluation-related methods in DoD acquisition programs.
This forum creates a culture that collectively shares industry analytics best practices to improve analytics and data literacy within the acquisition workforce. It brings together OUSD(A&S) and service component representatives to discuss analytics and ensure that the correct metrics are created and used to answer the strategic questions that informs the Analytics Framework and ultimately improve outcomes.
This board is comprised of the A&S DASD’s, key members from the services, and chaired by the PDASD, AE. It is a subset of the Acquisition Visibility Steering Group. The purpose of this board is to discuss current and future analytics initiatives in A&S at the strategic level.
Advana*, DoD’s big data platform for advanced analytics, has numerous free training materials* and third party training resources for its various tools, as well as webinars and office hours*. Please reach out to the Advana Help Desk* and initiate a Help Desk Ticket to inquire about or request a training.
*DoD network access only
DAU is teaming up with the world’s largest provider of massive online open courses to extend the reach of its DoD workforce training with online DoD-Coursera programs.
Relevant courses include Data Science, covering Programming in R, Practical Machine Learning, Statistical Inference, and more.
Data Mining uses the Analytic Framework to search through copious amounts of computerized data to find insightful patterns or trends.
Machine Learning uses statistical models and algorithms to give computers the ability to do new tasks based on patterns and inference, without being explicitly programmed. This diagram shows a small sample of the applications in which machine learning is currently being used.
Decision trees can be used to aid the description, categorization and generalization of a set of data, such as shown on the right with our model of total and procurement costs.
“Data has Shape, Shape has Meaning, Meaning drives Value.” -Gunnar Carlsson
Topological Data analysis studies the underlying shape of data by bringing together mathematics with computer science. It uses algorithms and concepts from algebraic topology to extract insights from complex multi-dimensional data structures.
In machine learning, Natural Language Processing (NLP) is the computer’s ability to understand and analyze human language. Text mining is useful in analyzing patterns and unexpected trends by measuring the frequency of words, which can indicate topics of concern or importance like the diagram on the right.