Solutions for Data Management and Analysis
Segue understands the need for accurate and up-to-date information to support mission-critical decisions, as well as the challenges faced in maintaining data security.
The need to ingest, validate, cleanse, and correlate data from multiple, disparate sources, execute complex analytics, and produce reports and visualizations for informed decision making is becoming more common across DoD organizations. With legacy enterprise portfolios composed of stove-piped systems, this emerging need remains difficult to meet without the development of flexible, scalable, and interactive data analytics environments.
Successful solutions in Advanced Data Analytics require not only experience with leading business intelligence tools and database technologies, but the ability to understand the data. Federal enterprise systems support large user communities and ingest large data sets often with multiple user interfaces. Addressing the myriad user requirements for these large systems and aligning them with the most appropriate analytics tools and technologies requires a deeply knowledgeable and experienced team. The ability to understand the data, in addition to the tools, is the critical piece which determines the success or failure of an integrated data solution.
Our data solutions tightly integrate the people and activities that an enterprise system seeks to connect. We work closely with subject matter experts to distinguish good data from bad, so that problems that existed in the legacy systems are not migrated to the new enterprise.
- Data Analytics - Business intelligence tools can provide analysts with an accurate view of their data with drill down and reporting capabilities, that may not be inherent in their existing information systems.
- Data Quality and Profiling - Decision makers need accurate and up to date information to meet their mission needs
- Enterprise Data Management - Connecting stove-pipes, integrating data from disparate systems and allowing accurate information flow is critical to enterprise system support
Identify the business processes and the critical /clean legacy data for migration to the enterprise.
Ensure data flows accurately across all organizational components.
Establish data standards which integrate the functions and processes of each group within the enterprise.
Develop and manage metrics that drive overall performance improvement.
The current fiscal environment is driving significant changes in the way that organizations must plan, budget, and execute their resources. The collection, processing, and analysis of relevant data will become increasingly important. In particular, it will be necessary to more precisely define purchasing needs, more successfully align resources with requirements, and more accurately measure performance.
Segue has a wealth of experience helping customers solve specific mission challenges of their existing enterprise systems, by making data reliable, available, and discoverable. We deliver comprehensive data analytics solutions, leveraging a wide-array of Enterprise data management and visualization tools, for analysis and reporting on data for billions of dollars in financial and manpower programs. For our Federal customers, we work in both SIPR and NIPR environments, interfacing (pulling and pushing) with numerous legacy data sources. Our data analytics solutions allow you to pull real-time snapshots from your repositories of information and data.
Data analytics solutions are only as good as the underlying data storage solutions upon which they're based. Segue focuses on data quality within systems and consistency between systems. Our data analytics solutions are tailored to our customer's needs, concentrated on relieving their existing information pain points. A well-crafted data analytics solution can help you identify and analyze your data of greatest value; providing you with actionable knowledge that informs your organization’s business decisions. Imagine better budgeting, management, and execution of your business processes – all supported by instant, fact-based information.
Data Quality and Profiling
Segue’s experience in enterprise data management provides us with a solid understanding of the challenges inherent to the large, shared information sources of enterprise systems. Numerous stakeholders and players, massive amounts of data, poor data quality, low data interoperability, interface issues, challenging requirements, and senior-level interest in meeting aggressive schedules all impact the goal of combining data into a reliable information source.
To cut through these hurdles, Segue focuses on the primary goal, releasing data from vertical stovepipes to allow information to flow horizontally through the enterprise. To achieve this, Segue employs a simple four-part approach to data storage solutions– based on LEAN principles:
Enterprise Data Management
Our enterprise data management approach has been refined through our experience integrating legacy stove-piped data sources into easy to use information management tools. Our process assures that you're able to access the specific data you need without the redundancies and errors that typically plague enterprise databases.
The quality of the enterprise system is tied to the quality of the data entering it. With data from numerous sources, a consistent approach to data cleansing and quality is essential for successful integration.
When connecting stove-piped systems into an enterprise, Segue works to first establish a governance structure for data standards, policies, and procedures in concert with a well-rounded team of experts from amongst all stakeholders. Then we provide recommendations and guidance to the organizations conducting data quality, in support of their efforts to prepare their data for import with our data storage solutions. We advocate the use of modern data dictionaries, data structure diagrams, and unambiguous, normalized data reporting standards.
Migrating bad data into the enterprise undermines the goal of generating accurate and actionable information. Failure to ensure data quality is one of the biggest factors in the failure of enterprise development efforts.