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Data Management

Organizations are realizing the importance of managing their data as they would any other enterprise asset. Data Management (DM), as defined by the Data Management Association (DAMA), is the development and execution of architectures, policies, practices, and procedures in order to manage the information lifecycle needs of an enterprise. Recognizing DAMA is an industry leader in DM, QSSI developed our DM Practice using the DAMA Data Management Body of Knowledge (DMBOK) as a foundation to define how to manage data. The QSSI approach to DM has resulted in solutions that effectively organize data, maximize the quality of data, and keep data secure.

Enterprise Data Warehousing

connectQSSI believes it is critical to begin investing in an Enterprise Data Warehouse (EDW) as early as possible so that valuable Business Intelligence (BI) is developed quickly, resulting in a higher return on investment. If not implemented correctly, an EDW can quickly lose its value, and BI & Analytics can become ineffective. The QSSI EDW design consolidates data into a “single version of truth” so that redundancy issues are all but eliminated. QSSI delivers an EDW in several small “sprints”, using an iterative approach so that an organization can maximize the quality of data.

Business Intelligence & Analytics

connectBusiness Intelligence (BI) management activities span the decision support lifecycle to plan, develop, implement, and control processes that will provide business analysis and decision-making data to the appropriate stakeholders. BI is often broken down into three categories - tactical, strategic, and operational – to support an enterprise view. QSSI adopts a holistic approach to BI based on multiple view points and develops solutions that avoid adding non-master data to a master data repository. Since this is not a one-time effort, the goal is for continuous process improvement; BI is only useful if its timely, accurate, and is viewed by the right people.

Predictive Modeling

PHRAll predictive models are not created equal. The effectiveness of a predictive model requires identifying the right content/structure, the right techniques, and the right processes. Predictive techniques are a specific subset of data mining analysis that is used to understand the relationship between data items and a given target/response. While most uses involve empirically derived statistical extrapolation from historical data, a more complex application of predictive models is required in fraud detection and compliance monitoring. As such, our techniques are based on correlation and declassification to identify individual targets based on anomalous behavior.

Big Data Engineering

HIHLarge volumes of complex data can hide important insights. Organizations that can extract specific facts from huge volumes of data can better control processes and costs, can better predict demand, and can build better solutions. QSSI solutions use fault tolerant storage systems like the HADOOP Distributed File System, or HDFS. HDFS is able to store huge amounts of information, scale up incrementally, and survive the potential failure of significant parts of the storage infrastructure without losing data.

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