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DATA COLLECTION FOR INTEGRATED PM

Whether your enterprise information flows count as “Big Data” might be somewhat of a subjective call. But using “Big Data” techniques to manage the information and workflows flows makes lots more sense than the use of a traditional architecture.

Data collection strategies for Integrated PM might be categorized into four quadrants; Social Analytics, Performance Management, Data Exploration, and Decision Science.

Social Analytics: Social analytics measures the vast amount of non-transactional data that exists today. Much of this data exist on social media platforms, email, interoffice instant messaging, help desks, and suggestion boxes, request queues, and websites. Social analytics measures three broad categories: awareness, engagement, and word-of-mouth or reach.

Performance Management: This category is about collecting performance measures on tasks, practices, processes, and projects. These performance measures such as latency, throughput, duration, counts, and asset turnover rates must are then put within a performance measure framework that supports continuous performance improvement.

Data Exploration: Data exploration makes heavy use of statistics to experiment and get answers to questions that managers might not have thought of previously. This approach leverages predictive modeling techniques to predict project behavior based on their previous business transactions and preferences. Cluster analysis can be used to segment customers into groups based on similar attributes that may not have been on analysts’ radar screens.

Decision Science: Decision science involves experiments and analysis of non-transactional data, such as historical projects, and departmental output. Many of the techniques used by decision scientists involve listening tools that perform text and sentiment analysis. With our Integrated PM technology, we leverage statistical analysis along with Watson and Cortana AI tools for classification and inference validation.


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Five years ago, most companies collected data that was part of their projects and daily tasks stored them in a database. This data was used primarily to keep track of operations or forecast needs. Today, both the sources and volume of data collected has exploded. At the same time, the need for cross-functional data to be transformed into knowledge and communicated to the enterprise, is no longer nice to have, but mandatory to maintain competitiveness. Knowledge Curation requires modern techniques not found in yesterday’s traditional tools.