Archive for August 2011

The Value of Real-Time Data

August 31, 2011

Real-Time data is a challenge to any process-oriented operation. But the functionality of the data is difficult to describe in such a way that team members not well versed in data management. Toward that end, four distinct phases of data have been identified:

  1. Real-Time: streaming data
    visualized and considered – system responds
  2. Forensic: captured data
    archived, condensed – system learns
  3. Data Mining: consolidated data
    hashed and clustered – system understands
  4. Predictive Analytics: patterned data
    compared and matched – system anticipates

A mnore detailed explanation of these phases may be:

Control and Supervision: Real-time data is used to provide direct HMI (human-machine-interface) and permit the human computer to monitor / control the operations from his console. The control and supervision phase of real-time data does not, as part of its function, record the data. (However, certain data logs may be created for legal or application development purposes.) Machine control and control feedback loops require, as a minimum, real-time data of sufficient quality to provide steady operational control.

Forensic Analysis and Lessons Learned: Captured data (and, to a lesser extent, data and event logs) are utilized to investigate specific performance metrics and operations issues. Generally, this data is kept in some form for posterity, but it may be filtered, processed, or purged. Nevertheless, the forensic utilization does represent post-operational analytics. Forensic analysis is also critical to prepare an operator for an upcoming similar process – similar in function, geography, or sequence.

Data Mining: Data mining is used to research previous operational events to locate trends, areas for improvement, and prepare for upcoming operations. Data mining is used identify a bottleneck or problem area as well as correlate events that are less than obvious.

Proactive / Predictive Analytics: The utilization of data streams, both present and previous, in an effort to predict the immediate (or distant) future requires historical data, data mining, and the application of learned correlations. Data mining may provide correlated events and properties, but the predictive analytics will provide the conversion of the correlations into positive, immediate performance and operational changes. (This utilization is not, explicitly AI, artificial intelligence, but the two are closely related)

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The Data-Information Hierarcy, Part 3

August 31, 2011

The Data-Information Hierarchy is frequently represented as
Data –> Information –> Knowledge –> Understanding –> Wisdom.

Or it is sometimes shortened to 4 steps, omitting Understanding. But, in fact, there are two predecessor steps: chaos and symbol. These concepts have been discussed in prior blogs (https://profreynolds.wordpress.com/2011/01/31/the-data-information-hierarcy/
https://profreynolds.wordpress.com/2011/02/11/the-data-information-hierarcy-part-2/).

Chaos is that state of lack of understanding best compared to the baby first perceiving the world around him. There is no comprehension of quantities or values, but a perception of large and small.

Symbol (or symbolic representation) represents the first stages of quantification. As such, symbolic representation and quantification concepts from the predecessor to Data.

So the expanded Data-Information hierarchy is represented in the seven steps:

Chaos –>
          Symbol –>
                    Data –>
                              Information –>
                                        Knowledge –>
                                                  Understanding –>
                                                            Wisdom

Continuing with this Data-Hierarchy paradigm, we can represent the five primary steps with the simple explanation:

  • Data and Information : ‘Know What’
  • Knowledge : ‘Know How’
  • Understanding : ‘Know Why’
  • Wisdom : ‘Use It’

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