Data Monitoring from assurance perspectives.

  1. Data monitoring activities
    1. What is data monitoring?
      1. Define monitoring
        Monitoring is the verb of a monitor. Google defines it as observe and check the progress or quality of (something) over a period of time; keep under systematic review. It is also a listen and report on activities that are important to maintain regular surveillance over.

        Synonyms to observe, watch, track, keep an eye, keep under observation, keep watch on, surveil, record, note, oversee.

        From the business dictionary, it is the supervising activities in progress to ensure they are on-course and on-schedule in meeting the objectives and performance targets.

        From, monitoring is something related to the control system that serves to remind or give warning. It is important to arrange for observing, detecting, or recording so that the activities/operation is under control.
      2. Define data monitoring
        Interesting facts from, they define Monitoring as the systematic process of collecting, analyzing and using the information to track the progress of any program toward reaching its objectives and to guide management decisions. It is aligned with my thinking (please refer to my earlier post about monitoring + data temporal) when this article mentioned the relationship between monitoring activity and process (related to performance, formative evaluation?) Thus, the program indicator is relative to the progress of time (start, durations, and end).

        One more thing, they always relate the activity of monitoring with the evaluation. This is because, in evaluation – it is a must to provide evidence-based information that is credible, reliable and useful. Thus, through monitoring, we can provide these kinds of evidence that can lead to future findings, recommendations, and the lessons to inform future decision making.Evaluation has been defined as a systematic assessment of any activities’ performance. It focuses on expected and achieved accomplishments, examining the results chain for the whole process, its contextual factors, and causality (wow!, it means everything – from input, activities, outputs, outcomes, and impacts). They also highlight the determining of the relevance, impact, effectiveness, efficiency and sustainability interventions as the result of the evaluation (for the time being, I don’t think I can cover it all since it is too wide).

      3. Define data monitoring activities.
        1. Examples of monitoring activities
  2. Assurance perspectives
    1. What is assurance?
    2. What the users want to be assured when they are doing the monitoring activities?
    3. Thus, from no 2 understanding – it is what we must cater when we are providing/presenting data for monitoring activities.
  3. In this case, let’s try focus and compare the assurance perspective from these five scenarios;
    1. Weather forecast
    2. Telco-data
    3. Trend analysis
    4. Project management monitoring (construction)
    5. Data Myra monitoring


  • Read more:
  • Dictionary.comQuite interesting – I will read more about monitoring and evaluating on these:
  • Frankel, Nina and Anastasia Gage. 2007. “M&E Fundamentals: A Self Guided Minicourse.” U.S. Agency for International Development, MEASURE Evaluation, Interagency Gender Working Group, Washington DC.
  • Gage, Anastasia and Melissa Dunn. 2009. “Monitoring and Evaluating Gender-Based Violence Prevention and Mitigation Programs.” U.S. Agency for International Development, MEASURE Evaluation, Interagency Gender Working Group, Washington DC.

Temporal Data and Weather Forecast

Weather data is temporal data. It changes according to time (hours, day, month or years). Since weather is crucial for us human to do our activities, people forecast it. By having forecast data, it helps business and people plan for their outdoor activities. In Malaysia – it is very crucial during kenduri kahwin, some people still believe and hire ‘bomoh hujan’ to forecast and prevent rain during the wedding day. More over, it helps business plan like transportation, construction and farmers for crop irrigation and protection. Eventhough the weather data in Malaysia is not as crucial as in four season country (since it will not help people on how to dress or either to bring extra coat for windy days) but forecast data can help in term of health issue like asthma and heat stress especially for children’sschool activities.

Since the data for Forecast weather is everywhere – from your own handphone, PC, TV and radio. I think for Haida (since you are from MANA – assurance course), it is time for us to check the accuracy between the forecast data and the real one. It will help to prove the accuracy of Jabatan Meteorologi Data. If the comparison has been done, why dont we visualize the comparison to ease the forecast data understanding.

Thus, the objectives for Haida research can be something like this

  1. Compare between forecast and real set of weather data.
  2. Visualize the comparison
  3. Identify the accuracy of the forecast data – reliability/assurance of the data OR maybe we can access how people trust/ the data?  (hmm.. this can give awareness about the credibility of jabatan meteorologi data)

In order to do that, what you need to do this week is:

  1. Identify and get the forecast and real set of weather data (try to get a set for 10 days first)
    1. Type of data – the general one, things like this:

Source: weather underground weather forecast.

  1. Bring that set of data for our next discussion (17 August 2017).
  2. Have a peek on your expected outcomes, something like this (but not necessarily exact):

  1. Read and understand this good to get some ideas for your LR (please explain to me your understanding about this article in our discussion later)

How is telecom industry benefiting their data?

How is telecom industry benefiting the big data?

Telecom industries are sitting on a gold mine, as they have plenty of data. But what they require is a proper digging and analysis of both structured and unstructured data to become a valuable asset to the industries.

Big Data from the perspectives of telecommunication industry

Through proper digging, they are able to get deeper insights into customers’:

  • Behaviour – combat fraud
  • Service usage patterns – marketing interest, marketing agility  (related to temporal data)
  • Preferences
  • Real time interests – real time customer insights (related to temporal data)

From Acker et al (2013), the telecom industry must experiment their own data. Demonstrating what they have on hand to see what kinds of connections and correlations it reveals, This process must be carried out iteratively to emerge the more efficient operations and more effective marking.

Source: Acker et al (2013)


  2. Ackers (2013) Benefiting from big data. A new approach for the telecom industry. published by Booz & Company.