Unemployment Analysis vs Graduan bergaji Rendah

Mungkin inilah sebabnya graduan sanggup bergaji rendah zaman milenia ini. Ramai tiada pekerjaan.  Mungkin kerana itu, walaupun bergaji rendah – syukur mereka ada kerja.

Hasil analisis dalam kelas Data Visualization and Interactive Design. Credit to Dir Ratna Adilla bt Ab Halim. FT04, Msc Business Intelligence and Analytics, UTM KL. Sumber data: Jabatan Statistik Malaysia (data.gov.my).

Temporal Data and Business Intelligence

Temporal Data and Business Intelligence

According to Aigner et al (2007), time is an outstanding dimension. In popular physics, time is the fourth dimension. For ages, scientists have been thinking about meaning and implications of time. Understanding temporal relations enables us to learn from the past to predict, plan, and build the future. In the world of computerization, time is like an invisible presence. Business systems mostly operate in some sort of existential present tense and programmed into a unique timestamp, flags and update. Hence, it is no surprise that time is also a key concern in Visual Analytics, where the goal is to support the knowledge crystallization process with appropriate analytical and visual methods [1]. Visualizing time-oriented data, which is the focus of this paper, is not an easy business. Even though many approaches to this task have been published in recent years, most of them are specific to only a particular analysis problem. The reason why most methods are highly customized is simple: it is enormously difficult to consider all aspects involved when visualizing time-oriented data.

In current years, the temporal data is still valid and significant to emphasize the value of Business Intelligence (BI). The temporal concept enables business users to explore past business events to understand problems, see trends (monitor and review the data) and these activities can support the decision making as well. Thus, in many cases, users get point-in-time views of the business at defined times, such as the daily or month-end close. However, as business operations have become increasingly real-time in nature, the need for a continuous history that provides an ongoing, complete and accurate record of transactions and business performance has become paramount. Temporal — and in particular, bi-temporal — data is thus central to effective BI processes and should be a core part of any data warehouse or data mart (Devlin, BI Expert Panel)

Furthermore, the business context is growing in the complexity of both BI needs and the temporal characteristics of data. Decision making is increasingly real-time in nature. Predictive analytics extends our interest from the past and present into the future. Big data and the Internet of Things take us into a new world where the people who use data often don’t control its structure or content. Even the bi-temporal model, Johnston is reluctantly forced to admit, may turn out to be insufficient to carry all the temporal meaning that a user may wish to impart to or extract from business data. A tri-temporal model, with three-time axes, may eventually be required to make full sense of data. Then quoted from Johnson, the understanding for the interconnection between Bi, analytics and data temporal is vital if we are to navigate the expanding world of big data. Without such an understanding – Devlin believes the implementation of BI was destined to crash and burn. Therefore, it should be seen as mandatory preparation to revisit the theory and practice of temporal data.

An important issue that concerns all previous points is task-orientation. This means that Visual Analytics systems should automatically suggest and parameterize visual, analytical, and interaction methods based on the users’ task at hand. Recently, an interesting analysis of possible visualization tasks has been published in [3]. That list of tasks can be used as a basis for future research on task-oriented Visual Analytics. In that regard, perceptual issues must be further investigated. Empirical tests have to be conducted to judge which forms of presentation (2D or 3D, static or dynamic, etc.) are best suited for particular analysis tasks.

Aigner, W., Miksch, S., Müller, W., Schumann, H. and Tominski, C., 2007. Visualizing time-oriented data—a systematic view. Computers & Graphics31(3), pp.401-409.

Devlin, B. Temporal Data Reality: In BI, time is of the essence. https://goo.gl/VdEt7T

Representations of Data Monitoring – e.g Telecommunication data

Definition of telecommunication: 
Communication over a distance by cable, telegraph, telephone or broadcasting. It is the transmission of signs, signals, messages, words, writings, images and sounds or intelligence of any nature by wire, radio, optical or other electromagnatic systems. Telecommunication occurs when the exchange of information between communication participants includes the use of technology. It is transmitted either electrically over physical media such as cable or electromagnatic radiation such as technology.

The value of data within telecommunication:
The most valuable telco data is an untapped source of customer information. Thus, the big companies nowadays turn telecom carriers since it has a valuable source of customer data (2017 study shows over than 300 brands in the US, UK and France finds than 67% of brands consider telecoms operators to be a better original source of data insights than Google, facebook, Apple and Samsung). Eventhough Google has 59% and Facebook has 52% remain the dominant brand partners for data insights and digital advertising, 26% of brands say they do currently partner with telcos that specialize in digital advertising. However, 48% of brands are not aware of telecoms operators’ ability to even offer these insights – and this is a good news for telco and it is our role to let them aware.

The potential that can be done with telco data:

  • Better original source of data insight
  • Underpin digital advertising – with high quality and compelling.

Special attention to Khairunnisa – please look at factor 2)better data analytics and 7)data was updated more regularly. The combination of these might bring us focus to on data temporal. See you on friday 11 August ’11 (Airis’ birthday)

Thanks to Ian Barker at https://goo.gl/fjQg3w and Ovum Survey https://goo.gl/vJbdnZ


Representation of Data Monitoring – Part 2: How

  1. Timeline:
    Line drawn on a suitable scale (days, month, years, centuries) on which key historical, planned or projected events and perionds marked in the sequence of their occurence (an incident or event; the fract or frequency of something happening, the fact of something existing or being found in a place or under a particular set of conditions) – this bring me to temporal data.