How is telecom industry benefiting their data?

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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)

References:

  1. http://bigdata-madesimple.com/11-interesting-big-data-case-studies-in-telecom/
  2. Ackers (2013) Benefiting from big data. A new approach for the telecom industry. published by Booz & Company.

Temporal Data and Business Intelligence

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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 & Graphics, 31(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

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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 – temporal data

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Temporal Data
synonym for time-oriented data, time-varying data, time-dependent data

Definition of temporal data:

Temporal data is data that varies over time (Jensen and Snodgrass, 1999)
A temporal data denotes the evolution of an object characteristic over a period of time (Daassi and Nigay, 2004)

Time dependent data is characterized by data elements being a function of time. In general, data takes the form of d = f(t), where data defined at discrete time stamps t, f is functions of time (Muller and Schumann, 2003)

Time series data is characterized by data elements being a function of time. In general, this data takes the following form, d = yt and y = f(t)

Key references for Data Temporal

  • Noah, S., Yaakob, S., & Shahar, S. (2009). Application of information visualization techniques in representing patients’ temporal personal history data. Visual Informatics: Bridging Research and Practice, 168-179.
  • [Daassi et al., 2004] Chaouki Daassi, Laurence Nigay, and Marie-Christine Fauvet. Visualization Process of Temporal Data, Lecture Notes in Computer Science, 3180, Springer-Verlag Heidelberg, 2004.
  • [Jensen and Snodgrass, 1999] Christian S. Jensen and Richard T. Snodgrass. Temporal Data Management, IEEE Transactions on Knowledge and Data Engineering, 11:36-44, 1999.
  • [Müller and Schumann, 2003] W. Müller and H. S. Schumann. Visualization Methods for Time-dependent Data – an Overview, In Proceedings of Winter Simulation 2003, New Orleans, USA, 2003
  • [Weber et al., 2001] Marc Weber, Marc Alexa, and Wolfgang Müller. Visualizing Time-Series on Spirals, In Proceedings of the IEEE Symposium on Information Visualization 2001 (InfoVis 2001), p. 7-14, October 2001.

 

Representation of Data Monitoring – Part 2: How

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  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.

Representation of Data Monitoring – Part 1: Definitions

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Presenting Data for Monitoring Activities – Part 1: Definitions

What is monitoring? (Definition of monitoring):
The act of carrying out planned observations, measurements, etc in order to collect, review and use information for a stated goal (to assess a particular situation) from – slideplayer.com/slide/7270385

Observer and check the progress or quality of (something) over a period of time – keep under systematic review; maintain regular surveillance over; listen to and report on. From google definition

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

Monitoring is one activity sub under management. Because within management, we also do planning, control, monitoring and reviewing any of the activities.

Where does the monitoring happen? 
In the organization – dashboard – business performance
In the industries – manufacturing process in the assembly line
In the project management – construction, physical activities.
In the security – airport, library, supermarket.

Definition of MYRA monitoring:
The act of carrying out planned observations or measurements related to the occurence changes of University KPIs (e.g. grants, publications and awards) in order to collect and analyse information needed to achieve stated KPT-KPI goals.

 

 

How doa works?

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When we make doa, we are expecting an immediate answer from Allah.

The thing is, our doa getting answered immediately or not is nothing to do with either Allah is happy with us or not. There is no connection between these two.

E.g. Nabi Nuh invited his nation for islam for 950 years. Do you think this kind of guy was not making doa for his son and wife?Nabi Muhammad was not making doa for his uncles – Abu Talib and Abu Lahab? Nabi Yaakub a.s not making doa for his twelwe sons?

Listen more to Nouman Ali Khan for How Doa works at https://goo.gl/VGsX4E

 

Pengesahan Jawatan di Universiti Teknologi Malaysia (akademik)

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Pengesahan Jawatan di UTM adalah dalam tempoh 1-3 tahun:

Dengan syarat 3 tahun berturut-turut dapat average eLPPT 80%.
Dengan syarat 2 tahun berturut-turut dapat average eLPPT 85%.
Dengan syarat 1 tahun berturut-turut dapat average eLPPT 90%.

Dan telah mengikuti program berkenaan.

  1. Program Transformasi Minda diadakan 2 kali setahun. Dalam tempoh 5 hari. Diadakan di UTM JB (6-10 Ogos 2017)
  2. Syarat tambahan – SPPT (Sijil Pengajaran Pengajian Tinggi). Anjuran UTM Lead. Bagi staf akademik sahaja (done – 9-26 Julai 2017)
  3. Peperiksaan perkhidmatan – akademik tidak perlu. (no need)
  4. Penilaian prestasi tahunan (eLPPT) – https://hffin.utm.my/smuhr – refer 1-3 tahun formula seperti di atas.
  5. Perakuan ketua jabatan – pegawai yang mengetuai sesuatu jabatan atau manamana pegawai yang diwakilkan kuasa secara bertulis oleh ketua jabatan untuk bertindak bagi pihaknya. Ketua jabatan boleh untuk tidak memperakukan pengesahan jawatan staf dibawah seliaannya jika prestasi tidak memuaskan.

Setelah Sah jawatan – akan dapat jawatan tetap, kemudian boleh pilih untuk caruman KWSP atau Skim pencen.

  1. Pencen
    • Pencen – dapat pencen bulanan dan faedah persaraan, GCR, faedah perubatan percuma untuk pesara dan tanggungan.
    • Pencen minimum 40 tahun, maksimum 60 tahun. 40 tahun ke atas boleh bersara pilihan – tetapi pencen hanya akan dapat apabila mencapai umur 55 tahun.
    • Pencen UTM sama mengikut pekeliling kerajaan.
  2. KWSP
    • KWSP – faedah persaraan, GcR, Faedah perubatan percuma untuk pesara, caruman KWSP anggota dan kerajaan, bantuan kematian RM2500 sekaligus, hilang upaya RM 5000 sekaligus

Jenis-Jenis Cuti bila bekerja di UTM

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Jenis-Jenis Cuti bila bekerja di UTM
Umumnya, jenis-jenis cuti di UTM adalah sama dan tertakluk kepada cuti dalam perkhidmatan kerajaan. Justeru, untuk maklumat lanjut – boleh rujuk pekeliling am yang berkaitan.

  • Cuti rehat – cuti bergaji penuh
    • Dapat 35 hari? – in my case dah dapat 35 hari. Call ext 4615 (please follow up this matter)
    • Cuti rehat yang tidak dapat dihabiskan – GCR.  Maksimum 150 hari (PP 18/2008).
  • Cuti Sakit
    •  ikut pekeliling kerajaan
    • sekiranya sijil sakit swasta lebih daripada 15 hari akan dikenakan potong gaji.
  • Cuti tanpa gaji
    • Syarat minimum 6 bulan perkhidmatan.
    • 30 hari bagi setiap satu tahun perkhidmatan
    • urusan persendirian yang mustahak di dalam dan diluar negara dengan tidak menjejaskan
    • CTG – tidak mengira GcR, tempoh layak untuk AJB (Anugerah Jasa Bakti) dan AJS (Anugerah Jasa Setia), kekananan, tamahan cuti apabilan genap 10 tahun perkhidmatan.
    • Maksimum 360 hari sepanjang perkhidmatan.
  • Cuti separuh gaji
    • Menjaga keluarga yang sakit (sanak-saudara yang rakat – pasangan, anak-anak, ibu bapa dan adik beradik).
    • 30 hari bagi genap perkhidmatan 1 tahun.
    • Maksimum 180 hari sepanjang perkhidmatan.
  • Cuti menjaga anak 
    • tidak melebihi 1825 hari (5 tahun) sepanjang perkhidmatan.
    • Sekiranya tidak bersambung dengan cuti bersalin – boleh ambil dengan maksimum 1 tahun atau anak berumur 4 tahun – mana yang terdahulu.
    • Sekiranya bersambung dengan cuti bersalin – boleh ambil maksimum 1825 hari (5 tahun).
  • Cuti haji
    • 40 hari sepanjang tempoh perkhidmatan
    • islam, berjawatan tetap dan disahkan dalam jawatan.
    • Sekiranya belum sah jawatan – boleh ambil cuti rehat atau CTG
  • Cuti mengikut suami bertugas di luar kawasan
    • Kepentingan untuk berada bersama pasangan
    • Kadar maksimum 3 tahun atau selamat tempoh berkhidmata berkursus pasangan pegawai mana yang lebih pendek.
    • Boleh guna untuk suami pergi further study.
  • Cuti Kuarantin
    • anak menghidap penyakit berjangkit – campak, chicken pox, demam campak, denggi, HFMD.
    • Terhad 5 hari atau selama tempoh perakuan – mana yang terpendek.
  • Cuti Tanpa rekod
    • 30 hari setahun  bagi tujuan menghadiri latihan atau khemah tahunan. Syarat (askar wataniah, pasukan simpanan)
    • Cuti untuk mengambil peperiksaan (SPA)
    • Cuti menghadirian mesyaurat persatuan ikhtisas
    • Cuti untuk mengambil bahagian dalam olahraga, suka
    • Cuti isteri bersalin – 7 hari setiap kali isteri bersalin x 5 kali sepanjang perkhidmatan.
    • 3 hari – cuti urusan kematian ahli keluarga terdekat (family + Parents + FIL + MIL)
    • Kecemasan am – banjir, tanah runtuh (perlu pengesahan daripada penghulu, balai polis berdekatan.