Journal of Network and Systems Management
Visualizing Internet Network Unavailability: Insights for Better Action
--Manuscript Draft-Manuscript Number:
Full Title:
Visualizing Internet Network Unavailability: Insights for Better Action
Article Type:
S.I. : Responsible Internet
Keywords:
Data analytics; Business Intelligence; Data visualization; informed decision making
Corresponding Author:
Mesfin Woldmariam, PhD
Addis Ababa University
Addis Ababa, ETHIOPIA
Corresponding Author Secondary
Information:
Corresponding Author's Institution:
Addis Ababa University
Corresponding Author's Secondary
Institution:
First Author:
Mesfin Woldmariam, PhD
First Author Secondary Information:
Order of Authors:
Mesfin Woldmariam, PhD
Order of Authors Secondary Information:
Manuscript Region of Origin:
FALKLAND ISLANDS (MALVINAS)
Funding Information:
Abstract:
Telecom companies capture network availability and unavailability data continuously
and automatically. But, it is not common to see them conduct analytics on the datasets.
As the result, managers usually miss which network stations are making them lose in
revenue and which of them bring more revenue? Which times do many networks go
unavailable? These questions cannot be answered by both design science and
behavioral science fields of information systems. By using network unavailability
datasets at Ethio-telecom, this study demonstrates how analytics based information
system can help managers to visualize communication network unavailability across
network stations in Ethiopia. The study reveal network unavailability forms some kind
of pattern across regions and time. For example, Southern regions has a persistent
high unavailability while central and western Addis Ababa has continuous high
availability of network. In terms of time May, June, and July has high unavailability
while March, December, September and November has the highest availability.
Thursday is the date many network unavailability is reported. It is very interesting to
analyze why this kind of uniform pattern emerged across regions and the months. This
kind of insights alerts decision makers to conduct more investigative studies for better
actions. From academic perspective, the author insists information system scholars
give attention to the overlooked field of analytics based information system. With the
ever increasing datasets, analytics based information system deserves more attention
than the past.
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Visualizing Internet Network Unavailability: Insights for Better Action
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Abstract
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Telecom companies capture network availability and unavailability data continuously and automatically. But, it is not
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common to see them conduct analytics on the datasets. As the result, managers usually miss which network stations
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are making them lose in revenue and which of them bring more revenue? Which times do many networks go
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unavailable? These questions cannot be answered by both design science and behavioral science fields of information
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systems. By using network unavailability datasets at Ethio-telecom, this study demonstrates how analytics based
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information system can help managers to visualize communication network unavailability across network stations in
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Ethiopia. The study reveal network unavailability forms some kind of pattern across regions and time. For example,
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Southern regions has a persistent high unavailability while central and western Addis Ababa has continuous high
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availability of network. In terms of time May, June, and July has high unavailability while March, December,
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September and November has the highest availability. Thursday is the date many network unavailability is reported.
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It is very interesting to analyze why this kind of uniform pattern emerged across regions and the months. This kind of
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insights alerts decision makers to conduct more investigative studies for better actions. From academic perspective,
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the author insists information system scholars give attention to the overlooked field of analytics based information
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system. With the ever increasing datasets, analytics based information system deserves more attention than the past.
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Keywords: Data analytics, Business Intelligence, Data visualization, informed decision making
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1. Introduction
Service outages in internet networks can occur due to factors like power outage, accidental fall of telecom tower due
to heavy rain and wind, sabotage by individuals, malfunctions in the core transmission network, major damage in the
core network elements, such as controllers, or in the switching functions [1]. Prevalence of such causes means a huge
income lose to service provider, which also affects businesses and individuals who use the network. For example,
individuals and business who are seeking to rent office space need information about which part of the city has regular
internet interruptions and which haven’t. Similarly, service providers also get information about which network sites
are more profitable and which are not? What is the pattern of network unavailability across the regions and zones?
When is the highest unavailability in the country etc? This means both service provider and service takers can make
informed decisions.
Service providers manage hundreds of thousands of connected network sites that are connected to central controlling
station. When network site encounter trouble/ fault, this information/data is captured by central system so that
maintenance department will fix. Promptly fixing such problems add value to the network reliability [2, 3]. Otherwise
delay in fixing problems results in revenue lose to service provider and dissatisfaction to service takers. Having
network availability rate without disturbance/ failure for all time service is mandatory for customer satisfaction as well
as better profitability. But, as a matter of fact, achieving 100% network availability is unrealistic as the causes are
beyond the control of telecom companies. Figure 1 shows how data about network problem is centrally and
automatically captured by the system.
Fig.1 Central data capturing system
Insights about the characteristics and patterns of network unavailability across stations help managers prioritize
maintenance and follow up. Network unavailability insights also help business and individuals to make informed
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decisions when renting office space. Imagine if you rented an office space and got continuous internet interruptions
and delayed maintenance.
Delay in fixing network problems and repetitive maintenance on specific network site costs telecom companies a lot.
Particularly, in developing nation, where almost all spare parts are imported, repetitive maintenance is expense and
counterproductive. Due to lack of data analytics departments and units in Ethio-telecom, this problem went unnoticed
by top level management of the company. As the result, Ethio-telecom has lost an estimated 500,000 USD from
November 17-to May 17, 2020.
In this perspective, this study demonstrates how data analytics give insights that is useful to managers and avoid extra
expenses [4, 5]. From academics point of view, this study demonstrates the need for analytics based information
systems, as stated by [6, 7].
2. The Need for Data Analytics Based Information Systems Study
Data analytics and decision making is a way of deciding based on insight in datasets. With the increasing digital
products, services, and interactions added with existence of smart systems and agents, organizations collect huge
datasets easily. Particularly, telecom companies have huge datasets which can be analyzed for better services. But
with respect to African countries like Ethiopia, it is not common to deduce insights from data and most of the decisions
are kind of business as usual. Thus, business managers are missing opportunities.
Every day, we create large tera bytes of data, which is so large and big that 90 % of the data in the world today have
been created in the last two years alone [8]. Such datasets is the backbone of data analytics and informed decision
making [9], which is the spring board efficient and effective performance of todays and future organizations.
The application of data analytics on telecom organizations’ day to day operation is becoming imperative due to its
capability of providing valuable insights, knowledge, and foreseen issues [10]. Making sense of both structured and
unstructured data through analytics helps organizations to revisit their routine operations, processes and decisions. For
this to be materialized, they need to turn their datasets into insights. Such practices can also help organizations gain
competitive advantages in the industry.
The application of analytics or knowledge discovery in telecom industry is reported by different scholars like [11, 12,
13]. These studies argued how information and data visualizations help executives to make informed decisions.
Unlike behavioral and design science research approaches of information systems research, research in data analytics
based information system need to be guided not by theories and frameworks but by set of exploratory questions whose
answers/ findings will be extracted from the dataset itself. This can be predictions, automated decisions, models that
learns from data, or any type of data visualization delivering insights for decision makers [6]. For example, building
dashboards from data visualization can help decision making [7].
In order to demonstrate the above argument, network unavailability datasets of Ethio-telecom for 2019 and 2020 was
analyzed. The following section give brief characteristics of the datasets.
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3. Research Datasets
The dataset has 6 columns as shown by table 1 below and there are 32,300 records. There are 23 telecommunication
regions and each of them have many network stations identified by their site IDs (column two).
Region
1.CAAZ
2.NAAZ
3.WAAZ
….
23. SR
Site ID
111104
111143
111202
……
….
TAG
Domain
Plan Date
31/01/2019
Normal
Power
Normal
Power
2/5/2019
Normal
Power
2/5/2019
….
….
….
…
….
…
Table 1: Sample format of original datasets
Close Date
5/2/2019
7/5/2019
7/5/2019
….
…
Column Name
Description
Region
This column lists out the names of the region where network unavailability is reported. The
column lists out all the regions like CAAZ, SR, SWR, etc
SITEID
Each region has hundreds of network sites and each of them have their own site ID.
TAG
This column has data about the problem on the site. It has values like Core, Normal, Solar,
which means problem on the core network, minor problem, and problem related to solar power
respectively.
Source of the problem. It has only one value as “power”
Domain
Planned Date
This column has information related to the date (day, month, and year) of the problem as
captured by the central system
Closed date
This column has information when the maintenance was completed.
Table 2: description of dataset format
4. Insights from the datasets
Accurate decision making needs accurate data analysis and presentation in a more consumable manner to
decision maker. In the big data era, this requires the use of different tools like: visualization [14], big data
analytics techniques [15]. In line with this, the present study used Tableau 2020.3 desktop version. This
tool is the leading visualization software in the industry. Before the analysis and visualization, the necessary
data preprocessing and transformation is made.
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Figure 2: Number of network unavailability days per regions /zones
4.1.
Network Unavailability across the Regions
As can be seen from the figure 2 above, south (SR), south western (SWR), and south eastern (SSWR) regions have
the highest number of network unavailable days. When there are interruptions and disturbances on the network
infrastructure, fixing them takes longer time (days) than other regions. Even though the data doesn’t tell why this is
happening in these regions, the insight is clear! These regions are making the company lose much than other regions.
Twenty five thousand network is unavailable is calculated as the sum of the number of days that the network sites are
out of services in those regions. For example, as indicated on figure 2 above, there are around 2,500 reported cases of
network sites failures in southern region. This is equal to (25,000/2,500) which means each network site failure lasts
for an average of 10 days without being fixed.
Given this insight, it is easy to understand customers of the company who reside in these regions are the most
disadvantaged ones and have less competitive advantage on the use of internet based services, products or business
models, as also reported by the study of [16,17] (Hailu, 2014; Tadesse, 2019). For individuals and enterprises who
intend to embark on digital business that highly depend on internet, this kind of information is crucial and worth
consideration.
Given the current Pandemic (COVID-19), many organizations (government and private), particularly those working
on the education sector are moving towards online education. Given the above internet network unavailability
information, those business and institutions planning to operate from southern part of the country will obviously face
a serious challenge. These areas are unreliable.
The uniform decline in the figure is also an interesting alarm to telecom managers. Under normal circumstance, the
figure should have look like zigzag than kind of liner decline. This kind of circumstances tell something is going
unnoticed.
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4.2.
Network Unavailability of 2019-2020 for the regions
Comparing network unavailability across the sites for the two years reveal that the trend is uniform. As seen by figure
3 below, those with high network unavailability in 2019 do have high unavailability in 2020 too and those with low
unavailability in 2019 have also low unavailability in 2020. This uniformity catches the attention of any stakeholders.
Why this has happened? In common sense and under normal circumstance, one can expect ups and down in the regions
for the two regions. But, the data tips presence of something which we cannot tell from the present data. Answering
this dilemma is not the objective of this paper. But the author strongly recommend management of the company assess
what is going behind the scenes.
Figure 3: Comparing network unavailability in the regions for two years
4.3.
Network Unavailability across the months for the regions
As illustrated by figure 4 below, southern regions have got the highest network unavailability for the months of May,
June, January, July and October. From this insight, it is interesting to explore what is going on in these regions during
these months.
Particularly, the scale of interruption during the month of May is an eye catching that needs further investigation.
Based on the information in literature, factors that lead to internet disturbances due to high scale of wind, rain, and
political instability might be the contributing factors. On the other hand, the months of March and November are the
two months with the least internet unavailability throughout the regions.
This kind of information from the dataset is very informative to service users and service providers. The service
provider can easily spot which network sites are bringing more revenue and which ones are costing them. Similarly,
service users (individuals/ firms), who intends to provide internet based services and products like e-learning, ecommerce etc can make informed decisions while selecting their operational location. It is also interesting to
investigate what is special with these months.
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Figure 4: Network unavailability for months
4.4.
Network Unavailability across days of the week
Analysis at a more granular level (day level interruption) reveal, Thursday has got the maximum network
unavailability report for all the regions, see figure 5 below. Again regions in the southern part of the country has got
the highest number of network unavailability. This information is also eye catching for telecom managers. It is
interesting to investigate what is going on throughout all network stations in the country on every Thursday.
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Figure 5: Network unavailability during the days of the week across the regions
5. Conclusion
Internet networks availability is the key for business that intend to provide digital products and services to their
customers. During the era of global information society, network availability to individual citizen is also like basic
need. Internet is the backbone of many business, stakeholders, and citizens. As the result it is common to see
organizations having websites, e-mails, social media presence, and even the use of collaboration technologies. But, it
is not possible to have the network all the time.
In this study, the application of data visualization technique reveal internet network unavailability in the country at
different level of aggregation. Regions, months, and days. From the data analysis, network stations in southern part of
the country have the highest number of days of internet unavailability. The months of May, June, and July are the
months with the highest unavailability. Thursday is exceptionally the most unavailability reported days throughout
the nation. This information needs close attention and investigation from service provider. More unavailable network
means more loss of revenue and customer dissatisfaction too.
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Another striking information is the uniform trend across all the regions for the two years, across the months, and even
days. Under normal circumstance, this kind of uniformity is not expected to happen. Some possible areas to investigate
are: technology infrastructures deployed in the regions, behavior of maintenance personnel and efficiency of
supervision, and service load on network equipment.
From academic point of view, the findings reported in this study demonstrates information system research problems
that did not get enough attention from information system community. These problems are not guided by design
science or behavioral science research approaches. Therefore, the author calls information system scholars to
reconsider the marginalized field of analytics based information systems, both in research and curricula.
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