If
the Data-Driven Decision-Making approach improves public service delivery,
optimises resource planning and allocation, encourages citizen participation,
and drives productivity, it is a
tangible asset for development planning and implementation. Therefore, such an
approach should be embraced by all executives, senior managers and managers in
decision making positions in any institution and country. It is
contended that "data-driven decisions are better decisions. The notion of
willingness to use data-driven decisions during development planning, policy
and decision making have been noticed in Namibia in recent years because data
producers have realised that hence statistical data is now enjoying high
profile than before. Big data brings opportunities for turning “digital trace'
into real-time statistics' practical during strategic decision making at all
levels.
In
summary, big data is data that is produced independently of any data collection
effort such as surveys- it is often data that is available as a by-product of
user-interaction with digital devices, such as mobile phone call records,
social media posts Twitter, Facebook, and online search records (for example,
search statistics, such as available through Google Trends). This type of data
is sometimes referred to as 'organic data' since they are not produced for
statistical purposes and emerge independent of data collection efforts such as
surveys, referred to as 'designed data'. However, such emerging data sources
can be mined and analysed to monitor human and societal behaviour in
near-real-time and potentially turned into statistics. Some scholars take big
data to mean too large datasets for traditional data-processing systems,
requiring new technologies. It is clear that existing data is underutilised and
that a data-driven decision-making approach might not even be applied in the
public sector to inform national and regional developmental decisions. It is
also supported by the mini survey results that revealed that 70 per cent of the
respondents come from Civic organisations that consist of government
ministries. Some of the approaches used to guide and facilitate the
decision-making process include a framework known as Data-Driven Decision
Making (DDDM). Hence"DDDM is the practice of basing decisions on the
analysis of data rather than purely on intuition. The DDDM approach improves public service delivery, optimises resource
planning and allocation, encourages citizen participation, and drives
productivity. The literature revealed that, often, institutions have the
data they need to tackle business problems and make informed decisions.
However, sometimes managers and decision-makers do not know how to utilise such
data to make critical decisions
Theories
of DDDM
Many
areas of human knowledge have extensively researched decision-making theories.
A decision is a response to a situation and comprehends judgment, expectations,
and evaluation. A brief literature analysis shows a relationship between
culture and decision-making as many organisations use a cultural-ethical
decision-making model. Decision and
behaviour could be the main elements of decision-making phenomena, which
involve thinking and reacting to external world stimuli
Optimal
Decision-Making Tree Algorithms theory
Decision
trees inducers are algorithms that robotically build decision trees from a
given data set. Archetypically, the aim is to find an optimal decision tree by
reducing the generalisation error. Induction of an ideal decision tree from a
given data is a various and arduous task. Optimal decision tree algorithms are
feasible for predicaments. Heuristic methods are needed for remedying
predicaments
Stochastic Sequential Decision-Making
Theories.
Dewey (1978) propounded the sequential model
of decision making, which enhanced the great philosopher Condorcet (1743-1794)
three stages model. The five stages were: 1) Identify and delineate the problem
2) Analyse the problem and establish criteria 3) Produce creative alternative
solutions 4) Assess the options and choose the best solution 5) Implement
selected alternative 6) Evaluate the solution. Stochastic sequential
decision-making predicaments encapsulate a configuration that emanates under
stochastic uncertainty because of a decision-maker's actions in the phases. A
stochastic process is a random process sprouting with time
Non
-Sequential Decision-Making Theories.
Non
-sequential decision-making theories are unprogrammed decisions when dealing
with non-routine predicaments that are unique and that generally require fast
remedies
Data-Driven Decision Adoption Theory
Tacit
decision making
Some
enterprise theories are incumbent upon intuition- and pragmatism -based
decision making to be efficacious, regardless of a corporate's intentions to
initiate a more data-driven tactic. The required data and information are
regularly "stored" uniquely in humans' brains, muscle memory or
subconscious cognitive, and are thus multifarious to quantify, codify, or even
delineate and share. In other ensamples, tacit decisions encapsulate creativity
or an enormous number of input determinants that would require colossal
computational power to augment. This is where humans beat computers by
deploying their instinct, which "stores" millions of years of
juxtaposed situations
Data
Aided Decision Making
These
decisions are enormously incumbent upon profound and prolific generated through
data but critically depend on a human input determinant. Empiricism for this
taxonomy can be propounded from Brynjolfsson and McElheran's rubric of the U.S.
census bureau data on manufacturing (2016b). The rationale to encapsulate human
input in the decision-making process are differentiated. In some ensamples,
adding tacit human capital to decisions can be of value to their results as an
ensample embracing customer interaction in the store level repurchasing process
of 7/11 in Japan, in others, a human ecosystem there is needed to mitigate a
potentially outsized risk
Local
The
first level for a corporate is regularly to execute pioneering data-driven
projects to assess the impact and return on investment. If the results are
satisfactory, the triumphant project is regularly institutionalised, and
eventually, DDD gets embraced on an isolated, functional level. Recurrently,
pioneering projects are within the arena of marketing since the influence and
effect of marketing collaborations can supposedly easily be scrutinised as a
standalone project. Particularly within marketing, there has been significant
ontogenesis in the automated and standardised deployment of analytics and data,
e.g., for a multifarious personalisation of communication and pricing. An
overview of this, however, the level of centralisation in local DDD espousal is
still relatively moderate.
Data-driven
Decision Making -Corporate Wide
One of the strategic ways DDD moots value in a
corporate is through the facilitation of cross-functional collaboration. At the
same time, plethoric concrete evaluations of collaborative value creation are
even more efficacious when they are accompanied by efforts to centralise some
of the decision makings and standardise best customs.
Source
Wiegert (2017)
Conceptual
Framework
Independent
Variables
Source Author (2021)
Evidence-based
DDDM
Scott (2020) asserts that evidence-based policymaking is the
deployment of sound and translucent data in making public policy decisions' approach affect how decisions are made and
who gets to make them. Evidence-based
policymaking in participatory situations means that, wherever feasible, public
policy decisions should be reached after an open debate informed by careful and
rigorous analysis using robust and transparent data. Making evidence-based
policymaking has become more vital in emerging states, given the increasingly
mixed methods of policy analysis
Capacity
and Human Skills
The
literature revealed that, often, institutions have the data they need to tackle
business problems and make informed decisions. However, sometimes managers and
decision-makers do not know how to utilise such data to make critical decisions
Application
of DDDMs
The
approach to decision making has been changed in recent years due to the
ever-increasing abundance of data, advancement of new technologies and
opportunities for data collection, storage, processing, and analysis. The case studies revealed that the companies that
adopted the DDDM framework have proven helpful in improving business efficiency
and productivity and creating competitiveness. The adoption is rapid but uneven
across industries. Two-thirds of U.S. companies have not adopted DDDM, but even
those who did, there is still heterogeneity among them given differences in
data infrastructure, data literacy and analytic capabilities that have been
identified as components of DDDM
Challenges
affecting DDDMs
Data Handling and its use to embrace
DDDM approach
Kiregyera (2015), Namibia falls under
data-demand constrained countries as per his twin challenge of data demand and
supply quadrants, where quantity and quality of data are improving. However,
data are not used for policy or decisions due to lack of information, limited
data access due to scattered data in different data producer institutions,
doubts about the veracity of available data and lack of knowledge about how to
access and effectively use data among data users. Blauer (2015) raised a concern
that even at the time of unprecedented
digital openness, institutions kept data locked up in proprietary databases or
published in cumbersome formats that limit the use of data. Wegner
(2013) also pointed out that needed statistical information is seldom readily
available in the format, quality and quantity required by decision-makers which
becomes a challenge to evidence-based outcomes. It is further pointed out that policymaking
decisions suffer due to the lack of clear policy indicators to guide the government
in addressing poverty-related issues (Paris-21 Secretariat, 2007).
Skills gap and abilities to analyse data
during the decision making
There is a lack of skill gaps such as
data literacy and the ability to analyse real-time data among consumers and
users of analytics, which is key to driving business performance. Companies are
advised to remove all barriers and friction between the users and effective use
of analytics; for example, managers are only able to tap the value created
through DDDM and make effective decisions if they are affluent with both data
and business languages (Marr 2017; Marr 2016; Mandinach 2012; Manyika et al.,
2011). Sophisticated predictive
analytics capabilities are needed within institutions to handle data and save
costs. Users have a deficiency in executing a profound analysis of data.
Universities and corporates are locked into paper-based methods for assessing
performance, and this entails that user cannot drill down to unravel the cause-and-effect
matrix and come up with a decisive enhancement decision-making process.
Data
security and regulations
Data security and regulations become
apparent when looking at the divergence in law and regulations regarding the
storage and use of third-person data for some geographies (e.g., U.S. and
E.U.). This is seen as a significant source of uncertainty, and more
convergence can be expected since it is still a very new field of the
jurisdiction (Weigert, 2017, p.27).
Resources
are meagre
Demand is high, and resources are
meagre. The demand for decision making information surpasses the time, money
and ICT resources present to meet the obligations. When the needed data for
decision-making is not delineated due to a lack of resources, data sources
cannot be recognised and implemented for user analysis and decision-making
needs
Trust in Data-Driven Decision Making
DDDM adopters need to foster trust in
data among all internal customers and users of analytics across their
organisations, including trust in the actual decision-making capabilities of
analytics. Institutions are advised to provide training and display maximum
transparency into the use of behavioural data. This approach will ensure useful
for DDDM adoption (Weigert, 2017).
Source
Mpunwa (2021)
Decision Support Systems during
decision-making activities
The process of embracing new opportunities
of the data revolution to make wise, better, and effective decisions with data.
In all sectors, the decision-making process relies on Decision, Support Systems
(DSS), a DSS systems are designed artefacts that have specific functionalities
to drive the decision-making process. There are five (5) more specific DSS
types: communications‐driven, data‐driven, document‐driven, knowledge‐driven, and model‐driven systems.
Communications technologies are central to communications driven DSS for
supporting decision‐making. Data‐driven DSS provide access to large data stores and
analytics to create information. Document‐driven DSS use documents to provide
information for decision making. Knowledge‐driven DSS are sometimes
generically called expert systems or recommender systems. Model‐driven DSS use
quantitative models for functionality and have been called model‐oriented
DSS and computationally
oriented DSS. Knowledge management system (KMS) encompasses both
document and knowledge driven DSS
Food
banking is the method of food that would then be dispensed of as waste is
improved and allocated to hungry people. Even though the custom of food banking
varies somewhat from place to place, the fundamental building blocks of food
banking configurations are analogous everywhere. A decision support system
(DSS) is crafted to assist Food banks in South Africa to manage their agency
database, systematise some of the daily dissemination decisions and simulate
apportionment policies
Adoption of Data-Driven Decision Making and the factors influencing its adoption
The
approach to decision making has been changed in recent years due to the new
advanced opportunities on data collection, storage, and processing
technologies. As a result, those occupying decision-making positions are using
data to make informed decisions and less relying on intuition are considered an
old tradition of decision making Brynjolfsson
Challenges
on the adoption of DDDM across industries.
Companies
that have adopted the DDDM approach have improved business efficiency,
productivity, and competitiveness
The concept of Big Data
According
to Prydz (2014), big data brings opportunities for turning “digital trace' into
real-time statistics' practical during strategic decision making at all levels.
In summary, big data is data that is produced independently of any data
collection effort such as surveys- it is often data that is available as a
by-product of user-interaction with digital devices, such as mobile phone call
records, social media posts (Twitter, Facebook, etc.) and online search records
(for example, search statistics, such as available through Google Trends). This
type of data is sometimes referred to as 'organic data' since they are not
produced for statistical purposes and emerge independent of data collection
efforts such as surveys, referred to as 'designed data'. However, such emerging
data sources can be mined and analysed to monitor human and societal behaviour
in near-real-time and potentially turned into statistics (Groves, 2011). Some
scholars take big data to mean too large datasets for traditional
data-processing systems, requiring new technologies (Provost & Fawcett,
2013).
It
is argued that the big data revolution is more potent than a used analyst in
the past since it allows managers to measure and manage development impacts
more precisely than ever before, make better predictions and smarter calculated
developmental planning decisions. Therefore, executives and senior managers can
use any available big data to target more effective developmental interventions
and do so in areas that have been dominated by guts and intuition (McAfee &
Brynjolfsonn, 2012). It is believed that decision-makers in Namibia responsible
for national and regional development planning and implementation have adequate
and comprehensive primary and secondary data to make informed decisions. The
literature revealed that, often, institutions have the data they need to tackle
business problems and make informed decisions, but sometimes managers and
decision-makers do not know how to utilise such data to make critical decisions
(Barton & Court, 2013).
Big
Data: emerging data sources to make near-real-time strategic decisions in the
21st century
The
wisdom statement says, "You cannot manage what you do not measure"
It
is further argued that while the principles of DDDM are relevant to both the private
and public sectors, it is a new phenomenon in the public sector that is lagging
in using data to make strategic and tactical decisions (Peppers & Rogers,
2013). McAfee and Brynjolfsonn (2012) advise that using data during
decision-making enables managers to decide based on evidence rather than
intuitions. Therefore, this is a serious call for the public sector in Namibia
and, specifically, the government ministries, the principal planners, and
primary data users to embrace the DDDM approach during development planning and
implementation. The institutional culture of using data-driven decisions
enables the development practitioners at a high level of decision making to
make smarter developmental decisions and better predictions at regional and
national levels towards improving the welfare of the Namibian people.
Methodology
Brecklin
(2009) used a case study to investigate the organisational structure,
assessment philosophy, and instructional cultures of one Wisconsin public
school district to learn how the district deployed assessment data to inform
reading instruction. The survey was situated within the context of the high
stakes testing environment created by No Child Left Behind legislation. Data
from plethoric sources informed the discourse, encapsulating documents,
interviews, and classroom observations. Participants embraced three district
administrators, three elementary building principals, three elementary reading
specialists and seven elementary classroom teachers
Study Setting
Ranchordias
and Klop (2018) executed a data-driven regulation and governments in city
cities; the research was conducted at the University of Groningen Faculty of
Law. This discourse unravels from the Eurocentric perspectives the notion of
data-driven legislation and governance in the environment of smart cities in
Tokyo, Delhi, and Mexico City by delineating how these urban epicentres attach
these technologies to gather and process data about citizens, traffic, urban planning,
or waste creation. It delineates how several intelligent cities through the
universe presently employ data science, big data, A.I., Internet of Things
('IoT'), and forecasting analytics to enhance the effectiveness of their
services and decision-making. Additionally, the discourse analyses the legal
ramifications of deploying these technologies to trigger or recognise local
regulation and governance content. The rubric explores in specific three
appropriate predicaments: the disconnect between conventional administrative
law frame of reference and data-driven regulation and governance, the impact of
the commercialisation of public services and citizen needs due to the
escalating outsourcing of smart cities technologies to private corporates; and
the restricted transparency and accountability that symbolises data-driven
administrative processes. Salem (2017) also conducted social media and the
internet of things towards data-driven policymaking in the Arab world and
highlighted the potential limits and concerns. Incumbent upon a regional survey,
the key findings on the deployment of social media data, IoT data, and their
conjunction as a significant data source. It also unravels the public
perspective and viewpoint on the potential role of this data in triggering
policymaking
Model
Chow (2017) researched the risk-sensitive and
data-driven decision-making using Markov decision processes (MDPs) model
furnished a mathematical framework for modelling sequential decision-making
where configuration evolution and cost/reward are incumbent on qualms and
control actions of a decision. MDP models have been extensively adopted in
plethoric domains such as automation, regulatory systems, finance, economics,
and manufacturing. At the same time, optimisation theories of MDPs serve as the
theoretical underpinnings to several dynamic programming and reinforcement
learning algorithms in stochastic control predicaments. This research fills a
gap in that it does not use a Markov Decision process as a model. However, it
borrows from the conceptual framework above adapted from Marsh et al. (2006),
showing the data-driven decision-making approach will guide the design of survey
instruments and methodology of this study the independent variables of human
capital, evidence-based decision making. Application of data-driven approach
and challenges affecting government Institutions. The dependent variables are
public sectors targeting all heads of department and management cadres.
Response
Rate for Head of Institutions
Source
Author (2021)
The
response rate was high from the survey conducted, in which 76.2% was achieved
from category 1 and 4.8% from category 2. The histogram demonstrates the
response rate. The mean for responses was 6.6 and standard deviation 23.5% the
Lilliefors p>0.01. The non-response
rate was 19% which is very low in terms
of statistics. Some
perceptions of the differences in response rate can be achieved by assessing
trends in response rate over duration for recurrent surveys. The surveys for
non-response rate measures the % of participants in which no members were
successful questioned
Descriptive Statistics for Demographic Variables for Head of
Institutions
Source Author (2021)
24 % of the participants were female,
while 76% of the respondents were male. Statistics demonstrate that it is a
male-dominated sample. The findings of this research are like Temey (2014), who
found industries as a male-dominated milieu. Male-female differential average
matters. The industry variable can be taxonomic as male-dominated, female-dominated,
or neutral. The classification rule reckons three groupings male-dominated,
female-dominated, and neutral. The classification rule reckons the proportions
of men and women in the labour force juxtaposed to men and women under
consideration. If the percentage of women industry within the labour force
extended to 1.25, it was reckoned as female-dominated if the same ratio was
below 0.75, the industry was reckoned as male-dominated
The
findings of this research are also advocated by the finding of Chauhan (2016)
on "Patriarchal benevolence" demonstrated by men in senior management
positions in their substratum for increased women's representation and
resources because interventions did not challenge their elite calibre at the
lower level, and such a move did not skirmish with their interest. In the
public sector, women's absence from senior positions and many occupations is
also connected to the promotion policies of the state of Pakistan. The rules
stipulate that promotion be based on seniority cum fitness. Even if quotas for
women were increased to 50 per cent at the entry-level, given the present rules
in the elite services with fast-track promotion, not many women have quotas,
for example, fishing quotas in Namibia. That is, it would still take 20 years
for a significant effect to be felt. Women entering the public sector through
quotas or on merit in competitive examinations still would not reach senior
positions until the minimum length of service is attained. This would result in
the preservation of gendered corporation hierarchies and the status quo as far
as women are concerned for some time
Source Author (2021)
The age group of participants were
between 40 to 49 years and 50 to 59 years of age. 41% of the participants were
between 50 to 59 years, while 59% were between 50 to 59 years of age. The
distribution of the sample indicates that they are comprised of mature
participants. However,
some respondents argued that the experience-related area
might initially favour mature age public sector, but the advantage of
having mature age participants has experienced inputs. In Accounting subject's
maturity tends to go hand in hand with business experience, which is a
distinct advantage. Mature-age participants, with more
fabulous experiences, give the younger participants confidence in what is
presented at work by articulating mature and experienced
issues. The inclusion of mature participants reflects their
increasing importance in the labour force, which can profoundly affect their
families
Source Author (2021)
11.76% are undergraduate with a first
degree, while 88.24% of the participants have post-graduate qualifications such
as Masters and PhD qualifications. Effectiveness is the ability to generate a specific
anticipated effect or result that can be evaluated.
Efficiency is the extent to which operations are executed out economically.
This custom encourages top Management to perceive
employees as consumers of capital rather than as a
long-term investment
Years of Experience with the Institution
Source Author (2021)
41.18% have 11 years and above working
for the public sector, while 11.76% have 6 to 10 years working with the
institution, and the remaining 47.06% have five years' experience with the
institution. The adage articulates that
character cannot be developed in ease and quiet. Only through experience of
work and hard work can the mind be strengthened, ambition inspired, and success
attained character
cannot be developed in ease and quiet. The statistics reveal that most
of the participants have a few years' experiences working for the public sector
as head of the institution, which could be due to high labour turnover. The
sharp increase in unemployment and discouragement among youth is due to hanging
by the thread position in the labour market. Research by Hofymer (2013) is like
the findings of this research in which many young people, due to lack of
experience and short histories with the corporates, would be the first to be
made redundant. Youth lack work experience and short histories with the
corporate who are the first to be retrenched. Youth who lack experience does
not have the right signals of their productivity and cannot tap into the
linkages of employed people. Public sectors are not willing to risk hiring
young people with limited experience as management heads
Most data suppliers
|
Descriptive Statistics |
|||||
|
|
N |
Minimum |
Maximum |
Mean |
Std.
Deviation |
|
Do
you get data from Namibia Statistics Agency (NSA) |
61 |
1 |
2 |
1.05 |
.218 |
|
Do
you get data from other government ministries/agencies/offices |
61 |
1 |
2 |
1.23 |
.424 |
|
Do
you get data from private sector |
61 |
1 |
2 |
1.67 |
.473 |
|
Do
you get data from international institutions (e.g., UN, WHO, ILO, WB, AU,
etc.) |
61 |
1 |
2 |
1.46 |
.502 |
|
Do
you get data from Internal Information Systems for Monitoring &
Evaluation |
61 |
1 |
2 |
1.52 |
.504 |
|
Other
data suppliers to your institution |
61 |
1 |
2 |
1.87 |
.340 |
|
Other
data reporting frequency |
61 |
1 |
2 |
1.84 |
.373 |
|
Valid
N (listwise) |
61 |
|
|
|
|
Source Author (2021)
When
Management was asked who your data suppliers are in descending preferences and
use, they articulated that they get data from other suppliers with the highest
mean score of 1.87. This is followed by other data reporting having a mean
score of 1.84, followed by the private sector with a mean score of 1,67.
Management cadres also get data from ICT with a mean score of 1.57, other
international organisations, such as the U.N., WHO, ILO, also supply them with
a mean score of 1.46. Ministries agencies and government operations also
furnish management cadres with a mean score of 1.23, the least supply of data
to management cadres was the Namibia Statistics Agency having a mean score of
1.05. Davenport and Dyche (2018) highlight that big companies are the suppliers
of big data corporate because they have data-warehouses such as Google, e-bay,
Linked In, Facebook, General Electric, UPS, Schneider National, MIPS UPS and
others. UPS tracks data on 16.3 million packages per 24 hours for 8.8 million
clients with a mean of 39.5 million tracking needs for 24 hours. These big data
suppliers contain data on videos, images, weblogs, documents, and PDFs. The
leading suppliers of big data are the internet, and 85% of data is created by
cyber-space users between the ages of 6-24 years
Big data at
Caesar Entertainment delineates self-driving cars as big data applications.
Netflix is employing and deploying big data for movies. Financial Institutions
such as Wells Fargo, Bank of America, and Discover use big data to appreciate
CRM. United Health care, such as corporate are chasing big data, Corporates
such as EMC dispose of substantial storage remedies such as cloud computing,
Other big data suppliers are 5 top property and Casualty Insurer which was
propounded by military corporates.
As "big data" becomes increasingly integrated
into many aspects of our lives, we hear more calls for revolutionary changes in
how researchers work. To save time in understanding the behaviour of complex
systems or predicting outcomes, some analysts say it should now be possible to
let the data "tell the story” Rather than developing a hypothesis and
going through painstaking steps to prove it. The success of companies such as
Google Inc. and Facebook Inc., which have transformed the advertising and
social media worlds by applying data mining and mathematics, has led many to
believe that traditional methodologies based on models and theories may no
longer be necessary. There are seral fields where massive amounts of data are
available and collected: drug discovery and pharmaceutical research; genomics
and species improvement; weather forecasting; the design of complex products
like gas turbines; and speech recognition
Source
Davenport and Dyches (2018) Big data suppliers in big companies
Relationships between Data Science, Big Data and
Data-Driven Decision Making
Provost
and Fawcett (2013) pointed out that "with vast amounts of data now
available, companies in almost every industry are focused on exploiting data
for competitive advantage. The volume and variety of data have far outstripped
the capacity of manual analysis, and in some cases have exceeded the capacity
of conventional databases" (p.51). The companies have realised they need
to hire data scientists to guide and extract information and knowledge from
data (closely related to the data mining concept) to improve decision-making.
Scholars have a considerable debate to pin down data science precisely since it
is intricately intertwined with other important concepts also of growing importance,
such as big data and data-driven decision making (DDDM)
55%
of the management cadres stated that the institution contributes most to the
Housing Sector, while 44% disagreed that the institution contributes most to
the Housing sector. Extensive data management encapsulates storage,
pre-processing, processing, security, governance, integration, housing, and
quality considerations. The desire to
grant affordable housing in Namibia was a national concern that has been
long-established on various grounds. However, bidding wars for the land is one
key determinant impacting the capability to provide affordable housing in
towns, especially in Windhoek. This is attributed to the insufficiency of land
suitable for development. A suitable quantity and quality of housing are
crucial for economic welfare, prosperity, social cohesion and the comprehension
of sustainable environmental goals and objectives as enunciated in the National
Development goals. Vision 2030 purpose, in line with the National Development
Goals (NDP 4) of Namibia, places much emphasis on furnishing access to
sufficient shelter for low- to ultra-low-income households and ensuring that
land is secured and enhanced address the issue of affordable housing in
Namibia. The state of Namibia initiated various policies and operational
programmes such as the Build-Together Programme (BTP), the National Housing
Enterprise (NHE), Shack Dwellers Federation of Namibia (SDFN) and some other
Central Government Initiatives to improve access to housing, the interpositions
to furnish affordable housing in Windhoek has not been attained. It should be noted that Namibia pollution of the
environment is less than that of China because of the few people and less
industrialisation, which pollutes the air.
While GHG emissions and GDP are mounting in tandem, some Chinese cities
may also be grasping a turning point at which ecosystem degradation commences
to degenerate while income proceeds to rise.
The essence of the
food bank in food security in Namibia is incessantly employed by farmers,
exclusively those living near. It is thought of as a necessary resource for food
storage after harvest, enhanced seeds for planting, and human capital knowledge
and skills
The
participants were asked how data-driven decision-making was applied in the
institution to manage public resources during development planning to achieve
national and regional development plans in tandem with V2030, HPP, NDPs, and
SDGs providing the necessary steps followed until data-driven decisions are
taken. Research conducted by Brim (1962) unravels the fourth step in DDM is
evaluations of Options through generated solutions. 4.9% of the participants stated
that data is also used during development projects' monitoring and evaluation
process. 3.3.% highlighted that decision making and monitoring of such
projects. 1.6% monitor and evaluate projects while 3.3% review performance of
units and individuals. 1.6 % stated we use the insights by the analysis to
drive change, present and distribute insights that data are presented to the
right people at the right time in a meaningful way. Those insights gained from
the data are deployed to inform decision making and ultimately improve
performance.1.6 per cent of the participants stressed that they apply regional
Balancing to ensure that each region benefits from land acquisition. 3.3 % also
highlighted that Line Ministries send our requests and proposals to the cabinet
to pass a resolution. The fourth step of DDM was identified by 9.8% of
participants, showing that 90.2 % of the management cadres were not conversant
with the DDM fourth stage.
The research findings reveal that 55%
(8.3% strongly agree and 46.7% agree) of the management cadres agree that
metadata, data information, is made available to the institution through the
broadest possible dissemination, such as the internet. Unfortunately, 40.2 % disagree that metadata
about data is made available to the institution through the broadest possible
dissemination, such as the internet, while 6.7% were not sure if metadata is
made available. Metadata is critical
since it provides a better understanding of data that advances its usability
among data users. Metadata's details on the who, how, when, where that includes
coverage and limitations assist data users to determine the usefulness of such
data during planning and decision-making processes.
|
Metadata which is information about
data (such as concepts, definitions,
scope and used methodology), is made available to the institution through the
broadest possible dissemination, such as the internet. |
|||||
|
|
Frequency |
Per
cent |
Valid
Percent |
Cumulative
Percent |
|
|
Valid |
Strongly
Agree |
5 |
8.2 |
8.3 |
8.3 |
|
Agree |
28 |
45.9 |
46.7 |
55.0 |
|
|
Disagree |
21 |
34.4 |
35.0 |
90.0 |
|
|
Strongly
Disagree |
2 |
3.3 |
3.3 |
93.3 |
|
|
Not
sure |
4 |
6.6 |
6.7 |
100.0 |
|
|
Total |
60 |
98.4 |
100.0 |
|
|
|
Missing |
System |
1 |
1.6 |
|
|
|
Total |
61 |
100.0 |
|
|
|
Source Author (2021)
The term metadata is a contestable
word. Metadata is machine-understandable information for the Web. Metadata at
COIN is understandable information for context mediation
The
role of the government is essential in improving regional development, planning
and coordination. Every planning for enterprise is affected by the business
cycle, and the macro-economic effects impact regional development—the rate of
interest rates, the exchange rate and government economic policies. The need to
appreciate economic policies making is not restricted to state policymakers. In
other words, regional councillors are fooling themselves when they claim issues
such as government economic policy have no relevance to their decision making;
everything is relevant to the strategic planning, for example:
|
Issue |
Government Action |
|
Growth of GNP |
Stimulate innovation |
|
Unemployment |
Reduce taxes |
|
Inflation |
Increase taxes |
|
The budget balance |
Increase taxes |
|
The role of markets |
Reduce regulation |
|
The trade balance |
Stimulate exports |
|
The exchange rate |
Sell currency reserves |
|
Pollution |
Tax polluting companies |
|
Fat bodies |
Introduce sugar tax on products |
|
High government expenditure |
Control government expenditure |
|
Business investment |
Improve confidence |
Source Napoli (2018)
Practise data-driven decision making within Institution
Source Author (2021)
On the practice of data-driven decision
making within the institution, most participants agree that managers are
responsible for development planning and policy design using a data-driven
approach to make better and wiser decisions rather than using intuition and
guts. The findings of this research differ from Faith (2017), who articulates
that some managers may scoff at the notion of making decisions based on
sentiments or intuition they construe the trader's role as one who persists
calm and collected, judiciously selecting the right course while those around
them are lobbed about their sentiments. If you are one of the managers who does
not have efficacy, that gut has any place. Managing from your intuition is a
way of tapping into the extra supremacy of the right hemisphere of the Cognito.
Managing from your intuition is a way of tapping into your extreme power of the
right hemisphere of the brain. Managing from your guts is competent, efficient,
proficient, and efficacious
Most managers also agree that using big
data as evidence during decision-making has excellent potential to contribute
to the institution's success significantly. The standard deviation is 1.1, and
the Kurtosis -2. The statistics show that spreading with a tall, narrow peak has positive
Kurtosis, and a low, flat peak is characteristic of negative
Kurtosis. Liebowitz (2018) research shows that big data is big news; some
signal it as the new oil. So, in the universe where computer-based applications
facilitate technologists to seize, curate, manage and process unimaginably
enormous amounts of complex data, is there any place for human intuition. The
big data recommend not the examination of petabytes of data which permits us to
articulate correlation is adequate without the need for causal models let alone
human judgement and the big data future promises simulations of the brain and
nervous system
Most
managers also agree that the highest-paid person in the institution makes the
most critical strategic decisions for development planning and implementation,
symbolising this is a mode of 2 and a standard deviation of 1.3. The Kurtosis
is negative two, with a standard deviation of 1.3 and a mode of 2. Figures show
that
distribution with a tall, narrow peak has positive Kurtosis, and a low, flat
peak is characteristic of negative Kurtosis. Strategic decisions
are vital for five key reasons: They are large-scale, risky, and hard to
reverse; they are a bridge between deliberate and emerging strategies; they can
be a crucial source of corporate learning. Strategic decisions are the bridge
between deliberate and emergent strategies. They can be a crucial source of
corporate learning, which play a pivotal role in the progression of managers
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