ADiBA Big Data Adoption Framework: Accelerating Big Data

Revolution 5.0

Norhayati Daut

, Naomie Salim

2,* b

, Chan Weng Howe

, Anazida Zainal

Sharin Hazlin Huspi

, Masitah Ghazali

and Fatimah Shafinaz Ahmad

Faculty of Computing and Informatics, Universiti Malaysia Sabah, Jalan UMS, 88400, Kota Kinabalu, Sabah, Malaysia

UTM Big Data Centre, Ibnu Sina Institute for Scientific and Industrial Research, Universiti Teknologi Malaysia,

Johor 81310, Malaysia

School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia, Johor 81310, Malaysia

Keywords: Big Data, Data Analytics, Big Data Adoption, Digital Transformation, Data-driven Organisation.

Abstract: Researchers have formulated the revolution of Big Data into several stages, from stage 1 using raw data until

stage 5 using operational intelligence and advanced analytics is used to provide wisdom. However, for

organisations to reap the values from big data adoption and implementation, they must embrace Big Data

Revolution 5.0: digital acceleration. At this stage, Big Data Analytics (BDA) becomes an asset from which,

businesses can get new insights and aid value creation, resulting in increased profits. BDA will play a large

part in extending an organisation's presence, which will lead to enticing possible investors and hasten global

growth. In this paper, we proposed a framework that aid organisations toward big data adoption and

implementation that can create the best value for the organisations. It covers the whole value chain of big data

adoption and implementation from the enculturation of big data in the organisation, to business understanding,

to data management and governance, to big data project planning, to data understanding, to data preparation,

to procurement, to analytics modeling, data product development, evaluation of model and data product

deployment, maintenance, and upgrades and inculturation of data analytics into business. The framework has

been successfully used in several Malaysian organisations, government, semi-government, and private

sectors.

1 INTRODUCTION

Big Data Analytics (BDA) uses advanced analytic

techniques for massive, diversified big data sets,

which might contain structured, semi-structured, and

unstructured data from various sources and sizes.

Besides, BDA is a sort of advanced analytics that

comprises complex applications that rely on analytics

systems to fuel predictive models, statistical

algorithms, and what-if scenarios. In an era when

technology has achieved its pinnacle of use and has

completely taken over our lives, the volume of data

https://orcid.org/0000-0003-1163-6445

https://orcid.org/0000-0001-8509-3055

https://orcid.org/0000-0003-0612-3661

https://orcid.org/0000-0003-0022-3039

https://orcid.org/0000-0002-7591-7249

https://orcid.org/0000-0001-5720-175X

https://orcid.org/0000-0001-8058-8606

exchanged is enormous. BDA has several advantages,

including improving decision-making and preventing

fraud. Thus, BDA is powering everything we do

online today, in every business. Most organisations

are now aware that if they capture all the data that

enters their operations, which may be in real-time,

they can utilise analytics to extract significant value.

It helps comprehend the current status of the business

or process and serves as a solid foundation for

forecasting future results. This is especially true when

advanced approaches like Artificial Intelligence (AI)

are used (Coeckelbergh, 2020). Businesses can use

Daut, N., Salim, N., Howe, C., Zainal, A., Huspi, S., Ghazali, M. and Ahmad, F.

ADiBA Big Data Adoption Framework: Accelerating Big Data Revolution 5.0.

DOI: 10.5220/0011351700003269

In Proceedings of the 11th International Conference on Data Science, Technology and Applications (DATA 2022), pages 549-556

ISBN: 978-989-758-583-8; ISSN: 2184-285X

549

BDA technologies and tools to make data-driven

decisions that improve their bottom line. The

methodologies, tools, and frameworks developed

because of BDA make this possible. BDA has several

advantages, including speed and efficiency, which

lead to more effective marketing, higher revenue

prospects, customer personalisation, and increased

operational efficiency. Businesses may now collect

real-time data and analyse big data to make faster,

more informed decisions. With the correct strategy,

these advantages can create competitive advantages

over competitors. The organisation's ability to work

faster and remain adaptable can gain a competitive

advantage they did not have previously.

The big data adoption revolution was coined by

(Mourtzis, 2021; Özdemir & Hekim, 2018). It was

made possible by developments such as quickly

expanding the amount of data available, speeding up

data storage capacity and processing at low cost, and

the evolution of Machine Learning approaches to

analyse complicated datasets. These stages can be

classified from industry revolution 1.0

(mechanisation) to industry revolution 5.0 (cognitive

computing) and will be explained in Table 1 in the

next section. Stage 1.0 mainly involved raw data

processed using linear programming. In stage 2.0, the

decision support system has used statistically

processed data. More variety of data is processed

using data mining techniques in stage 3.0. In stage

4.0, the whole data landscape is analysed using

artificial intelligence to overcome data challenges. In

stage 5.0, operational intelligence and advanced

analytics are used to provide wisdom.

To accelerate the digital transformation towards

data revolution 5.0, we develop a process framework

to help organisations establish and execute big data

adoption and implementation that can create value for

the organisation. Our framework has been

specifically formulated to eliminate data gaps in

creating values for organisations. We emphasise a

method of accelerated digital transformation context

as we strive to map the landscape of rights and data.

The structure of the paper is summarised below.

The literature review is described in section 2, which

clarifies the research gap. Section 3 describes the

research methods. The outcomes of this investigation

are thoroughly described in section 4. Finally, we

make some closing observations and

recommendations for further studies.

2 LITERATURE REVIEW

Many studies have explored the BDA adoption and

implementation in different types of organisations,

enterprises (Orenga-Roglá & Chalmeta, 2019),

government agencies (Qadadeh & Abdallah, 2020;

Thamjaroenporn & Achalakul, 2020), and industries

and sectors (Ponsard et al., 2017; Huber et al., 2019;

Massmann et al., 2020; Mathrani & Lai, 2021). These

studies explored and proposed a process framework

for adopting BDA based on several theoretical

models. The process framework consists of phases,

steps and activities that organisations can use as a

guideline to adopt BDA into their environment. It has

been found that many studies developed a process

framework based on the CRoss Industry Standard

Process for Data Mining (CRISP-DM) process model

(Li et al., 2016; Ponsard et al., 2017; Huber et al.,

2019; Qadadeh & Abdallah, 2020). Some studies

used project management methodology (Orenga-

Roglá & Chalmeta, 2019; Kastouni & Ait Lahcen,

2020) and data lifecycle model (

Blazquez & Domenech,

2018). These models and other models proposed in the

studies are used as the baseline in developing the

proposed initial ADiBA process framework in this

study.

In 2019, big data adoption was on everyone's

agenda (Bag et al., 2021). Innumerable businesses are

embarking on a data journey. However, according to

a new analysis, the issue is that businesses are not

getting the most out of data. Most businesses are now

increasingly starting with a data strategy, which is a

good idea. Even with a strong strategy and good

intentions, there is no guarantee of success. Only 9%

of respondents thought their company was very

effective at extracting value from data, while 48%

said it was somewhat effective.

The absence of data adoption across the organisation

is one of the reasons why data efforts fail. In addition,

understanding the stages of the big data revolution

can help us identify the data supply chain that can be

embedded into the organisation's value chain. In

Table 1, we summarise the revolution of Big Data to

help understand the stages of big data adoption and

implementation.

According to Table 1, the digital transformation

age must be accompanied by digital adoption, which

should be prioritised in implementing software,

technology, workflow, and even new cultural habits.

Laying a formal framework for digital adoption

ensures staff productivity, scale, and visibility for

executives. Learn how to create an agile workforce

that is naturally change-resistant. Today, an

abundance of data is being produced, which has

DATA 2022 - 11th International Conference on Data Science, Technology and Applications

550

Table 1: Digital Data Transformation Era, adapted from (Mourtzis, 2021; Özdemir & Hekim, 2018).

Digital

Transformation

1760

Revolution 1.0

1870

Revolution 2.0

1969

Revolution 3.0

2000

Revolution 4.0

2020

Revolution 5.0

Data

Complexity

Linear

programming

and Monte

Carlo

simulations

Decision support

system, Expert

system, Ad-hoc

reporting, and

Neural networks

Data Mining, Data

cubs, Statistical

analysis, and data

marts

Business

Intelligence,

Artificial

intelligence, text

mining, and image

anal

sis

Machine learning,

data visualisation,

data clusters,

operational

intelligence, and

advanced anal

tics.

Data Areas Accessible

Data focus on

opening data,

accountability,

and data

literac

Sustainable

Developments

Goals that

measure progress

on new indicators

and

rou

Data Innovation

emphasises big

data and new

technologies.

Data Landscape that

addresses systemic

challenges.

Mobilising Data for

the practical

proposition and

improving data

production.

Data

Challenges

Increasing data

literacy, the

ability to use

and interpret

data.

Benchmarking

data and

comparing its

progress across

new goals.

Innovation

increases the

quantity of data

and the

opportunity for

data improvement.

To comprehensively

build new and

emerging

technologies from

sources of data and

make system-wide

rovements.

The current

excitement about

data turns into

concrete

commitments for

lasting changes.

Data Scheme Observing the

facts

Sampling the data Compare the

information

Adjust the

knowled

Focus on the

wisdom

resulted in a slew of new opportunities and

challenges. Big data adoption provides the most

opportunity and value in cost, productivity, and

competitiveness. However, according to a Gartner

survey, only 14% of businesses have implemented

big data projects (Charles & Gherman, 2019). Recent

studies have demonstrated that big data investments

are increasing across businesses and worldwide.

While there are advantages to using big data, the

picture surrounding its acceptance is still hazy, as it is

with any new emergent technology. It exemplifies a

corporate adoption paradox that offers fast, but it

takes time to deploy successfully.

3 METHODOLOGIES

BDA is very different from the traditional statistical

approach to experimental design in terms of

methodology. Data is the foundation of analytics

(Handfield et al., 2019). There are 4 phases to be

conducted for this study. Phase 1 is conducting a

systematic literature review to investigate the process

and activities of BDA adoption that have been

outlined in past studies. An initial framework is then

developed based on the literature review findings. In

Phase 2, we performed data collection from an

industry case study. This includes discussions with

the BDA experts to see whether the proposed initial

framework is suitable and relevant. In Phase 3, the

initial framework developed in Phase 1 will be

refined based on the findings in Phase 2. All the

framework's guidelines and instruments for the

necessary steps of the related process are then

produced. The final Phase 4 is the framework

validation, where selected organisations apply the

updated and refined framework to their environment.

Surveys will be distributed to get feedback from using

the framework, and it will determine whether the

proposed framework needs to be refined again or not.

4 PROPOSED FRAMEWORK

We work together on their niche area focusing on big

data with the sub-niche of Artificial Intelligence (AI),

data management, cloud computing, and information

systems for the development that supports the data-

driven digital transformation of Malaysian

organisations. Our initial planning was to develop the

Malaysian Big Data Adoption Framework by

supporting big data self-serve platform with

recommendation systems. Thus, suggesting policies

and guidelines that can help more organisations

transform into digital organisations and have a more

mature level of data maturity. As illustrated in Figure

1, we designed and developed our proposed

Accelerating Digital Transformation through Big

Data Adoption that covers the whole value chain of

the big data adoption (ADiBA) framework.

ADiBA Big Data Adoption Framework: Accelerating Big Data Revolution 5.0

551

Figure 1: Proposed ADiBA framework.

Figure 2: Stages of ADiBA process framework.

Our ADiBA process framework covers the whole

value chain from data sources, consolidation, storage,

provisioning, discovery, data governance, data

management, and beneficiaries. Data sources focus

on structured and unstructured data for business

values consolidation emphasises the data selection,

extraction, transformation, wrangling, integration,

loading, and data integrity check. Data storage

focuses on the data dictionaries, data models, and

business rules. Data provisioning indicates the

descriptive, diagnostic, predictive, prescriptive,

visualisation, autonomous generation, and self-

service dynamic extract. Data discovery relies on

routine reporting, analytics dashboard, alert, OLAP

Analysis, Web Apps, Mobile Apps, and automated

system. Data governance focuses on the team and

charter, data stewardship, governance policies,

standards, data governance metrics, and analytics.

Besides, data management focuses on the strategy,

integration, quality management, metadata

DATA 2022 - 11th International Conference on Data Science, Technology and Applications

552

management, data operation, supporting the process,

and platform and architecture. The beneficiaries will

be internal users, local industries, and government

agencies.

The literature review found that studies used the

CRISP-DM process model, project management

methodology, data lifecycle, and others as the

baseline for developing the process framework. Each

model has its phases, steps and activities that can be

used to conduct BDA adoption in organisations.

These models' suitable and relevant phases are used

to develop the proposed ADiBA process framework.

The stages of the ADiBA process framework can

be divided from stage 0: prepare immersion of

analytic culture, stage 1: business understanding,

stage 2: data management and governance, stage 3:

project planning, stage 4: data understanding, stage 5:

data preparation, stage 6: tool, infrastructure, and

technology procurement and presentation, stage 7:

business analytics modeling, stage 8: data analytics

products development, stage 9: evaluation of model

and products, stage 10: data analytics product

deployment, stage 11: monitoring maintenance and

upgrades and stage 12: inculturation of big data

analytics into business, as illustrated in Figure 2.

Based on the ADiBA process framework, we have

identified and elaborated the 13 stages of the ADiBA

framework, tasks, and subtasks accordingly in Table 2.

Table 2: ADiBA process framework.

Stages Tas

Sub-Tas

0: Prepare Immersion

of Analytics Culture

Create Urgency Assess any potential threats that could arise in the near or distant

future.

Build a Guiding Coalition Identify the effective change leaders in the organisation and the

stakeholders.

Create a Vision for Change Determine the core values, define the ultimate vision and the

strate

ies for realisin

the bi

data initiatives in an or

anisation.

Communicate the vision Conduct a series of sharing sessions to communicate the analytic

culture convincingly.

Remove Barriers Ensure that the organisational processes and structure are in place

and ali

ned with the overall or

anisational vision.

Create Short-Term Wins Create short-term wins early in the change process.

Build on the Change Analyse what went right and what went wrong after each data

anal

tics initiative im

lementation.

Anchor the change in the

Organisation's Culture

Share the success stories related to change initiatives at every given

opportunity.

1: Business

Understanding

Identify Business Goal Understand & develop business overview write-up, identify

business goals and objectives, review current environment, identify

strength, weaknesses, challenges, and opportunities, related

regulations, acts and compliance needs regarding data.

Assess Situation Assess Decision Points of SWOT, functions by role and by

committee, ob

ective and

oal of enter

rise

Define Data Analytics goals

or insi

hts

Identify Analytics Goals of SWOT, functions by role and by

committee, ob

ective and

oal of enter

rise

2: Data Management

& Governance

Data Governance

Engagement Framework

Initialize engagement, Define DG capabilities, Identify scope and

constraints, Assess maturity of the organisation, Align planned DG

capabilities with business, Determine DG Principles, Policies and

Guidelines, strategic requirements.

Define Data Governance

Organisation

Determine DG Council and Data Management Core Team -

Stewards, Owners & DROs, Establish overall responsibility

assignment and overall data accessibility permissions, Develop DG

Charte

Develop Data Security,

Privacy, Sharing, Ethics and

Compliance Governance

Framewor

Identify security and privacy needs from act and regulations, data

and data product classification, data and data product threat and

risk, data and data product control measures, ethical measures, and

list of control measures to

rotect data.

3: Project Planning Identify Business Use Cases Identify business use cases to support data analytics goals, data

product for business use case, value of data product, how data

product is going to be enculturated into organisational business

process, and the priority of the data product. List data product and

users, and data

roducts for each unit, role, or committee.

ADiBA Big Data Adoption Framework: Accelerating Big Data Revolution 5.0

553

Table 2: ADiBA process framework (cont).

Sta

es Tas

Sub-Tas

3: Project Planning Estimate Resources Required Identify data requirements, analytics required, components of data

products, technology procurement cost, estimate value proposition

derivation of use-cases

Perform Cost-Benefit

Analysis

Estimate cost required for data, analytics and data product

development and operation, benefit of data product, perform value

osition-fit assessment

Develop Project Plan Assign priority levels to business use-cases, Identify short-term &

long-term use-cases projects, physical resources, financial and staff

implications, Develop big data analytic roadmap

4: Data

Understanding

Define Data Sources Identify current available data (Data Profiling), Perform data-

requirements mapping, Identify other data resource, Verify

availability of data, Revise business understanding

Design and Develop Data

Sandbox

Design master data management framework, overall sandbox

structure, data warehouse and data marts, data lakes and data

shards, integrated enterprise databases, data interchange APIs;

Define integrity checks, Develop data sandbox

Describe Data Define data dictionary and metadata management, Familiarize with

data, Describe data sources and data attributes, Develop data

dictionar

Develop Data Curation

ine

Develop data extraction engine, data input portal, data capturing

stem

4.5-Verify Data Quality Identify data quality requirements and data quality problems,

Develop and run data quality tests, Resolve data quality problems,

Validate data with users and experts

5: Data preparation Extract Data Extract Data

Transform Data Cleanse, Aggregate, Standardize, De-duplicate, Sort, Filter, Slice

Data

Explore and Visualize Data Provide Descriptive Statistics, Identify and Treat Missing Values,

Visualize data & detect outliers, Feature En

ineerin

Modify Data Join or Relate Necessary Table and Data; Cleanse, Re-format,

Normalize, Impute, Augment data; Remove Outliers

6: Tool,

Infrastructure, and

Technology

Procurement and

Presentation

Identify Required Tools,

Infrastructure and

Technology

Identify tools for data ingestion, data storing, data transformation,

data governance and data quality, data security analytics

visualization, techniques implementation, analytics and data

science, performance monitoring, and cloud computing tools &

latforms for Bi

Data

Evaluate Tools and

Technology

uest and evaluate

osal

Choose correct combination of tools

Procurement of Tools Arrange for procurement of tools

7: Business Analytics

Modelling

Identify Key Variables Identify Variables Involved in model

Select Modellin

Techni

ue Identif

and select techni

ues to

erform anal

tics

Desi

n Test Desi

n ex

eriment to evaluate, S

lit trainin

and test data

Build Model Desi

n, describe, and develo

model; set

arameters

Assess Model Run sample tests with data and validate output, Revise parameter

settin

, Ex

lore models, Re

ort

erformance of models

Manage Model Create model configuration management, Manage folders and

access

Model Plan and de

ment of the model to user/data

roduct develo

8: Data Analytics

Products

Development

Pre-Design Stage Determine the data product development process, Obtain

Requirements (USR) from PHASE-4

Design and Develop

Dashboards

Determine the dashboard type and design the dashboard, Design

dashboard Structure & Com

onents

Design and Develop Business

orts

Determine the report type (If applicable)

Develo

Alerts Develo

alerts in dashboards OR as a stan

-alone s

stems

DATA 2022 - 11th International Conference on Data Science, Technology and Applications

554

Table 2: ADiBA process framework (cont).

Sta

es Tas

Sub-Tas

9: Evaluation of

Model and Products

Perform Data Product

Testing

Develop a formal test plan; Perform component, integration, data

roduct, user acceptance, and satisfaction testing

Perform Pilot Test Verify and validate data product based

Pre

are Test Re

ort Record flaws from data

roduct and

ilot testin

, develo

ort

Determine Next Course of

Actions

Identify possible action, Make final decision

10: Data Analytics

Product Deployment

Plan Deployment Develop a plan deployment strategy, Identify steps, Define

instructions, Perform Trainin

Plan Monitoring and

Maintenance

Plan monitoring & maintenance

ort Final Results Develo

ment

ect re

ort, do final

resentation

Review Project Develop project experience report

11: Monitoring

Maintenance and

Upgrades

Monitor Performance Identify appropriate methods for performance evaluation, and

ossible of

erformance failure sand downtime

Correct Error Identify appropriate tools and solutions for correcting and fixing

the performance failures

Enhance Dashboard, Reports

and Alerts

Identify user experience (UX) enhancement methods for improving

user interfaces, and data-driven improvement that could help in

rovin

the user ex

erience

Replace or Discard System if

Obsolete

Identify project planning for solution / system / services

replacement which involve organisation budget and priorit

12: Inculturation of

Big Data Analytics

into Business

Generate Short-Term Wins Create short-term wins earl

in the chan

rocess.

Sustain: Build on the Change Analyse what went right and what went wrong after each data

analytics initiative implementation.

Anchor the Change in the

anisation Culture

Share the success stories related to change initiatives at every given

ortunit

Assess Impact of Big Data

Analytics

Check the pulse of the change initiative.

Uncover what works.

The ADiBA process framework will be validated,

including the tasks and sub-tasks. Several Malaysian

organisations have agreed to participate in the study,

where they can apply the framework to their

environment and give feedback for any further

refinements.

5 CONCLUSIONS

BDA is evaluating large amounts of data gathered

from many sources, such as digital data, social data,

and knowledge information. The major aim is to

identify patterns and relationships that have never

been seen before and gain fresh insights into the users

who created them. It is challenging to process using

traditional methods since it is so large, rapid, and

detailed. Our research contributes to the process

framework for BDA adoption and implementation in

organisations and the evaluation framework for

assessing the success and impact of the adoption. We

plan to create a process framework for BDA adoption

and implementation in businesses and an assessment

framework to examine the success and impact of the

adoption.

Our study's essential contribution is to structure

guidelines and instruments for each of the required

steps and activities, which will be provided as aids to

the process framework. The tools can assist

businesses in determining what is required for BDA

adoption and implementation on a step-by-step basis.

Influencing factors and user acceptance are critical,

especially at the start and end of the adoption process.

These considerations range from how an organisation

should begin the adoption process to any BDA

project's implementation and monitoring phases. The

process may alter based on the organisation's

structure, even though the processes and activities or

theoretical models utilised are the same.

Organisations will know what to expect, where to

start, and which direction they are traveling if they

use this framework. To adopt and implement BDA in

organisations, a process structure is required so that

these organisations may be directed through the entire

process. As a result, incorporating innovation into

businesses requires careful planning. Potential

adopters must identify the innovation and grasp how

ADiBA Big Data Adoption Framework: Accelerating Big Data Revolution 5.0

555

and why it works in the knowledge phase. The

persuasive phase will enter the picture when potential

adopters have ambivalent feelings about the

innovation. Because the major goal of this study is to

solve real problems, action research in real-world

circumstances using case studies is favored over

experimental investigations.

ACKNOWLEDGEMENTS

This research was funded by Ministry of Higher

Education, Malaysia

(JPT(BKPI)1000/016/018/25(58)) through Malaysia

Big Data Research Excellence Consortium

(BiDaREC) (Vot No: R.J130000.7851.4L933), (Vot

No: R.J130000.7851.4L942), (Vot No:

R.J130000.7851.4L938), (Vot No:

R.J130000.7851.4L936). We are also grateful to

(Project No: KHAS-KKP/2021/FTMK/C00003) and

(Project No: KKP002-2021) for their financial

support of our study and publication of this article.

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