About the Author(s)

Wandisa Nyikana Email symbol
Department of Information Technology, Faculty of Informatics and Design, Cape Peninsula University of Technology, Cape Town, South Africa

Tiko Iyamu symbol
Department of Information Technology, Faculty of Informatics and Design, Cape Peninsula University of Technology, Cape Town, South Africa


Nyikana, W. & Iyamu, T., 2023, ‘The logical differentiation between small data and big data’, South African Journal of Information Management 25(1), a1701. https://doi.org/10.4102/sajim.v25i1.1701

Original Research

The logical differentiation between small data and big data

Wandisa Nyikana, Tiko Iyamu

Received: 12 Apr. 2023; Accepted: 03 Oct. 2023; Published: 24 Dec. 2023

Copyright: © 2023. The Author(s). Licensee: AOSIS.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Background: The distinction between small data and big data is increasingly muted and has caused challenges and confusion in many quarters.

Objective: The objective of the study is to gain a deeper understanding of the confounded confusion that exists between small data and big data. Firstly, to develop a taxonomy that distinguishes between small data and big data. Secondly, it seeks to extract the value from the concepts, which can be of fundamental importance to an organisation.

Methods: This study follows the interpretive approach and employs qualitative methods, based on which 57 related materials were gathered, covering big data and small data, and analysed.

Results: The study reveals the factors that differentiate the concepts, which are of a technical front, business logic and data processing.

Conclusion: This study addresses the challenges which are increasingly of prohibitive ramifications for both academic and business domains. By removing the confusion, the classifications of small data and big data including associated attributes will be better understood. This increases their business use towards enhancement and competitive advantage.

Contribution: The article distinguishes between small data and big data, which has been missing, from both academic and business perspectives, since the emergence of the latter. The differentiation between small data and big data provides a guide to organisations in developing strategic frameworks and operational plans.

Keywords: big data; confusion between small and big data; taxonomic; small data; data differentiation; data classification; data nomenclature; data characteristics; logic difference; data analytics.


Many organisations are increasingly realising the importance and value of data. The realisation triggers organisations, to gain a better understanding and exploit the usefulness of their data from different angles, for effective business decisions (Ugur & Turan 2020). The academic domain, too, is increasingly conducting studies, to gain experimental and empirical studies, to contribute to the development and usefulness of data (Cockcroft & Russell 2018). In doing so, both business and academic domains characterise data into two main categories: small data and big data (Gelhaar, Groß & Otto 2021; Minami & Ohura 2021). The categorisation has infused confusion for many people in both business and academics (Rengarajan et al. 2022).

Small data often refers to data or normal data. Also, small data is often viewed and explained as a concept that uses tiny clues and specific attributes to uncover huge trends (Rengarajan et al. 2022). Kitchin and Lauriault (2015) argue that small data is characterized by limited volume, non-continuous collection and narrow variety. From a scientific angle, Ferguson et al. (2014) explain how the small data is a collective representation of entities for various purposes. Without contradiction, small data has been used for many years by businesses, to produce meaningful insights (Kitchin & Lauriault 2015) and to make operational decisions (Cekerevac et al. 2016).

Big data is defined by its characteristics known as the 4Vs: volume, velocity, veracity and variety (Sun, Strang & Li 2018; Osman 2019). According to Barham (2017), volume refers to size, which entails the scale of data. Velocity is the speed at which data travels, including how the data or set of data is streamed and flows in exchanges (Iyamu 2018). Veracity is the complexity and uncertainty of data (Lam et al. 2017). Variety refers to the different forms of data (Barham 2017). According to Bariki, Arvind and Hari (2017), value is another characteristic that defines big data, which depends on the importance an organisation associates with it. Small data, on the other hand, is the sample data retrieved by using sampling methods to understand certain problems (Cheng, Chen & Gong 2018). It is characterised by its limited volume and narrow variety (Kitchin & Lauriault 2015).

There are obvious and unclear similarities and differences between small data and big data. The differentiation can be clarified and put into perspective by a deeper understanding of the taxonomies of the concepts, which include their nomenclature. Nomenclature is the systematic way that is employed in naming things (Hugenholtz et al. 2021), or the rules that are used to form names or terms (Sterner & Franz 2017). Its purpose is to provide unambiguous clear meanings of names, to avoid misunderstandings or confusion. Sterner and Franz (2017) argue that nomenclature goes beyond understanding the information that surrounds the usage of those names. Thus, standard nomenclature is required for small data and big data that can be used by both humans and machines, to gain a better understanding of the concepts, idiosyncratically.

This study does not intend to redefine the concepts of small data and big data; rather, it focuses on the confusion and distinction the concepts pose to individuals and organisations (Nyikana & Iyamu 2023a). Primarily, the confusion remains because the concepts of small data and big data are not understood, distinctively. The confusion can be attributed to a lack of clarification of the taxonomies including the nomenclature of the concepts. This problem does not get easier because many studies either concentrate on big data or small data. Thus, it is hard to find studies that focus on both concepts, to increase their distinctiveness towards usefulness by organisations and stakeholders. Therefore, this study focuses on defining and establishing the taxonomies of small data and big data, for organisational purposes. This will help to provide clarity of the two concepts, eliminate confusion and increase their usefulness.

Classification between small data and big data is increasingly challenging, in both academic and business domains. It is hard to find studies that explicitly clarify the confusion between big data and small data, empirically or by experiment. Also, studies have identified the confusion and consistently stating or narrowing the difference between big data and small data, not about size. The confusion between big data and small data is increasingly prohibitive for many organisations including students at all levels. As a ramification of the confusion and its associated challenges, many decisions of a costly nature are being made by individuals and groups in organisations. Consequently, some of the areas that are negatively affected include data as a service, data manipulation and quality of service, which reflect in service value, competitiveness and sustainability. Thus, it is critically essential to address these growing challenges while the consequences are manageable.

In many organisations, the term and concept of big data remain a buzzword. This is attributed to the fact that many employees or stakeholders of organisations do not seem to observe or believe that there is a difference between small (normal) data and big data. In some organisations, small data is often mistaken for big data, and vice versa. Consequently, this type of confusion has a negative effect and influence on data structuring, management and planning for business enhancement. For example, despite the similarities, tools for big data analysis are purchased for small data purposes. In such an instance, two prohibitive things happen: (1) the cost of purchasing the tools for analysis or analytics and the scarce skill required (Mustapha 2022; Vassakis, Petrakis & Kopanakis 2018) and (2) an inappropriate tool is employed, which yields undesirable results (Kangelani & Iyamu 2020).

The small data contains some of the big data characteristics (Kitchin & Lauriault 2015). Hence, Cheng et al. (2018) claim that big data comes from small data but does not draw boundaries or distinctions between the two concepts. Also, the data analytics tools used to analyse big data can be applied to small data to extract information and gain useful insight. The overlapping of the two concepts induces more confusion; hence, it is important to understand the nomenclature for big data and small data. Yet, the characteristics including the nomenclature of both small data and big data are the same (Katal, Wazid & Gouda 2013). Faraway and Augustin (2018) explain how the confusion makes it difficult for both data analysts and data scientists to be skilled and confident that they have a good understanding of small data and big data. For example, some organisations are challenged with pricing the services of their data because they cannot differentiate small data from big data. Also, some organisations duplicate analytics tools because there is a lack of clarity.

The objectives of the study are twofold. Firstly, it is to develop a taxonomy that distinguishes between small data from big data, to remove the confusion, which often hinders understanding of their classification for business use towards enhancement. Secondly, it is to identify the value that the logical distinction can add to an organisation. In achieving the first objective, two steps were followed in examining the phenomenon: In step 1, the nomenclatural and the differences between small data and big data were examined; and in step 2, the scope and boundaries of each concept, small data and big data were understood better. This helps to gain a better understanding of what big data is if it is not about the size. From this understanding, a distinction is established. In the second objective, heuristics is applied, to extract the intended usefulness, which can be of fundamental value to organisations.

Literature review

This section presents the review of the literature conducted. It focuses on the core aspects of the study, which are the small data and big data, including the differentiation between the small and big data, and the concept of taxonomy.

Small data in organisations

Simplistically, Kitchin and Lauriault (2015) explained that small data uniquely focuses on answering specific questions. It consists of structured data sets. Ahmed et al. (2017) suggest that small data is characterised by low volumes, quantified velocities and structured varieties. Because of its manageable volumes, small data can be understood without the use of analytics (Dhaliwal & Shojania 2018). However, low or size can be subjective if there is no universal definition or measurable agreement. Such subjectivism allows an enterprise to decide on volume (big or small) in isolation. The emergence of big data invokes contrast in the category and boundary including differentiation between the two concepts (Faraway & Augustin 2018). Thus, it is essential to understand the characteristics and usefulness of the concepts in organisations towards enhancing activities and improving competitiveness.

Small data focuses on discovering and understanding what causes things to happen rather than the prediction (Faraway & Augustin 2018). Hence, it is used to determine current situations and conditions. In many academic institutions, some researchers make use of small data to assess and evaluate research outputs. Also, many organisations use small data to produce meaningful results and solutions (Vargas 2018) and to discover new useful insights (Dhaliwal & Shojania 2018). According to Cekerevac et al. (2016), organisations employ small data because it is most appropriate for developing initiatives. Also, it enables organisations to make key business decisions (Necsulescu 2017). This could be attributed to the fact that small data is granular and insightful.

From an analysis viewpoint, the use of small data with machine learning algorithms faces challenges because can be overfitting (Kong, Wang & Wang 2020; Li, Yao & Ma 2020). The machine learning algorithms do not provide robustness when applied to smaller data sets and this leads to poor performance, and expensive and complex processes (Kennedy et al. 2017; Vecchi et al. 2022). Also, other methods available for the analysis of small data have limited effectiveness and require skilled personnel (Kong et al. 2020). On the other hand, Kennedy et al. (2017) claim that since small data uses sample data, it cannot fully represent large data sets. Furthermore, small data focuses on answering specific questions or queries. Hence, it is difficult to apply its findings to large groups of events and activities (Ravi 2021).

Big data in organisations

Big data comes from various sources with several types of data formats and structures. It is collected using different devices (Iyamu 2020). Big data contains large, structured, semi-structured and unstructured data sets (Oussous et al. 2018). The concept is concerned with capturing, storing, analysing and evaluating the data that is created by human beings and devices using computer technologies (Herschel & Miori 2017).

Big data has become a crucial and useful resource for organisations. Cockcroft and Russell (2018) highlight big data as an asset in many organisations. It is recognised in many sectors and by different professionals such as scientists and healthcare practitioners (Iyamu 2020). Some organisations use it to address their processes and strategies (Barham 2017), while others use it for sustainability, efficiency and competitiveness (Iyamu 2018). Also, big data helps organisations to improve decision-making, to achieve their goals (Sivarajah et al. 2017). Moreover, it assists organisations to understand their operations (Ahmed et al. 2017) and to cut costs (Grable & Lyons 2018). Cekerevac et al. (2016) add that organisations use big data to gain new insights and for prediction. Financial institutions use big data to detect fraud (Cockcroft & Russell 2018), while pharmaceutical companies use it to trace defects in new products (Barham 2017).

Big data presents some challenges to organisations regardless of its usefulness. One of those challenges is the complexity of the integration of the data (Barham 2017). This is due to the different data structures that big data has and the high speed at which it flows (Barham 2017; Samsudeen & Haleem 2020). Also, some of the organisations still have data in legacy databases and this makes it difficult to gain value from big data. According to Mgudlwa and Iyamu (2018), processing data is complex because of the large size of the data. Moreover, it is complicated to process big data using traditional data processing applications. Nyikana and Iyamu (2022) highlight other challenges such as storage, skills, searching, security and privacy violations. On the other hand, the infrastructure for big data is inadequate and expensive, according to Sivarajah et al. (2017). Furthermore, the synchronisation of large data sets is another challenge (Nyikana & Iyamu 2023b).

Small data and big data differentiation

The confusion in differentiation between small data and big data is growing and it affects the logic and value associated with them (Kitchin & Lauriault 2015). Sacristán and Dilla (2015) suggest that organisations struggle to achieve the potential of big data and small data because many users find it difficult to differentiate between the attributes of the concepts. According to Letouze, Areias and Jackson (2015), the dichotomy between small data versus big data does not capture the complexity of their structures and ecosystems. Currently, there seems to be no consensus on the determinants of small data and big data (Aversa, Doherty & Hernandez 2018; Nyikana & Iyamu 2023a). In an attempt to gain an understanding of the challenges, Kitchin and Lauriault (2015) posit that the term ‘big’ is misleading as big data are characterized by much more than volume, and ‘small’ data can be large, such as national censuses.

Small data and big data are usually distinguished from each other using several factors, which include scope and volume. The capability, requirements and support mechanisms for small data are different from big data (Davenport, Barth & Bean 2012). The differences draw inferences from factors such as accessibility, conciseness and workability. Furht and Villanustre (2016) argue that there is a distinction between small data and big data but do not detail the differences. Wang (2017) highlights heterogeneity as one of the differences between the concepts of small and big data.

The concept of taxonomy

It is important to categorise and classify the concepts of big data and small data, which can be done through taxonomy. Rizk, Bergvall-Kåreborn and Elragal (2018) define taxonomy as the process of classification used in scientific fields. Gelhaar et al. (2021) explain that taxonomies provide a structure and organised knowledge that can be used by researchers to understand and analyse complex areas. Hence, developing the taxonomy of big data and small data would benefit both academics and the business domains, in gaining better insights and understanding of the existing knowledge about the concepts. Furthermore, taxonomy helps to develop theory.

Taxonomies are used in the literature about information systems (IS) to analyse and classify complex phenomena (Azkan et al. 2020). Also, to understand relationships among concepts (Rizk et al. 2018). According to Nickerson, Varshney and Muntermann (2013), there is a method that has been developed for IS researchers to use for taxonomy development to classify artefacts. Maslin (2002) explains that without taxonomy in biology, it is not easy to communicate and exchange information about organisms. Also, when the taxonomy is poorly defined, all the information linked to those defined names will be incorrect (Gkinko & Elbanna 2023; Prudencio, Maximo & Colombini 2023).

Taxonomy is widely used in different fields. Bloom’s taxonomy is a well-known taxonomy used in the academic domain for the classification of educational learning objectives (Aninditya, Hasibuan & Sutoyo 2019). In chemistry, the periodic table is another example where taxonomy has been used to understand the elements (Oberländer, Lösser & Rau 2019). In the field of IS, taxonomy has been used for the classification of digital technologies such as the Internet of Things (IoT), cloud computing and social media (Berger, Denner & Roeglinger 2018; Szopinski, Schoormann & Kundisch 2019). Healthcare uses taxonomies to classify diseases and medication to improve diagnosis (Haendel, Chute & Robinson 2018; Seyhan & Carini 2019). Furthermore, in health research, taxonomy is used to categorise the results of clinical trials, to improve knowledge discovery, which makes it easier for trials in the registries and databases (Dodd et al. 2018).

Research methods

A qualitative method was employed in this study. Primarily, this is because the qualitative method seeks to understand why things are the way that they are (Al-Ababneh 2020) and the study focuses on quality rather than quantity (Iyamu & Shaanika 2022). The method is suitable because the study seeks to understand the distinction between big data and small data, which is based on experiences, opinions and views. That distinction cannot be discovered by using the quantitative method as the method focuses more on numbers. Another reason for using the qualitative method is that it is exploratory by nature (Sovacool, Axsen & Sorrell 2018). Hence, it was used to explore the characteristics of big data and small data, to eliminate the confusion between the two concepts.

Document analysis was employed in the data collection, primarily because of wide coverage and historical purposes. According to Lakay and Iyamu (2022), the documentation focuses on collecting the existing data that is stable and may sometimes not be noticeable. Furthermore, it helps to provide broad knowledge and extensive coverage of the phenomenon being studied. Iyamu, Nehemia-Maletzky and Shaanika (2016) argue that the document analysis approach helps to provide balance and historical background over a period. Thus, the approach was employed to gain extensive and historical knowledge about big data and small data.

Criteria were set, consisting of two factors; source and period, to guide the collection of data. Firstly, the use of academic databases, to ensure credibility and reliability of the data. Secondly, a period of 10 years, to ensure extensive coverage of the meanings and attributes that have been associated with the concepts, historically. The data were collected from academic databases that include Google Scholar, AIS and Ebscohost. Over 450 articles were collected, requiring formulation and use of criteria. The criteria are as follows: (1) an article covering big data, small data, definition, and contrast between the concepts; and (2) an article published between 2012 and 2022. As shown in Table 1, the most appropriate articles were narrowed to a total of 57, of which 21 and 36 were for small data and big data, respectively.

TABLE 1: Source of data.

Analysing the qualitative data

We are aware that analysing data in a qualitative study can be cumbersome in that there are no specific guidelines or methods, as revealed and discussed in the literature (Dufour & Richard 2019; Lester, Cho & Lochmiller 2020). Thus, we carefully and methodologically employ the interpretive approach, from whose perspective the hermeneutics approach is applied to analyse the data in this study. It was methodological in that the analysis involves a process of describing, classification and interpretation of the data, to provide relevant, useful and meaningful information (Cassell & Bishop 2019; Taherdoost 2022). The authors adopted the framework by Boell and Cezec-Kecmanovic (2014), shown in Figure 1, for the analysis.

FIGURE 1: Hermeneutic Framework.

The hermeneutics approach is concerned with understanding and interpretation of data (Lakay & Iyamu 2022). According to Nigar (2020), the hermeneutics approach focuses on digging deep into text, to find new knowledge. Furthermore, it allows the researcher’s understanding and interpretation of the text. According to Nyikana and Iyamu (2022), the use of hermeneutics circles helps to gain a deeper understanding of the meanings that are associated with things, through repeated reading of the texts. The circles mean continuous interrogation of the text, by going forward and back until a satisfactory point where the researchers feel that a better understanding is gained.

Based on the focus of the hermeneutics, the approach is most appropriate for this study, primarily for two reasons. Firstly, the data are not first-hand. Existing materials (literature) are used as data in the study, as discussed in ‘Processing mechanism’ section. This means that the researcher needs to be thorough, to gain deeper insights into the authors of the literature’s perspectives. Secondly, the focus of the study, which is to determine the differentiation between small data and big data is unwieldy. Therefore, it requires unfathomable details, to achieve the goal of the study. Thus, reading of the 38 (see Table 1) related materials in circles, is inevitable.

The data analysis is conducted based on the objectives of the study, which are to; (1) examine the nomenclature and differences between small data and big data and (2) understand what big data is if it is not about the size.

Finding the distinction between the concepts

From the analysis, there are two main outcomes, which are; (1) the nomenclatural differences between small data and big data and (2) gaining a better understanding of what big data is because it is not about the size. The outcomes are presented in the remainder of this section.

The nomenclatural and differences between small data and big data

In understanding the nomenclature of both small data and big data, their attributes were identified, as tabulated in Table 2.

TABLE 2: The nomenclature.

This will help to understand the scope and boundaries of each concept, small data and big data. One of the similarities is that both big data and small data contribute value to organisations. According to Faraway and Augustin (2018), big data and small data are generated using the same sources, which include technological, business and societal factors. Small or big data drives innovation and productivity of businesses including decision-making (Hassani & Silva 2015). According to Jin et al. (2015), big data can enhance the competitive advantage of organisations, and economic growth of countries, and help to predict the future of enterprises. Doesn’t small data provide the same capability? The differences seem to hide within each other, small data and big data.

What is big data if it is not about size?

Table 3 provides a distinction between small data and big data, which explains the trajectory of the concepts. The distinction shows that the differences between the concepts are beyond size.

TABLE 3: The characteristics.

Based on the aim of the study which is to develop a taxonomy that distinguishes small data from big data, an in-depth investigation was conducted. The investigation focused on removing the confusion that exists between the two concepts. Thus, the two concepts – small data and big data – were investigated in two phases. Firstly, the concepts were investigated separately. Secondly, the concepts were mapped against each other. This approach helps to detect the similarities and differences, towards removing the confusion between small data and big data using its characteristics, as shown in Table 3. In addition, the approach will help to gain an understanding of how factors transform to form the taxonomies of each concept.

Ethical considerations

Ethical clearance was obtained from the Faculty of Informatics and Design (FID) Ethics Committee, Cape Peninsula University of Technology. It does not have project research number, but student number: 203168283.

Discussion of the differentiation

The study examined the small data and big data to gain a better understanding and taxonomic distinction between the two entities. The following discussion is based on the summation of the analysis of Table 2 and Table 3, which focused on the nomenclature and characteristics of small data and big data. From the tables, three factors of fundamental values are subjectively extracted, which gives credence to the distinction between the two concepts. The distinctive values are the technical front, business logic and data processing perspectives.

Technical front

From the technical front, small data and big data can be distinguished from three significant perspectives: architecture, storage and database. The architectural design provides ‘fit’ for both small data and big data, to enhance usefulness for organisational purposes. Although they are analogous, the architecture for small data and big data is not the same. Small data has a centralised architecture with structured data sets. According to Ahmed et al. (2017), the centralised architecture is sufficient for the small data because the data are in a central location. Big data encompasses distributed architecture with structured, semi-structured and unstructured data sets. The distributed architecture provides the capability to compile massive and heterogeneous data sets from various sources (Wang et al. 2020).

Furthermore, the storage of small data requires marts and stores data on a single entity or line-of-business (LOB), which can be limiting to business users (Jameel, Adil & Bahjat 2022; Najm et al. 2022). Hamoud et al. (2021) suggest that the use of data marts is limited because it is specific in terms of type and location of storage. Whereas data lakes is often used in storing big data, from various sources. Thus, big data can be stored in data lakes, in its original format and can only be transformed when it is needed (Mathis 2017). This eliminates the pre-processing and transformation of the data before it is loaded into a data warehouse (Khine & Wang 2018). From the database point of view, the small data uses a relational database; it is managed and accessed using a sequence query language. In its divergence, big data uses non-relational databases to manage the data. Wang et al. (2020) explain that the non-relational database is suitable for the characteristics of big data, which are the huge volume, high velocity and the variety of data. In addition, it provides highly scalable and reliable data storage.

Business logic

Business logic is an expression of organisational vision and strategy. Ruiz and Gandia (2022) suggest that business logic implies a logic of service in an organisation. Thus, it is the primary determinant of, the accumulation, manipulation, and use of small data and big data. According to Gerlitz, Gerken and Hülsbeck (2023), business logic dominates sustainability and strategic processes. In return, both small data and big data add value to the business of an organisation. Some of the values are, to gain insights, drive innovation and improve decision-making towards enhancing efficiency and effectiveness (Hanafizadeh et al. 2021; Vasile & Simion 2021). Primarily, this is to fortify sustainability and increase competitive advantage. From small data, insights are discovered using current data to understand what causes things to happen in the way that they do. Ahmed et al. (2017) state that the small data focuses on answering specific questions and addressing problems. Conversely, big data helps to comprehend the current state and to predict what could happen in the future. This helps an organisation to identify deficiencies, and threats to sustainability, as well as future opportunities and growth of the business. Furthermore, Custers and Uršič (2016) argue that the most innovative and profitable businesses have based their strategy on gathering and utilising big data.

Processing mechanism

Small data flows at a slower, constant speed as compared with big data. Also, small data is granular, meaning it can be processed and analysed, manually. Also, small data is often gathered on a human scale using a personal computer, with the focus on gaining an understanding of the causation (Faraway & Augustin 2018). However, statistical tools and business intelligence tools can also be used to optimise small data, where and when necessary. In contrast, big data flows at a higher speed and often lacks constancy in its velocity. Owing to its characteristics, it is difficult, near impossible to manually analyse big data. Thus, Kitchin and Lauriault (2015) argued the need for computational algorithms. This brings about big data analytics tools and machine learning techniques to process and analyse the data. Big data analytics tools can analyse diverse and versatile big datasets and extract useful information (Nyikana & Iyamu 2022). The aforementioned analysis reveals that the taxonomic distinction between small data and big data is not only about size, but about fundamental factors and values, such as data storage, database, data warehouse, data structure and data analysis.


The study advances our comprehension of the concepts of big data and small data, by formulating a taxonomy that distinguishes small data from big data, to remove the confusion that currently exists between the two concepts. This has immense significant contribution to business, in conducting a transaction that is dependent on data and assessing value. The study is significant for IT specialists, which include managers and data architects, as they strive to support and enable their organisation’s aims and objectives. Through a better understanding of the distinction between the concepts, data architects can design a less complex architecture from both business and technology perspectives. A less complex data architecture is intended to increase competitiveness and sustainability, for an organisation.

The study provides two distinctive values from nomenclature and characteristics entities, between small data and big data. From an academic viewpoint, each of these entities is a foundation for further development. From this perspective, the study contributes to the body of knowledge, which researchers and students, particularly, postgraduates can access for better understanding and clarifications concerning small data and big data.

The study provides clarity on an area that has been most confusing and conflicting through its categorisation of the attributes and characteristics of small data and big data. This enables individuals such as data scientists and managers, data architects and organisations at large to have a better understanding of the dimensions and myriad in carrying out activities such as analysis and computing of small data or big data. This can be used to define the value and contributions of either the small data or big data in an organisation. From an academic viewpoint, the study can be used as a baseline for developing a framework for data attribution platforms. The platform will focus on business-related services and value-creating mechanisms, to increase effective and efficient use of data in an organisation.

Although the study provides useful clarification for the confusion that exists between small data and big data, the work can be extended. For further studies, it will be useful and relevant to both academic and business domains, if a classification model is designed for the evaluation of the concepts.


This article is based on the thesis of the first author’s (Wandisa Nyikana) degree of Postgraduate diploma in Information Technology, Cape Peninsula University of Technology. The second author (Tiko Iyamu) was the supervisor of the project.

The authors would like to thank the Department of Information Technology, Cape Peninsula University Technology for its support. To our colleagues in the research forum, we appreciate your support.

Competing interests

The authors declare that they have no financial or personal relationships that may have inappropriately influenced them in writing this article.

Authors’ contributions

W.N. and T.I. are the only two authors. W.N. problematised the topic. T.I. guided W.N. in the data collection process. Both authors conducted the analysis of the data. Both authors contributed in writing the research report.

Funding information

The authors received no financial support for the research, authorship, and/or publication of this article.

Data availability

Access to academic journals was granted using student number. Access to documentation (peer-reviewed articles) was obtained from academic journals the university subscribes to. The authors of the materials used were clearly acknowledged through referencing.


The views and opinions expressed in this article are those of the authors and are the product of professional research. It does not necessarily reflect the official policy or position of any affiliated institution, funder, agency, or that of the publisher. The authors are responsible for this article’s results, findings, and content.


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