A Conceptual Framework for Assessing an Organization’s Readiness to Adopt Big Data
Abstract
:1. Introduction
2. Related Work
2.1. Big Data and Sustainability Issues
- resource scheduling based on purchase history, buying behaviors, and local weather and events;
- distribution and inventory optimization given current and predicted buying patterns and local demographic, weather, and events data;
- integrating analytics directly into products to create “intelligent” products; and
- insights about customers’ usage patterns, product performance behaviors, and overall market trends.
2.2. Maturity Models
- descriptive: used to determine an organization’s level of maturity;
- prescriptive: describing a desired state and assessing an organization’s distance to it;
- transitive: determining the steps that an organization must follow to reach the desired state.
2.3. Maturity Models for Big Data
- obtain information on their stage of use of advanced big data analytics, on their value creation process, and on their business models; that is, obtain information on their current state;
- identify the desired target state.
3. Framework of the Temporal Big Data Maturity Model (TBDMM)
3.1. Levels of Temporality
- static data: this data does not contain any temporal context, nor can this context be inferred from it;
- sequences: ordered sequences of static data with no direct time stamps (relative ordering, such as “earlier” or “later”);
- time-stamped sequences: sequences of static data stamped with time, which are collected in regular or irregular intervals; and
- fully temporal data: data that contains at least one time dimension; e.g., valid time or transaction time.
- static knowledge: this knowledge does not contain any temporal context, nor can this context be inferred from it. An example of such knowledge is the sentence: “Every organization has to conform to legal rules”;
- sequences: ordered sequences of events with no direct time stamps. These may be, e.g., events ordered by Allen’s temporal relations [91]. An example of sequential knowledge may concern the legal domain; namely, the sequential knowledge about a law-creating process: passing a law → signing of the law by the President → publishing the law;
- time-stamped knowledge: static knowledge that has been extended with time stamps (an example of which is a description of a license issuing process: Application for a license → decision → valid period of the license); and
- fully temporal knowledge: knowledge that possesses at least one time dimension, e.g., knowledge on the varying prices of shares.
- static rules: rules with no time context;
- temporally extended static rules; e.g., temporal descriptive rules; and
- rules that are proper to fully temporal knowledge; e.g., causal detection rules and temporal data mining rules.
3.2. The Temporal Big Data Maturity Model (TBDMM)
4. Reception of the TBDMM
4.1. Research Methodology and Research Sample
- the presence of temporal aspects in analysis and managerial decisions, and their significance;
- the maturity of the organization;
- the respondent’s understanding of the term “big data”;
- the importance of various business analytics types;
- the data/knowledge, IT solutions, and IT functionalities appropriate for BDA;
- the respondent’s assessment of their employees’ level of training in the context of BDA;
- the IT infrastructure and data quality in the organization; and
- the advantages of, and barriers to, BDA implementation.
4.2. Selected Survey Findings
4.2.1. Managers’ Understanding of Temporal Big Data
- “people from organizations having no advanced information technology solutions do not know what they mean while using the big data notion”;
- “big data does not exist, we focus on the analysis of a dataset’s portion”;
- “big data means creating and validating models based on machine learning tools, and using these models on complete datasets”;
- “linked heterogeneous datasets owned by various organizations”; and
- Hadoop, Spark, Cassandra, HBase, and NoSQL.
4.2.2. The Data/Knowledge Aspect in the TBDMM
4.2.3. The IT Solutions Aspect in the TBDMM
4.2.4. The IT Functionalities Aspect in the TBDMM
- multidimensional analytics/BI reporting;
- data mining and advanced data mining;
- big data analytics; and
- temporal inferences (with time as a distinctive feature).
5. Discussion. The Sustainable Development Context
- the possibility of creating new products and entering new markets;
- technology cost optimization;
- the possibility of using new technologies;
- the prediction of client insolvency risk, and a better understanding of global risks; and
- easier credit scoring.
- the ROI (Return on Investment) is hard to estimate (two indications);
- data cleaning and storage issues (two indications);
- the economy is not mature enough to make use of big data insights;
- a lack of internal data access procedures;
- a lack of appropriate hardware; and
- communication and legal barriers.
6. Concluding Remarks
Author Contributions
Funding
Conflicts of Interest
Appendix A
Author(s) | Definition |
---|---|
Technological Approach | |
[38] | “Data sets are generally quite large, taxing the capacities of main memory, local disk, and even remote disk. We call this the problem of big data. When data sets do not fit in the main memory (in core), or when they do not fit even on the local disk, the most common solution is to acquire more resources.” |
[39] | The big data term “describes data sets that are growing exponentially and are too large, too raw or too unstructured for analysis using relational database techniques”. |
[40] | “Big data involves the data storage, management, analysis, and visualization of very large and complex datasets.” |
[36] | “Datasets whose size is beyond the ability of typical database software tools to capture, store, manage, and analyze.” |
[41] | The big data term is “used to describe data sets so large, so complex or that require so rapid processing (…) that they become difficult or impossible to work with using standard database management or analytical tools.” |
[42] | “Big data refers to datasets with sizes beyond the ability of common software tools to capture, curate, manage, and process the data within a specified elapsed time.” |
[43] (p. 89) | “Big data (…) means that the organization’s need to handle, store, and analyze data (its volume, variety, velocity, variability, and complexity) exceeds its current capacity and has moved beyond the IT comfort zone.” |
[44] | “Big data involves the data storage, management, analysis, and visualization of very large and complex datasets. It focuses on new data-management techniques that supersede traditional relational systems, and are better suited to the management of large volumes of social media data.” |
[9] | “Big data consists of expansive collections of data (large volumes) that are updated quickly and frequently (high velocity) and that exhibit a huge range of different formats and content (wide variety).” |
[45] | “The broad range of new and massive data types that have appeared over the last decade or so.” |
[46] | “Data of a very large size, typically to the extent that its manipulation and management present significant logistical challenges.” |
[47] | “Big data: the data-sets from heterogeneous and autonomous resources, with diversity in dimensions, complex and dynamic relationships, by size that is beyond the capacity of conventional processes or tools to effectively capture, store, manage, analyze, and exploit them.” |
[48] | “Big data typically refers to the following types of data: (1) traditional enterprise data; (2) machine-generated/sensor data (e.g., weblogs, smart meters, manufacturing sensors, equipment logs); and (3) social data.” |
[49] | “Big data is an emerging paradigm applied to datasets whose size is beyond the ability of commonly used software tools to capture, manage, and process the data within a tolerable elapsed time.” |
[50] | “«Big data» means enormous volumes of data. The data can be divided as structured data and unstructured data. Various methods are applied to collect these data.” |
Organizational approach | |
[51] (p. 1) | “Consumers have unprecedented access to information and tools with which to consume information. Social media, mobile access, augmented reality and three-dimensional (3D) views of pictures and video have blurred the lines between our private and work personas and have fundamentally changed the way the consumer utilizes information.” |
[7] | “Big data, like analytics before it, seeks to glean intelligence from data and translate that into business advantage. However, there are three key differences: Velocity, variety, volume.” |
[52] | “Big data focuses on three main characteristics: the data itself, the analytics of the data, and presentation of the results of the analytics that allow the creation of business value in terms of new products or services.” |
[8] | “Big data is a combination of volume, variety, velocity and veracity that creates an opportunity for organizations to gain sustainable competitive advantage in today’s digitized marketplace.” |
[53] (p. 21) | “Companies today are overgrown with information, including what many categorize as big data. The jungle includes information about customers, competition, media and channel performance, locations, products, and transactions, to name just a few (…)” |
[24] (p. xxi) | “(…) big data is about leveraging the unique and actionable insights gleaned about your customers, products, and operations to rewire your value creation processes, optimize your key business initiatives, and uncover new monetization opportunities.” |
[54] | “A new attitude by businesses, non-profits, government agencies, and individuals that combining data from multiple sources could lead to better decisions.” |
Mixed (tech-organizational) approach | |
[55] | “Big data involves more than simply the ability to handle large volumes of data; instead, it represents a wide range of new analytical technologies and business possibilities. These new systems handle a wide variety of data, from sensor data to Web and social media data, improved analytical capabilities, operational business intelligence that improves business agility by enabling automated real-time actions and intraday decision making, faster hardware and cloud computing including on-demand software-as-a service. Supporting big data involves combining these technologies to enable new solutions that can bring significant benefits to the business.” |
[56] | Big data is “associated with the new types of workloads and underlying technologies needed to solve business problems that could not be previously supported due to technology limitations, prohibitive cost or both.” |
[6] | “Big data: high-volume, high-velocity, and/or high-variety information assets that require new forms of processing to enable enhanced decision making, insight discovery, and process optimization.” |
[57] | “Big data: a cultural, technological, and scholarly phenomenon that rests on the interplay of (1) Technology: maximizing computation power and algorithmic accuracy to gather, analyze, link, and compare large datasets; (2) Analysis: drawing on large datasets to identify patterns in order to make economic, social, technical, and legal claims; (3) Mythology: the widespread belief that large datasets offer a higher form of intelligence and knowledge that can generate insights that were previously impossible, with the aura of truth, objectivity, and accuracy.” |
[58] | “Big data is massively generated by uncountable online interactions between people, by human–systems transactions, and by sensor devices.” |
[59] | “Big data is no subject to sampling, it is linked with building databases from electronic sources, with no intention of statistical inference.” |
[5] | “Big data is high-volume, high-velocity and high-variety information assets that demand cost-effective, innovative forms of information processing for enhanced insight and decision making.” |
[60] | “Big data is large-scale data with various sources and structures that cannot be processed by conventional methods and that is intended for organizational or societal problem solving.” |
[61] | “Big data often represents miscellaneous records of the whereabouts of large and shifting online crowds. It is frequently agnostic, in the sense of being produced for generic purposes or purposes different from those sought by big data crunching. It is based on varying formats and modes of communication (e.g., text, image, and sound), raising severe problems of semiotic translation and meaning compatibility. Big data is commonly deployed to refer to large data volumes generated and made available on the Internet and the current digital media ecosystems.” |
Appendix B
Interview No | Business Activity | No of Employees | Annual Turnover for the Last 3 years | Capital Structure | Respondent’s Position | Sector | Period of Existence on the Market |
---|---|---|---|---|---|---|---|
1 | R&D | 50–249 | <EUR 50 million | National capital | Owner/Management Board | Professional, scientific, and technical | 6–10 years |
2 | Manufacturing | >250 | <EUR 50 million | Mixed (national and foreign) | Technology manager | Light industry | >10 years |
3 | Services | ≤9 | <EUR 2 million | National capital | Owner/Management Board | Professional, scientific, and technical | >10 years |
4 | Manufacturing | >250 | >EUR 50 million | Foreign capital | Manager/specialist planning and production management | Automotive industry | >10 years |
5 | Services | >250 | >EUR 50 million | Foreign capital | Advanced analytics manager | Telecommunications | 1–5 years |
6 | Manufacturing | >250 | >EUR 50 million | Foreign capital | ICT Manager/specialist | Consumer electronic | >10 years |
7 | Services | 10–49 | <EUR 2 million | National capital | Owner/Management Board | Professional, scientific, and technical | 6–10 years |
8 | Services | ≤9 | <EUR 2 million | National capital | Owner/Management Board | Administration services and support | >10 years |
9 | Services | ≤9 | <EUR 2 million | National capital | Owner/Management Board | ICT service and support | >10 years |
10 | Services | 50–249 | <EUR 10 million | National capital | ICT Manager/specialist | Production of ICT | >10 years |
11 | Services | 50–249 | <EUR 2 million | National capital | Senior SEO specialist | Transportation and storage | 6–10 years |
12 | Services | ≤9 | <EUR 2 million | National capital | Owner/Management Board | Construction | 6–10 years |
13 | Manufacturing | 10–49 | <EUR 2 million | National capital | Owner/Management Board | Production of ICT | 6–10 years |
14 | Banking | >250 | >EUR 50 million | Mixed (national and foreign) | Other-risk assessment manager | Finance | >10 years |
15 | Commerce | 10–49 | <EUR 2 million | National capital | Owner/Management Board | Professional, scientific, and technical | >10 years |
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Business Intelligence | Competitive Intelligence | Big Data | |
---|---|---|---|
Purpose | Analysis of internal business processes, improvement of operational and tactical decisions | Analysis of external environment (mainly competitors) | Analysis of the whole environment of the organization: internal resources, customers, suppliers, users of the Internet, and communities of practices |
Scope | Organization | Environment of organization (mainly competitors) | Whole environment of the organization |
Content/data | Well-structured information, internal data originating from databases, Enterprise Resource Planning, transaction systems | Semi-structured or unstructured information, external data originating from competitors, customers, and the Internet | Unstructured content, external data that comes from public, open resources, the Internet, mobile devices, and social media |
Used tools, technologies | Online Analytical Processing (OLAP), data mining, data warehouses | Advanced data mining, predictive modeling, web mining, text mining, and opinion mining | Advanced data mining, predictive modeling, web mining, opinion mining, text mining, exponential random graph models, search-based applications, dashboards, SOA (Service-Oriented Architecture), Hadoop, Spark, MapReduce, parallel processing, real-time processing, and machine learning techniques |
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Olszak, C.M.; Mach-Król, M. A Conceptual Framework for Assessing an Organization’s Readiness to Adopt Big Data. Sustainability 2018, 10, 3734. https://doi.org/10.3390/su10103734
Olszak CM, Mach-Król M. A Conceptual Framework for Assessing an Organization’s Readiness to Adopt Big Data. Sustainability. 2018; 10(10):3734. https://doi.org/10.3390/su10103734
Chicago/Turabian StyleOlszak, Celina M., and Maria Mach-Król. 2018. "A Conceptual Framework for Assessing an Organization’s Readiness to Adopt Big Data" Sustainability 10, no. 10: 3734. https://doi.org/10.3390/su10103734