PROF. DR. JARI KAIVO-OJA

UNIVERSITY OF TURKU, RESEARCH DIRECTOR OF FINLAND FUTURE RESEARCH CENTRE, RESEARCH Professor of Kazimieras SImonavicius university

Nowadays we are moving from Business Intelligence (BI) to Big Data analytics (BDA). In many fields of industries and in society, knowledge is a key strategic resource. Big Data represents a new technology and business paradigm for data that are generated at high velocity, and high volume and with high variety. Today Big Data is envisioned as a game-changer capable of revolutionizing the way businesses and societies operate in many fields. We need to understand the potential of integrated view of Big Data. The potential is surely larger than traditional data. Big Data is expanding every day. The growth of the three triangular dimensions, are negatively related to veracity, but positively related to complexity, variability, decay, and value (see Figure 1). This is fundamental reason why there are so many possibilities to create benefits and strategic advantages.

There have been Big Data available for many years (about over 20 twenty years), but only a small part of it is utilized. The first phase of Big Data 1.0 was in 1994-2004 (e-Commerce phase), second phase of Big Data 2.0 (social media phase) was in 2005-2014 and now we live Big Data 3.0 phase (IoT applications Plus BD 1.0 and Big Data 2.0). In current phase IoT applications can generate data in the form of images, audio, and video. This is a new technology environment. E-commerce websites and social media are still primary sources of Big Data, but IoT-based sensors read data from various equipment deployed for various operations. Streaming data analytics have great potential in a number of industries and financial and other welfare services, where streaming data are generated through human activities, machine data or sensor data.

Harnessing the power of Big Data is a key strategic challenge also for modern technology and digitalization roadmaps and especially for Industry 4.0 and Society 5.0 strategies.

Figure 1. An integrated view of Big Data (Lee 2017).

Data science and data analytics are needed also for better foresight analyses and better future decisions. Ignorance and misunderstanding are very bad enemies of decision-makers and ordinary citizens.

There are many problems in the field of Big Data management and development. Typical problems are:

• Why we need Big Data more?
• How do we find Big Data for our analyses? How we develop clean data instead of dirty data?
• How we can develop Big Data libraries for various stakeholders and real-life needs?
• How we can create synergies between different stakeholders and organizations in the field of Big Data analytics?
• What are the key advanced methods and models of Big Data foresight?
• Do smart cities need Big Data and how they create and develop it?
• How we discover, analyze, visualize and present Big Data analyses and results?
• How to become an active player in the Big Data analytics and markets?
• How to deploy and manage a lifecycle approach to Big Data analytics and problems?
• How to find good partners and synergic networks in the field of Big Data analytics?
• How to use Big Data in business and in innovative business modeling?
• How to use Big Data in innovation management and in the Triple Helix or extended Quartet Helix environment?
• How e-business players and start-up ecosystems could get benefits from Big Data analyses?
• Which are useful and most promising matching tools to use in business and public planning?
• How public decision-makers could develop Smart Specialization Strategy on the basis of Big Data analytics?
• How we define the endgame model for Big Data analytics? Do we need system theory to find such an endgame model?
• How Lithuanian Digitalization Roadmap will be implemented with the help of the Big Data foresight approach?
• How to test Big Data apps?

There are many good questions in the field of Big Data and data analytics. The grand challenge is that the Big Data field is in fast transformation and progress. There is not one self-evident “wonder code” or “wonder model” of Big Data. What is certain that hurried managers are surely stressed with Big Data challenges. The already fast invention of Big Data foresight reveals that the reality of Big Data innovation ecosystem is complex and changing. There are various alternative Big Data sources, BD solutions, and BD models. There good open source solutions but also interesting commercial tools. There is needs to make inventions and comparative analyses. What works? How does something work? How many resources are needed? etc. There are very good questions for us. Tailoring solutions for stakeholders will not be easy task in this complex decision environment.

The project “the Platforms of Big Data Foresight” has various work packages. WP:s are:

• Desk work analyses and integration of key R&D works
• Big Data Library Development
• Open Innovation Tools and Crowdsourcing with Big Data
• E-commerce Tools with Big Data
• Matching (meeting demand and supply) Tools with Big Data
• Testing and Piloting Apps.

We know that Big Data provides great potential for firms in new businesses, developing new products and services, and implementing business models and operations. Obvious benefits can be (1) personalization marketing, (2) better pricing, (3) cost reduction, (4) improved customer service. To gain such benefits requires that the challenges of Big Data are met professionally, (1) improving data quality, (2) improving data security, (3) providing privacy and trusted information systems, (4) proving professional investment justification, (5) developing data management and Big Data libraries, (6) educating qualified data scientists and (7) improving data learning facilities in workplaces and schools.

To meet these challenges of the Big Data field, we need better organizational awareness and incentive systems to utilize Big Data in various contexts. Too many incentives lead us to ignorance and bad knowledge management in organizations

Almost needless to say, all these PBDF work packages are challenging and ambient. Our research team will work with work packages and research questions. What we can expect is accumulated know-how of Big Data management and clear high-quality deliverables to all WPs. Hopefully we can also deliver something, which goes beyond all expectations in the field of Big Data foresight research.

Jari Kaivo-oja

Research Professor, Dr, Kazimiero Simonavičiaus University, KSU

Adjunct Professor, University of Helsinki, University of Lapland

Some references

Bzhalava, Levan, Kaivo-oja, Jari & Hassan, Sohaib  S. (2018) Data-based Startup Profile Analysis in the European Smart Specialization Strategy: A Text Mining Approach. European Integration Studies, No. 12, 118-128.

Bzhalava, Levan, Hassan, Sohaib S., Kaivo-oja, Jari & Köping Olsson, Bengt (2019) Mapping the Wave of Industry Digitalization by Co-word Analysis. A manuscript submitted to review process.

Chen, C. F., Qian, O., and Dai, Y. Z. (2014). Study on the Construction of Digital Library in the Age of Big Data. Library and Information Service, 58(7), 40–45.

EMC Educational Services (2015) Data Science and Big Data Analytics. Discovering, Analyzing, Visualizing and Presenting Data. John Wiley and Sons. Indianapolis, Indiana, USA.

Gandomi, Amir & Haider, Murtaza (2015) Beyond Hype: Big Data Concepts, Methods, and Analytics. International Journal of Information Management. 35, 137-144.

Hajirahimova, Makrufa, Sh. and Aliyeva, Aybeniz S. (2017). About Big Data Measurement Methodologies and Indicators. International Journal of Modern Education and Computer Science, 9 (10), 1–9.

Haukioja, Teemu, Kaipainen, Jouni, Kaivo-oja, Jari, Karppinen, Ari, Laitinen, Katja, Stenvall, Jari, Vähäsantanen, Saku (2019) Book review. Carlo Gianelle, Dimitris Kyriakou, Caroline Cohen, Marek Przeor (eds.), Implementing Smart Specialisation Strategies, A Handbook. European Spatial Research and Policy. Vol. 26, No. 1, 213-2018.

Kaivo-oja, Jari, Roth, Steffen & Westerlund, Leo (2017) Futures of Robotics. Human Work in Digital Transformation. International Journal of Technology Management. Vol.73, No. 4, 176 – 205.

Lee, In (2017) Big Data: Dimensions, Evolution, Impacts and Challenges. Business Horizons, 60, 293-303.

Oussos, Ahmed, Benjelloun, Fatima-Zahra, Laheen, Ayoub Ait & Belfkih, Samir (2018) Big Data Technologies: A Survey. Journal of King Saud University – Computer and Information Sciences. 30, 431-448.

Roth Steffen, Valentinov, Vladislav, Kaivo-oja Jari & Dana Leo-Paul (2018) Multifunctional organisation models. A systems-theoretical framework for new venture discovery and creation, Journal of Organizational Change Management, Vol. 31 No. 7, pp. 1383-1400 [SSCI 1.262, Scopus, CNRS**, CABS**]. This article won an Emerald Literati Award 2019 for Highly Commended articles and has therefore been made freely available for a period. Please click the banner to access the free version of the article or download it here once this period has expired. https://www.emerald.com/insight/content/doi/10.1108/JOCM-05-2018-0113/full/html

Roth Steffen, Schwede Peter, Valentinov Vladislav, Pérez-Valls Miguel, and Kaivo-oja, Jari (2019) Harnessing Big Data for a Multifunctional Theory of Firm. European Management Journal. © Elsevier Forthcoming. DOI: 10.1016/j.emj.2019.07.004 [SSCI 2.985, Scopus, CNRS**, CABS**, VHB***].

Smirnova, Ekaterina, Ivanescu, Andrada & Bai, Jiawei (2018) A Practical Guide to Big Data. Statistics and Probablity Letters, 136, 25-29.