Towards Data Smart Foresight – Exciting pivotal moments expected

Jari Kaivo-oja & Mikkel Stein Knudsen:

We have been told many news about data science. Some experts say that data science can call presidential races, reveal more about your buying habits than you would dare to tell your mother or wife, and predict just how many years those combined mega kebab hamburger pizzas have been shaving your life, and of trendsetting lifestyles – globally. Data scientists, the elite “python” men and women are today labelled “sexy” in various Harvard Review and MIT reviews articles. The famous slogan of W. Edwards Deming and Peter Drucker, “You can’t manage what you don’t measure”, is seen more and more relevant foundation for decision-makers (see e.g.  McAfee & Brynjolfsson 2012). However, it is not easy to verify “sex issues and sexuality” claims and that is why we should avoid overstatements of “sexy” Big Data.

We do not have to be “sexy” in all occupations of work, but we in the futures research community can develop foresight tools with Small or Big Data. We can develop new exciting ideas of foresight with data science and data analytics tools. The next level of your business and specialization in foresight analyses will probably happen with exciting data science tools that were not available only a few years ago.

The DPP paradigm and data smartness development

We all know that data is only “raw material” of information and knowledge. From data we can create information and knowledge – and finally even wisdom. Three key functions of foresight are Diagnosis, Prognosis and Prescriptions. The DPP paradigm is found for example in the For Learn -manual and in various discussions of European foresight programs. Of course, the analogy with medical sciences is obvious. Foresight specialist diagnose, prognose and deliver prescriptions like doctors do in the field of health and social care services. There is nothing mysterious in this professional practice. Also doctors and medical professionals apply Big Data tools and methods. Doctoral practice with diagnosis and prognosis is (also) based on Big Data-analytics. Such will also be the case for foresight specialists in the field of futures studies and applied foresight projects. Probably better foresight analyses can be provided to customers with Big Data than with Small Data.

Key deliverables of foresight are:

  • Desirability analytics
  • Probability analytics
  • Feasibility and impact analytics
  • Risk analytics
  • Strategic importance analytics
  • Network and stakeholder analytics
  • Spatial and global network analytics and
  • Decision model analytics (scenario multicriteria data for decision-making).

All key foresight deliverables can be based on Big Data analytics both in Numbers and Narratives data fields. Volume of big data from heterogeneous sources has considerably grown. Identification of the relevant data from the huge quantity of available Big Data lakes is still very challenging though, because Big Data can be very messy and cleaning it may take time and financial resources. It is obvious that more effort is needed in monitoring thematic data fields and deliver Big Data lakes to data scientists and foresight specialists.

Foresight synergy challenges with data analytics

Typically, foresight specialists and professionals deliver these kinds of knowledge intensive “goods” for organizations and decision-makers. They provide both soft and hard business and policy services. In many cases they also provide Knowledge Intensive Business Services (KIBS), which are tailored for private purposes. Data analytics can be applied both with qualitative and quantitative data. Current “science arena” of data analytics can be figured out in Fig. 1 (Kaivo-oja 2019). There are many challenges to use data science to transform information into insight and foresight. We can just mention well-known Narratives and Numbers approach in foresight research. Data analytics can enrich foresight with Numbers and Narratives analyses to the next level.

Figure 1. Data analytics field in the data science operations (Kaivo-oja 2019).

When we look at Fig. 1, it is important to underline potential synergies (1) between small data analytics and big data analytics and (2) between quantitative and qualitative research. These two synergy challenges are also huge challenge for futures of foresight research. We believe that the future true state-of-the-art foresight comes from the proper understanding and application of all four quadrants.

Towards Data Smart Foresight with Big Data ethics

As always, there are huge possibilities and treats in the field of big data analytics (Reinsel et al. 2017). Big Data flow increases volume, value, velocity, variety and veracity of data for organizations (Fig 2.) These 5 Vs are more and more relevant for decision-makers. Based on an IDC report prediction, the global data volume was predicted to grow exponentially from 4.4 zettabytes to 44 zettabytes between 2013 and 2020 (Hajirahimova & Aliyeva 2017).

Figure 2. Five Vs and Big Data.

Nowadays, people are more and more aware of privacy and social media risks. Even corporations are trying to be proactive in this field, like with the recent news of Google setting up an external advisory board for the responsible development of AI (Google, 2019b) or the publication of the Google AI Principles (Google, 2019a). Big Data-ethics will be critical topic of public and corporate ethics discussions. The following six principles are currently attributed to Big Data Ethics: (1) Ownership of small or big data – Individuals own their own data or sell their data with a contract. (2) Data Transaction Transparency – If individuals´ personal data is used, they should have transparent access to the algorithm design used to generate aggregate data sets, (3) Consent of data – If an individual or legal entity would like to use their personal data, one needs to be informed and explicitly expressed consent of what personal data moves to whom, when, how and for what purpose from the owner of the data, (4) Privacy of citizens – If data transactions occur all reasonable effort needs to be made to preserve privacy, (5) Currency – All individuals should be aware of financial transactions resulting from the use of their personal data and the scale of these transactions and (6) Openness – Aggregate data sets and data lakes should be freely available (see https://en.wikipedia.org/wiki/Big_data_ethics).

We can add other related ‘Big Issues for Big Data’ which needs to be added to the Big Data-ethics discussion (Raleigh, 2019): (7) Avoiding algorithm bias – Algorithm often unintendedly exacerbates underlying biases of real-world data and thereby harms specific populations (Dickson, 2018 provides numerous examples of this), (8) Data longetivity – As data gains value through use, its reliability over the long-term becomes more important; this creates emerging issues in cases of e.g. bankruptcy or decisions to discontinue management of data or data APIs.

On the other hand, data analytics can help us to manage some big risks like pandemic and climate change risks. Also, SMEs can have smarter business models and platforms with Big Data analytics. We are therefore also faced with dilemmas in which ethical boundaries might prevent us from achieving something we can intersubjectively agree as valuable (Wiren, 2019). Value search, value configuration and value delivery can be improved by the five Vs of Big Data. Also, governments and academia and civil society organizations can improve their services and value delivery to citizens by the services and good based on Big Data analytics. McKinsey (2018) argues that ‘Smart city applications can improve some key quality-of-life indicators by 10 to 30 percent’. In the case of Turku, using big data and ‘world class data science resources’ is now developed as a local strategic flagship project (Piippo, 2019).

Trend change from Business intelligence to Big Data analytics

The applications of Big Data foresight can be sometimes fascinating and sometimes alarming. We should be aware about possibilities and threats of Big Data analytics. In Fig 3, we can see that trends in the field of data analytics are changing, and we are moving from business intelligence to Big Data analytics, if we assess development with Google Trends database index numbers. Big Data analytics has been dominating people´s interest since 2014. Average Index number is 84 in years 2014–2019, while interest in business intelligence is decreasing near to 50 index levels (Average Index number 49,3 in years 2014–2019). Big Data started to gain more interest than business intelligence in 2013.

Figure 3. Business Intelligence Index and Big Data Index Trends in 2004-2019 with Linear Trend Lines (Index 0-100). Source: Monthly Global Data from Google Trends 24.3.2019. https://trends.google.fi/trends/?geo=FI).  

The importance of Big Data does not revolve around how much data an organization finally has in its files, but how an organization utilises the collected Big Data lakes. Every company and organization uses data in its own organizational way. Organization culture is have impacts on the use of Big Data in many ways. Leaders, management teams and workers have their own habits and beliefs of Big Data work like they have their habits in relation to business intelligence activities.

It is good to understand that there is an analogy between market square and the concept of platform. As we know, the market square enables producers and consumers to interact without external intermediaries. For producers, it would be time- and resource-consuming to find all customers and present offerings for everybody separately. Also for consumers, it would be similarly very inefficient to find various producers one by one. This situation is relevant for foresight and anticipation markets, where consumers and producers want to share knowledge intensive services and products in markets, business and networks. Big Data extends foresight market square in global settings.

The more efficiently a company uses its data lakes and adopts Big Data foresight, the more potential it has to grow, because of platform synergies and market square logic. The company and organizations can take data from various sources, but they have to think many issues before they can use data and information in in decision-making. Ethical codes of Big Data are highly relevant topics to discuss before making use of Big Data. Ethical thinking before serious action is always necessary in foresight and futures business.

Jari Kaivo-oja
Research Director, Finland Futures Research Centre, Turku School of Economics, University of Turku.
Research Professor (Kazimiero Simonavičiaus University, Platforms of Big Data Foresight, Foresight program)
Adjunct Professor (Planning and management sciences, University of Helsinki, Faculty of Science, Geosciences)
Adjunct Professor (Foresight and innovation research, University of Lapland, Department of Social Sciences)

Mikkel Stein Knudsen
Project Researcher (M.Sc., Pol. Science), Finland Futures Research Centre, Turku School of Economics, University of Turku                

References

Balcom Raleigh, Nicolas (2019). Current project insights: Potentials of big data for integrated territorial policy development in the European growth corridors. Dos and Don’ts of Big Data for Foresight, Turku Science Park, Turku, Thursday 28.2.2019.

Dickson, Ben (2018). What is algorithmic bias? TechTalks. Web: https://bdtechtalks.com/2018/03/26/racist-sexist-ai-deep-learning-algorithms/

FOR LEARN (2019). Support to mutual learning between Foresight managers, practitioners, users and stakeholders of policy-making organisations in Europe. Institute for Prospective Technological Studies. Joint Research Centre. Web: http://forlearn.jrc.ec.europa.eu/index.htm

Google (2019a). Looking Back at Google’s Research Efforts in 2018. 15.1.2019. Web: https://ai.googleblog.com/2019/01/looking-back-at-googles-research.html

Google (2019b). An external advisory council to help the responsible development of AI. 26.3.2019. Web: https://www.blog.google/technology/ai/external-advisory-council-help-advance-responsible-development-ai/

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. Web: http://www.mecs-press.org/ijmecs/ijmecs-v9-n10/IJMECS-V9-N10-1.pdf

Kaivo-oja, Jari (2019). Introduction:  The Challenges of Big Data Foresight. Lecture in Turku Science Park. Dos and Don’ts of Big Data for Foresight, Turku Science Park, Turku, Thursday 28.2.2019.

McAfee, Andrew and Brynjolfsson, Erik (2012) Big Data: The Management Revolution. Harvard Business Review, October 2012, Web: http://tarjomefa.com/wp-content/uploads/2017/04/6539-English-TarjomeFa-1.pdf

McKinsey Global Institute (2018). Smart Cities: Digital Solutions for a More Livable Future. Executive Summary, June 2018. Web: https://www.mckinsey.com/~/media/McKinsey/Industries/Capital%20Projects%20and%20Infrastructure/Our%20Insights/Smart%20cities%20Digital%20solutions%20for%20a%20more%20livable%20future/MGI-Smart-Cities-Executive-summary.ashx

Piippo, Tuomas (2019). Using world-class data science resources to create a smart and wise Turku. Dos and Don’ts of Big Data for Foresight, Turku Science Park, Turku, Thursday 28.2.2019.

Reinsel, David; Gantz, John and Rydning, John (2017). Data Age 2025: The Evolution of Data to Life-Critical (PDF). Framingham, MA, US: International Data Corporation. Web: https://www.seagate.com/files/www-content/our-story/trends/files/idc-seagate-dataage-whitepaper.pdf

Wikipedia (2019). Big Data Ethics https://en.wikipedia.org/wiki/Big_data_ethics.

Wiren, Milla (2019). Strategic Positioning in Big Data Utilization. Dos and Don’ts of Big Data for Foresight, Turku Science Park, Turku, Thursday 28.2.2019.

 

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