Accounting of information flows: Data balance sheet

Systematic accounting of data and information flows is about to be acknowledged as an integral part of regular internal and public reporting by organisations.  Alongside finances and corporate social responsibility, the topic of data has now found its way to annual reports. Forerunners publish even dedicated accounting reports for data and information flows, something which can be recommended in data-driven sectors.

For example, Finnish Transport Safety Agency Trafi recently published their second data balance sheet (tietotilinpäätös), an annual report describing their data strategy, related architectures and inventory of data and information flows. This supports Trafi in their aim to be a forerunner in collecting data but also opening it up for maximum use for societal benefit. Through digital public sector services and open data policy, Trafi among others encourages data flows between authorities, between authorities and (typically data-producing) users and towards companies to boost business. Examples of Trafi’s data include statistics and registers on vehicles, licences, permits and accidents. Another pioneer in data accounting is the Finnish Population Register Centre, having compiled data balance sheets since 2010, although due to the nature of the registers only a summary of the report is available for the public.

Why is this important?

Platform economy is all about unleashing the cornucopia of opportunities linked to data. Users and producers as well as the functioning of the platform create, process, store and exchange data, and these data and information flows form the key type of interaction in platform economy. Furthermore, many of the emerging technology areas linked to platforms, such as artificial intelligence, blockchain or automation, are extremely data-intensive.

Management of data has therefore become an increasingly critical and strategic part of activities of companies, public sector authorities and even individuals. On the one hand, data is an asset of real value, but on the other hand, this value can only come to fruition and grow through sharing and opening. This challenges existing business logics in many sectors, where data previously had little or no role or where data flows and information systems used to be strictly in-house matters.

Arguments favouring the introduction of data accounting to regular managerial and strategy work of organisations include both discovering opportunities but also addressing threats and uncertainties. Systematic data accounting helps internal monitoring and improvement, and an open approach helps to expand collaboration and partnerships with others (users, customers, companies and authorities). Accounting should also include responses and preparedness for safety and security issues as well as strategies related to data ownership, surveillance and fulfilment of possible regulatory requirements.

Things to keep an eye on

A significant change factor in the topic of data management in Europe is the data protection regulation (EU) 2016/679 that is to be applied in all European Union Member States in May 2018. This regulation addresses the protection of natural persons with regard to the processing of personal data and on the free movement of such data.

European Data Protection Supervisor lays out a definition of accountability in the meaning that organisations need to “put in place appropriate technical and organisational measures and be able to demonstrate what they did and its effectiveness when requested”. Suchlike measures include “adequate documentation on what personal data are processed, how, to what purpose, how long;  documented processes and procedures aiming at tackling data protection issues at an early state when building information systems or responding to a data breach; the presence of a Data Protection Officer that be integrated in the organisation planning and operations etc.”

Another great resource on the topic is the recent publication by the Finnish Government´s analysis, assessment and research activities on use and impacts of open data.  The report describes the openness of major data resources maintained by the public administration and on means to assess the economic impacts of open data in Finland. An analysis of the relationship between firms’ use of open data and their innovation production and growth is also provided. To conclude, the report proposes specific recommendations how to enhance the impact of open data in our society, including the use of tools such as data balance sheets.

The European Digital single market strategy and especially the subtopic of online platforms fits well into the above-mentioned discussion. Issues addressed under these activities include for example concerns about how online platforms collect and make use of users’ data, the fairness in business-to-business relations between online platforms and their suppliers, consumer protection and the role of online platforms in tackling illegal content online.

Guidance on how to prepare a data balance sheet is provided by for example the Finnish Data Protection Ombudsman in English and Finnish.

Selected articles and websites

General Data Protection Regulation (EU) 2016/679 – EUR-Lex
European Data Protection Supervisor: Accountability
European Commission: Digital single market – Online platforms
Valtioneuvoston kanslia: Avoimen datan hyödyntäminen ja vaikuttavuus
Liikenteen turvallisuusvirasto Trafi: Tietotilinpäätös 2016
Väestörekisterikeskus: Tietotilinpäätös
Data Protection Ombudsman: Prepare a data balance sheet
TechRepublic: Data’s new home: Your company’s balance sheet

Heidi Auvinen

Research Scientist VTT Technical Research Centre of Finland Ltd
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Persuasive computing

In the aftermath of the US election, the power of social media filter bubbles and echo chambers has again evoked discussion and concern. How much can algorithms influence our behaviour?

Why is this important?

Data is a key part of the functioning of any platform, and analysis and filtering of data streams allows, for example, tailoring of the platform’s offering based on user data. This is evident in content platforms such as Facebook or Youtube, which learn from your behaviour and customise the user view and suggested contents accordingly. This filtering for personalised experience is valuable and helps the user navigate in their areas of interests, but there are also various drawbacks.  Filtering and especially its invisibility can cause ‘filter bubbles’, where the user experience is threatened to limit to information that reinforces existing beliefs. This leads to polarization. What is even more troubling is that the algorithms can be tweaked to manipulate the feelings of users, according to a 2014 study done by Facebook without the users knowing.

Things to keep an eye on

The debate is now on-going as to how much algorithms can affect our actions. Some claim that the analysis and manipulation of social media feeds was instrumental in the US elections, while some say that the claims are overrated and the hype mostly benefits the analytics companies. In any case, the filtering of data is not inconsequential and there are increasing calls for more transparency to the filtering algorithms as well as for the ownership of the behavioural data collected through platforms. In part this issue becomes more and more topical with the advances in artificial intelligence, which makes data analysis more sophisticated and accessible. There are also interesting experiments – often with artistic goals – in confusing the algorithms in order to make the data they collect unusable by the platform owner.

Selected articles and websites

Will Democracy Survive Big Data and Artificial Intelligence?
The Rise of the Weaponized AI Propaganda Machine
The Truth About The Trump Data Team That People Are Freaking Out About
Robert Mercer: the big data billionaire waging war on mainstream media
How to hide your true feelings from Facebook
Persuading Algorithms with an AI Nudge

Mikko Dufva

Research Scientist VTT Technical Research Centre of Finland Ltd
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Use cases of AI for platforms

Why is this important?

There is currently a lot of hype around artificial intelligence (AI), but what opportunities does it offer for platforms? Roughly put: intelligent interfaces, comprehensive collection and advanced analysis of data. Chatbots and other conversational interfaces offer a natural way for people to communicate with a service offered by the platform, either via typing or voice. AI can offer customer service, online tutoring, expert advice or even a personal assistant. In the background it can go through massive amounts of data and recognise patterns. This has applications from health diagnosis to extracting information from street signs and from combating fraud to identifying key “influencers” of social media. The more data is fed to AI, more capable it gets.

Things to keep an eye on

All the big players are investing heavily in AI, with the hopes that they become the de facto platform for all things AI. How this plays out remains to be seen. It is also uncertain how the public opinion towards AI evolves: so far we have been a bit wary of letting always on personal assistants into our living rooms or trusting AI with our health data. The ethics of AI are still being discussed although the technology and services are advancing rapidly.

Selected articles and websites

Artificial intelligence and the evolution of the fractal economy
How Artificial Intelligence and Robots Will Radically Transform the Economy
7 Ways to Introduce AI into Your Organization
Google, Facebook, and Microsoft Are Remaking Themselves Around AI
Here’s What Artificial Intelligence Will Look Like in 2030
Why artificial intelligence is the future of growth
Would you want to talk to a machine?
Google’s Featured Snippets on Desktop Now Written By Artificial Intelligence
Uber Bets on Artificial Intelligence With Acquisition and New Lab

Mikko Dufva

Research Scientist VTT Technical Research Centre of Finland Ltd
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Deep learning and neural networks

Deep learning refers to an approach in machine learning, which aims at teaching machines to recognise abstract concepts based on large datasets. The leading edge currently is unsupervised machine learning, where the machine is left to make sense of the data on its own. Deep learning has made huge leaps in pattern recognition possible. Google Deepmind is one of the prominent companies utilising deep learning.

Why is this important?

For platforms deep learning offers the possibility to make sense of and recognise patterns from large amounts of data. Google provides an open source library called TensorFlow for this. Another benefit are the services that deep learning provides, such as voice recognition, chatbots etc. These can provide new functionality to the platform. On a broader view, the motivation is to use the deep learning to solve global problems.

Things to keep an eye on

The focus is now especially on unsupervised machine learning and “differentiable neural computers”, which can make sense of complex structured data. Examples of what deep learning algorithms such as the Google DeepMind can do range from lip reading to advanced translation to making sense of a metro map. One interesting development is making APIs to enable artificial intelligence algorithms to play games such as Starcraft and learn through it. This also means that artificial intelligence might be the future user of a platform. The big question then is will it benefit or exploit the platform.

Selected articles and websites

DeepMind has conquered games, London’s Underground and now it wants to help save the planet
Deep Learning Papers
Google’s DeepMind AI Said to Outperform Professional Lip-Readers
Zero-Shot Translation with Google’s Multilingual Neural Machine Translation System
Google’s AI creates its own inhuman encryption
Google DeepMind to Use Blizzard’s StartCraft II for AI Research Platform
Differentiable neural computers

Mikko Dufva

Research Scientist VTT Technical Research Centre of Finland Ltd
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Reputation Economy

Why is this important?

Platform economy often requires trusting strangers. One mechanism for ensuring that everyone plays nicely is to have a reputation system in place. Customers rate the service provider (e.g. Uber driver or Airbnb apartment) and the service provider in turn rates the customer. The ratings or at least their averages are public, which influences who we trust and how we behave in the platform. Reputation is thus a valuable asset in the platform economy.

Things to keep an eye on

Because reputation is valuable, the mechanisms that affect how it is created, shared and used are important. Can the reputation scores be transferred to other services or used in a way not originally intended? Will reputation economy become a new surveillance and control system, as depicted in dystopian images of future, which do not seem so far off given the failed startup Peeple and the Sesame Credit system in place in China.

Selected articles and websites

The Reputation Economy: Are You Ready?
We’ve stopped trusting institutions and started trusting strangers
The reputation economy and its discontents
China has made obedience to the State a game
Black Mirror Is Inspired by a Real-Life Silicon Valley Disaster

Mikko Dufva

Research Scientist VTT Technical Research Centre of Finland Ltd
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IBM Watson

Why is this important?

The IBM Watson ecosystem enables developers to include artificial intelligence to their services. Examples range from personalised health care advice to accelerated R&D to automated customer support. A key point to keep in mind is that the whole ecosystem is learning. As the ecosystem grows and more use cases are added, the quality and the range of things IBM Watson can do increases.

Things to keep an eye on

As the number of sensors and connected devices grows, the amount and ubiquity of data increases and this benefits machine learning services such as IBM Watson. Therefore it is useful to think how much data is gathered and where it is gathered. Another thing to consider is the potential public backlash from seeing artificial intelligence as a creepy ”big brother”. In addition, the question of who owns the data is crucial.

Selected articles and websites

IBM Watson ecosystem
R&D support: Inno360
Condé Nast Has Started Using IBM’s Watson to Find Influencers for Brands
Next Target for IBM’s Watson? Third-Grade Math

Mikko Dufva

Research Scientist VTT Technical Research Centre of Finland Ltd
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