Work is the application of our knowledge and capabilities. We work to produce outcomes, which are the products of our work. The better we work, the better our outcomes – our performance.
Work as a Data Value Chain
How do we create outcomes via work?
Picture 1: Work as a data value chain
Looking at picture 1 you can see, work is done by applying our knowledge and capabilities to create outcomes. The sum/essence of our knowledge is our intelligence. The quality of our knowledge includes knowledge about how to apply it, e.g., to one extent, insights achieved in one context are applicable in another context (Context). What parts of our knowledge can be seen as Consens and are widely accepted, and where there are different interpretations (Consent). And what part of our knowledge is explicitly and consciously available and what part is implicit knowledge (Consciousness).
Knowledge is created by learning. Learning is based on our reality experiences, the sum of all data that captures our attention. One person’s knowledge is raw data for another person unless the learning process has happened.
So much on how intelligence learns from data and its application to produce outcomes through work. AI is operating at a similar level as “knowledge”, but is lagging some qualities people can add to knowledge.
Depending on the kind of outcomes created by work, there are different way to “protect” the creator/worker or employer from unjust misuse of his work outcomes (e.g. copyrights and IP). However, in the digital sphere access and use are difficult to control and audit.
When considering people data collaboration, we mainly consider the application of people analytics, data science, artificial intelligence, and augmented intelligence. We can create collective knowledge and models that amplify our natural intelligence. by applying machine learning and training to our collective data.
Given that most of our work realities are already happening in the digital sphere, we could unlock huge collective potential to improve our work (Davenport, Harris, 2007).
Picture 2: People Data
In HR, it makes sense to distinguish between people data controlled by employers and data controlled by the individual or other entities, like cloud providers.
HR Data is typically at the core of people analytics and has the potential to improve HR, work, and workers. But there is also other people data.
Looking at the employer data sphere, there is the interesting bucket of “traces of work,” which represents any data collected because of the work a person is doing, e.g., usage data of software or machines. This data is typically under the employer’s control and is especially interesting for learning workers’ skills. The more digitized a skill or a job function is today, the higher the chances of training an AI based on it. It is worth mentioning that besides the employer, in many cases, the equipment manufacturer has access to the traces of work data of the users of their equipment or software.
Of course, the completeness of data about people can add colossal potential (and risks) for people insights. For example, if employers had access to some individual data, they could better align with people’s preferences and requirements. On the other hand, it would be very beneficial for employees to get data rights to their people data in HR systems, e.g., certificates of achievement and documentation about their capabilities. That means there is additional data collaboration potential for both stakeholder groups in people data collaboration beyond their data. However, it is also clear that many cloud-, social media- and AI providers have access to vast amounts of people data beyond the circle of influence of employees and employers. We will discuss this situation in our next article in this series of publications.
A 360-degree view of high-quality people data and the combination of natural human intelligence with artificial machine intelligence could unlock unique areas of the wasted work potential.
High-quality private data is the most crucial non-substitutable ingredient for the emergence of next-level AIs.
Positive-sum People Data Games
There is individual intelligence, team intelligence, and organizational intelligence. Communication, negotiation, alignment, management, diversity, and governance are required to put intelligence to work on different scales. If we are good at it, collective outcomes are better than individual ones. If we are bad at it, complexity slows down high performers and nothing or even damage might happen. So, there is positive-sum work but also negative-sum work (Surowiecki, 2004).
Alternatively, better phrased, there are positive-sum teams and organizations and zero-sum or negative-sum teams and organizations.
Creating outcomes as humans and machines becomes similar with AI, as described in the picture “work as a data value chain” above. However, the outcomes of people data collaboration can be data products, which are infinitely reusable with little to zero marginal cost, like anything in the digital sphere. Human capabilities and skills can be codified to allow value creation beyond the context from which they originated.
Zero-sum thinking leads to competition and even conflict. Positive-sum thinking leads to collaboration and even emergence.
Positive-sum People Data Games can be created if the potential loss of a participant (esp. Data Providers) is minimized while the potential gain is relevant. Our recent publication outlined which data collaboration core principles must be applied to enable such games. It is (1) a privacy-preserving foundation keeping data providers’ data private and entirely under her control at any time, (2) a fair distribution of value between the participants (most notably the data providers), and (3) joint data governance mechanisms that allow participants to decide how jointly-created data products are used jointly and leveraged (Fauler, 2023; WEF, 2019; BMWI, 2019).
This approach mitigates the risks reasonably well, but the extent of potential value needs to be understood through experimentation so far. Predicting the additional value joint big datasets can unlock over smaller single datasets is very difficult. However, the jointly addressed domain or “job to be done” and the quality of the group of participants can be quite a good approximation of value potential or at least its magnitude.
Picture 3: Data Product Co-Creation Phases
Therefore, Tapir has chosen to approach the creation of so-called data products in a co-creative and collaborative manner with leading subject matter experts (Co-shapers) and potential data providers who share an equally important “job to be done” (Seeding). The Seeding Phase is about conceptualizing a Data Product. After that, it can be passed over to the Prototyping and Testing Phase to create the Data Product and prove the value for all contributors. In the last phase, the Scaling phase, the Data Product can be opened to a broader audience, allowing for large-scale adoption or even independent monetization. The benefit for each participant is that all data providers’ combined number of datasets allows for data network effects and shared investments and collaboration on the data product’s ideation, design, and development.
We expect the overall Tapir community to experience synergies within one Data Product creation, as described above, and across Data Products. The effort of Seeding and Prototyping will also be shared across the different Data Products, meaning each member can benefit from the Data Products they have co-created and from other Data Products that reach the Scaling Phase without their early contribution.
However, what exactly can Data Products look like?
Data Products – Digitizing Human Skills
As mentioned, the outcomes of people data collaboration are people data products. Based on the available data structures, we examine different kinds of data products in the context of people data collaboration.
Picture 4: Applicability of Data
If you have knowledge, you can apply it immediately in the right contexts. There is no need for people-data collaboration. However, complementing knowledge, of course, adds up to more holistic insights.
Small data needs to be highly structured and meaningful. You can immediately take advantage of it, but you could add additional value by combining or comparing your small data insights with peers. This could steer your individual or even collective decisions. With small data, the value does not grow with the pure amounts of data but with the additional contexts of similar data. Small data collaboration can happen in areas such as benchmarking and statistics.
Big data sounds incredible and is in everybody’s mind right now. The idea is to derive insights from vast amounts of data. The challenge is that in many cases you need more data than one organization has available. This challenge is especially imminent if we look at complex problems to solve. Here, people data collaboration makes much sense, as we can unlock data network effects and jointly train (prediction) models and even AIs. However, predicting what value is possible with a given amount of data is difficult. As outlined in the earlier chapter, we need to collaborate on the data and the ideation, design, and development of Data Products.
As described above, these prediction models and AIs can act like knowledge. However, in many cases, they cannot be understood similarly.
Addressing People Challenges with Data Products
It is time to leave the realm of abstraction now. What exactly can these Data Products do? Of course, since we are talking about people data collaboration, it is about people-related topics. We have identified nine Domains that promise exceptional value.
Strategic HR | Strategic Planning | Labour Shortage | Skills |
HR Accelerators | DE&I | People Gen AI | Well-Being |
HR Lifecycle | Recruiting | Performance | Retention / Attrition |
Picture 5: Nine High-Impact People Data Collaboration Domains
The first value chain of people and HR is, of course, the “HR Lifecycle”. It defines the steps and phases a typical employee or worker goes through in an organization. Improvements on this level directly improve the performance of these processes.
On the top layer, we see “Strategic HR,” which aims to improve the people domain over a longer timescale, anticipate people trends, and ultimately improve an organization’s or region’s competitiveness.
In the middle layer, there are “HR Accelerators,” which can profoundly and at a more generalistic scale impact either the “HR Lifecycle” or “Strategic HR.”
Here are the Data Products we are currently working on. All of them are in the “Seeding” phase:
- Workforce Transformation
- Digital and Sustainability Skills
- ESG Reporting and Benchmarking
- Mental Health and Psychological Safety
- Sales Team Performance
Some of these Data Product candidates are already touching on our last article’s sources of wasted people potential. Still, you can imagine many more areas where people data collaboration can be applied and 100x your work.
Conclusions
People data is unique; it is the key to understanding and unlocking people potential. It even has the potential to augment people in unseen magnitudes and automate complete tasks or skills, disrupting the production factor work as we know it today.
Therefore, people data collaboration is the single most impactful opportunity for people organizations and should become a distinct tool in the HR toolbox of every HR function, especially more innovative ones.
People data collaboration is an HR Accelerator itself and can impact most of the wastes of human potential, paving the ground to 100x your work.
Like Data and AI approaches have changed other LOBs and Industries, they will also change the HR function for actionable insights, evidence-based decision making, and prompter adaptability (Marr, 2023). But they will also disrupt the production factor work by transforming data and knowledge into algorithms and (AI) models. Therefore, HR departments must take a leading role in any AI initiatives and act as enablers. Our last article in this series will touch on the new role of HR in Future Work.
We invite you to become an active member of the Tapir Community and help shape data- and AI-fueled Future Work.
Sources:
- Davenport, Harris (2007): The Data-Driven Workplace: How to Create a Culture of Analytics and Boost Performance, Harvard Business School Press
- Surowiecki, James (2004): The Wisdom of Crowds: Why the Many Are Smarter Than the Few and How Collective Wisdom Shapes Work, Economies, Societies, and Nations. Doubleday
- Fauler, Andreas (2023): Data Collaboration a new way to unlock data for the right causes, Rocketstar Foundation Medium
- WEF (2019): Data Collaboration for the Common Good: A Framework for Public-Private Partnerships. World Economic Forum
- BMWI (2019): Project GAIA-X – A Federated Data Infrastructure as the Cradle of a Vibrant European Ecosystem, Federal Ministry of Economic Affairs and Energy Germany
- Deloitte (2021): The Insights-to-Action Journey, Deloitte Report
- Marr, Bernard (2023): Data-Driven HR: How to Use AI, Analytics and Data to Drive Performance, Kogan Page
Andreas Fauler
Business
What this is about
Work creates outcomes through knowledge application. Data & AI can enhance this process, leading to better results when humans & machines collaborate.Share Story!
Andreas Fauler
Business