How to Become a Successful Data Professional

Remi Verhoeven (Senior Manager at KPMG) on: the character traits, technical skills and soft skills required in order to become a successful data profession. Listen to hear about the ways data professionals can improve their skills to increase their chances in the recruitment process.

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How to Become a Successful Data Professional
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Data Autonomy: Take Back Control Over Your Data

Dennis Groot on: data autonomy, what it is, why it matters and why it is important now. Dennis talks about how to implement data autonomy in complex architectures and how it relates to current trends like self-sovereign identity and the metaverse.

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Data Autonomy: Take Back Control Over Your Data
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How to boost data availability? Synthetic data is the answer!

Wim Kees Janssen (Co-founder of Syntho) on: data synthetisation, what it is, what it can be used for and how it adds value to organisations. Wim Kees talks about how synthetic data helps speed up Development, Test, Acceptance and Production (DTAP) cycles by making privacy a non-issue. Listen to hear how Syntho provides the technical solution to root out the need for production data and mitigate possible privacy risks.

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How to boost data availability? Synthetic data is the answer!
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Data strategy for small and medium-sized enterprises

Geneviève Meerburg (Director SME Services at van Spaendonck) on: implementing a data strategy within her organisation. Geneviève shares how the importance and value of data organically grew, leading to a concrete need for a data strategy. Listen to hear how van Spaendonck approached truly living through the principles set out in the data strategy and how it helped create new services for their clients. 

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Data strategy for small and medium-sized enterprises
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Don’t mention AI!

Bart Rentenaar (Enterprise Data Lead at Athora) on: implementing data innovation within his organisation. Bart shares examples of use cases that inspired him to get start with data innovations, the framework that employs to structure initiatives, examples of data innovations he implemented and the team that made that possible. Listen for tips when starting out with the implementation of data innovations.

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Don't mention AI!
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The Metaverse: new frontier that re-imagines retail & health. And data re-imagines the Metaverse.

D8A vision on metaverse

Avoid mistakes. How data dependent is the newest digital development?

The Metaverse, generated in science fiction and frequently applied to gaming platforms is trending in e-commerce and increasingly in healthcare.

Simply stated, the Metaverse is a connection between the physical and virtual worlds and is seen as the successor to the mobile internet.

Purchasing a digital product in one ecosystem in the Metaverse (e.g., Facebook) allows you to use it in another (like TikTok). Or buy a physical product, which includes a digital twin — a digital representation as well as a statistic — for your online persona. And vice versa, the digital twin could be used to increase sale at a physical location, if a physical product is not available at a shop, the digital twin can be shown as example. Want to model how a car would behave with the same conditions as in the physical world your are in right now (weather, population, other vehicles on the road) then you can in the Metaverse. Or just google Fortnite’s Ariana Grande concert in the Metaverse.

Meta-commerce is you like, beyond e-commerce. Metaverse is also beyond virtual reality. It is a hybrid of VR, AR, mixed reality and can interact with real life.

So interoperability between eco-systems is key. This is how the timestamps of blockchain can show its worth beyond crypto! The same goes for data. To understand the value of good data, you need to think beyond the structured data that often comes to mind when we talk about data, AI and innovations.

The Metaverse is all about unstructured data or files; e.g., images, videos, music, SEO. Often a neglected area when it comes to data quality concepts such as accuracy (high, medium, low quality), timeliness, versioning (which originates from archiving principles and is now becoming directly related to the core business & product life cycle management), format and completeness. Each file needs to have sufficient data quality rules, definitions and other meta data to enable the above mentioned interoperability. It needs to be totally clear that the digital twin you’re being is from this season or last season for instance. And don’t forget hygiene factors such as ownership (who owns the digital twin? Who can re-sell it?), customization possibilities, portability, sharing agreements, security and most of all privacy. Hot topic, privacy and The Metaverse. Being in accordance with legislation is key in a highly digital world.

From an analytics perspective, the Metaverse is similar to AR &VR. It needs high quality training data. Which means data sets needs to be accurate and fit for use, removing bias and including good data labeling — based on standard classifications.

The Metaverse within the healthcare sector seems a logical next move. Here the ownership, portability and privacy are even more significant. Further increasing the value of good data quality, governed by a fitting regime.

In short, the Metaverse is the upcoming opportunity to increase the value of good data. And for businesses to become further data driven.

Trusted data awareness

Arjan Pepping (Corporate Data Manager at MN) on: creating awareness around trusted data and the role of data in control for a pension provider. Listen for the golden tip on implementing data awareness.

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Trusted data awareness
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Good design teams embrace data quality

Marinka Voorhout (Director at Philips) on: data quality in design is becoming a pre requisite for innovations on data. Listen to practical approach tips and ideas to take data quality into account in user interfaces.

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Good design teams embrace data quality
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Trailer: Date with D8A

In this trailer of the Date with D8A podcast series, Simone from D8A explains the idea behind the D8A initiative and the what, why and how of the Date with D8A podcast.

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Trailer: Date with D8A
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Leading through ownership of personal data, this is what you should know!

#innovation #data

Enabling or blocking? Sovereignty of personal data.

Within the digital world, individuals are mostly viewed as — potential — consumers (obviously already a high share) or patients (currently growing share). The data of individuals needs to comply to the regulations within the country or region where the data is collected, i.e., it needs to fit with privacy and security.

Companies are building views on individuals, based from the name, address, email etc, which have been provided through every registration to an online service. As well as online behaviour, e.g., through tracking cookies. These centralised views or centralised identities are stored within silo-based platforms. Neither personal data or individual behaviour are well portable. This means that your digital identity exists in many small pieces with several companies knowing different information about you. This also means that you have to create a unique password for every profile you make, which can be cumbersome, and many tend to use the same password more than once. All of this creates security risks, since your personal data is being stored and managed by many entities and because a password breach might give access to several of your accounts.

An attempt to address these issues is federated identities. Individual identities are managed in a company or government centralized system. The system then distributes the data from the individual to a digital service. Examples where this is in use is within banks, insurers, retail and health. A federated identity enables easier digital activities through a single-sign-on solution However, a federated identity is still silo-based, since it only can be used with web services that accept this solution.

“………That’s right, SSI sets data ownership at the individual level.”

A next generation of identity solutions that is currently being developed and taken into use is self-sovereign identities (SSI). This type of digital identity is a user-centric identity solution that allows you to be in control of your data and only share the strictly relevant information. An example would a situation where you need to prove that you are of age. With an SSI you can document that you are over 18, without disclosing your exact age. Or documenting that you have received a specific vaccine, without disclosing information about all the vaccines you have ever gotten or other sensitive health data. Other examples are sharing that you have graduated to your — future — employer, your medical record with a hospital and your bank account with a store. In your own personal vault if you like (also: a ‘holder’ or ‘wallet’), you own and manage your data. That’s right, SSI sets data ownership at the individual level. Data ownership would resolve a large topic, that often proofs to be a blocker for companies to fulfill their digital ambitions. From this vault you decide to which companies & organisation you want to share your personal data to be defined per specific purpose. For this purpose, personal data needs to be classified (e.g., in accordance with privacy & security regulations) which data is open for all, which is private and which is secure data. The vault provider needs to have good technical solutions (e.g., with verifiers and encryption), a sufficient governance regime and controls in place to support this.

SSI will mean that individuals need to understand what ownership comprises of, what potential risks are and what good practices are to share data. Data literacy should be extended from mostly companies to more individuals. And companies should prevent technical, legal, ethical, fairness and security pitfalls (see also: 10 principles for SSI), e.g, for transparency for systems & algorithms as well as data monetization.

Innovate your data. Use a monetization strategy.

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What is data monetization? According to McKinsey, it is the process of using data to increase revenue, which the highest performing and fastest growing companies have adopted and made an important part of their strategy. Internal or direct methods including using data to make measurable business performance improvements and informed decision making. External or direct methods include data sharing to gain beneficial terms or conditions from business partners, information bartering, selling data outright, or offering information products and services (Definition of Data Monetization — IT Glossary | Gartner).

How to deploy a data monetization strategy

Companies that innovate through data monetization recognize this, monetization can be tricky. Get it right, and you have happy customers and users who are willing to pay for your product. But mis-prioritize and your audience numbers quickly drop, along with your revenue. Building data monetization based on the principles of ‘trusted data’ ( mitigates the risk of mis-prioritisations).

There is no clear-cut answer how a data-driven product generates revenue, or when that is appropriate. And there will, of course, be some products that never monetize.

Having a strategy will deliver guidance. A data monetization strategy is, simply put, a plan to generate revenue for data-driven products. Like with any plan, it guides and brings structure. It is not something that is fixed — it should be flexible enough to develop with the product, the market the product exists in and its users. Goals can and will change over time, and so strategies need to evolve to continuously achieve the goals they’re designed to target. Data products can be based on loyalty & subscription models or a on-time purchase model. It is important to understand at the beginning of the strategy which model(s) the data can leverage to create focus and scalable results.

Data monetization strategy must be built upon the following pillars:

* Understanding how data can be converted into value (see below) and the associated opportunities and challenges of data-based value creation;

* Strategic insights into improving and preparing data to support monetization;

* Strategic insights in the potential value, markets and ecosystems.

Opportunities for monetization

Data driven business models help to understand how data can discover new opportunities. This can be focused on value for efficiency (reducing costs and risks), value for legislation (comply with relevant regulations) and value by maximizing profits, by increasing impact on customers, partnerships and markets. This can include embedding data models, metadata and analytics into products and services. Data monetization needs to be scalable, flexible and user friendly, thereby providing advantages for the company and its customers.

Indirect monetization includes data-drive optimization. This involves analyzing your data to gather insights in opportunities to improve business performance. Analytics can identify how to communicate best with customers and understand customer behavior to drive sales. Analytics can also highlight where and how to save costs, avoid risk and streamline operations and supply chains.

“Having a full understanding of monetization possibilities will help to keep an open mind.”

Direct monetization of data is where things get interesting. It involves selling direct access to data, e.g. to third parties or consumers. This can be in raw form (heavily anonymised due to privacy regulations), aggregated, metadata only or transformed into analysis and insights.

Data-as-a-service

This is the most direct data monetization method. Data is sold directly to customers (or shared with partners for a mutual benefit). The data is e.g., aggregated and/or anonymised, to be fully in accordance with legislation. And to enable trusted data. Buyers mine the data for insights, including combining it with their own data. Or use it for AI solutions within a software. Ecosystem play is the newest area for Data-as-a-service.

Insights-as-a-service

This applies analytics to (combined) internal and external data. It focuses on the insights created using data, rather than the data itself. Either the insights are sold directly or provided as e.g. analytics enabled apps.

Ecosystems-as-a-service

This is a more flexible type of data monetization. The data ecosystem provides highly versatile, scalable and shareable data and/or analytics, when needed in real-time. Standardized exchanges and federated data management enable using data from any source and any format.

Analytics-as-a-service

This is the most advanced and exciting way of monetizing data. Analytics-as-a-service seamlessly integrates features such as dashboards, reports and visualization to new and existing applications. This opens up new revenue streams and provides powerful competitive advantage.

Having a full understanding of monetization possibilities will help to keep an open mind. Where many companies are focusing on analytics products & services, there are more opportunities! Always stay within legal & ethical boundaries, but explore all opportunity formats to grasp new markets.

Innovate or take up the L.E.T.S. reflex — that is the question

How the Legal, Economical, Technology and Scientific reflex impact data innovations.

Much has been said about proper data ownership. Many companies struggle to have successful data quality, data monetization and even advanced analytics, when ownership is unclear. Or not pro-actively practiced. In short: a data owner is accountable for correct and sufficient availability of good and trusted data quality. That accountability can only be applied if it is applied at the right organisational level, i.e. on the executive level. Any company that tries to divers data ownership to the tactical level (e.g. a data team lead) or operational level (e.g., a data steward) will not reach their data driven goals & objectives.

For those organisations without proper data ownership with a clear strategy, defined & monitored KPIs and supporting governance structure, there usually is only 1 road ahead.Employees on the organisational level will continuously firefight data issues, while e.g., AI solutions, data innovations and compliance goals are not met.

In highly sensitive organisational politics environments, substitute solutions for ownership are often sought. You can identify them as the L.E.T.S reflex:

  • Solutions with a high focus on Legislation (e.g., privacy, financial or health regulations);
  • Solutions with a high focus on Economical benefits (e.g., monetizing insights, increasing operational effectiveness through robotics);
  • Solutions with a high focus on Technology (e.g., a data platform/eco- systems, AI & Machine Learning, datawarehouses, BI tooling);
  • Solutions with a high focus on Science (e.g., researching data or analytics solutions, whitepapers, subsidized research).

Data driven companies will recognize at least one of these reflexes. Data innovations is complex, due to many factors. Which makes owning up to a solution for this complexity, a challenge. It is often easier to focus on one or multiple reflexes. Yet none of these reflexes can be seen as a substitute for data ownership.

Owning the use of data

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Why your company needs a Chief Data Officer.

It is time to increase acknowledgement of the importance of a chief data officer.

As companies move towards working data-driven, monetizing data in new and enhanced services and products is essential. Traditionally heavy regulated industries, e.g. financial and health, first focused on bringing their data in control. Their efforts concentrated mainly around data quality management, data privacy, data governance and E2E trusted data lineage. These efforts are often led — or owned — by a Chief Data Officer (CDO).

In this article, we advocate to shift or extend this focus of the chief data officer towards data in control AND data in use.

CDO’s define and communicate the companies vision on data management and data use. Through this vision, the CDO gives direction, guidance, advocates for change and sets priorities for running projects. Most companies CDO’s have to some extent achieved this for data management. The extended focus of Chief Data Officers, which we advocate for in this article, contains standard processes for the design, prototyping, development, productizing and use of data & insights products & services. Furthermore, it is the CDO who defines a standard set of technology to be used to support these processes and create these solutions. Where needed, this is based on the data management foundation as implemented by the CDO in previous years.

The Chief Data Officer ideally combines business expertise, technology background and analytics/BI. Extended by a common commercial sense, understanding of production processes and knowledge of relevant 3rd party partners to cooperate with. Organisations without an ‘extended CDO’ will experience difficulties and potential delays in reaching their data-driven goals — in accordance with new developments in the market. Without strategic guidance and steering, there is an increased risk that departments and units will define their own standard processes, set of technology and data-driven products and services. Making it harder to leverage pre-existing data foundations as well as cross-unit collaboration to enable effective market penetrations. Teams will struggle to escalate and address growing concerns as sufficient C-level representation is missing.

Concluding, companies benefit from a Chief Data Officer with a focus on data in control and data in use. Top-down ownership and alignment of data initiatives, standardisation of processes and data tooling and a clear escalation path for growing concerns are necessary to succeed as a data-driven company.

Data quality to resolve 3rd party cookies ban

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Marketeers and dedicated advertising benefit from good data quality

Google announced its intentions to kill off the tracking cookies (so called 3rd party cookies) within its Chrome browser. Cookies which advertisers use to track users around the web and target them with dedicated ads. Google is not the only major player altering the digital ad landscape. Apple has already made changes to restrict 3rd -party cookies, along with changes to mobile identifiers and email permissions. Big Tech altering 3rd party cookies is caused by the need to be respectful of the growing data privacy consciousness. Most consumers don’t like the feeling of being tracked across the internet (70% of U.S. adults want data regulation reform and 63% of internet users indicate that companies should delete their online data completely).

For most marketeers, this paradigm change presents huge challenges to enable customer acquisition by tracking users and targeting them with dedicated digital advertising.

On the other hand, 3rd party cookies are inherently problematic, from limited targeting capabilities, inaccurate attribution to the personalization & privacy paradox. Their loss presents an opportunity to provide a smaller group of high-value customers with higher-caliber and increasingly personalized experiences. In other words, losing these cookies might become a blessing in disguise.

Confronting data acquisition challenges in a cookie-less future

For all the shortcomings of 3rd party cookies, the marketing industry does not yet have a perfect answer for how to acquire customers without them. Marketeers are waking up to the impactfull change they are facing. One potential answer to the loss of 3rd party cookies can be that they will be replaced with 1st & 2nd* party data, i.e., gathering data shared directly by the customer, such as an email address, phone numbers and customer authenticators (see below). This data can become the mutual currency for the advertising business. First party data can be hard to obtain, you need to “earn” it, including solutions on how to gain good quality 1st party data.

Technology Section

Some solutions focus on technology, e.g.,Google’s Federated Learning of Cohorts (FLoC). A type of web tracking that groups people into “cohorts” based on their browsing history for interest-based advertising. Other technology solutions include building a 1st and 2nd party data* pool, i.e., a Customer Data Platform (CDP). CDPs are built as complete data solution across multiple sources. By integrating all customer data into individual profiles, a full view of customers can be profiled. Another solution are private identity graphs that hold all the identifiers that correlate with individuals. Private identity graphs can unify digital and offline first-party data to inform the single customer view and manage the changes that occur over time (LINK?). this helps companies to generate consistent, persistent 360-degree view of individuals and their relationship with the company, e.g., per product brand. All to enable stronger relationships with new and existing customers.

Earning good quality data will increase the need for standardized and good quality customer journeys. And therefore, the need for standardized and good quality data.
Where previously, design and data quality were not closely connected, the vanishing 3rd party cookies now acts as catalyst to integrate both.

Data quality is usually an unknown phenomenon for most designers**, design companies, front- & back-end software developers and marketeers. It requires a combined understanding of multiple domains, i.e., the user interface where data will be captured, the underlying processes which the captured data will facilitate, data storage & database structures and marketing (analyses) purposes.

Finding the expert that has all this combined knowledge is like finding a real gem. If you do, handle with care!
It will be more likely that all domains will need at least an understanding how their domain enhances and impacts the other domains.

For the (UI/UX) designer:

  • Have a good knowledge of data quality rule types. What is the difference between a format & accuracy type? Is timeliness of data relevant? What are pitfalls for data quality rules? How to integrate multiple purposes (e.g. processes, data integration & analytics) into a dedicated data quality rule.

For product owners:

  • Ensure that expertise of data entry and how data is used within processes at a granular level (i.e., on data field level). Onboard a so-called data steward who can facilitate the correct input for data quality. Let the data steward cooperate with front-end developers and designers.
  • Keep your data fresh. Data doesn’t last forever. Make sure data stewards support data updates and cleansing.
  • Data stewards should work with designers and front-end developers to determine which fields are considered as critical. These fields should be governed by a strict regime, e.g., for the quality and timeliness of data as well as for access to the data and usage purposes.
  • Personal authentication is a separate topic that needs to be addressed as such. Relying on big tech firms as Facebook or Google can seem an easy solution, however increases the risk of being dependent on an external party. Yet authentication needs to be earned to build authentic customer relationships. When customers give a company a verifiable durable pieces of identity data, they are considered authenticated (e.g. signing up for a newsletter or new account via email address). This will be a new way of working for most companies. Therefore, data stewards need to up their game and not only know existing processes but extend their view, understanding and knowledge towards new developments.
  • Data stewards must align with the Data Privacy Officer on how to capture, store and process data. When it comes to privacy, compliance and ethics, you can never play it too safe.

For data storage & databases:

  • Ensure that data architecture (or at least a business analyst) is involved in the design process. This is sometimes resolved by the back-end developer (who cannot work without aligning with the architect office on data integration, models for databases and data definitions).
  • If standardized data models and/or data definitions reside within the organization, this should be part of the database development. Refer to authoritative source systems where possible.
  • If the application is made via low-code, standardization of existing data models/architecture, data definitions and data quality rules is often part of the approach. Yet, data quality checks should always take place as separate activity.

For marketeers:

  • Understand how customer journeys can facilitate 1st and 2nd party cookies. Determine which data is needed for insights. Gather insights requirements and work together with the data steward to define data quality rules that facilitate your insights. Now that the 3rd party source is limited, the value of the customer journey for marketing increases!
  • Privacy is one of the catalysts to make 3rd party cookies disappear. This requires a new approach for acquiring personal data for marketing and ad targeting. New developments that require new skills and more importantly, a new cooperation between existing domains. Companies that enable this, will lead this new way of working.

Footnotes:

* Data from 1st party cookies = occur only within a company’s own domain. & data from 2nd party cookies = ca be used within and outside a companies’ own domain. This article takes mostly 1st party data into account. For 2nd party data, you can further investigate e.g., ‘data co-ops’, complementary companies that share data. Each member of the co-op should relate to the others in a meaningful way because outside of your own web domain, you’ll be able to reach customers only on your partner sites — and this reflects on your brand.

** Of course, there are designers work who work with data enabled design. In the view of this article, this is a different topic, more focused on tracking & logging data, which is then analyzed to improve the design. This article is about good data quality when data is entered via a UI, e.g., as part of a customer journey.

Value of data

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Transform data as a by-product into data-fueled value streams

Up until recent, data — in most cases — is perceived as by-product. With the potential to deliver (new) insights. This is the case for e.g., IoT sensor data which is mainly used for process automation & preventive maintenance within manufacturing or healthcare. A secondary stream of data usage. Companies with an open mind for opportunities are now realizing that by embedding these IoT sensors across product lines, manufacturing facilities or patient journeys, the generated data — and the services it can deliver — can become a strategic asset.

Any company should ask the following: Does the company generate lots of data? Or is it a data rich company that produces and/or maintains market leading products?

Answering this question requires a real-data driven mindset. There is no doubt that the value of preventive maintenance is valid. Yet, it is not scalable enough to become that data rich company. And if the investment for (IoT) data can be returned in multiples, companies should be creative and look for opportunities beyond just predicting equipment issues and maintenance requirements.

Don’t just act data-driven, be it! Look beyond the initial goal of the IoT sensors and the data that is generated. Get at the research & business chair. And drive new products and services. Data initially generated by IoT* for automation, can provide new insights in e.g. live performance of products, providing customers with valuable services. A good example is the Rolls Royce IntelligentEngine. The IntelligentEngine IoT sensor data — aggregated and analyzed in the cloud — is providing Rolls-Royce with unprecedented insights into the live performance of its machinery. A modern passenger jet generates an average of 500GB of data per flight(!) and several terabytes on long-haul routes. The thousands of sensors in each Rolls-Royce engine track everything from fuel flow, pressure and temperature to the aircraft’s altitude, speed, and air temperature, with data instantly fed back to Rolls-Royce operational centers. The company’s aircraft availability center is continuously monitoring data from 4,500 in-service engines. Rolls-Royce can tap into a — cloud based — ecosystem of small, specialist 3rd parties to analyze different parts of available data. And that data capability is rapidly evolving to providing customers with valuable aftermarket services that range from showing airlines how to optimize their routes to keeping a survey ship in position in heavy seas.

Next to this, the company recently launched another data-centric initiative; R2 Data Labs. This act as an acceleration hub for data innovation. “Using advanced data analytics, industrial artificial intelligence and machine-learning techniques, R2 Data Labs will develop data applications that unlock design, manufacturing and operational efficiencies within Rolls-Royce, and creates new service propositions for customers,”.

Combining data & analytics into potential new value streams is often a first step within companies that are aiming to become more data driven. In practice, this can bog down into analytics MVP without generating a scalable product or services which is understood, carried and supported by business stakeholders, account management and sales. This connection needs to be in place to be successful [ link naar andere artikel MV]. The R2 datalab shows what additional circumstances and behavior is needed for success. At its heart, Data Innovation Cells will comprise experts drawn from multiple disciplines across the company and apply cutting-edge DevOps principles to rapidly explore data, test new ideas, and turn those into new innovation and services. In other words, successful data products & services comprise of a fusion of the following elements: a technological (ecosystem) environment, sufficient internal & external data, understanding the value of data, trusted data & analytics, an understanding existing & new markets and product development.

Rolls Royce had the advantage of having available data, sufficient circumstances as well as a very good market understanding. They were part of a mature market and ‘just had to tap into’ data to deliver relevant new products.

There are multiple examples of data driven value streams within existing and upcoming companies. You should check e.g., Google Spin-off Examples of The Climate Corporation / ClimateEngine, that enable crop insurances based on satellite data or Twiga Foods that introduces smart crates with tags for real-time data collection, thereby enabling food distribution in Kenya. Examples where data is driving new value models. First Access and Tala use data from mobile phones to provide alternative credit scoring services that help financial services providers assess the risk of people at the base of the pyramid. BBOXX has developed the Bboxx Pulse® platform which harnesses remote (real-time payment) monitoring data and IoT data to deliver energy access in a scalable and distributed model.

Value models

The above mentioned examples show how data can be monetized via different value models. By comparing monetization models and determining which is best suited data value offerings, companies can increase revenue margins and introduce beneficial new products and features, while expanding customer relationships and delivering specific value that keeps your clients coming back for more.

1. Perpetual model: the traditional model where customer pay for a product once upfront. And then have a perpetual rite to use the product (e.g. the raw data, aggregated data, meta data or insights). The seller has full responsibility for upkeep and updates.

2. Subscription (‘as a service’) model: here; Data-as-a-Service or DaaS. The customer buys service subscriptions to access of the data, right to use the data and updates & support. Advantage is that DaaS provides a predictable revenue stream that can be projected into the future. This revenue predictability has made the subscription model — similar to the software industry — increasingly popular.

3. Usage model: In this leading-edge monetization model, customers pay providers based on specified usage metrics ( e.g., pay per tick, when data is needed real time. Number of tests performed per data set. Or another data-related (technically it is software) example is the data cloud providers metric where customers are charged based on terabytes of data storage) with periodical invoicing. A customer pays for what they use.

4. Outcome model: A quite leading-edge and interesting model. Suppliers are not selling data products or services, they’re selling an outcome. Something like those law firm commercials that say, “We don’t get paid unless you see cash,” this model is about achieving a defined business result rather than delivering individual IoT data. This model is used by Rolls Royce and Climate (now Monsanto).

5. Impact model: Companies, e.g., some the largest agricultural companies in the world are collaborating to help eradicate Malaria by 2040. This is funded by donors. Some business models can create value by measuring social impact and reporting it to relevant bodies, such as government departments, donors and impact investors. This enables companies to think beyond data business models. Additional value streams for this could be generated by payments as grants or in ‘pay by results’ schemes.

*Note, this article has examples of IoT sensor data, which mostly does not include privacy related data. However, for all data monetization efforts it is applicable that they always must be performed within the requirements of the data privacy regulations in force as well as companies’ ethical guidelines.