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

A brief guide to productize data

There has never been a better time to create impact with data & analytics. More and more data is available, computing power is increasing fast and analytical techniques are getting mature. Being data driven is the talk of the town, for sure it is part of the strategy of your organization. In the last decade, most companies have invested in data & analytics initiatives to enhance efficiency, increase sales and comply to regulation. Yet, these initiatives have not yet resulted into full business value. Organisations are getting ready for the next wave; getting value out of data & analytics products.

Model to realize data-driven products
Best practice model to realize data-driven products and services

1. Climbing fast; the importance of data in value creation

Data is an asset and has (future) economic benefits. During the last years, the volume, complexity and richness of data has grown exponentially — mainly driven by e-commerce and Internet of Things or sensor data-and is expected to continue to do so (see also: McKinsey). In fact, so much of the world is instrumented that it is difficult to actually avoid generating data. We have entered a new era in which the physical world has become increasingly connected to the digital world. Data is generated by everything from cameras and traffic sensors to heart rate monitors, enabling richer insights into human and “thing” behavior.

Add to that the current growth in analytical power (e.g. analytics, machine learning, artificial intelligence). And the confluence of data, analytical and computational power today is setting the set for the next wave of digital disruption and data driven potential for growth.

“… the confluence of data, analytical and computational power today is setting the set for the next wave of digital disruption …”

This growth has a number of preconditions. Of course, organisations need to recognize that data is an asset. It also requires that required data is correct, available and (re-)usable. And potential revenue generation need to be qualified, e.g. through data marketplaces, data-as-a-service integration, digitization of customer interactions, product development, cost reduction, optimize operations and improving productivity.

In the last ten years data & analytics initiatives within organisations mainly focused on:

Controlled data & analytics, e.g. data organisation, data governance, privacy or trusted analytics;

Centralized source of available data, e.g. data platform or data engineering;

Insights value chain, e.g. deriving insights through use-case based machine learning, process mining or BI self-service by a team of analytics experts.

Not all initiatives bring the desired results. For example, deriving new insights is often considered as innovative, but any executive will recognize the sprawl of self-generated BI reports each claiming their own version of the truth, making it complex and time consuming to turn insights in to company steering. And although these initiatives are by-and-large based on business cases such as efficient reporting, comply to regulations or end-of-life for legacy systems, controlling and centralizing data & analytics, they are in fact supporting hygiene factors. And there is the trend that algorithms seem to be commoditizing, e.g. Google and Amazon are providing free access to their analytics libraries. In the end, this trend will transform any insights value chain into becoming a hygiene factor as well.

In any case, the results of most current data & analytics initiatives are not a breakthrough innovation or digital disruption.

2. Approach your data with a product mindset

So, while most data & analytics efforts are — still — performed to facilitate and improve the insights value chain, the real innovation is productization of data & analytics. Organisations need to look beyond their team of skilled data & analytics professionals with governed data sources, latest analytics tools and technologies if they want to leverage data to improve and increase revenue. To actively contribute to this, organization should start viewing data & analytics through a product development lens.

This means that we need to transform from data & analytics (point) solutions mainly focused on internal value towards the creation of full-fledged data & analytics products. Productization involves abstracting and standardizing the underlying principles, algorithms and code of successful point solutions until they can be used to solve an array of similar and relevant business problems. In the end, this should lead to a robust portfolio of data & analytics products.

To enable this organisations need to have the following foundation in place:

Bridge the gap between data & analytics and business – In many organisations, data & analytics and business execution are totally separate. The business lacks understanding of what is possible and therefore will ask for everything, without prioritization and lacking a requests funnel. This leads to the development of data & analytics point “solutions” without full business potential. Move beyond the current hype of ‘data literacy’ and actually involve relevant (business) stakeholders into data & analytics. Embrace change. And be practical by starting with data quality, ownership or relevant use-cases to improve daily operations through analytics, BI or robotics. Expand from there and be persistent. Truly and sustainably embedding data & analytics in an organisation is a long-term process.

Anchor data & analytics competences at executive level – Business impact from data-derived insights only happens when data & analytics is implemented deep within and consistently throughout the organization. This requires commitment, ownership, sponsorship and direction of a leader with the authority and sufficient understanding of data & analytics and its potential.

Understand potentials— the value of data & analytics depend on uniqueness and end uses
How to monetize the potential of data & analytics? Its value comes down to how unique it is, how it will be used, and by whom. Understanding the value is a tricky proposition, particularly since organizations cannot nail down the value until they are able to clearly specify its uses, either immediate or potential. Data & analytics may yield nothing, or it may yield the key to launching a new product line or making a scientific breakthrough. It might affect only a small percentage of a company’s revenue today, but it can be a key driver of growth in the future. General rule of thumb is that uniqueness of data will increase its value, so find that (hiddenn) gem. Where possible, join unique sets to further enhance the value and potential of data.
Product development takes an investment in time, people and technology. Set up a — technical — test-and-learn lab environment where pilots and beta-version products can be developed and by which the value can be further explored and understood. Include domain experts, data scientists, data experts. Capture client- and end-user needs in this lab environment and transform it into solutions and products. Identify quick wins for early adopter clients, to learn and develop how products work in a client environment. Set up a cooperation with sales departments and potential partners. Standardise, improve and scale products .

Take sufficient time — be lean where possible – Many organisations have invested resources and investments in data & analytics initiatives, e.g. hiring and/or educating data scientists, data lake implementations and data ownership. They are eager to finally monetize the data so that it indeed is ‘the new oil’. But if products are unclear or without market relevance, there is the risk of missing targets and being overtaken by competitors. At the same time, be opportunistic for quick results, perform pilots as much as possible to create an early adopters client base.

3. The coming wave: data & analytics product opportunities

Potential data driven product opportunities are well researched, identified and described. Think about e.g. IoT-based analytics for leasing companies and car insurers, real-time supply and demand matching for automotive, logistics and smart cities, personalized e-commerce and media, data integration between banks and B2B customers and data driven life sciences discovery. Besides, resolving the above mentioned foundation, a detailed approach on to realise these opportunities is less clearly defined. This paragraph contains the 5 main steps that all organisations should follow:

Step 1: Conceptualize the product

Identify a data & analytics product that meets the market needs within the lab environment. To identify relevant opportunities, include product expert, business groups (e.g. super users, sales and marketing) and -potential — clients. The process involves product definition and identification of data required for the product (which should include sourcing data creatively). Organisations with unique internal data have an increased opportunity to create highly valuable products with a good competitive edge. E.g. a bank with an agricultural background can use unique data which are highly sought after by other financial institutes. And a supply chain company can enhance their planning software with integrated robotics to increase efficiency for their clients, enhancing churn and sales opportunities. Take the uniqueness of available data into account early on in the process. 
Determine the market position and potential business model for the prototype. There are three main prototype categories, i.e. a data-as-a-service product, algorithms code performing robotics and analytics & BI and software code containing interactive insights based on analytics algorithms.

Step 2: Acquire and build

Data as foundation for new products is traditionally captured internally and externally to support daily operations and reporting & insights. Given the vast amounts of data being available from commercial and public sources, extend the purpose of data acquisition for productization. Acquired data needs to be correct, timely, understandable, with a clear provenance — including restrictions for usage & storage and in accordance with regulatory compliance such as privacy laws.

To design and build correct algorithms supporting robotics and/or analytics products, an analytics pipeline needs to be established. In this pipeline correctness, reusability, bias, quality and provenance of algorithms and quality of code will be managed. Integrating CI/CD (continuous integration / continuous development) supports a lean and agile analytics pipeline with fast testing of the prototype value. Data and code need to be stored in an agile, scalable and secured environment. And finally, data & analytics products gain value from the context of their use, user interface and/or ease of use. So, incorporate UX design into the product development approach.

Step 3: Refine and validate

Once data is identified and (algorithm and software) code and user interfaces are designed and build, they need to be enriched, refined and validated.

Step 4: Readiness

Store data in an advanced environment where it can be integrated, queried, processed and searched. This makes data sufficiently, fast and reliable available for data-as-a-service products. Ensure that this is supported by a solid and robust data architecture. Distribution channels of algorithms and software code can be numerous, e.g. in a cloud environment where it can integrate with web- and mobile solutions. Custom made build into client environments. Or through pre-defined (API) connections made to measure for clients.

Step 5: Market and AI feedback

The competitive nature of the information product space, availability of new data sources and demand for timely decision support require an ongoing emphasis on innovation, pricing and monitoring product usage. Adding this step at this stage of the analytics-based data product development process is consistent with the iterative nature of product development in a “lean startup” context. Once again, the evolution of new technologies has provided a mechanism for facilitating a feedback and information extraction process from the marketplace

Brief recap: companies are eager to utilize the new data-oil. Not every organisation is able to do that successfully. By taking a comprehensive approach, persevering through sufficient knowledge building on ALL organisational level and starting small based on a step by step approach, you can be successful with data products and services.

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!

Gambit or Gamble? Exploit your data value with this successful strategy!

In the digital world, there are two main flavours, those with extensive data and those that require extensive data.

Find your data entrepreneurship

– In this article, we leave out the data-native (Big Tech) companies -.

Those with extensive data, are in fact the (international) corporations with trusted brands, mature system landscapes and long long-lasting relationships with customers and partners. They can build upon large quantities of (historical) data, consistently used for existing processes and products. These could do much more with their data, maneuvering (the Gambit) real value out of their data.

Most corporations already invested in structural advantages for a competitive data edge: a supporting platform infrastructure, data quality monitoring, established data science teams and a data steward / data scientist attitude. For a maximal return on those investments, companies need to go the extra mile.

A strategy for data

The most common pitfall of a data strategy is that it becomes an overview of big words only, with a (too!) high focus on technology and analytics.  Yet technology should be an enabler. And analytics is just a manifestation. Don’t gamble with data (products), a good data strategy starts with a clear vision, related to market, technology and (regulatory) developments. Include a target operating model to achieve the strategy. But most of all, include on the value of data. Determine use-case types that will create most value. Large corporations have an unparalleled knowledge of industry and markets and are uniquely positioned to oversee this. Of course, there are value-cases for efficiency gains and productivity improvements. Limiting to these obvious values, tends to close doors on new opportunities. Companies must have a clear ambition pathway to data-driven revenue. This new revenue can include rewiring customer interaction, creating a completely new product or business and stepping into new markets.

In practice, data driven revenues proof to be more difficult than imagined. The effort to introduce new products within new markets combined with uncertain results make companies hesitant. Without a solid and funded ambition and a defined risk appetite, this can result into only minimal innovations, such as adding data features (apps!). Compared to data-native companies, this minimal innovation sometimes seems small potatoes. A clear data strategy gives companies mature guidance for innovation KPIs, investments, risks, and market opportunities. The data strategy will help to build success and develop new services, products and even ventures.

Data equals assets

In general, there are two flavours when it comes to data within companies. Companies have less data than they realize. Or companies have more data than they realize and have an under-utilization of the data, due to insufficient awareness of its value. Understanding the value of your data is based on 5 pillars:

  • History

Historical data cannot be easily replicated, years of data about customers, productions, operations, financial performance, sales, maintenance, and IP are enormously valuable. Such historical data is beneficial for increasing operational efficiency, building new data products and growing customer intimacy. Although Big Tech companies have been around for some years already, they can not compete with dedicated historical data sets. If the (meta) data is of good quality, the value increases even more. Mapping where this data resides gives an up-to-date overview of relevant data throughout the system landscape.

  • Privacy

Corporations are highly aware of the relevance of privacy regulations and have adopted data privacy measures and controls into their data operations. This way, the data that is available is for sure in accordance with (global) data privacy legislation.

  • Integration

Being part of a – traditional – chain with external suppliers and receivers (e.g. supplying materials to a manufacture who sells it to a retailer) can leverage the data into multiple views on e.g. sourcing and warehouse management. Established corporations are uniquely situated to build data-chains. Having a trusted brand creates traction for cooperation and partnerships to capture, integrate, store, refine and offer data & insights to existing and new markets.

“Understanding the value of data means requires real entrepreneurship”

  • Extension

Large corporations can enhance existing & new products with data, e.g., through sensor data. Big Tech companies are doing that now mostly for software products. More traditional companies are particularly capable to do this for hardware products. This way of thinking is still very much underdeveloped, because it is difficult to introduce a new product or even worse, enter a new market with a new product. Yet, it is also the ultimate opportunity! Build data entrepreneurship, by starting small while understanding the full potential of data. Examples of small starts are identifying if a data model can be IP – e.g., when it is part of a larger hardware product. In real life, starting small often means focusing on a solution that is close to home, e.g., joining multiple data sets into one and/or build dashboard, which can be offered to customers as extended service. These are often chosen because of feasibility reasons. From a data product perspective, don’t consider such an approach as not small; consider it as not even starting. Companies that do not progress beyond these products should at least have a simultaneous experimental track, building and failing new products and services for lessons learned what works and what doesn’t. Understanding the value of data requires entrepreneurship (see also the example of Rolls Royce here.)

  • Data entrepreneurship

Large and established corporations are the epiphany of entrepreneurship. It is at their very core. Yet, often not enough for data. Data can be so alien to them that experimenting for value is hesitant or not happening. And this is where start-up companies are not lacking. They might not have the large historical data sets, trusted data chains or easy connections with available hardware products. They do have the entrepreneurial spirit and are highly aware of the value of data. And have the capability to experiment and become successful with new products.

Becoming data entrepreneurial means knowing which data you have, understanding the (potential) value and daring to look beyond the obvious.

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

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!

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

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.

Date with D8A
Date with D8A
Good design teams embrace data quality

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.

Date with D8A
Date with D8A
Trailer: Date with D8A

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.