Let’s be honest, speed, agility, and scalability are paramount to meet business expectations. Low code has quickly gained popularity as a powerful tool that complements full code solutions, enabling data teams and business users. In this article, we will summarize the benefits of low-code in data consultancy, and explore its impact on the development cycle.
Fast Explorative Analytics (FEA): First to mind is the speed of delivery, applying the fail-fast method. Low code empowers data-savvy users to quickly prototype, experiment, and iterate through various scenarios. By abstracting complex coding processes, low-code tools enable users to visually build and customize data pipelines, perform data transformations, and apply machine learning algorithms with minimal coding effort. This accelerates time-to-insight and facilitates fast explorative analytics for product development, trend analysis, and other data-driven decision-making processes.
Democratization and Scalability: By empowering business users and data teams low-code democratize access to data and analytics by reducing the dependency on technical expertise. Business users, who possess domain knowledge but may lack coding skills, can leverage low-code solutions to extract valuable insights from data without relying on data specialists. This empowers organizations to scale their data-driven initiatives and leverage resources efficiently.
Controlled Traceability for Auditability: Through enhanced traceability, low-code solutions support auditability of decision-taking & product development. Low code provides built-in mechanisms for version control, data lineage, and documentation. This traceability facilitates audits, compliance reporting, and the ability to trace back decisions made based on specific data and analytics pipelines.
Potential Pitfalls and Mitigating Measures: While low-code offers significant advantages, there are potential pitfalls to be mindful of:
Scalability Challenges: As the complexity and scale of the projects increase, low-code solutions may encounter limitations. It is crucial to regularly assess the scalability requirements and evaluate if additional custom code or full code solutions are needed to meet the growing demands.
Over-Reliance on Pre-Built Components: Low-code platforms often provide pre-built components and integrations. However, relying solely on these components may limit customization and flexibility. It is important to strike a balance between leveraging pre-built functionalities and having the ability to extend and customize as needed.
Security and Data Governance: While low-code platforms offer governance features, data security and privacy must still be diligently addressed. Organizations should ensure proper access controls, encryption, and compliance measures are in place to safeguard sensitive data and comply with regulatory standards.
Low code has proven itself as a valuable complement to full code solutions in the field of data. It facilitates fast explorative analytics, empowers business users, and ensures controlled traceability for auditability. By leveraging low-code platforms, organizations can accelerate their data-driven initiatives, democratize data access, and scale their analytical capabilities.
Central for de-central data teams: start acting as a (temporary) data owner to service business requirements.
Set up data service desks to be flexible for business requirements.
Data is often seen as the raw material to produce new products, frequently with analytics and AI as the innovative machinery enabling the end-result. Recent years have proven that the value of data more often serves as an enabler of multiple business results, leading to efficiency savings, profits, and the ability to maintain existing markets while expanding into new ones
Data as fundamental oil
Whether it is automated payments and invoicing, online customer interactions, or digital manufacturing, data is the underlying oil that can make your business operations run smoothly.
Or does it? Why is it that, despite the abundance of data, businesses often run sub-optimally, sometimes even relying on manual activities, in this digital age?
“So, if data is the new oil,
why isn’t everything running smoothly?”
All companies generate, receive, and process data to some extent. Data is abundant these days. So, if data is the new oil, why isn’t everything running smoothly?
Here is why: the data itself is complex, and the usage of data is complex. Many companies have tried to resolve this combined complexity through centralized standardization. Many projects aimed at establishing a single data model have become famous, often leading to disappointments. Alternatively, data solutions seek refuge in technology, often resulting in an increase in applications, which can add to the complexities instead of alleviating them. Above all, centralized standardization requires control, which does not adequately serve the business.
Move from control to empower
The very essence of any business is flexibility, the ability to innovate and develop new products and markets. The business needs to be facilitated by data. So, move from controlling towards empowering.
Empowering means understanding that there is no one-size-fits-all when it comes to data, as in the above-mentioned complexities. Data is very similar to machinery. Just compare the oil for ball bearings to petrol. They share the same raw material but differ in volume, characteristics, substance, and processing for different purposes.
How do we see the solution?
With the extensive rise in data volume, complexity, and velocity, a central data team supported by data stewards and architects is no longer sufficient. It requires more decentralized teams that can facilitate specific business needs while adhering to central requirements. Use the motto: control only where needed – for example, using one standard product or client ID across systems, and facilitate where possible, such as adding an additional product ID to support a regional process. Of course, this requires more effort. It is evident that any additional data requires more maintenance. However, the benefits for the business are immediate and significant. There is no need for the business to change processes, systems, or reporting. Immediate possibilities emerge to make more local variations of products and insights, facilitating specific market requirements. This approach maintains the possibility of working with central initiatives and the option to upgrade or downscale data where possible without affecting central requirements.
“The answer is easy, the deployment is more complex “
How do you facilitate this? The answer is easy; the deployment is much more complex. Have dedicated data teams in place with a close relationship to the business. That data team should consist of senior, well-trained data management experts, data analysts, and data engineers to facilitate and guide the local solutions, including the link to central platforms. The team should be able to answer business questions through a so-called service desk. Such a service desk requires a thorough understanding of the business processes and systems and the translation of data requirements into existing (or missing) data within systems. Preferably, the service desk should have the capability to identify, flag, and resolve regulatory questions on privacy, financial legislation, and health legislation. Make sure that the service desk is enabled by a ticketing workflow, including a dashboard displaying their effort and impact. Finally, that data team should be able to guide the business stakeholders in the best approaches and solutions. Don’t expect business stakeholders to deliver data requirements; they will have business requirements. If you didn’t know better, this team almost acts like a data owner.
Of course, some data needs to be strictly governed and controlled. There are overarching business requirements (e.g., insights in sales volumes) that require consistency and quality of trusted data. Identify these key data elements and manage them with a strict and tight regime. These key data elements can. for example, be linked to key reporting, identified as being used for most processes, or be the primary key within multiple systems. Current data volumes can make this identification a tough job. A good start can be using the Dublin Core standard to identify the right regime. The standard uses the following guidance: – which data is related to which process, system, product and report? – where is data being used? – why is the data needed (purpose)? – Who uses the data? – How is data labelled and referenced? – What is the relevance of the data (e.g., static or dynamic)? – How is data related to other data?
New way
Teams acting as (temporary) data owners is a new, almost revolutionary way of looking at data. The traditional view, based on data standards (e.g., DAMA, DCAM, ISO), all revolves around governance and ownership. That view is based on having data ownership within the business. If you step away from that theoretical view and fall back on lessons learned, then don’t expect business stakeholders to take up sufficient data ownership. For decades, they have perceived data as a by-product.
Most business stakeholders will stay away from data ownership simply because it is unknown territory for them. It is up to the data team to translate do’s and don’ts regarding data and take up data ownership for the business. In theory, this might even support the embracing of data ownership by business stakeholders through the principle of show and tell.
So, a team of experts is required. Such a team goes beyond the effort of some companies to “simply” assign a data steward who reports on the content of specific fields within systems. Companies should build dedicated teams across the organization, which will often need to invent the wheel themselves. The way of working will differ per objective. For project goals, make sure you can act fast, agile, and dedicated. For sustainable solutions, ensure that you stay completely aligned with company standards (and enhance a few where needed) to avoid the “not invented here syndrome.” For any purpose, make sure you take the time to understand and align data, data requirements, and business requirements. And actually build solutions – not just on paper, but within apps, databases, data pipelines, and systems.
All of this will require a solid, robust, and senior data leadership team which can manage, sustain, guide and facilitate data responsibilities. Invest in that team.
For examples on data standards, visit: DAMA, DCAM or ISO
Ori Yudilevich (Chief Technology Officer at Materials Zone) on: the history of Materials Zone, the company and its product. Ori explains how Materials Zone’s Materials Informatics platform applies material science techniques to save costs. He also explains what challenges with regards to data they usually come across.
Ryan Price (Executive Data & Artificial Intelligence at Avanade) on: trends and developments within data analytics consulting. Ryan talks about how Avenade approached data and AI on a global scales and discusses his views regarding important topics around data and AI.
D8A Directors
Trends and Developments in Data Analytics Consulting
Caroline Fluit (Global VP Digital Product Engineering at IKEA) on: data innovation within the retail industry. Caroline shares her experience and lessons learned with regards to data innovation as well as leadership.
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.
“Where is your data stored?” is not asked often enough. An interesting topic, brought up by an owner of a Dutch cloud company during a radio program.
It surprised me. As a Data Professional who has lost count of the number of times I have asked this question. Why is this still not a common concern?
When I ask the question, the reactions vary. Often times the answer is a simple “I don’t know” When I do find the right specialist to tell me these details, the conversation goes something like this:
“Where is the data stored?” “In the cloud.“ “Yes, but where are those servers located?” “In Europe” “Yes, what country in Europe?” “In The Netherlands. “ “Yes, do you know in which city?” “Yes, in Amsterdam.”
The tedious process goes to show that it’s a question that is not asked often enough. But it matters.
It’s interesting, it seems we don’t often enough realise that although data is digital, it always has a physical aspect to it. Much like us, the data has to live somewhere. In turn, cyber security is not just about the digital security of who can access your data, but also the physical one. What physical measures are in place to ensure that no one breaches your data centres? Do you know who can access the building where your data lives? However, security is but one concern when it comes to the physical location of data.
Laws and regulations mandate where data may be stored and processed. And processing includes ‘viewing’ data. GDPR, for instance, requires that data is stored in Europe. That means that some international cloud providers are automatically a non-option, when they don’t have data centres in Europe. If your data centres are located in a different country, you’re automatically dealing with cross-country transfers. Be vocal about this towards your cloud provider. If you don’t decide where your data is physically stored, they will. Then it’s out of your control. And most likely not in line with legal requirements.
Did you know that there are countries that demand their data is stored and processed locally only?
Location may also influence the reliability of you data. If your data centres are located in an area where power outages are common, you will be dealing with limited availability. Or if it is a politically unstable region, your data centre may be put out of the running all together. Next to that, the further away the physical location, the higher the risk for limited connectivity. Both the quality of the network and the distance can greatly impact connectivity. All these aspects can influence your ability to deliver the data-driven digital products and services your customers are paying for. In other words, thinking about the location of your data is business critical.
So my takeaway for you, start asking the question: do you know where your data is stored?
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 Roycehere.)
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.
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.
Date with D8A
Data strategy for small and medium-sized enterprises
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.
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.
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.
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.
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 maximizingprofits, 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.
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.
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 incontrol AND data inuse.
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.
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.
How important is the structure of data teams in the organisation?
Picture an organisation that wants to become more data-driven. It implements an updated strategy by hiring data specialists in business intelligence, data architecture, data engineering & data science. The organisation does not yet have a clear vision on how to structure & manage this new field of specialists. Small data teams pop up within the various business departments & IT. They work in close collaboration with business experts to create an impact & an appetite for data-driven change. As the number of data specialists in the organisation grows, it creates a need for standardisation, quality control, knowledge sharing, monitoring and management.
Sounds familiar? Organisations worldwide are in the process of taking this next step. In this blog, we will discuss how to structure & integrate teams of data specialists into the organisation. We will base these discussions on Accenture’s classification and AltexSoft expansion on these.
Two key elements are essential when discussing the structure and management of data teams.
The element of control
Customers and the organisation need work to be delivered predictably with quality in control. In other words, tooling, methods & processes need to be standardised among data specialists. Output can be planned & communicated, delivery of output is recognisable & easy to use, and assurances can be given on the quality of work by adherence to standards. Adding to the control element are the practices of sharing knowledge & code base between specialists and centralised monitoring & management.
The element of relevance
Data specialists rely on domain expertise to deliver output that is relevant to the organisation and its customers. Domain expertise is gained by working closely with business experts within and outside the organisation. Expertise building is slow & specific to the domain. Relevance and speed in delivering go hand in hand. Data specialists create maximum value when working closely & continuously with business experts. Adding to the element of relevance are the practices of customer-centricity, value-driven developing adaptability to the given situation in tooling, methods & processes.
The elements of control & relevance determine the success of the next step in data-driven change. The structure & integration of data teams depends on the amount of control and relevance required by the organisation. We will discuss three common approaches for structuring teams.
Decentralised approach
This approach maximum leverages the relevance element. In the decentralised approach, specialists work in cross-functional teams (product, functional or business) within the business departments. This close collaboration allows for flexibility in the process & delivery of output. Communication lines within the cross-functional teams are short, business knowledge for the data specialist is generally high. Central coordination & control on tooling, methods & processes is minimal as expertise & people are scattered across the organisation. Organisations implementing this approach may have less need for control or, in many cases, are just starting data-driven work and therefore lack the need for elaborate control measures.
Centralised approach
As the name suggests, this approach centralises data expertise in one or more teams. This approach leverages the control element. Data specialists work closely together, enabling fast knowledge sharing. As the data specialist work in functional teams management, including resource management, prioritisation & funding is simplified. Centralisation encourages career growth within functional teams. Standardisation of delivery & results are common & monitoring on these principles is done more efficiently. Communication lines with business experts & clients are longer. Creating the danger of transforming the data teams into a support team. As team members work on a project by project base, business expertise is decentralised within functional data teams, adding lead time to projects. Organisations implementing this approach may have a high need for control & standardisation. Furthermore, as work is highly prioritised & resource management coordinated, the centralised approach supports organisations with strict budgets to take steps to data-driven work.
Center of Excellence (CoE) approach
The CoE is a balanced approach trying to optimise both the element of control & relevance. In this approach local pockets of data specialists work within businesses teams (cross-functional) while a Center of Excellence team enable knowledge sharing, coordination and standardisation. The data-savvy professionals — aligned with the business teams — build business expertise and have efficient communication lines within business departments. The CoE team provides a way for management to coordinate and prioritise (cross-) department tasks and developments by enabling CoE team members to support the local data specialist (commonly called SWAT technique). Furthermore, the CoE team is tasked with standardising delivery & results across the organisation. Organisations implementing the CoE approach need local expertise within units to support daily operations and a more coordinated team to support & standardise. As data specialists work in both the business department and the Center of Excellence, the organisation needs to support both groups financially. A higher level of maturity in working data-driven is required to justify the SWAT-like approach in high priority data projects.
Concluding control & relevance are two critical elements to consider when deciding how to integrate & structure data teams within an organisation. We elaborated on three common approaches, centralised, decentralised and the Center of Excellence. Each balancing control & relevance. Which structure will work for your organisation depends on the current level of maturity and the need for either control or relevance.
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.
