Digital procurement, PPDS and multi-speed datafication -- some thoughts on the March 2023 PPDS Communication

The 2020 European strategy for data ear-marked public procurement as a high priority area for the development of common European data spaces for public administrations. The 2020 data strategy stressed that

Public procurement data are essential to improve transparency and accountability of public spending, fighting corruption and improving spending quality. Public procurement data is spread over several systems in the Member States, made available in different formats and is not easily possible to use for policy purposes in real-time. In many cases, the data quality needs to be improved.

To address those issues, the European Commission was planning to ‘Elaborate a data initiative for public procurement data covering both the EU dimension (EU datasets, such as TED) and the national ones’ by the end of 2020, which would be ‘complemented by a procurement data governance framework’ by mid 2021.

With a 2+ year delay, details for the creation of the public procurement data space (PPDS) were disclosed by the European Commission on 16 March 2023 in the PPDS Communication. The procurement data governance framework is now planned to be developed in the second half of 2023.

In this blog post, I offer some thoughts on the PPDS, its functional goals, likely effects, and the quickly closing window of opportunity for Member States to support its feasibility through an ambitious implementation of the new procurement eForms at domestic level (on which see earlier thoughts here).

1. The PPDS Communication and its goals

The PPDS Communication sets some lofty ambitions aligned with those of the closely-related process of procurement digitalisation, which the European Commission in its 2017 Making Procurement Work In and For Europe Communication already saw as not only an opportunity ‘to streamline and simplify the procurement process’, but also ‘to rethink fundamentally the way public procurement, and relevant parts of public administrations, are organised … [to seize] a unique chance to reshape the relevant systems and achieve a digital transformation’ (at 11-12).

Following the same rhetoric of transformation, the PPDS Communication now stresses that ‘Integrated data combined with the use of state-of the-art and emerging analytics technologies will not only transform public procurement, but also give new and valuable insights to public buyers, policy-makers, businesses and interested citizens alike‘ (at 2). It goes further to suggest that ‘given the high number of ecosystems concerned by public procurement and the amount of data to be analysed, the impact of AI in this field has a potential that we can only see a glimpse of so far‘ (at 2).

The PPDS Communication claims that this data space ‘will revolutionise the access to and use of public procurement data:

  • It will create a platform at EU level to access for the first time public procurement data scattered so far at EU, national and regional level.

  • It will considerably improve data quality, availability and completeness, through close cooperation between the Commission and Member States and the introduction of the new eForms, which will allow public buyers to provide information in a more structured way.

  • This wealth of data will be combined with an analytics toolset including advanced technologies such as Artificial Intelligence (AI), for example in the form of Machine Learning (ML) and Natural Language Processing (NLP).’

A first comment or observation is that this rhetoric of transformation and revolution not only tends to create excessive expectations on what can realistically be delivered by the PPDS, but can also further fuel the ‘policy irresistibility’ of procurement digitalisation and thus eg generate excessive experimentation or investment into the deployment of digital technologies on the basis of such expectations around data access through PPDS (for discussion, see here). Policy-makers would do well to hold off on any investments and pilot projects seeking to exploit the data presumptively pooled in the PPDS until after its implementation. A closer look at the PPDS and the significant roadblocks towards its full implementation will shed further light on this issue.

2. What is the PPDS?

Put simply, the PPDS is a project to create a single data platform to bring into one place ‘all procurement data’ from across the EU—ie both data on above threshold contracts subjected to mandatory EU-wide publication through TED (via eForms from October 2023), and data on below threshold contracts, which publication may be required by the domestic laws of the Member States, or entirely voluntary for contracting authorities.

Given that above threshold procurement data is already (in the process of being) captured at EU level, the PPDS is very much about data on procurement not covered by the EU rules—which represents 80% of all public procurement contracts. As the PPDS Communication stresses

To unlock the full potential of public procurement, access to data and the ability to analyse it are essential. However, data from only 20% of all call for tenders as submitted by public buyers is available and searchable for analysis in one place [ie TED]. The remaining 80% are spread, in different formats, at national or regional level and difficult or impossible to re-use for policy, transparency and better spending purposes. In order (sic) words, public procurement is rich in data, but poor in making it work for taxpayers, policy makers and public buyers.

The PPDS thus intends to develop a ‘technical fix’ to gain a view on the below-threshold reality of procurement across the EU, by ‘pulling and pooling’ data from existing (and to be developed) domestic public contract registers and transparency portals. The PPDS is thus a mechanism for the aggregation of procurement data currently not available in (harmonised) machine-readable and structured formats (or at all).

As the PPDS Communication makes clear, it consists of four layers:
(1) A user interface layer (ie a website and/or app) underpinned by
(2) an analytics layer, which in turn is underpinned by (3) an integration layer that brings together and minimally quality-assures the (4) data layer sourced from TED, Member State public contract registers (including those at sub-national level), and data from other sources (eg data on beneficial ownership).

The two top layers condense all potential advantages of the PPDS, with the analytics layer seeking to develop a ‘toolset including emerging technologies (AI, ML and NLP)‘ to extract data insights for a multiplicity of purposes (see below 3), and the top user interface seeking to facilitate differential data access for different types of users and stakeholders (see below 4). The two bottom layers, and in particular the data layer, are the ones doing all the heavy lifting. Unavoidably, without data, the PPDS risks being little more than an empty shell. As always, ‘no data, no fun’ (see below 5).

Importantly, the top three layers are centralised and the European Commission has responsibility (and funding) for developing them, while the bottom data layer is decentralised, with each Member State retaining responsibility for digitalising its public procurement systems and connecting its data sources to the PPDS. Member States are also expected to bear their own costs, although there is EU funding available through different mechanisms. This allocation of responsibilities follows the limited competence of the EU in this area of inter-administrative cooperation, which unfortunately heightens the risks of the PPDS becoming little more than an empty shell, unless Member States really take the implementation of eForms and the collaborative approach to the construction of the PPDS seriously (see below 6).

The PPDS Communication foresees a progressive implementation of the PPDS, with the goal of having ‘the basic architecture and analytics toolkit in place and procurement data published at EU level available in the system by mid-2023. By the end of 2024, all participating national publication portals would be connected, historic data published at EU level integrated and the analytics toolkit expanded. As of 2025, the system could establish links with additional external data sources’ (at 2). It will most likely be delayed, but that is not very important in the long run—especially as the already accrued delays are the ones that pose a significant limitation on the adequate rollout of the PPDS (see below 6).

3. PPDS’ expected functionality

The PPDS Communication sets expectations around the functionality that could be extracted from the PPDS by different agents and stakeholders.

For public buyers, in addition to reducing the burden of complying with different types of (EU-mandated) reporting, the PPDS Communication expects that ‘insights gained from the PPDS will make it much easier for public buyers to

  • team up and buy in bulk to obtain better prices and higher quality;

  • generate more bids per call for tenders by making calls more attractive for bidders, especially for SMEs and start-ups;

  • fight collusion and corruption, as well as other criminal acts, by detecting suspicious patterns;

  • benchmark themselves more accurately against their peers and exchange knowledge, for instance with the aim of procuring more green, social and innovative products and services;

  • through the further digitalisation and emerging technologies that it brings about, automate tasks, bringing about considerable operational savings’ (at 2).

This largely maps onto my analysis of likely applications of digital technologies for procurement management, assuming the data is there (see here).

The PPDS Communication also expects that policy-makers will ‘gain a wealth of insights that will enable them to predict future trends‘; that economic operators, and SMEs in particular, ‘will have an easy-to-use portal that gives them access to a much greater number of open call for tenders with better data quality‘, and that ‘Citizens, civil society, taxpayers and other interested stakeholders will have access to much more public procurement data than before, thereby improving transparency and accountability of public spending‘ (at 2).

Of all the expected benefits or functionalities, the most important ones are those attributed to public buyers and, in particular, the possibility of developing ‘category management’ insights (eg potential savings or benchmarking), systems of red flags in relation to corruption and collusion risks, and the automation of some tasks. However, unlocking most of these functionalities is not dependent on the PPDS, but rather on the existence of procurement data at the ‘right’ level.

For example, category management or benchmarking may be more relevant or adequate (as well as more feasible) at national than at supra-national level, and the development of systems of red flags can also take place at below-EU level, as can automation. Importantly, the development of such functionalities using pan-EU data, or data concerning more than one Member State, could bias the tools in a way that makes them less suited, or unsuitable, for deployment at national level (eg if the AI is trained on data concerning solely jurisdictions other than the one where it would be deployed).

In that regard, the expected functionalities arising from PPDS require some further thought and it can well be that, depending on implementation (in particular in relation to multi-speed datafication, as below 5), Member States are better off solely using domestic data than that coming from the PPDS. This is to say that PPDS is not a solid reality and that its enabling character will fluctuate with its implementation.

4. Differential procurement data access through PPDS

As mentioned above, the PPDS Communication stresses that ‘Citizens, civil society, taxpayers and other interested stakeholders will have access to much more public procurement data than before, thereby improving transparency and accountability of public spending’ (at 2). However, this does not mean that the PPDS will be (entirely) open data.

The Communication itself makes clear that ‘Different user categories (e.g. Member States, public buyers, businesses, citizens, NGOs, journalists and researchers) will have different access rights, distinguishing between public and non-public data and between participating Member States that share their data with the PPDS (PPDS members, …) and those that need more time to prepare’ (at 8). Relatedly, ‘PPDS members will have access to data which is available within the PPDS. However, even those Member States that are not yet ready to participate in the PPDS stand to benefit from implementing the principles below, due to their value for operational efficiency and preparing for a more evidence-based policy’ (at 9). This raises two issues.

First, and rightly, the Communication makes clear that the PPDS moves away from a model of ‘fully open’ or ‘open by default’ procurement data, and that access to the PPDS will require differential permissioning. This is the correct approach. Regardless of the future procurement data governance framework, it is clear that the emerging thicket of EU data governance rules ‘requires the careful management of a system of multi-tiered access to different types of information at different times, by different stakeholders and under different conditions’ (see here). This will however raise significant issues for the implementation of the PPDS, as it will generate some constraints or disincentives for an ambitions implementation of eForms at national level (see below 6).

Second, and less clearly, the PPDS Communication evidences that not all Member States will automatically have equal access to PPDS data. The design seems to be such that Member States that do not feed data into PPDS will not have access to it. While this could be conceived as an incentive for all Member States to join PPDS, this outcome is by no means guaranteed. As above (3), it is not clear that Member States will be better off—in terms of their ability to extract data insights or to deploy digital technologies—by having access to pan-EU data. The main benefit resulting from pan-EU data only accrues collectively and, primarily, by means of facilitating oversight and enforcement by the European Commission. From that perspective, the incentives for PPDS participation for any given Member State may be quite warped or internally contradictory.

Moreover, given that plugging into PPDS is not cost-free, a Member State that developed a data architecture not immediately compatible with PPDS may well wonder whether it made sense to shoulder the additional costs and risks. From that perspective, it can only be hoped that the existence of EU funding and technical support will be maximised by the European Commission to offload that burden from the (reluctant) Member States. However, even then, full PPDS participation by all Member States will still not dispel the risk of multi-speed datafication.

5. No data, no fun — and multi-speed datafication

Related to the risk that some EU Member States will become PPDS members and others not, there is a risk (or rather, a reality) that not all PPDS members will equally contribute data—thus creating multi-speed datafication, even within the Member States that opt in to the PPDS.

First, the PPDS Communication makes it clear that ‘Member States will remain in control over which data they wish to share with the PPDS (beyond the data that must be published on TED under the Public Procurement Directives)‘ (at 7), It further specifies that ‘With the eForms, it will be possible for the first time to provide data in notices that should not be published, or not immediately. This is important to give assurance to public buyers that certain data is not made publicly available or not before a certain point in time (e.g. prices)’ (at 7, fn 17).

This means that each Member State will only have to plug whichever data it captures and decides to share into PPDS. It seems plain to see that this will result in different approaches to data capture, multiple levels of granularity, and varying approaches to restricting access to the date in the different Member States, especially bearing in mind that ‘eForms are not an “off the shelf” product that can be implemented only by IT developers. Instead, before developers start working, procurement policy decision-makers have to make a wide range of policy decisions on how eForms should be implemented’ in the different Member States (see eForms Implementation Handbook, at 9).

Second, the PPDS Communication is clear (in a footnote) that ‘One of the conditions for a successful establishment of the PPDS is that Member States put in place automatic data capture mechanisms, in a first step transmitting data from their national portals and contract registers’ (at 4, fn 10). This implies that Member States may need to move away from manually inputted information and that those seeking to create new mechanisms for automatic procurement data capture can take an incremental approach, which is very much baked into the PPDS design. This relates, for example, to the distinction between pre- and post-award procurement data, with pre-award data subjected to higher demands under EU law. It also relates to above and below threshold data, as only above threshold data is subjected to mandatory eForms compliance.

In the end, the extent to which a (willing) Member State will contribute data to the PPDS depends on its decisions on eForms implementation, which should be well underway given the October 2023 deadline for mandatory use (for above threshold contracts). Crucially, Member States contributing more data may feel let down when no comparable data is contributed to PPDS by other Member States, which can well operate as a disincentive to contribute any further data, rather than as an incentive for the others to match up that data.

6. Ambitious eForms implementation as the PPDS’ Achilles heel

As the analysis above has shown, the viability of the PPDS and its fitness for purpose (especially for EU-level oversight and enforcement purposes) crucially depends on the Member States deciding to take an ambitious approach to the implementation of eForms, not solely by maximising their flexibility for voluntary uses (as discussed here) but, crucially, by extending their mandatory use (under national law) to all below threshold procurement. It is now also clear that there is a need for as much homogeneity as possible in the implementation of eForms in order to guarantee that the information plugged into PPDS is comparable—which is an aspect of data quality that the PPDS Communication does not seem to have at all considered).

It seems that, due to competing timings, this poses a bit of a problem for the rollout of the PPDS. While eForms need to be fully implemented domestically by October 2023, the PPDS Communication suggests that the connection of national portals will be a matter for 2024, as the first part of the project will concern the top two layers and data connection will follow (or, at best, be developed in parallel). Somehow, it feels like the PPDS is being built without a strong enough foundation. It would be a shame (to put it mildly) if Member States having completed a transition to eForms by October 2023 were dissuaded from a second transition into a more ambitious eForms implementation in 2024 for the purposes of the PPDS.

Given that the most likely approach to eForms implementation is rather minimalistic, it can well be that the PPDS results in not much more than an empty shell with fancy digital analytics limited to very superficial uses. In that regard, the two-year delay in progressing the PPDS has created a very narrow (and quickly dwindling) window of opportunity for Member States to engage with an ambitions process of eForms implementation

7. Final thoughts

It seems to me that limited and slow progress will be attained under the PPDS in coming years. Given the undoubted value of harnessing procurement data, I sense that Member States will progress domestically, but primarily in specific settings such as that of their central purchasing bodies (see here). However, whether they will be onboarded into PPDS as enthusiastic members seems less likely.

The scenario seems to resemble limited voluntary cooperation in other areas (eg interoperability; for discussion see here). It may well be that the logic of EU competence allocation required this tentative step as a first move towards a more robust and proactive approach by the Commission in a few years, on grounds that the goal of creating the European data space could not be achieved through this less interventionist approach.

However, given the speed at which digital transformation could take place (and is taking place in some parts of the EU), and the rhetoric of transformation and revolution that keeps being used in this policy area, I can’t but feel let down by the approach in the PPDS Communication, which started with the decision to build the eForms on the existing regulatory framework, rather than more boldly seeking a reform of the EU procurement rules to facilitate their digital fitness.

Emerging risks in digital procurement governance

In a previous blog post, I drew a technology-informed feasibility boundary to assess the realistic potential of digital technologies in the specific context of procurement governance. I suggested that the potential benefits from the adoption of digital technologies within that feasibility boundary had to be assessed against new governance risks and requirements for their mitigation.

In a new draft chapter (num 8) for my book project, I now explore the main governance risks and legal obligations arising from the adoption of digital technologies, which revolve around data governance, algorithmic transparency, technological dependency, technical debt, cybersecurity threats, the risks stemming from the long-term erosion of the skills base in the public sector, and difficult trade-offs due to the uncertainty surrounding immature and still changing technologies within an also evolving regulatory framework.

The analysis is not carried out in a vacuum, but in relation to the increasingly complex framework of EU digital law, including: the Open Data Directive; the Data Governance Act; the proposed Data Act; the NIS 2 Directive on cybersecurity measures, including its interaction with the Cybersecurity Act, and the proposed Directive on the resilience of critical entities and Cyber Resilience Act; as well as some aspects of the proposed EU AI Act.

This post provides a summary of my main findings, on which I will welcome any comments: a.sanchez-graells@bristol.ac.uk. The full draft chapter is free to download: A Sanchez-Graells, ‘Identifying Emerging Risks in Digital Procurement Governance’ to be included in A Sanchez-Graells, Digital Technologies and Public Procurement. Gatekeeping and experimentation in digital public governance (OUP, forthcoming). Available at SSRN: https://ssrn.com/abstract=4254931.

current and Imminent digital governance obligations for public buyers

Public buyers already shoulder, and will very soon face further digital governance obligations, even if they do not directly engage with digital technologies. These concern both data governance and cybersecurity obligations.

Data governance obligations

The Open Data Directive imposes an obligation to facilitate access to and re-use of procurement data for commercial or non-commercial purposes, and generates the starting position that data held by public buyers needs to be made accessible. Access is however excluded in relation to data subject to third-party rights, such as data protected by intellectual property rights (IPR), or data subject to commercial confidentiality (including business, professional, or company secrets). Moreover, in order to ensure compliance with the EU procurement rules, access should also be excluded to data subject to procurement-related confidentiality (Art 21 Dir 2014/24/EU), and data which disclosure should be withheld because the release of such information would impede law enforcement or would otherwise be contrary to the public interest … or might prejudice fair competition between economic operators (Art 55 Dir 2014/24/EU). Compliance with the Open Data Directive can thus not result in a system where all procurement data becomes accessible.

The Open Data Directive also falls short of requiring that access is facilitated through open data, as public buyers are under no active obligation to digitalise their information and can simply allow access to the information they hold ‘in any pre-existing format or language’. However, this will change with the entry into force of the rules on eForms (see here). eForms will require public buyers to hold (some) procurement information in digital format. This will trigger the obligation under the Open Data Directive to make that information available for re-use ‘by electronic means, in formats that are open, machine-readable, accessible, findable and re-usable, together with their metadata’. Moreover, procurement data that is not captured by the eForms but in other ways (eg within the relevant e-procurement platform) will also be subject to this regime and, where making that information available for re-use by electronic means involves no ‘disproportionate effort, going beyond a simple operation’, it is plausible that the obligation of publication by electronic means will extend to such data too. This will potentially significantly expand the scope of open procurement data obligations, but it will be important to ensure that it does not result in excessive disclosure of third-party data or competition-sensitive data.

Some public buyers may want to go further in facilitating (controlled) access to procurement data not susceptible of publication as open data. In that case, they will have to comply with the requirements of the Data Governance Act (and the Data Act, if adopted). In this case, they will need to ensure that, despite authorising access to the data, ‘the protected nature of data is preserved’. In the case of commercially confidential information, including trade secrets or content protected by IPR, this can require ensuring that the data has been ‘modified, aggregated or treated by any other method of disclosure control’. Where ‘anonymising’ information is not possible, access can only be given with permission of the third-party, and in compliance with the applicable IPR, if any. The Data Governance Act explicitly imposes liability on the public buyer if it breaches the duty not to disclose third-party data, and it also explicitly requires that data access complies with EU competition law.

This shows that public buyers have an inescapable data governance role that generates tensions in the design of open procurement data mechanisms. It is simply not possible to create a system that makes all procurement data open. Data governance requires the careful management of a system of multi-tiered access to different types of information at different times, by different stakeholders and under different conditions (as I already proposed a few years ago, see here). While the need to balance procurement transparency and the protection of data subject to the rights of others and competition-sensitive data is not a new governance challenge, the digital management of this information creates heightened risks to the extent that the implementation of data management solutions is tendentially open access. Moreover, the assessment of the potential competition impact of data disclosure can be a moving target. The risk of distortions of competition is heightened by the possibility that the availability of data allows for the deployment of technology-supported forms of collusive behaviour (as well as corrupt behaviour).

Cybersecurity obligations

Most public buyers will face increased cybersecurity obligations once the NIS 2 Directive enters into force. The core substantive obligation will be a mandate to ‘take appropriate and proportionate technical, operational and organisational measures to manage the risks posed to the security of network and information systems which those entities use for their operations or for the provision of their services, and to prevent or minimise the impact of incidents on recipients of their services and on other services’. This will require a detailed assessment of what is proportionate to the cybersecurity exposure of a public buyer.

In that analysis, the public buyer will be able to take into account ‘the state of the art and, where applicable, relevant European and international standards, as well as the cost of implementation’, and in ‘assessing the proportionality of those measures, due account shall be taken of the degree of the entity’s exposure to risks, its size, the likelihood of occurrence of incidents and their severity, including their societal and economic impact’.

Public buyers may not have the ability to carry out such an assessment with internal capabilities, which immediately creates a risk of outsourcing of the cybersecurity risk assessment, as well as other measures to comply with the related substantive obligations. This can generate further organisational dependency on outside capability, which can itself be a cybersecurity risk. As discussed below, imminent cybersecurity obligations heighten the need to close the current gaps in digital capability.

Increased governance obligations for public buyers ‘going digital’

Public buyers that are ‘going digital’ and experimenting with or deploying digital solutions face increased digital governance obligations. Given the proportionality of the cybersecurity requirements under the NIS 2 Directive (above), public buyers that use digital technologies can expect to face more stringent substantive obligations. Moreover, the adoption of digital solutions generates new or increased risks of technological dependency, of two main types. The first type refers to vendor lock-in and interoperability, and primarily concerns the increasing need to develop advanced strategies to manage IPR, algorithmic transparency, and technical debt—which could largely be side-stepped by an ‘open source by default’ approach. The second concerns the erosion of the skills base of the public buyer as technology replaces the current workforce, which generates intellectual debt and operational dependency.

Open Source by Default?

The problem of technological lock-in is well understood, even if generally inadequately or insufficiently managed. However, the deployment of Artificial Intelligence (AI), and Machine Learning (ML) in particular, raise the additional issue of managing algorithmic transparency in the context of technological dependency. This generates specific challenges in relation with the administration of public contracts and the obligation to create competition in their (re)tendering. Without access to the algorithm’s source code, it is nigh impossible to ensure a level playing field in the tender of related services, as well as in the re-tendering of the original contract for the specific ML or AI solution. This was recognised by the CJEU in a software procurement case (see here), which implies that, under EU law, public buyers are under an obligation to ensure that they have access and dissemination rights over the source code. This goes beyond emerging standards on algorithmic transparency, such as the UK’s, or what would be required if the EU AI Act was applicable, as reflected in the draft contract clauses for AI procurement. This creates a significant governance risk that requires explicit and careful consideration by public buyers, and which points at the need of embedding algorithmic transparency requirements as a pillar of technological governance related to the digitalisation of procurement.

Moreover, the development of digital technologies also creates a new wave of lock-in risks, as digital solutions are hardly off-the-shelf and can require a high level of customisation or co-creation between the technology provider and the public buyer. This creates the need for careful consideration of the governance of IPR allocation—with some of the guidance seeking to promote leaving IPR rights with the vendor needing careful reconsideration. A nuanced approach is required, as well as coordination with other legal regimes (eg State aid) where IPR is left with the contractor. Following some recent initiatives by the European Commission, an ‘open source by default’ approach would be suitable, as there can be high value derived from using and reusing common solutions, not only in terms of interoperability and a reduction of total development costs—but also in terms of enabling the emergence of communities of practice that can contribute to the ongoing improvement of the solutions on the basis of pooled resources, which can in turn mitigate some of the problems arising from limited access to digital skills.

Finally, it should be stressed that most of these technologies are still emergent or immature, which generates additional governance risks. The adoption of such emergent technologies generates technical debt. Technical debt is not solely a financial issue, but a structural barrier to digitalisation. Technical debt risks stress the importance of the adoption of the open source by default approach mentioned above, as open source can facilitate the progressive collective repayment of technical debt in relation to widely adopted solutions.

(Absolute) technological dependency

As mentioned, a second source of technological dependency concerns the erosion of the skills base of the public buyer as technology replaces the current workforce. This is different from dependence on a given technology (as above), and concerns dependence on any technological solution to carry out functions previously undertaken by human operators. This can generate two specific risks: intellectual debt and operational dependency.

In this context, intellectual debt refers to the loss of institutional knowledge and memory resulting from eg the participation in the development and deployment of the technological solutions by agents no longer involved with the technology (eg external providers). There can be many forms of intellectual debt risk, and some can be mitigated or excluded through eg detailed technical documentation. Other forms of intellectual debt risk, however, are more difficult to mitigate. For example, situations where reliance on a technological solution (eg robotic process automation, RPA) erases institutional knowledge of the reason why a specific process is carried out, as well as how that process is carried out (eg why a specific source of information is checked for the purposes of integrity screening and how that is done). Mitigating against this requires keeping additional capability and institutional knowledge (and memory) to be able to explain in full detail what specific function the technology is carrying out, why, how that is done, and how that would be done in the absence of the technology (if it could be done at all). To put it plainly, it requires keeping the ability to ‘do it by hand’—or at the very least to be able to explain how that would be done.

Where it would be impossible or unfeasible to carry out the digitised task without using technology, digitalisation creates absolute operational dependency. Mitigating against such operational dependency requires an assessment of ‘system critical’ technological deployments without which it is not possible to carry out the relevant procurement function and, most likely, to deploy measures to ensure system resilience (including redundancy if appropriate) and system integrity (eg in relation to cybersecurity, as above). It is however important to acknowledge that there will always be limits to ensuring system resilience and integrity, which should raise questions about the desirability of generating situations of absolute operational dependency. While this may be less relevant in the context of procurement governance than in other contexts, it can still be an important consideration to factor into decision-making as technological practice can fuel a bias towards (further) technological practice that can then help support unquestioned technological expansion. In other words, it will be important to consider what are the limits of absolute technological delegation.

The crucial need to boost in-house digital skills in the public sector

The importance of digital capabilities to manage technological governance risks emerges a as running theme. The specific governance risks identified in relation to data and systems integrity, including cybersecurity risks, as well as the need to engage in sophisticated management of data and IPR, show that skills shortages are problematic in the ongoing use and maintenance of digital solutions, as their implementation does not diminish, but rather expands the scope of technology-related governance challenges.

There is an added difficulty in the fact that the likelihood of materialisation of those data, systems integrity, and cybersecurity risks grows with reduced digital capabilities, as the organisation using digital solutions may be unable to identify and mitigate them. It is not only that the technology carries risks that are either known knowns or known unknowns (as above), but also that the organisation may experience them as unknown unknowns due to its limited digital capability. Limited digital skills compound those governance risks.

There is a further risk that digitalisation and the related increase in digital capability requirements can embed an element of (unacknowledged) organisational exposure that mirrors the potential benefits of the technologies. While technology adoption can augment the organisation’s capability (eg by reducing administrative burdens through automation), this also makes the entire organisation dependent on its (disproportionately small) digital capabilities. This makes the organisation particularly vulnerable to the loss of limited capabilities. From a governance perspective, this places sustainable access to digital skills as a crucial element of the critical vulnerabilities and resilience assessment that should accompany all decisions to deploy a digital technology solution.

A plausible approach would be to seek to mitigate the risk of insufficient access to in-house skills through eg the creation of additional, standby or redundant contracted capability, but this would come with its own costs and governance challenges. Moreover, the added complication is that the digital skills gap that exposes the organisation to these risks in the first place, can also fuel a dynamic of further reliance on outside capabilities (from consultancy firms) beyond the development and adoption of those digital solutions. This has the potential to exacerbate the long-term erosion of the skills base in the public sector. Digitalisation heightens the need for the public sector to build up its expertise and skills, as the only way of slowing down or reducing the widening digital skills gap and ensuring organisational resilience and a sustainable digital transition.

Conclusion

Public buyers already face significant digital governance obligations, and those and the underlying risks can only increase (potentially, very significantly) with further progress in the path of procurement digitalisation. Ultimately, to ensure adequate digital procurement governance, it is not only necessary to take a realistic look at the potential of the technology and the required enabling factors (see here), but also to embed a comprehensive mechanism of risk assessment in the process of technological adoption, which requires enhanced public sector digital capabilities, as stressed here. Such an approach can mitigate against the policy irresistibility that surrounds these technologies (see here) and contribute to a gradual and sustainable process of procurement digitalisation. The ways in which such risk assessment should be carried out require further exploration, including consideration of whether to subject the adoption of digital technologies for procurement governance to external checks (see here). This will be the object of forthcoming analysis.

Digital procurement governance: drawing a feasibility boundary

In the current context of generalised quick adoption of digital technologies across the public sector and strategic steers to accelerate the digitalisation of public procurement, decision-makers can be captured by techno hype and the ‘policy irresistibility’ that can ensue from it (as discussed in detail here, as well as here).

To moderate those pressures and guide experimentation towards the successful deployment of digital solutions, decision-makers must reassess the realistic potential of those technologies in the specific context of procurement governance. They must also consider which enabling factors must be put in place to harness the potential of the digital technologies—which primarily relate to an enabling big data architecture (see here). Combined, the data requirements and the contextualised potential of the technologies will help decision-makers draw a feasibility boundary for digital procurement governance, which should inform their decisions.

In a new draft chapter (num 7) for my book project, I draw such a technology-informed feasibility boundary for digital procurement governance. This post provides a summary of my main findings, on which I will welcome any comments: a.sanchez-graells@bristol.ac.uk. The full draft chapter is free to download: A Sanchez-Graells, ‘Revisiting the promise: A feasibility boundary for digital procurement governance’ to be included in A Sanchez-Graells, Digital Technologies and Public Procurement. Gatekeeping and experimentation in digital public governance (OUP, forthcoming). Available at SSRN: https://ssrn.com/abstract=4232973.

Data as the main constraint

It will hardly be surprising to stress again that high quality big data is a pre-requisite for the development and deployment of digital technologies. All digital technologies of potential adoption in procurement governance are data-dependent. Therefore, without adequate data, there is no prospect of successful adoption of the technologies. The difficulties in generating an enabling procurement data architecture are detailed here.

Moreover, new data rules only regulate the capture of data for the future. This means that it will take time for big data to accumulate. Accessing historical data would be a way of building up (big) data and speeding up the development of digital solutions. Moreover, in some contexts, such as in relation with very infrequent types of procurement, or in relation to decisions concerning previous investments and acquisitions, historical data will be particularly relevant (eg to deploy green policies seeking to extend the use life of current assets through programmes of enhanced maintenance or refurbishment; see here). However, there are significant challenges linked to the creation of backward-looking digital databases, not only relating to the cost of digitisation of the information, but also to technical difficulties in ensuring the representativity and adequate labelling of pre-existing information.

An additional issue to consider is that a number of governance-relevant insights can only be extracted from a combination of procurement and other types of data. This can include sources of data on potential conflict of interest (eg family relations, or financial circumstances of individuals involved in decision-making), information on corporate activities and offerings, including detailed information on products, services and means of production (eg in relation with licensing or testing schemes), or information on levels of utilisation of public contracts and satisfaction with the outcomes by those meant to benefit from their implementation (eg users of a public service, or ‘internal’ users within the public administration).

To the extent that the outside sources of information are not digitised, or not in a way that is (easily) compatible or linkable with procurement information, some data-based procurement governance solutions will remain undeliverable. Some developments in digital procurement governance will thus be determined by progress in other policy areas. While there are initiatives to promote the availability of data in those settings (eg the EU’s Data Governance Act, the Guidelines on private sector data sharing, or the Open Data Directive), the voluntariness of many of those mechanisms raises important questions on the likely availability of data required to develop digital solutions.

Overall, there is no guarantee that the data required for the development of some (advanced) digital solutions will be available. A careful analysis of data requirements must thus be a point of concentration for any decision-maker from the very early stages of considering digitalisation projects.

Revised potential of selected digital technologies

Once (or rather, if) that major data hurdle is cleared, the possibilities realistically brought by the functionality of digital technologies need to be embedded in the procurement governance context, which results in the following feasibility boundary for the adoption of those technologies.

Robotic Process Automation (RPA)

RPA can reduce the administrative costs of managing pre-existing digitised and highly structured information in the context of entirely standardised and repetitive phases of the procurement process. RPA can reduce the time invested in gathering and cross-checking information and can thus serve as a basic element of decision-making support. However, RPA cannot increase the volume and type of information being considered (other than in cases where some available information was not being taken into consideration due to eg administrative capacity constraints), and it can hardly be successfully deployed in relation to open-ended or potentially contradictory information points. RPA will also not change or improve the processes themselves (unless they are redesigned with a view to deploying RPA).

This generates a clear feasibility boundary for RPA deployment, which will generally have as its purpose the optimisation of the time available to the procurement workforce to engage in information analysis rather than information sourcing and basic checks. While this can clearly bring operational advantages, it will hardly transform procurement governance.

Machine Learning (ML)

Developing ML solutions will pose major challenges, not only in relation to the underlying data architecture (as above), but also in relation to specific regulatory and governance requirements specific to public procurement. Where the operational management of procurement does not diverge from the equivalent function in the (less regulated) private sector, it will be possible to see the adoption or adaptation of similar ML solutions (eg in relation to category spend management). However, where there are regulatory constraints on the conduct of procurement, the development of ML solutions will be challenging.

For example, the need to ensure the openness and technical neutrality of procurement procedures will limit the possibilities of developing recommender systems other than in pre-procured closed lists or environments based on framework agreements or dynamic purchasing systems underpinned by electronic catalogues. Similarly, the intended use of the recommender system may raise significant legal issues concerning eg the exercise of discretion, which can limit their deployment to areas of information exchange or to merely suggestion-based tasks that could hardly replace current processes and procedures. Given the limited utility (or acceptability) of collective filtering recommender solutions (which is the predominant type in consumer-facing private sector uses, such as Netflix or Amazon), there are also constraints on the generality of content-based recommender systems for procurement applications, both at tenderer and at product/service level. This raises a further feasibility issue, as the functional need to develop a multiplicity of different recommenders not only reopens the issue of data sufficiency and adequacy, but also raises questions of (economic and technical) viability. Recommender systems would mostly only be susceptible of feasible adoption in highly centralised procurement settings. This could create a push for further procurement centralisation that is not neutral from a governance perspective, and that can certainly generate significant competition issues of a similar nature, but perhaps a different order of magnitude, than procurement centralisation in a less digitally advanced setting. This should be carefully considered, as the knock-on effects of the implementation of some ML solutions may only emerge down the line.

Similarly, the development and deployment of chatbots is constrained by specific regulatory issues, such as the need to deploy closed domain chatbots (as opposed to open domain chatbots, ie chatbots connected to the Internet, such as virtual assistants built into smartphones), so that the information they draw from can be controlled and quality assured in line with duties of good administration and other legal requirements concerning the provision of information within tender procedures. Chatbots are suited to types of high-volume information-based queries only. They would have limited applicability in relation to the specific characteristics of any given procurement procedure, as preparing the specific information to be used by the chatbot would be a challenge—with the added functionality of the chatbot being marginal. Chatbots could facilitate access to pre-existing and curated simple information, but their functionality would quickly hit a ceiling as the complexity of the information progressed. Chatbots would only be able to perform at a higher level if they were plugged to a knowledge base created as an expert system. But then, again, in that case their added functionality would be marginal. Ultimately, the practical space for the development of chatbots is limited to low added value information access tasks. Again, while this can clearly bring operational advantages, it will hardly transform procurement governance.

ML could facilitate the development and deployment of ‘advanced’ automated screens, or red flags, which could identify patterns of suspicious behaviour to then be assessed against the applicable rules (eg administrative and criminal law in case of corruption, or competition law, potentially including criminal law, in case of bid rigging) or policies (eg in relation to policy requirements to comply with specific targets in relation to a broad variety of goals). The trade off in this type of implementation is between the potential (accuracy) of the algorithmic screening and legal requirements on the explainability of decision-making (as discussed in detail here). Where the screens were not used solely for policy analysis, but acting on the red flag carried legal consequences (eg fines, or even criminal sanctions), the suitability of specific types of ML solutions (eg unsupervised learning solutions tantamount to a ‘black box’) would be doubtful, challenging, or altogether excluded. In any case, the development of ML screens capable of significantly improving over RPA-based automation of current screens is particularly dependent on the existence of adequate data, which is still proving an insurmountable hurdle in many an intended implementation (as above).

Distributed ledger technology (DLT) systems and smart contracts

Other procurement governance constraints limit the prospects of wholesale adoption of DLT (or blockchain) technologies, other than for relatively limited information management purposes. The public sector can hardly be expected to adopt DLT solutions that are not heavily permissioned, and that do not include significant safeguards to protect sensitive, commercially valuable, and other types of information that cannot be simply put in the public domain. This means that the public sector is only likely to implement highly centralised DLT solutions, with the public sector granting permissions to access and amend the relevant information. While this can still generate some (degrees of) tamper-evidence and permanence of the information management system, the net advantage is likely to be modest when compared to other types of secure information management systems. This can have an important bearing on decisions whether DLT solutions meet cost effectiveness or similar criteria of value for money controlling their piloting and deployment.

The value proposition of DLT solutions could increase if they enabled significant procurement automation through smart contracts. However, there are massive challenges in translating procurement procedures to a strict ‘if/when ... then’ programmable logic, smart contracts have limited capability that is not commensurate with the volumes and complexity of procurement information, and their development would only be justified in contexts where a given smart contract (ie specific programme) could be used in a high number of procurement procedures. This limits its scope of applicability to standardised and simple procurement exercises, which creates a functional overlap with some RPA solutions. Even in those settings, smart contracts would pose structural problems in terms of their irrevocability or automaticity. Moreover, they would be unable to generate off-chain effects, and this would not be easily sorted out even with the inclusion of internet of things (IoT) solutions or software oracles. This comes to largely restrict smart contracts to an information exchange mechanism, which does not significantly increase the value added by DLT plus smart contract solutions for procurement governance.

Conclusion

To conclude, there are significant and difficult to solve hurdles in generating an enabling data architecture, especially for digital technologies that require multiple sources of information or data points regarding several phases of the procurement process. Moreover, the realistic potential of most technologies primarily concerns the automation of tasks not involving data analysis of the exercise of procurement discretion, but rather relatively simple information cross-checks or exchanges. Linking back to the discussion in the earlier broader chapter (see here), the analysis above shows that a feasibility boundary emerges whereby the adoption of digital technologies for procurement governance can make contributions in relation to its information intensity, but not easily in relation to its information complexity, at least not in the short to medium term and not in the absence of a significant improvement of the required enabling data architecture. Perhaps in more direct terms, in the absence of a significant expansion in the collection and curation of data, digital technologies can allow procurement governance to do more of the same or to do it quicker, but it cannot enable better procurement driven by data insights, except in relatively narrow settings. Such settings are characterised by centralisation. Therefore, the deployment of digital technologies can be a further source of pressure towards procurement centralisation, which is not a neutral development in governance terms.

This feasibility boundary should be taken into account in considering potential use cases, as well as serve to moderate the expectations that come with the technologies and that can fuel ‘policy irresistibility’. Further, it should be stressed that those potential advantages do not come without their own additional complexities in terms of new governance risks (eg data and data systems integrity, cybersecurity, skills gaps) and requirements for their mitigation. These will be explored in the next stage of my research project.

Urgent: 'no eForms, no fun' -- getting serious about building a procurement data architecture in the EU

EU Member States only have about one year to make crucial decisions that will affect the procurement data architecture of the EU and the likelihood of successful adoption of digital technologies for procurement governance for years or decades to come’. Put like that, the relevance of the approaching deadline for the national implementation of new procurement eForms may grab more attention than the alternative statement that ‘in just about a year, new eForms will be mandatory for publication of procurement notices in TED’.

This latter more technical (obscure, and uninspiring?) understanding of the new eForms seems to have been dominating the approach to eForms implementation, which does not seem to have generally gained a high profile in domestic policy-making at EU Member State level despite the Publications Office’s efforts.

In this post, I reflect about the strategic importance of the eForms implementation for the digitalisation of procurement, the limited incentives for an ambitious implementation that stem from the voluntary approach of the most innovative aspects of the new eForms, and the opportunity that would be lost with a minimalistic approach to compliance with the new rules. I argue that it is urgent for EU Member States to get serious about building a procurement data architecture that facilitates the uptake of digital technologies for procurement governance across the EU, which requires an ambitious implementation of eForms beyond their minimum mandatory requirements.

eForms: some background

The EU is in the process of reforming the exchange of information about procurement procedures. This information exchange is mandated by the EU procurement rules, which regulate a variety of procurement notices with the two-fold objective of (i) fostering cross-border competition for public contracts and (ii) facilitating the oversight of procurement practices by the Member States, both in relation to the specific procedure (eg to enable access to remedies) and from a broad policy perspective (eg through the Single Market Scoreboard). In other words, this information exchange underpins the EU’s approach to procurement transparency, which mainly translates into publication of notices in the Tenders Electronic Daily (TED).

A 2019 Implementing Regulation established new standard forms for the publication of notices in the field of public procurement (eForms). The Implementing Regulation is accompanied by a detailed Implementation Handbook. The transition to eForms is about to hit a crucial milestone with the authorisation for their voluntary use from 14 November 2022, in parallel with the continued use of current forms. Following that, eForms will be mandatory and the only accepted format for publication of TED notices from 25 October 2023. There will thus have been a very long implementation period (of over four years), including an also lengthy (11-month) experimentation period about to start. This contrasts with previous revisions of the TED templates, which had given under six months’ notice (eg in 2015) or even just a 20-day implementation period (eg in 2011). This extended implementation period is reflective of the fact that the transition of eForms is not merely a matter of replacing a set of forms with another.

Indeed, eForms are not solely the new templates for the collection of information to be published in TED. eForms represent the EU’s open standard for publishing public procurement data — or, in other words, the ‘EU OCDS’ (which goes much beyond the OCDS mapping of the current TED forms). The importance of the implementation of a new data standard has been highlighted at strategic level, as this is the cornerstone of the EU’s efforts to improve the availability and quality of procurement data, which remain suboptimal (to say the least) despite continued efforts to improve the quality and (re)usability of TED data.

In that regard, the 2020 European strategy for data, emphasised that ‘Public procurement data are essential to improve transparency and accountability of public spending, fighting corruption and improving spending quality. Public procurement data is spread over several systems in the Member States, made available in different formats and is not easily possible to use for policy purposes in real-time. In many cases, the data quality needs to be improved.’ The European Commission now stresses how ‘eForms are at the core of the digital transformation of public procurement in the EU. Through the use of a common standard and terminology, they can significantly improve the quality and analysis of data’ (emphasis added).

It should thus be clear that the eForms implementation is not only about low level form-filling, but also (or primarily) about building a procurement data architecture that facilitates the uptake of digital technologies for procurement governance across the EU. Therefore, the implementation of eForms and the related data standard seeks to achieve two goals: first, to ensure the data quality (eg standardisation, machine-readability) required to facilitate its automated treatment for the purposes of publication of procurement notices mandated by EU law (ie their primary use); and, second, to build a data architecture that can facilitate the accumulation of big data so that advanced data analytics can be deployed by re-users of procurement data. This second(ary) goal is particularly relevant to our discussion. This requires some unpacking.

The importance of data for the deployment of digital technologies

It is generally accepted that quality (big) data is the primary requirement for the deployment of digital technologies to extract data-driven insights, as well as to automate menial back-office tasks. In a detailed analysis of these technologies, I stress the relevance of procurement data across technological solutions that could be deployed to improve procurement governance. In short, the outcome of robotic process automation (RPA) can only be as good as its sources of information, and adequate machine learning (ML) solutions can only be trained on high-quality big data—which thus conditions the possibility of developing recommender systems, chatbots, or algorithmic screens for procurement monitoring and oversight. Distributed Ledger Technology (DLT) systems (aka blockchain) can manage data, but cannot verify its content, accuracy, or reliability. Internet of Things (IoT) applications and software oracles can automatically capture data, which can alleviate some of the difficulties in generating an adequate data infrastructure. But this is only in relation with the observation of the ‘real world’ or in relation to digitally available information, which quality raises the same issues as other sources of data. In short, all digital technologies are data-centric or, more clearly, data-dependent.

Given the crucial relevance of data across digital technologies, it is hard to emphasise how any shortcomings in the enabling data architecture curtail the likelihood of successful adoption of digital technologies for procurement governance. With inadequate data, it may simply be impossible to develop digital solutions at all. And the development and adoption of digital solutions developed on poor or inadequate data can generate further problems—eg skewing decision-making on the basis of inadequately derived ‘data insights’. Ultimately, then, ensuring that adequate data is available to develop digital governance solutions is a challenging but unavoidable requirement in the process of procurement digitalisation. Success, or lack of it, in the creation of an enabling data architecture will determine the viability of the deployment of digital technologies more generally. From this perspective, the implementation of eForms gains clear strategic importance.

eForms Implementation: a flexible model

Implementing eForms is not an easy task. The migration towards eForms requires a complete redesign of information exchange mechanisms. eForms are designed around universal business language and involve the use of a much more structured information schema, compatible with the EU’s eProcurement Ontology, than the current TED forms. eForms are also meant to collect a larger amount of information than current TED forms, especially in relation to sub-units within a tender, such as lots, or in relation to framework agreements. eForms are meant to be flexible and regularly revised, in particular to add new fields to facilitate data capture in relation to specific EU-mandated requirements in procurement, such as in relation with the clean vehicles rules (with some changes already coming up, likely in November 2022).

From an informational point of view, the main constraint that remains despite the adoption of eForms is that their mandatory content is determined by existing obligations to report and publish tender-specific information under the current EU procurement rules, as well as to meet broader reporting requirements under international and EU law (eg the WTO GPA). This mandatory content is thus rather limited. Ultimately, eForms’ main concentration is on disseminating details of contract opportunities and capturing different aspects of decision-making by the contracting authorities. Given the process-orientedness and transactional focus of the procurement rules, most of the information to be mandatorily captured by the eForms concerns the scope and design of the tender procedure, some aspects concerning the award and formal implementation of the contract, as well as some minimal data points concerning its material outcome—primarily limited to the winning tender. As the Director-General of the Publications Office put it an eForms workshop yesterday, the new eForms will provide information on ‘who buys what, from whom and for what price’. While some of that information (especially in relation to the winning tender) will be reflective of broader market conditions, and while the accumulation of information across procurement procedures can progressively generate a broader view of (some of) the relevant markets, it is worth stressing that eForms are not designed as a tool of market intelligence.

Indeed, eForms do not capture the entirety of information generated by a procurement process and, as mentioned, their mandatory content is rather limited. eForms do include several voluntary or optional fields, and they could be adapted for some voluntary uses, such as in relation to detection of collusion in procurement, or in relation to the beneficial ownership of tenderers and subcontractors. Extensive use of voluntary fields and the development of additional fields and uses could contribute to generating data that enabled the deployment of digital technologies for the purposes of eg market intelligence, integrity checks, or other sorts of (policy-related) analysis. For example, there are voluntary fields in relation to green, social or innovation procurement, which could serve as the basis for data-driven insights into how to maximise the effects of such policy interventions. There are also voluntary fields concerning procurement challenges and disputes, which could facilitate a monitoring of eg areas requiring guidance or training. However, while the eForms are flexible, include voluntary fields, and the schema facilitates the development of additional fields, is it unclear that adequate incentives exist for adoption beyond their mandatory minimum content.

Implementation in two tiers

The fact that eForms are in part mandatory and in part voluntary will most likely result in two separate tiers of eForms implementation across the EU. Tier 1 will solely concern the collection and exchange of information mandated by EU law, that is the minimum mandatory eForm content. Tier 2 will concern the optional collection and exchange of a much larger volume of information concerning eg the entirety of tenders received, as well as qualitative information on eg specific policy goals embedded in a tender process. Of course, in the absence of coordination, a (large) degree of variation within Tier 2 can be expected. Tier 2 is potentially very important for (digital) procurement governance, but there is no guarantee that Member States will decide to implement eForms covering it.

One of the major obstacles to the broad adoption of a procurement data model so far, at least in the European Union, relates to the slow uptake of e-procurement (as discussed eg here). Without an underlying highly automated e-procurement system, the generation and capture of procurement data is a main challenge, as it is a labour-intensive process prone to input error. The entry into force of the eForms rules could serve as a further push for the completion of the transition to e-procurement—at least in relation to procurement covered by EU law (as below thresholds procurement is a voluntary potential use of eForms). However, it is also possible that low e-procurement uptake and generalised unsophisticated approaches to e-procurement (eg reduced automation) will limit the future functionality of eForms, with Member States that have so far lagged behind restricting the use of eForms to tier 1. Non life-cycle (automated) e-procurement systems may require manual inputs into the new eForms (or the databases from which they can draw information) and this implies that there is a direct cost to the implementation of each additional (voluntary) data field. Contracting authorities may not perceive the (potential) advantages of incurring those costs, or may more simply be constrained by their available budget. A collective action problem arises here, as the cost of adding more data to the eForms is to be shouldered by each public buyer, while the ensuing big data would potentially benefit everyone (especially as it will be published—although there are also possibilities to capture but not publish information that should be explored, at least to prevent excessive market transparency; but let’s park that issue for now) and perhaps in particular data re-users offering for pay added-value services.

In direct relation to this, and compounding the (dis)incentives problem, the possibility (or likelihood) of minimal implementation is compounded by the fact that, in many Member States, the operational adaptation to eForms does not directly concern public sector entities, but rather their service providers. e-procurement services providers compete for the provision of large volume, entirely standardised platform services, which are markets characterised by small operational margins. This creates incentives for a minimal adaptation of current e-sending systems and disincentives for the inclusion of added-value (data) services potentially unlikely to be used by public buyers. Some (or most) optional aspects of the eForm implementation will thus remain unused due to these market structure and dynamics, which does not clearly incentivise a race to the top (unless there is clear demand pull for it).

With some more nuance, it should be stressed that it is also possible that the adoption of eForms is uneven within a given jurisdiction where the voluntary character of parts of the eForm is kept (rather than made mandatory across the board through domestic legislation), with advanced procurement entities (eg central purchasing bodies, or large buyers) adopting tier 2 eForms, and (most) other public buyers limiting themselves to tier 1.

Ensuing data fragmentation

While this variety of approaches across the EU and within a Member State would not pose legal challenges, it would have a major effect on the utility of the eForms-generated data for the purposes of eg developing ML solutions, as the data would be fragmented, hardly representative of important aspects of procurement (markets), and could hardly be generalisable. The only consistent data would be that covered by tier 1 (ie mandatory and standardised implementation) and this would limit the potential use cases for the deployment of digital technologies—with some possibly limited to the procurement remit of the specific institutions with tier 2 implementations.

Relatedly, it should be stressed that, despite the effort to harmonise the underlying data architecture and link it to the Procurement Ontology, the Implementation Handbook makes clear that ‘eForms are not an “off the shelf” product that can be implemented only by IT developers. Instead, before developers start working, procurement policy decision-makers have to make a wide range of policy decisions on how eForms should be implemented’ in the different Member States.

This poses an additional challenge from the perspective of data quality (and consistency), as there are many fields to be tailored in the eForms implementation process that can result in significant discrepancies in the underlying understanding or methodology to determine them, in addition to the risk of potential further divergence stemming from the domestic interpretation of very similar requirements. This simply extends to the digital data world the current situation, eg in relation to diverging understandings of what is ‘recyclable’ or what is ‘social value’ and how to measure them. Whenever open-ended concepts are used, the data may be a poor source for comparative and aggregate analysis. Where there are other sources of standardisation or methodology, this issue may be minimised—eg in relation to the green public procurement criteria developed in the EU, if they are properly used. However, where there are no outside or additional sources of harmonisation, it seems that there is scope for quite a few difficult issues in trying to develop digital solutions on top of eForms data, except in relation to quantitative issues or in relation to information structured in clearly defined categories—which will mainly link back to the design of the procurement.

An opportunity about to be lost?

Overall, while the implementation of eForms could in theory build a big data architecture and facilitate the development of ML solutions, there are many challenges ahead and the generalised adoption of tier 2 eForms implementations seems unlikely, unless Member States make a positive decision in the process of national adoption. The importance of an ambitious tier 2 implementation of eForms should be assessed in light of its downstream importance for the potential deployment of digital technologies to extract data-driven insights and to automate parts of the procurement process. A minimalistic implementation of eForms would significantly constrain future possibilities of procurement digitalisation. Primarily in the specific jurisdiction, but also with spillover effects across the EU.

Therefore, a minimalistic eForms implementation approach would perpetuate (most of the) data deficit that prevents effective procurement digitalisation. It would be a short-sighted saving. Moreover, the effects of a ‘middle of the road’ approach should also be considered. A minimalistic implementation with a view to a more ambitious extension down the line could have short-term gains, but would delay the possibility of deploying digital technologies because the gains resulting from the data architecture are not immediate. In most cases, it will be necessary to wait for the accumulation of sufficiently big data. In some cases of infrequent procurement, missing data points will generate further time lags in the extraction of valuable insights. It is no exaggeration that every data point not captured carries an opportunity cost.

If Member States are serious about the digitalisation of public procurement, they will make the most of the coming year to develop tier 2 eForms implementations in their jurisdiction. They should also keep an eye on cross-border coordination. And the European Commission, both DG GROW and the Publications Office, would do well to put as much pressure on Member States as possible.