IEO evaluation of the Bank of England's use of data to support its policy objectives

Foreword from the Chair of Court

Data are critical to the work of a central bank. The Bank of England has long recognised this. Most recently, we defined ‘decision-making informed by the best available data, analysis and intelligence’ as a timeless enabler of our mission. And, to deliver on that, in 2021 we made ‘modernise the Bank’s ways of working’ a strategic priority for the years 2021 to 2024.

At the same time, the pace of innovation in data and analytics continues to increase, as the recent advances in the capabilities of large language models make clear. Every day, the Bank makes decisions that affect millions of the UK’s people and businesses – the Bank’s data and analytics capabilities support and power that decision-making process. It is therefore vital that we stand back and consider whether our data capabilities will remain fit for purpose in a rapidly changing world such that we deliver our timeless enabler – and ultimately our mission.

To that end, in October 2022 the Bank’s Court of Directors commissioned its Independent Evaluation Office (IEO) to conduct an evaluation of the Bank’s use of data to support its policy objectives.

The IEO’s report is clear. Overall, and despite many positive steps, looking forward the Bank must ensure that its data capabilities advance to match its ambition, especially as data and analytics best practice advances rapidly. While the Bank is not alone in facing this challenge, addressing it is strategically critical. The Bank will therefore need to set itself up for success by stepping up the pace of change, investing in its technology and people, and overcoming the barriers that will impede progress in a rapidly changing data and analytics landscape.

The IEO’s recommendations provide a foundation for doing so. They make 10 detailed recommendations, grouped into three broad themes: committing to a clear vision for data and analytics, supported by a comprehensive strategy and effective governance; overcoming the institutional, cultural and technological barriers faced by organisations as they move to new and emerging data-centric ways of working to keep in step with a changing world; and ensuring the Bank’s staff have the support and skills they need.

At our 22 September meeting, Court welcomed the Bank’s commitment in taking forward these recommendations. We will monitor their implementation as part of the IEO’s follow-up framework.

David Roberts, Chair of Court
October 2023

Overview

Data have long been at the heart of central banking. But the availability of data and the capabilities to draw insights from them have developed rapidly over the past decade or so. These changes, when coupled with expanding remits and global shocks, have created both opportunities and challenges for central banks. In that context, in October 2022 the Court of Directors (the Bank’s board) commissioned its IEO to conduct an evaluation of the Bank’s use of data to support its policy objectives.

In response to rapid change, central banks have innovated in multiple dimensions, from institutional structures to technological infrastructure, to new analytical methods and data sources. But, like many organisations, they have faced a range of challenges along the way, whether from legacy systems, established working practices or the practicalities of cloud migration.

The Bank of England has been on a similar journey to peer central banks. It made data a prominent feature of the 2014 ‘One Bank’ strategy and in the strategic priorities for the next three years that it set out in 2021. It created the role of Chief Data Officer supported by an expanding team. It has developed a sequence of data strategies (Box A), founded on credible problem statements. It has rolled out new analytical and storage capabilities with associated training for staff. And, supported by its emerging centres of excellence, it has done pioneering analysis with new techniques and data sources – with examples ranging from the use of machine learning to predict individual bank distress from regulatory returns and plausibility checking returns from regulated firms, through to tracking the macroeconomy at high frequency during the pandemic with unconventional measures of activity. ^{footnote [1] footnote [2]}

The Bank’s current data and analytics operating model devolves a large amount of responsibility for data and analytics to its business functions. The central Data and Analytics Transformation Directorate, led by the Chief Data Officer, is responsible for enabling those areas in their delivery of the central data strategy, which is half of one of the Bank’s seven strategic priorities for 2021–24. This model is currently in transition, partly in response to our evaluation and partly as a result of leadership change, with a new Chief Data Officer having started in role in April 2023.

Our evaluation took the overarching research question ‘Is decision-making to support the Bank’s policy objectives informed by the best available data, analysis and intelligence, and can it expect to be so in the future?’. We adapted this from the Bank’s ‘timeless enabler’ on data, which was set out alongside the 2021–24 strategic priorities. We broke that down into four detailed areas of investigation, covering broad questions of strategy and governance and three detailed areas of data management: acquisition and preparation; storage and access; and analysis and dissemination. Our evidence gathering involved: conducting around 175 interviews, across the Bank and a range of other organisations, including peer central banks and regulators; a staff survey, complemented by targeted focus groups; and consulting an advisory group of senior Bank staff and, separately, two external expert advisors.

With best practice in data and analytics advancing rapidly, the Bank will need to step up the pace of change – and associated investment – if it is to take advantage of new opportunities. While progress has been made using a devolved operating model, data capabilities are inconsistent across the organisation and in some cases the current approach is sub-optimal. To progress further, management will need to systematically address a range of foundational technology and process issues and build the capabilities necessary to enable the Bank to take advantage of new data tools so it can be in the best position to deliver on the Bank's mission. We make 10 detailed recommendations, which we grouped into three broad themes: committing to a clear vision, supported by a comprehensive strategy and effective governance; breaking down institutional, cultural and technological barriers to keep in step with a changing world; and ensuring staff have the support and skills they need.

Table A: Recommendations

In addition, the Bank is currently taking a range of actions to strengthen key foundational enablers. Successful execution of these wider initiatives will be crucial to fully delivering the Bank’s data ambitions: i) improvements to the approach to setting organisational strategy, prioritisation and budgeting; ii) tackling technology obsolescence; and iii) strengthening of the Bank’s central services and change management capabilities. The appointment of an Executive Director to lead a new Change and Planning function, the delivery of the Central Services 2025 programme, and future iterations of the Bank’s wider talent strategy will contribute across these areas of focus.^{footnote [3]}

The evaluation was conducted by a dedicated project team reporting directly to the Chair of Court.^{footnote [4]} The IEO team benefited from feedback and challenge from a Bank-wide senior-level advisory group (including Bank Governors). David Craig (founder and former CEO, Refinitiv, former Head of Data and Analytics, LSEG, and Executive Fellow, London Business School) and Kanishka Bhattacharya (Expert Partner, Bain & Company, and Adjunct Associate Professor, London Business School) provided support and independent challenge to the team and reviewed and endorsed the findings in this report.

This report was approved for publication by the Chair of Court at the September 2023 Court meeting.

Context for the evaluation

Data have long sat at the heart of central banking, including at the Bank of England. At least since the heyday of the gold standard, monetary policy makers have drawn on data to determine the stance of monetary policy. The Bank’s Quarterly Bulletin, the Bank’s flagship publication from its introduction in 1960 through to the 1993 launch of the Inflation Report, offered a detailed commentary on economic and financial developments, supported by an extensive range of statistics. These days, the Monetary Policy Report and Financial Stability Report continue to provide detailed coverage of the data and analytics that have gone into policy formulation. Supervisors now work with a broad range of regulatory returns, with the volume of supervisory data available having increased materially since the global financial crisis.

Nonetheless, the world of data has been changing rapidly and central banks have had to adapt to at least three continuing developments. Global events have presented new policy challenges, most notably the global financial crisis and Covid-19 pandemic. Central banks have often broadened their focus, with some taking on additional macroprudential, microprudential and supervisory roles. More broadly, technological change has led to both vastly more data being available to central banks and the development of powerful new tools to interpret them.

Central banks have had to innovate in response to these developments, although this has not always been easy. They have explored institutional change, including appointing chief data officers, adopting data strategies and experimenting with a range of structures for data governance and management. Many have migrated to cloud. In 2020, 80% of respondents to a BIS survey said that they were using ‘big data’ sources, up from 30% in 2015.^{footnote [5]} But, at the same time, they have struggled with legacy systems and migrating to new technology, including the unfamiliar IT arrangements that this can involve. New analytics and data practices have needed to fit into existing policy frameworks, including generating reliable results that can be interpreted by policymakers.

The Bank of England has been on the same journey as its major peers. It acquired new responsibilities following the global financial crisis, including: a statutory committee responsible for macroprudential policy; and microprudential policy for, and supervision of, banks, insurers and financial market infrastructures. It has had to adapt to major economic events, including the global financial crisis, the UK’s exit from the European Union, the Covid-19 pandemic and, most recently, Russia’s invasion of Ukraine. And over the past decade or so the Bank has acquired large amounts of new data including microdata on firms and households, regulatory data on banks and insurers and asset or even transaction-level data on key financial products and unconventional data from operational, administrative and digital sources.

The Bank made data a prominent feature of its 2014 ‘One Bank’ strategy and the strategic priorities for the next three years that it set out in 2021, with both strategies supported by credible assessments of the Bank’s analytics and data capabilities. In 2014 it created the role of Chief Data Officer, initially at a relatively junior level, before it was made an Executive Director role in 2019. Its data transformation efforts have been supported by an expanding team; from a small Division reporting to the Chief Information Officer it has grown to a full Directorate, bringing together data transformation with Divisions that were already part of the Monetary Policy area, covering advanced analytics and the collation and publication of statistical and regulatory data.

Over the past decade the Bank has taken significant steps to enhance data and analytics. It launched a rationalised and improved suite of analytical tools, which allowed it to focus support and training resources more effectively. It has expanded the range of storage options available, most notably introducing the Data and Analytics Platform to host large data sets. In 2014 it created an Advanced Analytics Division, to act as a centre of excellence. Together, these steps have facilitated increased uptake of programmatic analytical tools and have allowed further centres of excellence to emerge across the organisation.

As a result, the Bank has been able to conduct innovative data and analytics work. Notable examples include: embedding machine learning into the plausibility checking of returns from regulated firms; a predictive analysis tool to support selection of regulated firms for the Prudential Regulation Authority’s (PRA’s) Watchlist; tools to analyse insights from firms’ management information; and, during the pandemic, the rapid adoption of high-frequency indicators from unconventional sources to track economic developments.^{footnote [6]}

The Bank’s current operating model for data and analytics

The Bank’s current approach to delivering its mission of ‘promoting the good of the people of the United Kingdom’ is summarised in its strategic priorities, which support cross-Bank prioritisation. Data appear twice within the current strategic plan, both as a ‘timeless enabler’ of the Bank’s mission – ‘decision-making informed by the best available data, analysis and intelligence’ – and as Strategic Priority 7, ‘modernise the Bank’s ways of working’. Strategic Priority 7 has two sponsors at Executive Director level, the Chief Data Officer and the Chief Information Officer, as the majority of actions fall to the Data and Analytics Transformation (DAT) and Technology Directorates. The Bank’s data strategy is an integral part of Strategic Priority 7 and is led by the Chief Data Officer and DAT.

Figure 1: Data have a prominent role in the Bank’s strategic priorities, as both a ‘timeless enabler’ and part of Strategic Priority 7

Strategic priorities 2021–24

Footnotes

• Source: Bank of England Annual Report and Accounts 2021–22.

The 2021 data strategy had three broad strands. The first focused on ‘enabling’, consisting primarily of expanding and refining existing offerings around data collection, storage, support and training. The second consisted of targeted improvements in business outcomes, such as the work in the PRA on ‘RegTech’. The third was the Transforming Data Collection programme, run jointly with the Financial Conduct Authority (FCA), which aimed to ensure ‘regulators get the data they need to fulfil their mission, at the lowest possible cost to industry’.^{footnote [7]}

DAT, under the ultimate oversight of the Deputy Governor for Monetary Policy and (since 2022) the Chief Operating Officer, is a central function with four roles, all relevant to delivering the data strategy:

1. Being the Bank’s centre of expertise for the compilation, dissemination and publication of regulatory and statistical data, a role that sits with the Data and Statistics Division.
2. Using advanced analytical techniques and data science to help the Bank excel as a leading research and analytics institution, led by the Advanced Analytics Division.
3. Providing the tools, platforms, services, training and specialist support to enable data and analytics across the Bank. This is done by the Data Strategy Implementation Division (DSID).
4. Developing and promoting innovation in data and analytics tools and technologies to support analysis, policy and decision making across the Bank, a responsibility of the whole DAT Directorate.

DSID plays a key role in several components of the Bank’s data strategy. They provide key support services, including: management of the Data and Analytics Platform; a Data and Analytics email-based helpdesk; provision of training and guidance to support analytical and data management best practice; and ownership of the Data Catalogue, which is intended to support data governance and act as a repository of key data sources in use across the Bank. DSID also partners with business areas to help them to deliver their priority outcomes through better use of data and analytics. That is supported by the Data and Analytics Business Partners team, which provides a formal link between business areas and experts in the data function, and the Analytics Enablement Hub (AEH), which works with business areas on targeted projects. AEH also provides training and guidance to support the use of a range of modern analytical tools (eg R, Python), strategically selected for the Bank’s use cases. DSID and the PRA are also working with the FCA and industry to transform data collection from the UK financial sector and have recently established a cross-Bank taskforce to more effectively combine expertise.^{footnote [8]}

More broadly, many of DAT’s functions are intended to enable business areas to deliver the central data strategy in line with business-area priorities. The management of data assets and many data transformation initiatives sit with individual business areas. For example, after DAT processes and plausibility-checks collections, regulatory data on banks are held and managed by the PRA, while Monetary Analysis manages a database of macroeconomic time series. Business areas across the Bank have begun to experiment with different approaches to strengthening data science skills, whether through training or hiring. Many areas have developed their own centres of analytical excellence specialising in data science techniques. For example, the PRA ‘RegTech’ team has developed natural language processing tools and acts as a co-ordinating ‘hub’ for other data specialist teams working in PRA supervision and policymaking areas. Similarly, the Financial Markets Infrastructure Directorate’s Data team has developed expertise in techniques required to analyse large transaction-level markets data sets. With extensive autonomy, some areas have well-developed data strategies focused on business area priorities – in addition to the transforming data collection agenda, the PRA’s data strategy covers how regulatory data are accessed, the development of dashboards for supervisors, as well as coaching and digital skills – while others have more minimal arrangements. This dispersion of responsibilities was mirrored in oversight, which at the outset of this evaluation was spread across a large number of data or (for investment) programme boards, as well as strategic committees like the Executive Policy Co-ordination Committee, the Executive Operational Co-ordination Committee and the Operations and Investment Committee.

Overall, the current operating model for data and analytics is in transition, partly in response to our evaluation and partly as a result of leadership change.^{footnote [9]} More broadly, important enablers of the Bank’s data activities are undergoing change: a new plan is being drawn up to tackle technology obsolescence, alongside a cloud migration strategy; central services are being upgraded through the Central Services 2025 (CS2025) programme; and the Bank’s change capabilities are being strengthened by the appointment of an Executive Director for Change and Planning. Successful execution of these wider initiatives will be crucial to fully delivering the Bank’s data ambitions.

Approach to our evaluation

We took the overarching research question ‘Is decision-making to support the Bank’s policy objectives informed by the best available data, analysis and intelligence, and can it expect to be so in the future?’. We adapted this from the Bank’s ‘timeless enabler’ on data, which was set out alongside the 2021–24 strategic priorities (Figure 1). We broke that down into four evaluation criteria, each underpinned by a set of benchmarks:

1. Are data roles, responsibilities and decision-making criteria clearly understood?
2. Does the Bank identify and acquire the right data to meet its policy objectives, efficiently and effectively preparing them for analysis?
3. Does the Bank have the right systems and structures, centrally and locally, to ensure data are appropriately stored and are accessible across the organisation?
4. Does the Bank have the right technology and skills in place to analyse data effectively and efficiently to support policymaking?

We conducted an extensive evidence-gathering exercise, drawing on three main sources:

• Interviews: around 175, conducted across the Bank and externally. Within the Bank, we spoke to every business area and to the central support functions. External engagements included peer policy institutions, academics, think tanks and a range of private sector bodies.
• Staff engagement: we conducted a survey open to all staff and received 311 responses, well-distributed across the organisation. We followed that up with five targeted focus groups, covering: researchers; data scientists; data owners and stewards; analysts and economists; and supervisors.
• Advisors: we regularly consulted both an internal panel of senior Bank staff, consisting of 15 members drawn from across the organisation, and two external advisors with significant experience in the field of data and analytics.

Themes and recommendations

Theme 1: Agree a clear vision for data and analytics, supported by a comprehensive strategy and effective governance

The Bank has consistently set itself a high level of ambition on data and analytics over the past decade and the effective use of data appears prominently in its current strategic plan. The Bank’s ambitions have been grounded in convincing assessments of the Bank’s data and analytics capabilities. However, progress has been inconsistent across the organisation with variation in the degree to which ambition has been matched by resources, plans and management oversight. This highlights the challenges inherent in a more devolved data operating model, especially during times of significant transformation in the external data and analytics landscape. Prompted by emerging findings from this evaluation and the arrival of a new Chief Data Officer in April, this area has seen the most change since our evaluation began, which offers a strong foundation for addressing our Theme 1 recommendations around vision, resources, strategy and governance.

Recommendation 1: Agree and champion a vision for data use, matching funding to ambition

Ensuring consensus around the Bank’s vision for data will be vital, because delivering it will require funding that consistently matches ambition and more concerted championing. The broad support that we heard from the Bank’s Executive for the current level of ambition suggests the Bank’s existing timeless enabler could continue to serve as a benchmark for the Bank’s ambitions. But the renewed strategic conversation currently occurring is needed to restate the case and galvanise support. A renewed vision will need to be met with plans and budgets that consistently match the level of ambition, even if the Bank faces competing priorities. The Bank will also need to review how it tracks spending on data and analytics and make sure funding remains consistent with plans. The Bank’s senior leaders will need to build on the efforts of the new Chief Data Officer to ensure the centrality of effective data use to the Bank’s mission is understood both inside and outside the Bank. The PRA has piloted a data skills coaching programme for senior leaders which, if extended Bank-wide, would help support championing efforts.

Recommendation 2: Collaboratively design deliverable Bank-wide and local business area data strategies to meet measurable business outcomes

With an agreed vision and commitment to funding (Recommendation 1), the Bank’s central functions and business areas will need to work together to refresh the Bank’s data strategy. This collaborative approach will require common understanding of the ‘art of the possible’ and of the Bank-wide and individual business areas’ target operating models, encompassing data, technology and skills. These inputs would support the development of enterprise, data and technology architectures describing their current and target states.^{footnote [15]} As the Bank-wide strategy is refreshed, business areas will need to develop local strategies that are embedded within that and champion them. Nor can the data strategy stand alone – it will need to be consistent with, and perhaps developed alongside, supporting strategies for technology and people. In order to build trust, it is important that stakeholders can see measurable progress. That would be aided by: quantified and planned expected benefits ex-ante; mechanisms to track progress; and evaluated outcomes ex-post.

Recommendation 3: Ensure governance structures can support the agreement, co-ordination and monitoring of data transformation, with clear accountability for delivery

The newly established Data and Analytics Board fills an executive-level gap identified in the early stages of the evaluation and will need to ensure its membership, terms of reference and supporting structures allow the refreshed data strategy (Recommendation 2) to be developed, co-ordinated and monitored during implementation.^{footnote [16]} The Board is a promising development; as it becomes established, its co-chairs – the Chief Data Officer and Chief Information Officer – and membership will want to ensure that it: remains a forum that effectively convenes central functions and business areas; forges consensus on Bank-wide data and analytics priorities, including the details of the strategy, such as benefits, deliverables and timescales; ensures all the Bank’s data and analytics transformation activities are consistent with the organisational strategy; keeps abreast of the latest technological developments (Recommendation 7); monitors progress; and holds its members to account for delivery of the strategy and key dependencies. As part of this, it will need to review what supporting structures it requires, including: subcommittees (for example, to ensure data and technology initiatives are consistent with agreed strategies and architectures); monitoring tools (for example, executive scorecards); and accountability devices (such as published documents and member objectives).^{footnote [17]} Our external advisors recommended that governance structures should evolve over time as data maturity increases, suggesting the Bank requires stronger central direction at the early stages of the journey before it can move to a more decentralised approach.

Theme 2: Break down institutional, cultural and technological barriers to keep the Bank's data and analytical practices in step with a changing world

The data analysis produced by Bank staff is highly regarded. Policy committees praise the staff’s outputs and its centres of excellence conduct innovative analysis with data. The Bank also continues to rank among the most transparent of central banks. But, not unlike other specialist organisations, the Bank has wrestled in recent years with a range of barriers to making the most of the large amounts of data it acquires. Notwithstanding progress made in recent years, difficulties remain. As with other large, specialist organisations the Bank has found it difficult to combine different types of expertise and to collaborate effectively across business areas, and between business areas and central functions, with local areas preferring to develop their own solutions. A perhaps understandable risk aversion has contributed to: a relatively constrained approach to data sharing, both internally and externally, beyond that necessary due to statutory prohibitions; and a nervousness around adopting new technologies, notably cloud solutions, or working practices. Our recommendations focus on breaking down these institutional, cultural and technical barriers, through: strengthening collaboration, particularly through the use of partner roles linking central functions and local areas; articulating principles to guide greater sharing of data and analytics, internally and externally; strengthening the technological foundations of the Bank’s data and analytics, particularly by migrating to cloud; and finding ways to draw more extensively on external technical expertise. Continued development of a unified data and technology architecture, supported by improved governance structures, will also be crucial.

Recommendation 4: Improve day-to-day collaboration across the business on data and analytics

The Bank should consider structures that could strengthen collaboration and more effectively combine expertise. This applies across business areas, between business areas and central functions, and between different professions (particularly data, change management and technology specialists). Its business partnerships programme – if fully implemented – offers a promising start, focused on building collaboration between business areas and the data function. This could play a crucial role in helping: central functions understand desired business outcomes; business areas understand what is possible; and the Bank in ensuring that data projects can be incorporated within Bank-wide data and technology architectures. The Bank will need to monitor progress on business partnerships, including a balanced assessment of how business areas have engaged with it, perhaps at the Data and Analytics Board. Further action may be needed to reinforce cultural change. The Bank should review lessons from the partnerships and the newly established cross-Bank AI and data collection taskforces when considering the most effective ways to bring together people with common interests and expertise. More broadly, we came across interesting models at peer central banks, including those focused on combining data and technology experts with business area specialists to produce repeatable products. There are also a range of models (eg guilds, tribes) and delivery frameworks (eg the Data Management Capability Assessment Model) established in the data management profession for combining expertise.^{footnote [18]} This will have wider implications, since CS2025 also proposes partnership models for the Bank’s Technology and People Directorates.

Recommendation 5: Agree the approach to sharing data and analytics inside and outside the Bank

Greater openness around data and analytics, internally and externally, would foster greater scrutiny and challenge, helping the Bank gain additional insights and keep up with a rapidly evolving world – the Bank produces large amounts of extremely valuable data and analysis, but much of it is only easily available to subsets of Bank staff. The Bank could adopt a presumption of sharing, but would need to further consider the implications and appropriate guardrails. The Bank has important legal obligations and constraints when it comes to sharing data but, within those, it should articulate and highlight a set of principles for disseminating data and analytics, internally and externally. Guiding principles would allow the Bank to consider how to safely open up wider access to data and analysis and might facilitate external collaboration. This is consistent with the IEO’s Research Evaluation, which recommended that the Bank needed to support access to data for external co-authors to broaden the expertise and perspectives that the Bank can draw on. We note that some other organisations that face similar binding restrictions have found means to facilitate access to internal data, for example the ONS’ Secure Research Service.^{footnote [19]} Moving to cloud (Recommendation 6) could help overcome technical barriers to sharing.

Recommendation 6: Narrow the gap with modern data and analytics practices, with the most impactful initial step being a phased migration to cloud

The Bank will need to develop an achievable plan to modernise most of its data and analytics practices, to avoid falling further behind a rapidly evolving frontier. A reliance on inefficient manual processes generates risks and staff frustration. A move to cloud would be the most powerful technological step the Bank could take to close this gap, supported by common standards (Recommendation 8) and upskilling (Recommendation 10). While a move to cloud is no panacea and brings new challenges, we have seen that peers and other organisations have been able to unlock capabilities through the use of modern tools and provide increased computing capacity. Cloud offers a range of enhanced capabilities that could improve data collection, discoverability (for example, automation of data cataloguing) and analysis, and allow some embedding of modern data management practices (Recommendation 8). This could include being more open to buying in ‘off the shelf’ tools than is currently the case. Peers’ experience suggests cloud migration might also help with other issues such as efficient use of licenses, facilitating access for external experts (Recommendation 5) and obsolescence, with cloud providers keeping tools up to date. As a ‘late adopter’ of cloud, the Bank can learn lessons from other organisations’ experience of making the transition.

Recommendation 7: Systematically monitor and experiment with new approaches and technology for data and analytics

The Bank will need to consider the role of its emerging centres of excellence in raising data maturity; centres of excellence like Advanced Analytics and local business area data science hubs have brought deep expertise into the Bank and, through collaboration, have helped develop others’ skills. The Bank should ask whether there is more it can learn from others’ experience of innovation hubs and how their role should evolve as maturity rises. Further mechanisms might include an external advisory board made up of experienced experts to provide challenge to the Data and Analytics Board; the Bank has used such arrangements effectively in a number of areas and the Monetary Authority of Singapore have used it for data and technology.^{footnote [20] footnote [21]} Coaching senior staff on data could be expanded Bank-wide (Recommendation 1) and draw on lessons from elsewhere, for example reverse-mentoring, where talented analysts and data specialists coach senior staff on the art of the possible.

Theme 3: Ensure staff have the support and skills they need to work effectively with data

The Bank has long understood the importance of enabling individuals to work effectively with data. The 2014 data strategy focused on enabling business areas and ‘encouraging individuals to get and make better use of data within their roles’ is a key outcome in the Bank’s 2021 data strategy, with DAT identifying data, tools, platforms, training and other support services as important enablers to facilitate that outcome. While the Bank has introduced data specialist roles and built up its training offering, developing data skills and embedding new, more modern approaches takes time. Recent technological developments have increased analytical capabilities and offer the potential to automate more processes, freeing staff time to focus more on higher value-added analysis. This may have implications for the skills the Bank wants to develop and how staff best work with each other. We have identified three recommendations to help the Bank make progress by ensuring staff have the support and skills they need: embed common standards to make data and analysis easily discoverable and repeatable; provide staff with accessible support and guidance across the data lifecycle; and develop a comprehensive data skills strategy encompassing hiring, training, retention and skills mix.

Recommendation 8: Embed common standards to make data and analysis easily discoverable and repeatable

The Bank’s data and analytics guidance needs to be comprehensive, easier to find and better incentivised. The Bank has recently refreshed its guidance on data management, which will be launched this year. It would benefit from doing the same for analytical common standards, not least to ensure that greater use of programmatic analytical tools is suitably resilient. When these are established, the Bank should consider how to raise engagement and adherence. Training is one option, and is standard at induction in professional services firms, consultancies and banks. Other options include greater leadership championing, recognition of good practice in performance reviews, mandatory training, audits and individuals attesting to compliance with the standards through the ‘Our Code’ process.^{footnote [22]} In the past, the Bank has used the performance management process to influence behaviour, or enforced compliance top-down. Best practice could be built into future data and analytics platforms as part of cloud migration (Recommendation 6), which could include automation of data cataloguing which our advisors indicated was widely adopted in data-mature private sector firms.

Recommendation 9: Provide staff with the easily accessible support and guidance they need across the data lifecycle

The Bank could go further in joining up existing data and analytics support. This would materially enhance the support accessible to staff, who currently struggle to navigate the fragmented range of services available. A single front door, clearly visible on the desktop or intranet front page and spanning the data lifecycle, could effectively triage data and analytics requests, directing them to existing resources or escalating to deeper support, as appropriate. The Bank already operates elements of this approach in interactions between hubs, helpdesks and users – joining it up would significantly ease the experience of staff. The Bank should define how this service would relate to that offered by business partners and how those business partners are resourced to meet any increased demand.

Recommendation 10: Develop a comprehensive data skills strategy encompassing hiring, training, retention and role mix

The Bank should develop a comprehensive data skills strategy, embedded within wider initiatives around talent and skills. Such a strategy will need to consider the career proposition for data specialists, including both data scientists and the technology specialists that support data work. It will need to articulate where skills should be developed inside the Bank, across all levels of seniority and supported by a training offer, or hired into the organisation. It will need to be informed by a clearly defined operating model (Recommendation 2) that articulates the role of data specialists relative to other skillsets in the organisation, including analysts and technology specialists. This should be linked to the People Directorate’s wider talent strategy. The Bank can draw lessons from the mix of approaches business areas have adopted when experimenting with building area-wide data skills.