Developing And Institutionalising The Western Cape Government Of South Africa’s Master Plan For Province-Wide Data Governance: First Lessons Learned.

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The Master Plan for Province-wide Data Governance for the Western Cape Government of South Africa is a strategic response to the complexity in which Government operates within the area of managing disparate data and application systems. The Plan encompasses the vision for Business Intelligence; an Implementation Roadmap, Solutions Architecture and a Resource Plan. The institutionalisation is done through a three (3) stage approach; being conceptualisation and design, pre-implementation and implementation. The main value is that although it is centrally driven by the Department of the Premier, the governance arrangements takes a collaborative approach. The establishment of a Data Competency Centre is key to manage the availably, quality, sharing and integrity of data as a strategic asset. The session will map out the first lessons learned of the journey embarked upon by the Western Cape Government, to institutionalise data governance for improved performance, efficiency and desired service delivery impact.

Zeenat Ishmail, Chief Director: Strategic Management Information, Western Cape Government; [email protected]
Zeenat will be speaking at the Data Governance Conference & Master Data Management Summit 14-17 May 2018, London; on the subject Developing and Institutionalising Data Governance – a Case Study at the Western Cape Government of South Africa

The Western Cape Government (WCG) realised the strategic, operational and public value of data governance; and to this effect, established a strategic programme on Province-wide Data Governance (PWDG1).

In this Paper, the Chief Directorate: Strategic Management Information, on behalf of the WCG, provides a first update on the development and institutionalisation of PWDG within the WCG. This paper is in line with the First Annual Update: 2017/18 on the development and institutionalisation over the period 2015-2018; and the institutional approach followed that constitutes the Master Plan on PWDG. This first update covers the period April 2015 to March 2018.

Although a number of progress reports have to date been presented to various reporting structures in WCG, this publication attempts to bring together a coherent view of the context and progress. In doing so, it also narrates the WCG as a case study in the area of PWDG.

This Paper constitutes an integral part of the Master Plan of PWDG. It provides the content for a comparative review against similar global initiatives on matters such as Data Quality, Master Data Management (MDM) and Business Intelligence (BI).

Introduction

The development and institutionalisation of PWDG is an initiative of the Department of the Premier (DotP), WCG. DotP is the custodian of the PWDG Framework that was endorsed by Cabinet on 9 November 2015; and the Master Plan on PWDG that was approved by Cabinet on 6 December 2017.

PWDG refers to the overall management of the availability, usability, integrity and security of data assets within the WCG and has identified four (4) high-level benefit areas.

These are:

• Improved data management;

• Common data quality standards;

• Collaboration with partners; and

• Efficiency gains.

For the WCG this means that provincial departments will have easy access to relevant and accurate data; and Heads of Department will have the benefits of using data and information to make evidencebased decisions, whether it is for policy development, planning or service delivery. Correct service delivery decisions ultimately contribute to improved citizen experiences and better socio-economic outcomes.

The programme on PWDG has been established to incrementally deliver these benefits. This cannot be achieved in isolation, but is integrally interdependent on other key initiatives within the WCG, specifically the Digital Government Strategy (DGS) and the Departmental e-Vision Strategies.

In December 2017, DotP collaborated with six (6) key provincial departments and agreed to establish a Data Competency Centre (DCC2). It serves as a vital governance mechanism to facilitate the coordination and institutionalisation of PWDG; and evolves from the initial Interim Business Intelligence Competency Centre (BICC2)

Journey towards Province-wide Data Governance

This brief diagnostic provides a snapshot of how the journey started.

In the WCG, Performance Management and BI complement each other in the generation of strategic data and information. The role of Monitoring and Evaluation (M&E), and the broader Performance Management has been evolving and expanded beyond reporting to the Executive. The public are also expecting better services and information on government service delivery processes

Within this context, PWDG was initiated as a response to the evolvement of the Resultsbased Monitoring and Evaluation (RBM&E3) System, its related seven-phased model; and the need to have quality indicators and data sources for measurement to improve evidence-based decision-making.

The evolvement and the associated risks were managed through the risk management processes; and the subsequent consultative audit process that produced an Internal Audit Report on a Data Quality Framework for Strategic Management Information, which was electronically signed on 15 October 2015. Many of the required actions within the said Internal Report have been incorporated in the Master Plan on PWDG and the related Project Plan.

A diagnostic review on Province-wide Monitoring and Evaluation provided evidence that there is a demand for good quality indicator and data management systems. In line with the policy frameworks on Government-wide M&E, and role of the Offices of the Premier regarding M&E, the Chief Directorate: Strategic Management Information in the DotP is the custodian for provincial M&E. In this regard, the DotP has the mandate to lead data governance within the context of M&E. There is however a lack of a policy mandate for the Department within the broader context of PWDG. Notwithstanding the challenges on the discharge of mandates, considerable work has been done in the area of PWDG.

The journey on PWDG to date, is documented and articulated in accordance to the calendar years and processes achieved during the said year. In terms of the Master Plan, the period beyond November 2016 was about conceptualising and writing up of the Master Plan, the period beyond September 2017 was about preimplementation and implementation to commence 1 April 2018. A number of consultative engagements were held with the Executive during its development.

Context of Province-wide Data Governance

Situational Analysis

PWDG is key to address the situation surrounding the following:

• Government accounts for the effectiveness of its planning and budgetary systems, as they relate to relevant policy outcomes and budget and resource allocation for service delivery impact.
• Improving the provincial government business operations by putting BI capabilities in place, in producing information from multiple sources of data and linkages between sources, including administrative data sets across the spheres of government, and data sets from surveys done by other external parties.
• Provincial departments to have easy access to relevant and accurate information; and
• HODs benefit from using data and information to make evidence-based decisions.

Within this context, the WCG required a focused and standardised response to data governance issues, such as the lack of coherence in the use and production of data and information across government departments.

The WCG is to note that the public sector across a number of countries, are showing an interest in making better use of data for evidence-based decision-making.

The WCG needed to be responsive to the emerging data governance issues that improve data, statistics and information as a public good; and for the WCG data to be relevant and reliable for better planning on service delivery, monitoring, budgeting, and resource allocation.

WCG response to Data Governance

The direction of the WCG is to improve the use and production of quality data and information for better development outcomes and service delivery. This is ultimately done to achieve an outcome at an institutional level that is geared towards “coherence in data use and production across departments.”

In institutionalising PWDG, the DotP is pursuing four (4) ambitious missions simultaneously. This means the DotP wants to:

• Produce reliable and accurate data and information across WCG;
• Partner in data and information products and systems
• Ensure accountably and transparency of data and information systems; and
• Keep stakeholders informed.

The DotP in collaboration with key departments are to implement these through four (4) equally important and interdependent streams. These are aligned to data governance focus areas, as well as the audit quality domains used within the consultative audit process.

Stream 1: Effective coordination of data systems across sectors in terms of a logical data warehouse;

Stream 2: Better policy design in terms of strategic frameworks/plans, standards for data management, privacy, compliance and security;

Stream 3: Effective implementation in terms of consistent application of indicators, data quality and metadata standards; and

Stream 4: Advocacy (Information, Communication, and Learning).

The Master Plan for Province-wide Data Governance

What the Master Plan communicate

A contextual summary of each of the four (4) chapters of the Master Plan for institutionalising PWDG is outlined:

Chapter 1

Chapter 1 contextualises the business need, defines BI and its vision, taking into account the challenges of government to account for effectiveness of its planning and budgetary systems; as it relates to the relevant policy outcomes, budget and resource allocation.

It outlines the methodologies and processes for BI; and the gaps in terms of the data value chain. It also outlines the principles for a Province-wide BI solution, for example, data ownership and common standards. It sketches the direction through five (5) building blocks. These are strategy, data coordination and governance, leadership (people and culture), technology and the financing of the business goal.

Chapter 2

This chapter highlights the challenges for improved data and information; and documents a roadmap as a response to these challenges. It emphasises the importance of being responsive to the emerging responsibility that data, statistics and information as a public good have to be relevant and reliable.

The chapter contextualises the vision, destination, definition, objectives and levers as outcomes for PWDG. It also provides the four (4) missions to be undertaken over the years, and the benefits to be achieved across departments.

The strategic direction it takes is a four (4) stream approach with steps and actions. This is ultimately about architecture and integration, privacy, compliance and security, data quality and management alignment.

The chapter further highlights the structures already established as a start to kick off the coordination across the four (4) streams.

Chapter 3

Chapter 3 contextualises the business need, defines Enterprise Architecture (EA); and its vision, taking into account the business capability that is usually delivered as a technology platform. It outlines the EA methodology and processes inclusive of the gaps. It also outlines the principles for an EA that overarches improved data management. It then sketches the direction in terms of strategy, focusing on data governance and data architecture. The implementation approach is identified to be delivered amongst others, a ‘Fit for Purpose’ BI and MDM.

Chapter 4

This chapter provides the motivation for the resource plan based on previous work done, a province-wide diagnostic; and the eventual beneficiaries. It makes reference and linkages to all chapters. It further translates the five (5) building blocks articulated into investment themes with linkages to the four (4) missions and benefits. The projected project funding is aligned to the five (5) investment themes. The work required is listed within each investment theme.

Vision, Direction, Objectives, Outcomes and dependencies

The vision or strategic intent is “better data information, for better decisions, known to all

The direction of the WCG is to improve the use and production of quality data and information for better development outcomes and service delivery.

The key objective for PWDG is for WCG to be a data-driven organisation. This means:

• To drive business performance through better evidence and decisions; consequently, improving the use and application of BI leads to improved government operations; and performance for service delivery improvement.

• To improve the use and production of quality data and information for better development outcomes and service delivery.

The outcome, at an institutional level, is geared towards “coherence in data use and production across departments.

The basic interdependency elements are highlighted in a dependency model for the establishment of a LDW.

Approach to developing and landing the Master Plan

A three (3) stage approach is adopted to ensure that the Master Plan for institutionalising PWDG is understood and finds resonance across all departments. A high-level approach is also adopted in reporting progress to e-PTM. These stages and associated work are outlined as follows:

Stage 1: Conceptualisation and Design
In this stage the four (4) chapters of the Master Plan were compiled. This means that the vision was articulated; and the approach to the roadmap, as well as the solutions for the architecture, were defined. This stage also included the compilation of a resource plan and number of stakeholder engagements across departments.

Stage 2: Pre-implementation.
This stage is about determining baseline assessments, identifying best practices and governance arrangements. This includes the identification and use of departmental data initiatives to experiment and pilot methodologies and tools; a Regulatory Impact Assessment (RIA4) and establishing the culture and capacity to analyse, learn and use evidence for decision making.

Stage 3: Implementation.
This stage is about implementing the project plan within the WCG. This stage is inclusive of the continuous monitoring of the identified work breakdown structure, and ultimately evaluating the roadmap towards the achievement of the institutional outcome.

Summary of the Status of Implementation

The following section provided a synopsis on the progress and achievements in the PWDG project.

Stage 1: Conceptualisation and Planning
In terms of compiling the four (4) chapters for the PWDG master plan, all four (4) chapters have been finalised and communicated. A common vision for PWDG has been articulated and consulted with relevant departments and HODs. Since the inception there have been 53 different discussions on PWDG. A roadmap to establish project governance has also been developed and disseminated to detail PWDG project deliverables and timelines. Appropriate social, economic and governance structures were developed and liaised with to communicate progress on various PWDG initiatives.

Stage 2: Pre-implementation
This stage includes doing a readiness assessment, identifying best practices, conducting stakeholder engagements and governance arrangements to institutionalise PWDG. Two (2) data governance stakeholder engagements were held on 5 April and 20 November 2017 respectively. The objective of the first session was to get buy-in from key departmental representatives on the PWDG vision and objectives. The second session provided data governance champions with the opportunity to discuss and present on key deliverables such as the APM, MDM and PWDG governance structures.

To assess the readiness of departments to implement PWDG, a tool was developed to measure BI maturity across WCG departments. A BI value assessment was conducted to measure the perceived value of BI systems using a controlled sample.

A RIA is also in the process of being finalised to amended appropriate provincial and national legislation, on data and information governance.

For identifying the best PWDG practices, a first-level of analysis on key departmental data sources was conducted. This analysis provided key insights into the critical data sources which departments utilise. The results of the first-level analysis were also aligned to the APM; to analyse the percentage of key data sources which could be sourced electronically.

To improve on the integration and quality of these data sources; and promote the culture to analyse, learn and use evidence for decision making, MDM was identified as a best practice methodology.

To test this methodology, three (3) case studies were identified. These case studies were: Pass rate and teenage pregnancy; testing the long term effectiveness of early childhood development centres; and sexual assault/rape victims not followed up by social workers.

Additionally, a standard classification to serve as minimum norms and standards in the area of indicators were developed for various international, national and subnational policy priorities such as the Sustainable Development Goals (SDGs), the National Development Plan (NDP), the Provincial Strategic Plan (PSP) and the Whole of Society Approach (WoSA5).

The institutionalisation of appropriate PWDG governance arrangements is critical to ensuring that coherent data are being used to inform decision making.

The concepts thus for the LDW and a DCC for BI related services, governance and competencies, were recommended to deliver BI maturity transversally across WCG departments; thereby institutionalising PWDG.

Governance Arrangement through Collaboration

To take the institutional outcomes and work stream design objectives forward within departments, a cross-government approach was developed and institutionalised to ensure that coherent data are used to inform departmental plans and budgets.

Figure 1 shows the configurations of the crossgovernment approach, which includes multiple data champions from key WCG departments leading specific sectors.

There are essentially four (4) structures to ensure collaborative governance. This includes Provincial Top Management dealing with ICT matters (e-PTM), the Transversal Applications Steering Committee (TAPSC) that coordinates provincial ICT matters and the DCC, which is the coordinating steering committee for Data Governance; and also includes the oversight of a clearing house function and advanced data analytics.

The four (4) streams of the data governance project report to the DCC on progress, challenges and risks associated with institutionalising data governance. The four (4) streams are managed by a programmeand a project manager.

The technical committees per sector (or data user and producer forums) deal with the operational matters relating to data governance. They feed their requirements and proposals to the DCC. All departments are represented in these technical committees.

A Provincial Data Governance Council which would consist of external experts on data governance is also proposed. The Council would advise the Executive, e-PTM and TAPSC.

Success factors and Key Lessons Learnt

The following are the success factors and key lessons learnt during the WCG journey to institutionalise data governance; and to improve performance, efficiency and service delivery; while getting a proper handle on the data and systems applications.

A key to success is for PWDG to be driven centrally and governed collaboratively. The adoption of PWDG must be seen as a measure of “success”. It is vital to establish a governance structure for PWDG that reports to existing structures, such as the TAPSC, to ensure that collaboration takes place. There should be clearly defined and documented mandates, roles and responsibilities.

To achieve this, it is vital that there is research on the appropriate legal framework for data and records governance via the RIA process. This is crucial for the legislative and regulatory environment within which PWDG should be implemented.

A governance structure is required to drive this process. A version or combination of the proposed DCC / BICC should drive the essentials of PWDG. For a governance structure to make a meaningful contribution, departments must accept, willingly take on and drive data governance in their respective departments.

The governance structure should manage the availably, quality, sharing and integrity of data as a strategic asset. The scope of the structure should aim to achieve collaboration and a formal governance arrangement, which can lead to additional structures to accommodate data users and producers.

There must be a shift from uncoordinated efforts and individual capabilities to an integrated data management effort. Shared responsibility and coordinated efforts between departments is critical, especially during austere times. This will improve operational efficiencies across departments.

The data governance agenda must find traction in departmental planning and/or data-driven initiatives.

Establishing incentives will encourage data users and producers to comply with the data governance prescripts (including the ability to use authority effectively to enforce change). Data should be managed and used as a strategic asset, which will make the monitoring and targeting of complex service delivery activities less challenging. The institutionalisation of PWDG across departments is required to ensure the overall management of the availability, use, sharing and privacy of data as a strategic asset. Departmental Data Governance Champions must be identified for data initiatives and priorities via a change management programme, which defines the work and responsibilities of data governance champions.

The DotP must be recognised as the trusted “Operator”6 to facilitate data sharing and integration between departments; and to be the overall coordinator of both Information Technology and data governance to support business. Appropriate Memorandums of Agreement (MOAs) thatdefines DotP as the Operator must be developed.

It is important to understand the current data topography within WCG, matched against the demand for data and information. Analysis of the data landscape in departments by initially focussing on the 20 per cent of data sources that address 80 per cent of the business problems. Do an analysis on the indicators and the associated data sources, and seek correlation and/or identify gaps between the demand and supply of the data sources. The data at the municipal level would also need to be brought into the fold. This will ensure that data and information at grassroots level will be considered.

The departmental competencies in terms of data governance should be assessed. This includes an understanding of how data is being gathered; the maturity of data process flows, etc. These required competencies should be shared across departments to build the data governance expertise within the WCG.

Departments should engage more transversally at a practical and technical level on strategic data initiatives. Wider application of credible data sharing models such as MDM and standardised key or base datasets, should be identified to ensure that data integration takes place in a coherent and accurate manner. One of the areas where integration can take place is within the spatial context, so an address database is essential. As citizens are serviced transversally, consideration should be given to all datasets that contain personal data in terms of creating a master record for each client.

Better use and improvement of the Application Portfolio Management (APM) will ensure that appropriate systems, data architecture and technology are used to achieve integration and enhanced applications while preventing duplication.

One of the aspects that should be dealt with at an early stage is a common lexicon or language, to ensure that all terms, terminology and definitions are understood in the same way. This avoids confusion and ensures that all are on the same page, and have a common understanding.

Decide early on what assessment tool should be used to measure data quality. The PWDG team decided to use the South African Statistical Quality Assessment Framework (SASQAF7) for administrative data, to assess the quality of WCG administrative data. The same framework will be used to assess the standards set for indicators and data sources. Shared norms and standards for indicator development and data sources are needed. It is important to have shared ways of bringing data together. Without these common features across government, it is unlikely to effectively evaluate what is being done currently and what will change over time.

Way Forward – Actions for 2018/2019

Based on all the discussions between various governance structures, a number of resolutions for the new financial year were tabled. The resolutions that were adopted in collaboration with WCG departments, in support of data governance are translated into key actions that is aligned to the Master Plan for institutionalising PWDG. These are outlined below.

• Commence the RIA.

• Integrate current ICT and data governance mechanisms and enhance their functioning.

• Migrate the interim BICC into a transversal DCC.

• Strengthen advocacy of the PWDG purpose as set out in Chapter 2 of the Master Plan, and develop and manage this roadmap.

• Rationalise the current APM

• Build a LDW, with adequate protection of personal data and data quality.

• Use MDM as a key capability to enable data integration across multiple departmental data sources.

• DotP to be assigned as the trusted ‘Operator’; and should facilitate data sharing and integration between departments.

• A BI Maturity assessment framework to be developed and implemented.

• Build on and implement departmental best practice examples

• Institutionalise data governance across the WCG to improve both the efficiency and impact on service delivery.

Conclusion

This update provides a documented overview of work done up to the preimplementation stage of PWDG. It also sets the context for the implementation stage. The journey to date was exciting: one that gives meaning to role of being a public official in Government.

Ms Zeenat Ishmail currently holds the position of Chief Director: Provincial Strategic Management Information within the Department of the Premier, Western Cape Government (WCG) since 2008. She has provided strategic leadership to ensure the effective design of the Province-wide Monitoring and Evaluation System (PWMES); including putting in place a Results-based M&E model. She has been instrumental in provincialising the National Evaluation System for the WCG. She currently drives the institutionalisation of Province-wide Data Governance across the provincial departments. Zeenat is the WCG representative on the South African Statistics Council. She also oversees the management of the province-wide forums for M&E, programme and project management, Spatial Information, Business Intelligence and Data Governance.

Zeenat Ishmail, Chief Director: Strategic Management Information, Western Cape Government

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