DMMA MATURITY ASSESSMENT REPORT CGIAR, SEPTEMBER 2020 Copyright © 2020 Accenture. All rights reserved. 1 Center Type Area End Content Area CONTENTS EXECUTIVE SUMMARY KEY INSIGHTS AND OBSERVATIONS CURRENT MATURITY ASSESSMENT SCORECARD DEEP-DIVE INTO CHALLENGES & SOLUTIONS RECOMMENDATIONS & ROADMAP Copyright © 2020 Accenture. All rights reserved. 2 Center Type Area End Content Area GLOSSARY Data Architecture – The organization and structure of an organization’s data as it Analytics Strategy – A single view of the capabilities within the organization and the transforms from pure data elements into information and insights way in which analytics are delivered Data Interoperability – Ensure consistency with a “golden version” of contextual Visualization & Analytics - Visualization techniques, algorithms and statistical data values and creation of a single point of reference for critical enterprise-wide techniques to learn from the data patterns and use this learning to predict future data trends Data Quality – The assurance that your data comes from the right source, is of Human Augmented AI & ML – Seamless integration between humans and machines standard quality and is used by the right people in the right context. at scale Data Governance – Guidelines and standards to ensure compliance and enable Data Value Tracking – Encompasses the benefit realization framework to measure effective interoperability of systems the true value addition of data Data Strategy – Details on the data landscape strategy such as principles, patterns, Data driven Organization – an enterprise model that aims at promoting the wider capabilities, & technology components interlinkages, to establish reliable, secure, adoption of data and insights across the enterprise scalable and agile data pipelines. Data driven business model – An integrated approach to identify, distribute and Data Security – Data security refers to legal constraints, regulatory requirements monetize the data/insight and transparency. It encompasses Data protection issues and privacy rules and how these rules are respected Copyright © 2020 Accenture. All rights reserved. 3 EXECUTIVE SUMMARY Copyright © 2020 Accenture. All rights reserved. 4 EXECUTIVE SUMMARY SUMMARY 10 Weeks 13 Centers 35 Stakeholder conversations CONTEXT APPROACH KEY FINDINGS WHAT NEEDS TO CHANGE • This was a Data & Analytics Maturity Assessment • CGIAR customized Data & Analytics Maturity Mature use of data management & • It is critical to have an updated data engagement conducted by Accenture, working closely Assessment framework methodology aligned to statistical analytics in research projects management policy at the right level of with CGIAR stakeholders and led by the CGIAR industry standards across the organization granularity Internal Audit Function • 30 interviews over 10 weeks, across 12 Focus on digital strategy • A structured collaboration model with the • The aim was to assess the current state, identify pain capabilities and 13 Centers right representation from various stakeholders CGIAR Open Access & Data Management points and areas of improvement and detail key data is critical to move ahead • Robust Quantitative Scoring Framework to policy is being revised management recommendations measure current state maturity • Leadership support is critical to bring the vision Data Quality is an area of concern • The approach relied on review of documents of a target state to reality through funding and • Future state vision defined based on CGIAR provided, interviews with stakeholders and self- There is no structured governance council policies Data Community inputs and Accenture domain assessment by stakeholders where documentation is which brings together data community experience • Rigor in data management best practices and not available across the organization guideline to ensure improved research quality. Enforcement of defined policies is not • Motivate & incentivize the researchers to adequate prioritize data management Further details on page 7. Detailed recommendations can be found on page 25. Note: Alliance-Bioversity and CIAT are out of scope for this assessment due to ongoing integration of the Centers Copyright © 2020 Accenture. All rights reserved. 5 KEY INSIGHTS Copyright © 2020 Accenture. All rights reserved. 6 Center Type Area End Content Area KEY INSIGHTS HIGHLIGHTS FROM THE PROGRAM WHAT IS GOING WELL WHAT IS NOT GOING WELL Extensive use of data management & statistical analytics in research projects Enforcement of defined data management policies is inadequately monitored across the organization and measured in most research projects. CGIAR Open Access & Research Data Management Policy under revision A structured collaboration model with the right representation from various currently stakeholders is critical to move ahead Data quality is an area of concern. It is especially challenging for data collected Focus on Digital Strategy initiatives across the organization by non-digital methods, such as socio-economic data Quality controls are in place for majority of the financial data and implemented Raw data locked in siloed data platforms by the software tool ( based on self-assessment by operational data managers ) Finance data is managed by mature software applications, supported Operational data is managed through different software tools such as OCS and by consultant effort and software vendor teams ( based on self-assessment by SAP with customized configurations. So it is an expensive and challenging effort operational data managers or finance managers, the current project execution is to consolidate data into a common data platform for insight generation. working without much of a challenge ) Copyright © 2020 Accenture. All rights reserved. 7 KEY INSIGHTS KEY SOLUTION THEMES COMMUNITY POLICIES & PEOPLE & CULTURE LEADERSHIP RIGOR IN GOVERNANCE STRATEGY SUPPORT TECHNOLOGY • A structured collaboration • It is critical to have a • Motivate & incentivize • Leadership support is • Rigor in data model needs to be defined regularly updated data the researchers to critical to bring the management best with the right management policy at prioritize data vision of a target state practices and guideline representation from the right level of management in their to reality through is needed to ensure various stakeholders. granularity. research work. funding and policies. improved research • Roles & responsibilities • Processes and metrics • Foster a culture of • Leadership can work quality. need to be defined for need to be defined to trust to encourage with funders to make • Processes & policies are different stakeholders in enhance data value collaboration & data a priority due to its often defined for data alignment with leadership. tracking. knowledge sharing. impact on research management, but • There is a need for quality. suitable monitoring increased focus on and enforcement is analytics as a capability. necessary. Copyright © 2020 Accenture. All rights reserved. 8 KEY INSIGHTS KEY INSIGHTS INTO DATA CAPABILITIES DATA MANAGEMENT DATA ARCHITECTURE INTEROPERABILITY DATA QUALITY DATA GOVERNANCE DATA STRATEGY DATA SECURITY • A few Centers raised • Ontologies are not well • There is no tool used or • There appears to be no • There is no common data lake or • Published datasets are open- concerns regarding CGIAR defined for hydro and socio- assessment done to structured alignment of data storage for raw data, which is access, so in a lot of cases Centers policy being not detailed economic terms and funding measure data quality. It is managers within the System. expensive to collect. do not consider data security a enough or due for revisions. is not adequate to address driven by individual • Most Centers do not budget • There is a need to digitize data high priority issue when it comes • It is not practical to align on these gaps. commitment. separately for DM in projects, collection. Some Centers are to project execution. technology across different • Metadata repository is • Data quality is better at which will have increased focus already doing it. • Contingencyy planning for data research projects. This available at research project source for digitally from funders in coming days. security breaches needs to be needs to be considered level. collected data. more pro-active. during audit and risk assessment exercises. INSIGHTS & VALUE REALIZATION STRATEGY ADVANCED ANALYTICS AUGMENTED AI VALUE TRACKING DATA DRIVEN ORGANIZATION DATA BUSINESS MODEL • There is advanced analytics • Statistical modelling is not • Human Augmented AI • KPIs to track success of data • There is opaqueness on how • Few Centers suggested work being done in research automated. Researchers and is hardly leveraged in projects is highly inconsistent insights and assets are shared considering alternate revenue projects, but there is no data managers need to decide any of the Centers. across different Centers, and across research projects before streams for data within the strategy defined for if it is applicable at a project • There is no policy often driven eternally by they are being published. provisions. analytics both at Center and level. available on funders. • It is a difficult to identify the • Evaluate CGIAR stand on use of the System level. Analytics is • There is little use of data responsible AI. • KPIs are often not baselined and knowledge experts in such a published datasets by commercial not always considered a visualization tools. Power Bi re-evaluated at a regular level widely distributed organization. entities. capability on its own. and Salesforce Einstein can be • No incentive to measure or • Initiating the conversation would leveraged. report KPIs. help defining the direction. Copyright © 2020 Accenture. All rights reserved. 9 MATURITY ASSESSMENT SCORECARD Copyright © 2020 Accenture. All rights reserved. 10 Center Type Area End Content Area ASSESSMENT CGIAR IS AT LEVEL 3 WITH ROOM FOR IMPROVEMENT 05. OPTIMIZED 04. DM processes are fully MANAGED automated, regularly improved, and optimized 03. based on formalized DM practices are executive sponsorship and DEFINED automated across the analysis of changing enterprise. They are organizational goals. 02. managed and governed Represents top of line Data Management through quantitative industry data management. REPEATABLE practices are aligned measures of process with strategic performance. 01. organizational goals Activities are deliberate, and standardized INITIAL documented and across most areas; performed consistently however practices are across the full BU not fully adopted. Activities are informal, organization ad hoc and team specific. Dependent on heroic efforts and lots of cleansing. Note: Maturity scores reflect the current state assessment of data capabilities in research data domain. We also received inputs from operational data managers for a complete view. CCooppyyrriigghhtt © 22002200 AAcccceennttuurree.. AAllll rriigghhttss rreesseerrvveedd.. 11Operational Data Management was not assessed separately due to insufficient inputs,. ASSESSMENT SCORECARD DMMA MATURITY SCORECARD OVERALL SCORE Maturity Stage: L3-Defined 3/5 Current State Future State DATA ARCHITECTURE 3.7 3.7 5 Current State Future State DATA INTEROPERABILITY 3.0 3.0 5 Current State Future State DATA QUALITY 3.4 3.4 5 Current State Future State DATA GOVERNANCE 3.1 3.1 5 Current State Future State 3.8 5 DATA STRATEGY 3.8 Current State Future State DATA SECURITY 3.0 3.0 5 Copyright © 2020 Accenture. All rights reserved. 12 ASSESSMENT DMMA MATURITY SCORECARD OVERALL SCORE Maturity Stage: L3-Defined 3/5 Current State Future State ANALYTICS STRATEGY 1.7 1.7 5 Current State Future State DATA VISUALIZATION & 2.8 2.8 5 ADVANCED ANALYTICS Current State Future State HUMAN AUGMENTED AI 0.7 0.7 5 Current State Future State DATA VALUE TRACKING 2.6 2.6 5 Current State Future State 3.8 5 DATA DRIVEN ORGANIZATION 3.8 Current State Future State DATA DRIVEN BUSINESS MODEL 4.0 4.0 5 Copyright © 2020 Accenture. All rights reserved. 13 ASSESSMENT CAPABILITY HEAT MAP ACROSS THE 13 CENTERS WE WORKED WITH Data Data Data Visualization & Data Value Data-driven Data driven Business Data Architecture Data Interoperability Data Quality Analytics Strategy Human Augmented AI Governance Strategy Security Analytics Tracking Organization Model Info not available Maturity Guide High Med Low Not applicable Copyright © 2020 Accenture. All rights reserved. 14 DEEP-DIVE INTO CHALLENGES & SOLUTIONS Copyright © 2020 Accenture. All rights reserved. 15 CHALLENGES GAP ANALYSIS REPORT FOR RESEARCH DOMAIN CAPABILITY ASSESSMENT SCORE CURRENT STATE TARGET STATE* Centers are largely aligned with the last updated CGIAR Policy needs to be updated with a suitable level of Data Architecture 3.7 Open Access and DM Policy, but it is outdated and too granularity and incentivized for adoption in the research high level leaving a lot of room for individual community. interpretations. Ontologies are not well defined for hydro and socio- Ontologies need to be defined for all terms and the latest Data Interoperability economic terms. Funding or skillsets are not adequate version needs to be adopted across all Centres for true 3.0 to address these gaps. data accessibility Data quality challenges exist, but it is difficult to quantify Data Quality metrics are defined, measured, assessed Data Quality 3.4 the extent of the problem due to no structured tool or and remediated. A data quality monitoring process needs framework for measuring data quality. Also quality to be established to enable the above monitoring is ad-hoc in nature. There is a data community in place, but no defined A target operating model including a data governance Data Governance governance structure and roles/responsibilities, so council should exist for a cohesive and empowered data 3.1 meaningful collaboration cannot be conducted. Also, community. It can only happen with the right level of they are not empowered by leadership. leadership buy-in. Data workflows can be time consuming, especially when A common data lake should be set for raw data which is Data Strategy there are data quality challenges at source. Not much expensive to collect. Leveraging DevOps principles for 3.8 documentation is available automated data pipelines can help streamline long- running projects There are policies defined around data classification and Data Security and privacy breach prevention initiatives role-based data access control, however there is no are in place and followed consistently across all the Data Security clarity on adoption and adherence of these policies Centers including documented and distributed Data 3.0 across business units and Data Domains Security standards and policies that are updated rigorously Copyright © 2020 Accenture. All rights reserved. 16 CHALLENGES GAP ANALYSIS REPORT FOR RESEARCH DOMAIN CAPABILITY ASSESSMENT SCORE CURRENT STATE TARGET STATE Analytics is not strategized at a Center or System level even though it happens quite extensively within a An institutional and organizational strategy to guide the Analytics Strategy 1.7 project. This limits the ability to generate insights for data community on how to leverage more value out of leadership strategizing. their data and for improved funder buy-in. Visualization & Self-service analytics should be available to researchers Analytics 2.8 Data Visualization tools are not utilized widely. Also use and leadership for their individual needs. Analytics need of analytics outside research projects is very limited. not be limited to research data. Human Augmented AI With the potential of this technology in research areas, it 0.7 This capability is at a nascent stage and no policies is important to define a strategy, especially defining the defined around it. guidelines on responsible AI. Data Value Tracking CGIAR Dashboards are used for reporting, but there is The value of data as an asset needs to be defined in 2.6 no defined metrics defined to track the value of data as terms of measurable metrics, baselined with target values asset. and re-evaluated periodically. Data is available for analysis and hence the relatively Availability of data is not sufficient for data driven Data driven high score reflect the confidence of data managers but it organization. It is a cultural and process shift to be guided Organization 3.8 conceals that truly data driven organization need to have by data driven metrics for decision making. defined data value tracking. Data monetization is not happening at any of the Conversation needs to be guided by policy regulations, Data driven Business Centers, but there is a curiosity around exploring other but it may be worthwhile to address the commercial use models 4.0 avenues of value within the mandate. of published datasets. Copyright © 2020 Accenture. All rights reserved. 17 CHALLENGES DATA CAPABILITIES KEY CHALLENGES AND OBSERVATIONS (1/5) CAPABILITY KEY CHALLENGES DESCRIPTION HURDLES TO OVERCOME IMPLICATIONS The Open Access and Data Management Having an updated document with the A high level and outdated policy is open OUTDATED DATA policy has not been updated for a few right level of granularity and making to individual interpretations and Data Architecture POLICY years and often they are too high level to sure research community has the updates, which can lead to a divergent be useful. incentive to adopt it. approach. Data is collected digitally in some cases. Data cleansing is a tedious task and bad Data Quality NON-STANDARD DATA But a lot of it is still not standardized and Digital data collection is difficult to data puts the onus on data quality on COLLECTION depends on integrity & diligence of field implement for all kinds of data. the researcher to ensure research worker. quality. Data Interoperability ONTOLOGY FOR ALL Ontologies are not defined for all terms, Without standard metadata, data is not Skillset is not easy to find to address especially hydro terms and socio- truly accessible even if it is free for TERMS this challenge.economic terms. public access. Opportunity lost for cross-project Analytics Strategy Analytics is driven by individual projects A vision and strategy needs to be SILOED ANALYTICS collaboration as well as self-service for the purposes of research only. defined first. analytics by leadership. NO COMMON Technology platform to store and Raw data is owned by the research access the huge volume of raw data. Raw data is expensive to collect and Data Architecture REPOSITORY FOR RAW project. Also an operating model will need to be clean, but reusability is limited. DATA defined. Copyright © 2020 Accenture. All rights reserved. 18 CHALLENGES GOVERNANCE STRUCTURE KEY CHALLENGES AND OBSERVATIONS (2/5) CAPABILITY KEY CHALLENGES DESCRIPTION HURDLES TO OVERCOME IMPLICATIONS Organization wide changes are difficult GOVERNANCE to implement. The data community is Data Governance There is no data governance council This can happen only with leadership too large with no assigned COUNCIL empowered and tasked with oversight. buy-in and stakeholder alignment. responsibilities to design and enforce changes RESEARCHER Some level of funder push is necessary, Having policies and budget allocated Researchers are not incentivized to follow Data Governance since this a primarily publicly funded will not result in changes, unless MOTIVATION up on DM guidelines and policies. organization. researchers adopt the guidelines. FUNDING Often budget is available for DM, but it Metrics need to be defined in More funds will not automatically lead Data Governance does not get routed to the right set of DISTRIBUTION alignment with project managers. to solutions.activities. Data driven CULTURAL There is a lack of trust and motivation This needs to be addressed both from It can be difficult to achieve the One Organization when it comes to sharing knowledge and project governance perspective and CHALLENGES CGIAR vision without addressing this.insights. employee empowerment perspective. Data security breaches are not proactively Data security goes beyond There can lead to legal and goodwill Data Security DATA SECURITY addressed and often documentation is anonymization of PII data and access damages, which are difficult to not available to evaluate. control. reinstate. Copyright © 2020 Accenture. All rights reserved. 19 CHALLENGES LEADERSHIP ATTENTION KEY CHALLENGES AND OBSERVATIONS (3/5) CAPABILITY KEY CHALLENGES DESCRIPTION HURDLES TO OVERCOME IMPLICATIONS Data Value DATA VALUE Effort needed to identify the suitable There is no way to quantify the value Tracking Metrics are not defined for data projects. TRACKING KPIs to track and baseline those. realized from data as an asset. Data driven FUNDER FOCUS ON In a publicly funded organization, major Needs to be addressed by leadership It would be difficult to enforce and Organization changes can be effected only with funder with a better visibility to funder DATA sustain major changes.buy in. priorities. FUNDING Ramping up new solutions can be It needs to be addressed by leadership Data Strategy challenging and require additional who has better visibility into funder Changes will not happen. AVAILABILITY financial support. priorities. DM may not always get the top priority Data is the fuel for quality research, and Data Strategy Leadership has multiple priorities and LEADERSHIP SUPPORT for leadership. But it still needs to be poorly managed data can lead to often DM gets ignored. planned properly. reduced credibility in research. Data driven LACK OF TRAINING Training and reskilling opportunities are Funds are often pulled out of project, It can lead to knowledge and skillset Organization OPPORTNITIES sporadic and not widely available. limiting the scope. gap among researchers. Copyright © 2020 Accenture. All rights reserved. 20 CHALLENGES DATA COMMUNITY VOICE KEY CHALLENGES AND OBSERVATIONS (4/5) CAPABILITY KEY CHALLENGES DESCRIPTION HURDLES TO OVERCOME IMPLICATIONS Not all data is equally valuable, classifying Introducing an overhead of too many Visualization & DATA ASSET them in right categories can make it It would be challenging to define and processes in a small project can Analytics CLASSIFICATION easier to prioritize limited resources in align the classification of data assets. discourage people from following the right areas. guidelines. High attrition on data teams and Need to analyse the root cause for It leads to lower employee motivation Data Governance STAFF TURNOVER departments, making it difficult to bring turnover. and delay in implementing changes. changes. Human Unless a policy is defined early in the Not much work happening in this area Augmented AI RESPONSIBLE AI No policies or guidance on responsible AI. game, it can lead to unintentional yet. malpractices or misuse of AI. Copyright © 2020 Accenture. All rights reserved. 21 CHALLENGES OPERATIONAL DATA MANAGEMENT KEY CHALLENGES AND OBSERVATIONS (5/5) CAPABILITY KEY CHALLENGES DESCRIPTION HURDLES TO OVERCOME IMPLICATIONS It will be difficult to implement One Centers are independent There is no incentive for Centres to LACK OF A COMMON CGIAR vision without defining organizations with their own collaborate and work towards a Data Architecture uniform administrative management VISION organizational structures, policies common vision from finance plan which has direct impact on data and procedures. perspective. management Different ERP and HR tools such as OCS is implemented in 9 centres, It can be an auditing, data security WIDE ARRAY OF SAP, Oracle or MS used across all Data Interoperability including System Organization. We and data reporting challenge if data centers for financial data PLATFORMS need to evaluate the necessity for a needs to be consolidated from management, and other areas as unified ERP platform across centres. different ERP platforms. well. Alignment in terms of processes, Loosely defined processes can lead to GOVERNANCE roles & responsibilities within There is a working collaboration model, blind spots in terms of data Data Governance institute as well as System but difficult to analyse objectively STRUCTURE management and it also discourages Organization is not well defined and without documentation. collaboration across centers. documented. Analytics is mostly driven by the There are siloed data platforms across It is difficult to enable analytics for a capabilities of the software which is Analytics Strategy ANALYTICS different operational platforms such as data driven organization if data used by the operational finance, HR, payroll, logistics, etc. consolidation is an expensive activity. departments. Copyright © 2020 Accenture. All rights reserved. 22 CHALLENGES HOW TO ADDRESS THE CHALLENGES S.NO CHALLENGE SOLUTION THEMES DESCRIPTION 1 Outdated Data Policy 2 Non-standard Data collection DEFINE the collaboration model along with roles & COMMUNITY GOVERNANCE responsibilities 3 Ontology for all terms 4 Siloed Analytics 5 No common repository for raw data ESTABLISH a regularly updated Open Access and 6 Governance Council POLICIES & STRATEGY Research Data Management Policy 7 Research Motivation 8 Funding Distribution MOTIVATE & incentivize the researchers to foster a 9 Cultural Changes PEOPLE & CULTURE culture of trust and collaboration 10 Data Security 11 Data Value Tracking 12 Funder focus on Data MOBILIZE leadership support for funding and LEADERSHIP SUPPORT collaborating with the funders 13 Funding Availability 14 Leadership Support 15 Lack of training opportunities ENFORCE rigor in Data Management practices and 16 Data Asset Classification RIGOR IN TECHNOLOGY guidelines defined in the policy 17 Staff turnover 18 Responsible AI Copyright © 2020 Accenture. All rights reserved. 23 RECOMMENDATIONS & ROADMAP Copyright © 2020 Accenture. All rights reserved. 24 Center Type Area End Content Area RECOMMENDATIONS TO IMPLEMENT SOLUTION THEMES RECOMMENDATIONS SOLUTION THEMES RECOMMENDATIONS Establish a data governance council, including data architecture board with representation from System, IT, Research, Data Managers COMMUNITY and leadership GOVERNANCE Establish a Data Value Office across the Centers tasked with the benefit & value monitoring from data Explore new value streams for research data in line with good provision mandate Update the Open Access and DM Policy and share with stakeholders POLICIES & STRATEGY Establish and adopt a data asset classification to prioritize higher adoption of DM policies in high value data assets Draft an archival & retention strategy, including data disposal after project end Introduce recognition program across the Centers to foster sense of community & collaboration PEOPLE & Measure and incentivize the adoption of standard DM Policy and best practices CULTURE Establish policies & guidelines to build trust and confidence to share data Establish guidelines for the use of Responsible AI Prioritize data management in the upcoming Digital Strategy aligned with leadership vision Estimate & budget for DM effort as part of project planning LEADERSHIP Review and refine the disbursal of funding available for DM activities SUPPORT Define a structured data quality monitoring approach, including a data quality score Sensitize funders and leadership on the value of data management Share guidelines for building a risk register focusing on data security Digitize data collection methods to ensure better data quality RIGOR IN Define ontologies for all terms TECHNOLOGY Implement a common data repository as a platform for raw data Establish a uniform platform for operational data management Copyright © 2020 Accenture. All rights reserved. 25 Center Type Area End Content Area RECOMMENDATIONS HOW THE RECOMMENDATIONS ARE PRIORITIZED 01 02 03 ASSESS THE BENEFIT ESTIMATE THE EFFORT PLAN FOR DEPENDENCIES Assess the benefit of each initiative in Effort is estimated based on potential complexity Dependencies have been accounted for based on organizational context based on stakeholder of the task, funding and stakeholder experience information available. Initiatives dependent on inputs and Vision Workshop feedback based on past efforts to solve a challenge the completion of other initiatives or initiatives which need a longer planning phase have been identified as long-term projects Copyright © 2020 Accenture. All rights reserved. 26 RECOMMENDATIONS RECOMMENDATION PRIORITIZATION 1 Prioritize data management in the upcoming Digital Strategy aligned with leadership vision 2 Update Open Access and DM Policy and share with stakeholders 3 Estimate & budget for DM effort as part of project planning Long-Term Initiatives Critical Enablers Quick Wins 4 Share guidelines for building a risk register focusing on data security R7 R2R1 R6 5 Introduce recognition program across the Centers to foster sense of community & collaboration. R14 R10 R3 R5 6 Establish and adopt a data asset classification to prioritize higher adoption of DM policies in high R4 value data assets R17 R11 7 Establish a data governance council, including data architecture board with representation from R9 System, IT, Research, Operations, Data Managers and leadership 8 Draft an archival & retention strategy, including data disposal after project end R13 R12 9 Establish a Data Value Office across the Centers tasked with benefit & value monitoring from R15 R16 R8 data R18 10 Review and refine the disbursal of funding available for DM activities 11 Define a structured data quality monitoring approach, including a data quality score R19 12 Measure and incentivize the adoption of standard DM Policy and best practices R20 13 Digitize data collection methods to ensure better data quality 14 Define ontologies for all terms 15 Establish policies & guidelines to build trust and confidence to share data 16 Implement a common data repository as a platform for raw data De-prioritized 17 Sensitize funders and leadership on the value of data management De-prioritized 18 Explore new value streams for research data in line with good provision mandate Low Medium High 19 Establish a uniform platform for operational data management EASE OF IMPLEMENTATION 20 Establish guidelines for the use of Responsible AI ( Indicative of effort ) Copyright © 2020 Accenture. All rights reserved. 27 BENEFIT Low Medium High RECOMMENDATIONS QUICK WINS 1,2 Accelerate creation of Open Access and Data Management Policy & 4 Update Risk register to focus on data security and privacy prioritize in digital strategy & One CGIAR vision WIN STRATEGY Effort WIN STRATEGY • Align with ICT & operational data managers for a complete and uniform • Incentivize and enforce adoption of the new policy in research community cybersecurity approach • Align with operational data managers for One CGIAR vision BENEFITS BENEFITS • Improved protection against data security breach related litigation costs and • Improved research quality due to data being managed better goodwill loss • Improved leadership data awareness due to easy access to insights 3 Include data management cost in project proposal (effort underway to 5 Recognize and motivate the researchers to prioritize Data currently do this for various projects) Management WIN STRATEGY WIN STRATEGY • Work with functions to mandate inclusion of data processing cost in their project • Work with Centers to mandate inclusion of data management effort in their approval process and budget planning research projects and motivate the researchers to be an advocate by introducing rewards & recognition programs BENEFITS • More accurate picture of true cost of project execution BENEFITS • Prevent overruns on project time and budget • Researchers become advocates for data management. SUB-CAPABILITIES Data Architecture, Data Security, Data Governance, Data Strategy Copyright © 2020 Accenture. All rights reserved. 28 RECOMMENDATIONS RECOMMENDED DATA ASSET CLASSIFICATION 6 RECOMMENDATION KEY STAKEHOLDERS EFFORT BENEFIT Establish and adopt a data asset classification Owners: High High • Establish protocols to measure and identify high value data assets • System Organization • Currently CGIAR has a data prioritization framework that tries to quantify • Research Data Managers "high-value" data in terms of value, risk, and cost. It can be refined, if required and be pushed for wider reference Collaborators: • Prioritize resources and DM efforts on projects involving high value data Medium MediumIT assets Researchers • Reuse existing Data Prioritization Framework KEY BENEFITS SUB-CAPABILITIES: Low Low • Projects can focus their limited resource on high value data. Data Governance, Data Architecture • Small projects or projects dealing with low value data may not be motivated to invest heavily on DM efforts, thus avoiding an overhead cost for them. • Identifying high value data assets can help in enriching data security efforts as well. Such projects need to have a high focus on data security and access RISKS & DEPENDENCIES control during project execution phase as well, and it should be regularly • Definition of a high value data asset needs alignment from Centers monitored, and risk assessed. • Data Management should not be ignored in projects with low value data assets Copyright © 2020 Accenture. All rights reserved. 29 RECOMMENDATIONS RECOMMENDED ORGANIZATION STRUCTURE 7, 9 RECOMMENDATION KEY STAKEHOLDERS EFFORT BENEFIT Instituting Data Governance Council to enable collaboration and oversight in Owners: data community High High • CGIAR Leadership • Global champion deputed to Centers to enable a truly hybrid org structure for Data & Analytics. Also tasked with building a Data Community of Practice • Funders (CoP) Collaborators: • Establish a Data Value Office tasked with benefit quantification & monitoring Medium Medium value from Data and Analytics projects Data Governance Council • Constitute a data architecture review board that works closely with Institutional Data Managers Researchers, Data Managers & IT governance boards Research Data Mangers • Include a focused group of operational data managers within the governance Operational Data Mangers council Low Low Researchers KEY BENEFITS IT • Data Community of Practice will help create an environment of creativity, SUB-CAPABILITIES: innovation & collaboration around data Data Governance • Encourages sharing of data and insights across Centers • Better rigor around Data & Analytics projects. The Value office can also work RISKS & DEPENDENCIES with the Internal Communication teams to broadcast business value from successful projects, which can help build business trust and excitement • Cultural resistance to new org structure around Data & Analytics projects • Lack of incentives for Centers to collaborate • Data architecture review board will help govern changes to data architecture, • Needs active support and buy-in from leadership enforcing consistency and better governance Copyright © 2020 Accenture. All rights reserved. 30 RECOMMENDATIONS IMPROVE DATA MANAGEMENT PROCESS RIGOR 8, 11, 13 RECOMMENDATION KEY STAKEHOLDERS EFFORT BENEFIT Enforce process excellence for better data quality at source, better technology Owners: enablement and cost efficiencies in managing data High High • System Organization • Enforce stronger data quality checks at source • Include accuracy of data entry by field workers • Research Data Manager • Substitute manual processes with an automated approach for data pipeline & • Operational Data Manager workflows Medium Medium Collaborators: • Define in structured data quality monitoring, including a data quality score Leadership • Draft an archival & retention strategy for raw data at the end of a project. Educate Centers on the same. Researcher Low Low KEY BENEFITS • Improved research quality due to better quality data SUB-CAPABILITIES: • Enhanced credibility of published datasets Data Architecture, Data Quality, Data Strategy • Saving of effort in data cleansing activities, which can then be routed towards research-oriented activities RISKS & DEPENDENCIES • Reduced litigation risks due to mis-managed data archival and retention. • Adding ML-based tooling capabilities will require resources with niche skills • Digitized data collection is difficult to implement for all kinds of raw data Copyright © 2020 Accenture. All rights reserved. 31 RECOMMENDATIONS ENHANCING FUNDING DISTRIBUTION 10 RECOMMENDATION KEY STAKEHOLDERS EFFORT BENEFIT Owners: Review and refine the disbursal of funding available for DM activities. High High • Data Management should be budgeted for during the project approval phase • Leadership • Project Manager and Research Manager should align and plan on the • Project Manager disbursal of funds, to ensure it is spent on DM activities. Collaborators: • Encouraging value tracking for data projects will encourage funding Medium Medium Funders availability from funders. System Organization KEY BENEFITS SUB-CAPABILITIES: Low Low • Prioritization based on Risk-based approach to data security, which ensures Data Governance value for money • Improved data security especially for confidential/sensitive data • Facilitates data privacy compliance such as demonstrating proactive action in case of a data breach (for instance data has been encrypted) RISKS & DEPENDENCIES • Requires time and persistent effort to raise awareness • This initiative will require approvals from multiple stakeholders at all levels Copyright © 2020 Accenture. All rights reserved. 32 RECOMMENDATIONS INCREASING RESEARCHER PARTICIPATION 12, 15 RECOMMENDATION KEY STAKEHOLDERS EFFORT BENEFIT Measure and incentivize the adoption of standard DM Policy and best practices Owners: High High • Introduce rewards and recognitions programs to recognize researchers who • Leadership are setting standards in exemplary Data Management • Project Manager • Enforce the adoption of new policies and guidelines • Build a governance model which encourages researchers to share data and Collaborators: insights with their peer group Medium MediumFunders System Organization KEY BENEFITS SUB-CAPABILITIES: Low Low • Success of any recommendation will eventually be driven by on-ground Data Governance participation. • Research quality will improve if DM policies can be implemented without impacting the independence of research work RISKS & DEPENDENCIES • Requires time and persistent effort to raise awareness • This initiative will require approvals from multiple stakeholders at all levels Copyright © 2020 Accenture. All rights reserved. 33 RECOMMENDATIONS LONG TERM WINS 14 Define ontologies for all terms, including hydro terms & socio- 16 Implement a common data repository for raw research data economic data WIN STRATEGY WIN STRATEGY • Define the technology platform for a data lake to storEe ftfhoer rtaw research • Allocate funds and identify the right skillset to address the challenges data • Define operating model for continued usability & maintenance BENEFITS • Improved semantic interoperability for data BENEFITS • Increased data accessibility for published datasets • Expensive raw data is available for re-use • Easier insight generation 17 Sensitize funders and leadership on the value of data management 18 Explore new value streams for research data in line with good provision mandate WIN STRATEGY • In a publicly funded organization, leadership needs to align funders on the value of data WIN STRATEGY as an asset and mobilize their support for effective adoption • Work with leadership to explore new funding streams within the good BENEFITS provision mandate • Researcher participation is easier to enforce with funder support. BENEFITS • Drives more targeted funding for better DM capabilities. • Increased funding for research work SUB-CAPABILITIES Data Interoperability, Data Driven Business Model, Data Strategy Copyright © 2020 Accenture. All rights reserved. 34 RECOMMENDATIONS LONG TERM WINS 19 Establish a uniform platform for operational data management 20 Establish guidelines for Responsible AI WIN STRATEGY WIN STRATEGY Effort • Define uniform data platform for operational data which is currently fragmented across • With AI being an increasingly used technology in the field of genomics and Centers cross-breeding, it is important to define the principles of Responsible AI in line with government regulations BENEFITS • Effort savings in data consolidation BENEFITS • Improved insight generation • Future-ready 21 Enhanced documentation in operational data management 22 Establish Analytics strategy to build a capability WIN STRATEGY WIN STRATEGY • Detailed documentation is recommended to ensure continuity and consistency in • Analytics is happening in siloed manner as part of research projects, but implementation, especially with independent ERP platforms being used Centers and the System do not have a defined Analytics strategy which helps them build capability and generate insights BENEFITS • Align with operational data managers • Uniformity in business implementation and rules BENEFITS • Enhanced insight generation for improved funder relationships • Leadership visibility into work done by Center SUB-CAPABILITIES Data Strategy, Data Architecture, Human Augmented AI, Data Visualization & Analytics Copyright © 2020 Accenture. All rights reserved. 35 RECOMMENDATIONS ROADMAP FOR THE RECOMMENDATIONS Quick Wins Critical Enablers Long-Term Initiatives R7 Establish a data governance council with representation from System, IT, Research, Data Managers and leadership R9 Establish a Data Value Office across the Centers tasked with R18 Explore new value streams for research data in line benefit & value monitoring from data with good provision mandate Process Modifications R2 Update Open Access and DM Policy and R8 Draft an archival & retention strategy, including share with stakeholders data disposal after project end. R6 Establish and adopt a data asset classification to prioritize higher adoption of DM policies in high value data assets R12 Measure and incentivize the adoption of R15 Establish policies & guidelines to build trust and standard DM Policy and best practices confidence to share data R5 Introduce recognition program across the Centers to foster R20 Establish guidelines for the use of sense of community & collaboration Responsible AI R1 Prioritize data management in the upcoming Digital R10 Review and refine the disbursal of funding R17 Sensitize funders and leadership on the value of Strategy aligned with leadership vision available for DM activities data management R11 Define a structured data quality monitoring R3 Estimate & budget for DM effort as part of approach, including a data quality score project planning R13 Digitize data collection methods to ensure better data quality R14 Define ontologies for all terms R4 Share guidelines for building a risk register focusing on data security R16 Implement a common data repository as a platform for raw data R19 Establish a uniform platform for operational data management Copyright © 2020 Accenture. All rights reserved. 36 Leadership People & Rigor in Technology Policies & Community Support Culture Strategies Governance ADDENDUM Copyright © 2020 Accenture. All rights reserved. 37 MATURITY MODEL MATRIX THE INFORMATION BELOW WAS USED TO DETERMINE THE CURRENT STATE AND AMBITION BEHIND CGIAR’S VISION 1 2 3 4 5 “INITIAL “REPEATABLE” “DEFINED” “MANAGED” “OPTIMIZED” DATA ARCHITECTURE Ineffective and ad-hoc Fragmented and limited Central repository, Enterprise Data Model Methodical and complete Integrated in the ecosystem with partners Interrelationships between Master Data Complete and up-to-date Master and Reference Master Data identified but DATA INTEROPERABILITY Not present elements clear / Master Data managed. Data. Meta Data (incl. Data Definitions and Managed and in controlFragmented and limited Meta Data DATA FOUNDATION Accessible Metadata Lineage). Data Catalogs are governed. & ARCHITECTURE Scattered, local data quality Critical data elements are backed up by Proactive and part of culture. Defined measurement and unclear data measured data quality controls and Centrally steered DQ process. Data quality goals Ad hoc. No data quality standards thresholds for critical business elements DATA QUALITY quality. Application or line of requirements. Data security and access is are established and used to prioritize are published and data quality self-monitoring activities business level profiling and analysis specific for data; checked and includes remediation processes. are defined and followed. occurs elements of information classification Centrally steered data management processes Proactive data management and part of Data Stewards (local) / IT Led and organization-wide governance. Enterprise Not filled in; no sponsor for data Data Stewards (value stream; data owners culture; respected and accepted governance / Data owners partly and Local Data Governance sustainability DATA GOVERNANCE governance and data owner role are assigned on a tactical basis and Governance. Compliance to data policies named. Policies focused on routines are in place. All data management is not defined fragmented Governance per domain and standards is inherited and designed in regulatory compliance topics are covered by policies and standards and all processes. Compliance by design compliance is measured DATA STRATEGY & Business decisions are always checked to GOVERNANCE Business decisions are not Very few business decisions are Only important business decisions are Business decisions are checked to ensure Data DATA STRATEGY ensure Data driven decisions were correct checked afterwards checked afterwards checked afterwards driven decisions were correct in practice in practice Policies regarding Data access, Data There are no clear policies Policies regarding Data access, Data Policies regarding Data access, Data Policies regarding Data access, Data ownership ownership & Data authorization exist in DATA SECURITY regarding Data access, Data ownership & Data authorization are ownership & Data authorization exist at & Data authorization exist in the organization the organization and are fully ownership, Data authorization in progress the business unit level but are not fully implemented implemented Parts of analytical usage are driven Analytical strategy is defined and drives Analytical operating model in place and driving Analytics Strategy is fully embedded in the ANALYTICS STRATEGY No analytics Strategy in place by strategy progress in analytical maturity value business strategy VISUALIZATION & Limited usage of analytics; usage Usage of analytics for focused use Enterprise-wide analytical toolset; central Continuous and coordinated analytics INSIGHTS & Broad usage of advanced analytical techniques of data-driven analyses on an ad- cases; analytics is part of specific analytics CoE; early examples of advanced process improvement and value and data science ANALYTICS ANALYTICS hoc basis role descriptions analytics realization HUMAN AUGMENTED AI Significant usage of AI and strong Ethical use of AI is top of mind and AI Full deployment of AI to augment business No use of AI Ai Proof-of-Concepts and early usage connection between AI and Analytics; CoE oversight is not optional; AI is embedded & ML processes; C-level support and ownership of AIfor AI in normal way of doing business The value delivered by data is tracked Ad-hoc tracking of the value of data The value of data is clear in pockets of the Data value tracking is embedded in all DATA VALUE TRACKING No tracking of the value of data enterprise-wide and a clear overview of the in specific initiatives organization data related approaches realized benefits is available DATA DRIVEN Major strategic decisions are Initial initiatives in data education Formal data education programs in place Data and data driven insights are an intrinsic Data drives decision making within the DATA VALUE made based on instincts instead and data is used to support major and data is a respected contributor to part of decision making; organizational culture organization and data literacy is REALIZATION ORGANIZATION of data strategic decisions decision making drives using data to support decisions widespread in the enterprise Data is shared across an enterprise’s DATA DRIVEN BUSINESS Data monetization is used to a certain Data is not used to drive new boundaries; new data drive business models are Data is used as the key lever for strategic Early examples of data monetization extent to drive profitability. There’s a data MODELS business models regularly launched, and a scheme exists to scale growthmonetization strategy successful new business models Copyright © 2020 Accenture. All rights reserved. 38 HOW PATH TO FUTURE SHOULD LOOK LIKE WE LISTENED TO THE STAKEHOLDERS AND THE DATA COMMUNITY AND SOME THEMES RESONATED ALL ACROSS VISION Data driven insights Ontology in Graph DB Automated data pipeline Leadership SupportConnected Data Collaboration Model Incentive for researchers Data Interoperability Governance Council Training Funding for DM Common data repository Well-defined meta data KPI definition KPI definition Funding Distribution Data citation New value streams API Driven database Performance Appraisal Institutional Data Manager Data as an Asset Updated policy document Standard guidelines Copyright © 2020 Accenture. All rights reserved. 39 MATURITY ASSESSMENT APPROACH OVERVIEW Framework Definition Data & Analytics Maturity Assessment Vision Workshop The Data & Analytics Maturity assessment framework The Data & Analytics Maturity Assessment is carried out The purpose of this Workshop is to build the understanding ensures that we are taking a holistic and structured view of through the following steps: of the desired maturity level of CGIAR’s Data & Analytics the landscape. • Document Review capabilities in future in the short, medium and long term • Surveys or Interviews ( as is applicable for each During this discussion, the capabilities and sub-capabilities stakeholder ) During this workshop, the core assessment team and CGIAR around Data & Analytics streams of CGIAR will be defined. stakeholders will come together and establish the desired This activity will help us to prioritize and customize the During this assessment, a connect will be established with maturity level that CGIAR would like to achieve for each of framework to include the unique needs for CGIAR. the data & analytics stakeholders to understand the current the capabilities & sb-capabilities.. This will then be used as maturity state of their organization/Center. an input to create the final version of Recommendations Outcome of this discussion will drive the entire Maturity Outcome of this phase will drive the definition of current and Roadmap for prioritization of the security initiatives Assessment phase and ensure that we are focusing in the maturity level across capabilities which will be an input to after doing gap analysis. right areas, the gap-analysis phase. We will be leveraging Accenture Data & Analytics Maturity The Assessment workbook with capability score will speak In this workshop we will come to a common understanding Assessment framework and customize it based on the about the current state maturity and desired state maturity of CGIAR’s data and analytics desired maturity level for discussions from this discussion. along the various sub capabilities future roadmap. EXTENDED DMMA FRAMEWORK ASSESSMENT WORKBOOK VISION WORKSHOP Copyright © 2020 Accenture. All rights reserved. 40 Snapshots Deliverables Purpose Data & Analytics Value Chain Data Foundation Data Strategy Insights & Data Value & Architecture & Governance Analytics Realisation Identification and securing of Core architectural components Governance mechanisms that Analysis of data-leveraging use value opportunities created by addressing metadata models, data ensure appropriate controls are in cases and analytic tools to create analytics in order to drive security and access place across the operating model compelling insights outcomes Data Architecture Data Governance Analytics Strategy Data value Tracking Data Driven Organization Data Interoperability Data Strategy Visualization & Analytics Data Quality Data Security Human Augmented AI & Data Driven Business ML Model People, Process and Technology Copyright © 2020 Accenture. All rights reserved. 41 DATA FOUNDATION & ARCHITECTURE DESCRIBES HOW AN INFORMATION DRIVEN ORGANIZATION INTEGRATES DATA IN A STRUCTURED WAY ESTABLISHING THE RIGHT DATA FOUNDATION & ARCHITECTURE ENSURES THAT AN ORGANIZATION: • knows what data it has, where it resides, where it flows, who uses it and how • establishes and assesses indicators of ‘right data’ DATA ARCHITECTURE DATA INTEROPERABILITY DATA QUALITY The organization and structure of a organization’s data as it Ensure consistency with a “golden version” of contextual data values The assurance that your data comes from the right source, is transforms from pure data elements into information and and creation of a single point of reference for critical enterprise-wide of standard quality and is used by the right people in the insights data right context The elements are: The elements are:The elements are: PRINCIPLES | Guidelines for developing, deploying & ▪ META DATA MANAGEMENT | Creation of a single ▪ DATA QUALITY RULES | Business rules refer to the ▪ understanding and communication of Data quality using data-related IT resources aligned to business point of reference for critical enterprise-wide data, includes data definition and glossary strategy objectives ▪ MASTER DATA MANAGEMENT | Collection of DESIGN | Design data pipeline plumbing mechanism in ▪ QUALITY MONITORING | Monitoring encompasses the ▪ people, processes, and technology components order to enable data flow different procedures to assess Data quality as well as working together to ensure quality of metrics and processes to verify the quality of Data. Master/Reference Data ▪ DATA PROFILING| Data profiling is the statistical analysis and assessment of Data values within a Data set for consistency, uniqueness and logic ▪ DATA CLEANSING| Data cleansing makes sure data is correct and accurate in a data source. Copyright © 2020 Accenture. All rights reserved. 42 DATA STRATEGY & GOVERNANCE DESCRIBES HOW AN INFORMATION DRIVEN ORGANIZATION MANAGES DATA AS A STRATEGIC ASSET Having the right DATA STRATEGY AND GOVERNANCE ensures that an organization: • Understands and has the competencies to understand and drive/manage value from data • has the right roles to understand and manage all aspects of the data value chain • has the right data to measure business KPIs DATA GOVERNANCE DATA STRATEGY DATA SECURITY Guidelines and standards to ensure compliance and enable Details on the data landscape strategy such as principles, patterns, Data security refers to legal constraints, regulatory effective interoperability of systems capabilities, & technology components interlinkages, to establish requirements and transparency. It encompasses Data reliable, secure, scalable and agile data pipelines. protection issues and privacy rules and how these rules are respected The elements are: The elements are: The elements are: ▪ GOVERNANCE | The culture, functions, processes and ▪ DATA SUPPLY CHAIN | The technology that enables data to flow ▪ RISK & COMPLIANCE | Compliance rules & regulations authorities that shape the execution, control and efficiently through the entire organization and throughout each management of the enterprise (internal and external) data organization’s ecosystem of partners ▪ POLICIES & STANDARDS| Policies and standards refer to how the organization transfers its privacy needs in terms of ▪ OPERATING MODEL | The team structures and process ▪ DATA PLATFORMS | Tools, templates, architecture patterns and policies and the implementation of these policies, standards definitions required to run an effective data governance accelerators to enable the rapid design and build out of core data and tools function infrastructure (enterprise data lake) to support large scale data storage and processing ▪ CONTROL & FAILURE MECHANISMS| Control and failure mechanisms refer to how the organization controls the respect of these procedures and how the organization reacts in case of failure. Copyright © 2020 Accenture. All rights reserved. 43 INSIGHTS & ANALYTICS DESCRIBES HOW A DATA DRIVEN ORGANIZATION CONVERTS DATA INTO ACTIONABLE INSIGHTS CONVERTING DATA INTO ACTIONABLE INSIGHTS VIA INSIGHT & ANALYTICS ENSURES THAT AN ORGANIZATION: • The right use cases identified and right data to feed into these use cases? • Established the right capabilities to generate insights from data? • Built the right governance around analytics? ANALYTICS STRATEGY VISUALIZATION & ANALYTICS HUMAN AUGMENTED AI & ML Seamless integration between humans and machines at A single view of the capabilities within the organization Visualization techniques, algorithms and statistical techniques scale and the way in which analytics are delivered to learn from the data patterns and use this learning to predict future trends The elements are: The elements are: The elements are: ▪ ANALYTICS STRATEGY | the vision and objectives behind ▪ VISUALIZATION | Powerful visual representations of data that ▪ AI STRATEGY | Devising use cases that can be driven by the enable decision makers to readily grasp a key summary of the data the analytics journey of the organization and the underlying right balance of human and machine intelligence and spot important trends and patterns in the data operating model for the analytics team ▪ AUGMENTED INTELLIGENCE | A wide range of techniques ▪ DATA SELF SERVICE | Giving business users & citizen data ▪ VALUE TARGETING | Identification of high potential such as machine learning, deep learning, natural language scientists, the ability to easily explore data, prepare data and opportunities to create valuable insights from the data generate simple reports by themselves, with minimal IT support processing, computer vision etc. that can be used to train machines to learn from patterns and perform tasks that ▪ NARRATIVE | Interpreting the data trends, patterns and insights, require intelligence within the context of business, to build a story that brings data to life ▪ ALGORITHMS | The statistical methods and AI techniques used to build models that make predictions, perform complex analysis and offer recommendations Copyright © 2020 Accenture. All rights reserved. 44 DATA VALUE REALIZATION DESCRIBES VALUE EXTRACTION AND TRACKING AS WELL AS ORGANIZATIONAL CHANGES REQUIRED DATA VALUE REALIZATION ENSURES THAT AN ORGANIZATION CAN… • Leverage data to create/improve competitive differentiation • Use data to fuel innovation around products and services • Make a cultural shift towards being a data-driven organization • Improve operational efficiencies and bottom-line DATA VALUE TRACKING DATA DRIVEN ORGANIZATION DATA DRIVEN BUSINESS MODELS Encompasses the benefit realization framework to an enterprise model that aims at promoting the wider adoption is an integrated approach to identify, distribute and measure the true value addition of data of data and insights across the enterprise monetize the data/insight The elements are: The elements are: The elements are: ▪ BENEFIT REALIZATION | Measure whether the data ▪ DATA-DRIVEN DECISION MAKING | Making decisions at all ▪ MONETIZATION STRATEGY | Enable an enterprise’s initiatives and projects are adding the expected value to the levels in the enterprise, based on data rather than instinct journey from data to dollar by devising ways to either sell organization enterprise data in compliance with regulations or use data ▪ DATA LITERACY | Training employees and empowering as the key lever for profitable growth them with access to the right datasets and tools so that they can use data in their day-to-day job and for innovating ▪ DATA SHARING | Frameworks and technology platforms beyond it that enable sharing data beyond enterprise boundaries, to make the enterprise truly connected with vendors, partners, customers and other stakeholders in the ecosystem Copyright © 2020 Accenture. All rights reserved. 45