Data & Analytics Capability Maturity Assessment (Tech) [Comments Optional]

Welcome to Utilligent’s Data & Analytics capability self-assessment. You will be asked to consider the maturity levels of your data management capabilities. We have curated 16 Data & Analytics topic areas from our Data and Analytic maturity model. For each area, you will be asked to indicate your level of agreement with statements describing the capability. It will take you about 25 minutes to complete the questionnaire and please provide additional information as you see fit.

Once completed, we will carefully review your answers and information. Our Data and Analytic team will compile a consolidated report of your results including our perspectives of where you are on your data maturity journey. In addition, we will provide specific insights into areas of opportunity and improvement. You can expect our report to be emailed to you within 3 to 5 working days.

Together with your report, you will receive an invitation to schedule a free follow-up conversation with our team to review your results and discuss any questions you may have.

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Data Strategy

An enterprise data strategy is a business plan for leveraging an enterprise’s data assets to maximum advantage; it sets the direction and goals and defines an approach to solving the key data challenges facing the enterprise.

A data strategy should include business plans to use information to competitive advantage and support enterprise goals. Data strategy must come from an understanding of the data needs inherent in the business strategy: what data the organization needs, how it will get the data, how it will manage it and ensure its reliability over time, and how it will utilize it. Typically, a data strategy requires a supporting Data Management program strategy – a plan for maintaining and improving the quality of data, data integrity, access, and security while mitigating known and implied risks. The strategy must also address known challenges related to data management.

In many organizations, the data management strategy is owned and maintained by the Chief Data Officer (CDO) and enacted through a data governance team, supported by a Data Governance Council. Often, the CDO will draft an initial data strategy and data management strategy even before a Data Governance Council is formed, to gain senior management’s commitment to establishing data stewardship and governance

Thinking about Data Strategy at your organization, please indicate the level to which agree with the assertions on the left in the matrix below.

	This assertion is just not correct at all	While I partly agree, this assertion is substantially not correct	I agree with some of this assertion but not all; there are key qualifications	I agree with this assertion but with minor qualifications	I agree with this assertion without any qualification
1. Data strategy is part of enterprise goal setting and cascaded down across the whole organization
2. The data strategy addresses the key challenges, vision, objectives and outcomes across the enterprise
3. The data strategy details the specific types of data or information that needs to be managed in each domain across the enterprise
4. The data strategy includes a comprehensive and accurate SWOT analysis and roadmap to achieve the required capability
5. The data strategy process is regular and ongoing and reflects emerging market trends and leading practices
6. The data strategy is communicated to all those involved in or affected by data governance across the enterprise

Your additional comments for Data Strategy

Please feel free to provide us with additional comments and information about this topic.

Data Valuation

Many organizations recognize that their data is a vital enterprise asset. Despite that recognition, few organizations actively manage data as an asset from which they can derive ongoing value. Deriving value from data does not happen in a vacuum or by accident. It requires intention, planning, coordination, and commitment. It requires management and leadership.

Thinking about Data Valuation at your organization, please indicate the level to which agree with the assertions on the left in the matrix below.

	This assertion is just not correct at all	While I partly agree, this assertion is substantially not correct	I agree with some of this assertion but not all; there are key qualifications	I agree with this assertion but with minor qualifications	I agree with this assertion without any qualification
1. Data is valued as an asset across the enterprise on a par with other intangible assets
2. A consistent set of principles have been agreed for valuing data across the enterprise
3. The case for EDM investment is aligned to strategic drivers and tangible organizational outcomes
4. The funding model for EDM has been established, approved and adopted by the organization
5. EDM funding is measured and evaluated against tangible enterprise objectives
6. The assessment of costs and benefits guides ongoing EDM program decisions

Your additional comments for Data Valuation

Please feel free to provide us with additional comments and information about this topic.

Policy & Principles

Data management (DM) principles are usually formulated during data strategy development as part of a DM framework and are the highest order propositions that serve as the foundation for DM.

Typically, the first principle within the DM framework will state something like data is recognized as a valuable, enterprise-wide asset. The DM principles within the DM framework are supported by policies, standards, and controls.

DM policies are short statements of management intent and fundamental rules governing data from creation through to destruction. Standards and controls are the measures and mechanisms by which the enterprise maintains the acceptable performance of its DM.

Thinking about Policy & Principles at your organization, please indicate the level to which agree with the assertions on the left in the matrix below.

	This assertion is just not correct at all	While I partly agree, this assertion is substantially not correct	I agree with some of this assertion but not all; there are key qualifications	I agree with this assertion but with minor qualifications	I agree with this assertion without any qualification
1. The EDM strategy incorporates developing, deploying and maintaining EDM policies and principles
2. A comprehensive set of EDM policies and principles are now in place across the data lifecycle
3. The EDM policies and principles are well known and reflected in operational processes and procedures
4. There is a defined process that allows receiving feedback and inputs to improve EDM policy and principles
5. The effectiveness of EDM policies and principles is measured and widely reported
6. There is routine and consistent periodic review of EDM policies and principles

Your additional comments for Policy & Principles

Please feel free to provide us with additional comments and information about this topic.

Organization & Culture

A Data Management (DM) professional is any person who works in any facet of DM (from technical management of data throughout its lifecycle to ensuring that data is properly utilized and leveraged) to meet strategic organizational goals. DM professionals fill numerous roles, from the highly technical (e.g., database administrators, network administrators, programmers) to strategic business (e.g., Data Stewards, Data Strategists, Chief Data Officers).

In order for data governance to make its best impact, several working groups, capability centers and decision-making bodies will need to be established and staffed with DM professionals and stakeholders.

Thinking about Organization & Culture as it pertains to data at your organization, please indicate the level to which agree with the assertions on the left in the matrix below.

	This assertion is just not correct at all	While I partly agree, this assertion is substantially not correct	I agree with some of this assertion but not all; there are key qualifications	I agree with this assertion but with minor qualifications	I agree with this assertion without any qualification
1. EDM oversight and leadership is an executive management responsibility
2. Clear ownership has been assigned for all the data the enterprise has and needs
3. Roles, responsibilities, and accountabilities, are established across the enterprise
4. The EDM strategy describes the target state and roadmap for EDM organization, roles and responsibilities including an EDM function
5. Data stewards are reporting to the executive stakeholders and / or matrixed to business owners
6. EDM training starts at employee induction and is supported by role specific skills development and refresher programs

Your additional comments for Organization & Culture

Please feel free to provide us with additional comments and information about this topic.

Architecture & Standards*

Enterprise Data Architecture defines standard terms and designs for the elements that are important to the organization. The design of an Enterprise Data Architecture includes depiction of the business data as such, including the collection, storage, integration, movement, and distribution of data.

Ideally, Data Architecture should be an integral part of enterprise architecture. If there is not an enterprise architecture function, a Data Architecture team can still be established. Under these conditions, an organization should adopt a framework that helps articulate the goals and drivers for Data Architecture. These drivers will influence approach, scope, and roadmap priorities.

The principal goals are to:

Identify data storage and processing requirements.
Design structures and plans to meet current and long-term enterprise data requirements
Strategically prepare the enterprise to quickly evolve products, services, and data to take advantage of business opportunities inherent in emerging technologies.

Thinking about Architecture & Standards with regards data at your organization, please indicate the level to which agree with the assertions on the left in the matrix below.

	This assertion is just not correct at all	While I partly agree, this assertion is substantially not correct	I agree with some of this assertion but not all; there are key qualifications	I agree with this assertion but with minor qualifications	I agree with this assertion without any qualification
1. There is documented data architecture that covers all data assets in your organisation
2. There is a defined process to manage adding or editing data architecture for projects
3. Architectural guidelines and standards address all aspects of the data lifecycle from acquisition to destruction
4. Architectural guidelines and standards effectively communicated to all (IT and non-IT) stakeholders across the enterprise
5. Projects and regular operations are highly compliant with enterprise architectural standards and guidelines
6. Architectural guidelines and standards are subject to regular review and continuous improvement

Your additional comments for Architecture & Standards

Please feel free to provide us with additional comments and information about this topic.

Data Storage & Operations*

Data Storage and Operations includes the design, implementation, and support of stored data, to maximize its value throughout its lifecycle, from creation/acquisition to disposal.

Data Storage and Operations includes two sub-activities:

Database support focuses on activities related to the data lifecycle, from initial implementation of a database environment, through obtaining, backing up, and purging data. It also includes ensuring the database performs well. Monitoring and tuning are critical to database support.
Database technology support includes defining technical requirements that will meet organizational needs, defining technical architecture, installing, and administering technology, and resolving issues related to technology.

Database administrators (DBAs) play key roles in both aspects of data storage and operations. The role of DBA is the most established and most widely adopted data professional role, and database administration practices are perhaps the most mature of all data management practices. DBAs also play dominant roles in data operations and data security.

Thinking about Data Storage & Operations at your organization, please indicate the level to which agree with the assertions on the left in the matrix below.

	This assertion is just not correct at all	While I partly agree, this assertion is substantially not correct	I agree with some of this assertion but not all; there are key qualifications	I agree with this assertion but with minor qualifications	I agree with this assertion without any qualification
1. There are documented processes and procedures for data classification, collection, admin, access and release, disposal, audit and quality
2. There is a defined process to manage the use of data models in data-day (BAU) operations. (e.g. Models are updated as a result of regular change)
3. Data management tools are subject to enterprise governance and control
4. Data storage and operations monitoring, maintenance and support ensure the integrity, availability and performance of all enterprise data
5. Change management processes ensure the integrity of entire data lifecycle
6. SLA and OLA measurement and reporting address all aspects of the efficiency and effectiveness of data storage and operations

Your additional comments for Data Storage & Operations

Please feel free to provide us with additional comments and information about this topic.

Modelling, Design, & Sourcing*

Data modelling is the process of discovering, analyzing, and scoping data requirements, and then representing and communicating these data requirements in a precise form called the data model. Data modelling is a critical component of data management. The modelling process requires that organizations discover and document how their data fits together. The modelling process itself designs how data fits together.

Data models depict and enable an organization to understand its data assets. exist at three levels of detail: conceptual, logical, and physical. Each model contains a set of components. Examples of components are entities, relationships, facts, keys, and attributes. Once a model is built, it needs to be reviewed and once approved, maintained.

Within the context of databases, a data definition language (DDL) to used define a data model; a data manipulation language (DML) to insert/update/delete data records and data control language (DCL) is used to control access to data. Data query languages (DQLs) simply the process of retrieving data records which meet the criteria of interest (e.g. all customers in arrears) without the need to know how the data is stored in the database.

Thinking about Modelling, Design, & Sourcing with regards data at your organization, please indicate the level to which agree with the assertions on the left in the matrix below.

	This assertion is just not correct at all	While I partly agree, this assertion is substantially not correct	I agree with some of this assertion but not all; there are key qualifications	I agree with this assertion but with minor qualifications	I agree with this assertion without any qualification
1. Modeling techniques and standards in use are subject to enterprise governance and control
2. Approved business definitions are assigned to defined taxonomies (fully-attributed conceptual models)
3. A data management plan, including authoritative data source for master data, is part of solution architecture documentation
4. Designs leverage and inform the enterprise data modelling processes and repositories
5. Designs lead to an optimal Information architecture that enables the acquisition, production, storage, and delivery of data to meet business and technical objectives
6. Sourcing ensures that all enterprise data needs are met efficiently and effectively

Your additional comments for Modelling, Design, & Sourcing

Please feel free to provide us with additional comments and information about this topic.

Master & Reference Data

Reference data is used to organize or categorize other data, or for relating data to information both within and beyond the boundaries of the enterprise. Usually consists of codes and descriptions or definition. For example, Order Status: New, In Progress, Closed, Cancelled; may incorporate external standards, e.g. postal address or industry classification; it may be used for computation, e.g. currency exchange rate; it may also be quite abstract, e.g. synonym list.

Master Data, which encompasses reference data, is data about the business entities (e.g., employees, customers, products, financial structures, assets, and locations) that provide context for business transactions and analysis. Entities are represented by entity instances, in the form of data / records. Master Data should represent the authoritative, most accurate data available about key business entities. When managed well, Master Data values are trusted and can be used with confidence.

In most organizations, various projects and initiatives, mergers and acquisitions, and other business activities result in multiple systems executing much the same functions, isolated from or uncoordinated with each other. These conditions inevitably lead to inconsistencies in data structure and data values between systems. Both can be reduced through the management of Master Data and Reference Data.

Thinking about Master & Reference Data with regards data at your organization, please indicate the level to which agree with the assertions on the left in the matrix below.

	This assertion is just not correct at all	While I partly agree, this assertion is substantially not correct	I agree with some of this assertion but not all; there are key qualifications	I agree with this assertion but with minor qualifications	I agree with this assertion without any qualification
1. The enterprise has an agreed set of reference and master data under management
2. The definition and maintenance of reference and master data is controlled and managed
3. All data is reconciled to and quality-assessed against an authoritative source of master and reference data
4. The master and reference data processes and tools enable easy, cost-effective sharing of data across the enterprise
5. Linkages between internal master data and external meta data is well coordinated
6. The enterprise has automated testing of master data business rules

Your additional comments for Master & Reference Data

Please feel free to provide us with additional comments and information about this topic.

Warehousing & Big Data*

A Enterprise Data Warehouse (EDW) is a combination of two primary components:

An integrated decision support database and the related software programs used to collect, cleanse, transform, and store data from a variety of operational and external sources.
To support historical and analytical requirements, an EDW may also include dependent, copied subsets of data known as data marts

Data in placed in the EDW through a so called “Extract, Transform, Load” or ETL process. A properly designed ETL system extracts data from the source systems, enforces data quality and consistency standards, conforms data so that separate sources can be used together.

Big data is a term that has become associated with warehousing where the volume of data poses specific challenges that are unique to large scales of data, which are often solved through a range of techniques including parallel and distributed processing.

In many respects big data can be conceived simply as “bigger” data. Despite Moore’s law and the rapid progression of technology, the amount of data that can be captured, processed, and analyzed, relative the amount of data in the observable world, is small.

Thinking about Warehousing & Big Data with regards data at your organization, please indicate the level to which agree with the assertions on the left in the matrix below.

	This assertion is just not correct at all	While I partly agree, this assertion is substantially not correct	I agree with some of this assertion but not all; there are key qualifications	I agree with this assertion but with minor qualifications	I agree with this assertion without any qualification
1. Processes and tools enable consistent extraction/collection, cleansing, transformation, control and loading of warehouse data from all operational and external sources
2. Warehousing, including dependent data marts, support analysis of current and historical data across all data domains
3. Warehousing builds on the atomic data, aggregating and summarizing to meet requirements and ensure performance without loss of the atomic data.
4. Warehousing promote transparency and self-service, ensuring stakeholders understand what is available and how it is assembled
5. Warehousing links to enterprise meta-data, enabling consumers to understand lineage and collaborate with other data stakeholders
6. All data needed by the enterprise, regardless of volume, velocity (including real-time) and variety (structured, unstructured) is available for consumption and analysis

Your additional comments for Warehousing & Big Data

Please feel free to provide us with additional comments and information about this topic.

Analytics & Monetization

Analytics, which is mostly associated with the decision-making objectives of data management, is really part of a decision support. Analytics, an evolution of the decision support arena encompasses all creation, cultivation, and curation of insights.

Thinking about Analytics & Monetization with regards data at your organization, please indicate the level to which agree with the assertions on the left in the matrix below.

	This assertion is just not correct at all	While I partly agree, this assertion is substantially not correct	I agree with some of this assertion but not all; there are key qualifications	I agree with this assertion but with minor qualifications	I agree with this assertion without any qualification
1. Diagnostic analytics, visualization and dashboarding tools are available and in use in order to look at past performance to determine what happened and why
2. Descriptive Analytics methods and tools are readily available and in use across the enterprise to enable operational understanding of what is happening now
3. Predictive Analytics methods and tools are readily available and in use across the enterprise to facilitate insights to future events
4. Prescriptive Analytics methods and tools are readily available and in use across the enterprise to support decision making on "next best actions"
5. Deep learning methods and tools facilitates clustering, classification and modelling of data and the discovery of new abstractions, relationships and insights
6. Machine learning methods and tools support implementation and automation of enterprise performance improvement

Your additional comments for Analytics & Monetization

Please feel free to provide us with additional comments and information about this topic.

Integration & Interoperability*

Integration and Interoperability describes processes related to the movement and consolidation of data within and between data stores, applications, and organizations. Integration consolidates data into consistent forms, either physical or virtual.

Data Interoperability is the ability for multiple systems to communicate. DII solutions enable basic data management functions on which most organizations depend:

Integration of vendor packages into an organization’s application portfolio
Providing operational intelligence and management decision support
Data sharing between applications, and across organizations
Distributing data across data stores and data centers
Integrating structured and unstructured data
Data consolidation into hubs or marts
Archiving data
Managing data interfaces
Data migration and conversion
Obtaining and ingesting external data

Thinking about Integration & Interoperability with regards data at your organization, please indicate the level to which agree with the assertions on the left in the matrix below.

	This assertion is just not correct at all	While I partly agree, this assertion is substantially not correct	I agree with some of this assertion but not all; there are key qualifications	I agree with this assertion but with minor qualifications	I agree with this assertion without any qualification
1. Data integration activities are subject to enterprise governance and control
2. The set of data integration disciplines and tools used by the organization provides bulk transport and load, change data capture, versioning and configuration, metadata capture and management, and in-line data quality checks and controls.
3. The creation and verification of data source precedence rules is subject to enterprise governance and control
4. Changes to the integration environment, including upstream sources and downstream targets, are controlled, coordinated and logged, and are under enterprise governance and control
5. Changes to data sources, transformations, and destination are auditable through the data governance process
6. Selected highly shared data is fully integrated, centrally managed, and delivered as needed to integration data stores

Your additional comments for Integration & Interoperability

Please feel free to provide us with additional comments and information about this topic.

Document & Content Management*

Document and Content Management entails controlling the capture, storage, access, and use of data and information stored outside application (primarily relational) databases.

Its focus is on maintaining the integrity of and enabling access to documents and other unstructured or semi-structured information which makes it roughly equivalent to data operations management for relational databases. The key goals are to:

To comply with legal obligations and customer expectations regarding Records management.
To ensure effective and efficient storage, retrieval, and use of Documents and Content.
To ensure integration capabilities between structured and unstructured Content.

In many organizations, unstructured data has a direct relationship to structured data. Management decisions about such content should be applied consistently.

In addition, as are other types of data, documents and unstructured content are expected to be secure and of high quality. Ensuring security and quality requires governance, reliable architecture, and well-managed Metadata.

Thinking about Document & Content Management with regards data at your organization, please indicate the level to which agree with the assertions on the left in the matrix below.

	This assertion is just not correct at all	While I partly agree, this assertion is substantially not correct	I agree with some of this assertion but not all; there are key qualifications	I agree with this assertion but with minor qualifications	I agree with this assertion without any qualification
1. Records appraisal considers physical volume, frequency of use, operational need, regulatory obligations, historical significance
2. There are high quality and comprehensive tools in place for management of all enterprise records that are seamlessly integrated with underlying operational platforms
3. Records in all forms (documents, emails, texts, images, sound, video, etc.) can be easily captured, controlled, monitored, searched and located
4. Digitization of records and extraction of comprehensive meta-data is simple, accurate, precise and easily automated
5. Document and content management process and tools should facilitate controlled access, collaboration, authoring, and versioning
6. Document and content management process and tools should seamlessly support approval routes and publishing across multiple channels (web, mobile, etc.)

Your additional comments for Document & Content Management

Please feel free to provide us with additional comments and information about this topic.

Metadata Management*

Metadata describes data (e.g., databases, data elements, data models), the concepts the data represents (e.g., business processes, application systems, software code, IT infrastructure), and the connections (relationships) between the data and concepts. An example of metadata is the information on a catalogue card in a library describing a book, or a list of column names in a database table.

Metadata management is the end-to-end process and governance framework for creating, controlling, enhancing, attributing, defining, and managing a metadata. The Dewey Decimal Classification is a metadata management system developed for libraries.

Thinking about Metadata Management at your organization, please indicate the level to which agree with the assertions on the left in the matrix below.

	This assertion is just not correct at all	While I partly agree, this assertion is substantially not correct	I agree with some of this assertion but not all; there are key qualifications	I agree with this assertion but with minor qualifications	I agree with this assertion without any qualification
1. Meta-data processes and tools address all information about the technical and business processes, data rules and constraints, and logical and physical data structures used to manage enterprise data.
2. Meta-data is readily accessible and expressed in a language that all business and technical stakeholders can understand
3. An enterprise metadata repository spans all relevant categories and classifications of data assets under management, and accurately reflect the implemented data layer of the organization.
4. Roles and responsibilities clearly defined across the enterprise for the capture, updating, and use of meta-data
5. The processes and tools used to maintain the meta-data repository ensure that that it is complete, current and correct
6. Meta-data types and data definitions support consistent and automated import, subscription, and consumption practices

Your additional comments for Metadata Management

Please feel free to provide us with additional comments and information about this topic.

Data Quality

No organization has perfect business processes, perfect technical processes, or perfect data management practices; consequently, all organizations experience problems related to the quality of their data. However, organizations that formally manage the quality of data have fewer problems than those that leave data quality to chance.

Formal data quality management is like continuous quality management for other products. It includes managing data through its lifecycle by setting standards, building quality into the processes that create, transform, and store data, and measuring data against standards. Managing data to this level usually requires a Data Quality program team.

The Data Quality program team is responsible for engaging both business and technical data management professionals and driving the work of applying quality management techniques to data to ensure that data is fit for consumption for a variety of purposes. The team will likely be involved with a series of projects through which they can establish processes and best practices while addressing high priority data issues.

Thinking about Data Quality at your organization, please indicate the level to which agree with the assertions on the left in the matrix below.

	This assertion is just not correct at all	While I partly agree, this assertion is substantially not correct	I agree with some of this assertion but not all; there are key qualifications	I agree with this assertion but with minor qualifications	I agree with this assertion without any qualification
1. There is an integrated, organization-wide strategy to achieve and maintain the level of data quality required to support the enterprise goals and objectives.
2. Data profiling skills, tools and methods are consistently applied across the enterprise to identify data quality issues
3. Data quality assessment results include recommendations for remediation with supporting rationale.
4. Impact analysis includes estimates of the costs of fixes, the level of effort, characterization of the business impact, and tangible and intangible benefits.
5. The organization addresses root causes of defects and other issues based on an understanding of the meaning, technical characteristics, and behavior of the data over time.
6. There is extensive use of automation to facilitate to identify and resolve data quality issues at the source

Your additional comments for Data Quality

Please feel free to provide us with additional comments and information about this topic.

Security, Privacy, & Compliance*

Data Security includes the planning, development, and execution of security policies and procedures to provide proper authentication, authorization, access, and auditing of data and information assets.

The specifics of data security (which data needs to be protected, for example) differ between industries and countries. Nevertheless, the goal of data security practices is the same: To protect information assets in alignment with privacy and confidentiality regulations, contractual agreements, and business requirements.

The goals of data security activities include:

Enabling appropriate access and preventing inappropriate access to enterprise data assets
Enabling compliance with regulations and policies for privacy, protection, and confidentiality
Ensuring that stakeholder requirements for privacy and confidentiality are met

Thinking about Security, Privacy, & Compliance with regards data at your organization, please indicate the level to which agree with the assertions on the left in the matrix below.

	This assertion is just not correct at all	While I partly agree, this assertion is substantially not correct	I agree with some of this assertion but not all; there are key qualifications	I agree with this assertion but with minor qualifications	I agree with this assertion without any qualification
1. All enterprise data is classified and tagged in terms of business criticality and sensitivity
2. Role based security is implemented (e.g. access on a "need to know" basis) and updated in real-time when roles change (elevated or revoked)
3. There is sufficient investment in and maintenance of systems to minimize the number of manual and/or out-of-process changes to data
4. The enterprise monitors access and changes to sensitive data and takes action in real-time to protect enterprise data
5. The enterprise uses de-identification and statistical disclosure techniques for sensitive data to prevent un-intended access or disclosure
6. Regular risk assessments are conducted from a data perspective as part of an overall enterprise risk management regime

Your additional comments for Security, Privacy, & Compliance

Please feel free to provide us with additional comments and information about this topic.

Monitoring, Audit, & Reporting*

Monitoring and logging are fundamental to the exercise of authority, control, and shared decision making (for planning, design, operations monitoring, and enforcement) over the management of all enterprise data assets.

Monitoring and logging are intrinsic to many aspects of data management including:

Database storage and operations (especially availability, performance and tuning)

Privacy, regulatory compliance, and auditing

Data sharing and integration

Data quality

Operationally, real-time database monitoring tools automate monitoring of key metrics, such as capacity, availability, cache performance, user statistics, etc., and alert administrators of database issues.

More broadly a monitoring strategy will identify what needs be monitored; how it will be done (e.g., native resource monitoring)

Thinking about Monitoring, Audit, & Reporting with regards data at your organization, please indicate the level to which agree with the assertions on the left in the matrix below.

	This assertion is just not correct at all	While I partly agree, this assertion is substantially not correct	I agree with some of this assertion but not all; there are key qualifications	I agree with this assertion but with minor qualifications	I agree with this assertion without any qualification
1. Deviation from EDM controls are recognized and remediated quickly
2. An EDM function provides oversight and visibility of all aspects of enterprise data governance
3. Internal and external auditing ensures that all required business controls over data are consistently and comprehensively applied
4. An EDM function provides and facilitates effective decision making regrading data across the enterprise
5. Key performance indicators are defined and reported across all aspects of EDM
6. KPI and compliance reporting drives continuous improvement in EDM practice across the enterprise

Your additional comments for Monitoring, Audit, & Reporting

Please feel free to provide us with additional comments and information about this topic.

Congratulations, your are done with the assessment questions!

In order to better interpret your responses and gain a further understand your perspective, please answer the following questions.

What is your role at the organization

What deliberate initiatives has your organization undertaken with regards data maturity?

Rank the following aspects of data maturity apparent at your organization

The Utilligent Data & Analytics Maturity Framwork

Architecture & Standards
Data Storage & Operations
Modelling, Design, & Sourcing
Master & Reference Data
Warehousing & Big Data
Analytics & Monetization
Integration & Interoperability
Document & Content Management
Monitoring, Audit, & Reporting
Security, Privacy, & Compliance
Data Quality
Metadata Management
Data Strategy
Data Valuation
Policy & Principles
Organization & Culture

What are your biggest challenges/opportunities for Data & Analytics at your organization?

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Data & Analytics Capability Maturity Assessment

Data & Analytics Capability Maturity Assessment (Tech)

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The Utilligent Data & Analytics Maturity Framwork