Data is the new currency of the information economy, the lifeblood of businesses, and the present era’s new oil. They are growing at an unprecedented rate, and are becoming an integral part of human lives. Every moment petabytes of data are being generated and analyzed for decision-making. Data are also a double-edged sword as it has the potential to make or break an economy.

If used properly Data can help to improve the way programs and policies are developed and implemented, drive economies, and empower citizens. The same data falls at risk of abuse if left ungoverned. This leads to a fundamental question; What kind of data management and Data governance arrangements are needed to support the generation and use of data in a safe, ethical, and secure way while also delivering value equitably?

This situation arises due to:

• Lack of understanding of the gravity of Data by organizations.
• Challenges in skill sets of professionals handling Data within the organization.
• Lack of understanding and enforcing new policies and compliances in alignment with the organization’s requirements.

As a result, modern data management and governance frameworks are difficult to implement. It requires a focused approach of right intention, planning, coordination, and commitment. It has to follow a top-down approach where the management takes the lead.

Data Management and Data Governance cover a wide range of activities from decision-making to technical deployment. Collaboration between business and IT roles ensures high-quality data meeting strategic requirements to leverage data assets for value.

The primary goal is to maximize benefits, as with financial and physical assets. A Data Management and Governance assessment identifies areas for improvement to ensure legal compliance, and privacy protection, and prevent data misuse. Tracking improvements and evaluating growth and Data maturity level through digital transformation assessment is essential for organizations to evolve, attain Data maturity, achieve a competitive advantage, and maximize business value while staying focused on business needs and strategic goals.

Data Maturity models are beneficial to rate the level of readiness in the Data Management and Governance parameters

Data Maturity models describe process characteristics’ progression through various levels. It is an approach to process improvement based on a globally followed framework developed in the US. When an organization comprehends the process characteristics, it can assess its Data maturity level and devise a plan to improve its capabilities. It can also measure its progress and compare itself to competitors or partners tailor-made and guided by the levels of this model. At each new level, process execution becomes more consistent, predictable, and reliable, leading to improvement. Progression occurs in a predetermined order as mentioned below.

Data Maturity Models Levels commonly include

• Level 1 Initial / Ad Hoc: General-purpose data management using a limited tool set, with little or no governance. Data handling is highly reliant on a few experts. Roles and responsibilities are defined within silos. Each data owner receives, generates, and sends data autonomously. Controls, if they exist, are applied inconsistently. Solutions for managing data are limited. Data quality issues are pervasive but not addressed. Infrastructure supports are at the business unit level.

Assessment criteria may include the presence of any process controls, such as logging of data quality issues.

• Level 2 Repeatable: Emergence of consistent tools and role definition to support process execution. In Level 2, the organization begins to use centralized tools and to provide more oversight for data management. Roles are defined and processes are not dependent solely on specific experts. There is organizational awareness of data quality issues and concepts. Concepts of Master and Reference Data begin to be recognized.

Data Maturity models Assessment criteria might include formal role definitions in artifacts like job descriptions, the existence of process documentation, and the capacity to leverage toolsets.

• Level 3 Defined: Emerging data management capability. Level 3 sees the introduction and institutionalization of scalable data management processes and a view of DM as an organizational enabler. Characteristics include the replication of data across an organization with some controls in place and a general increase in overall data quality, along with coordinated policy definition and management. More formal process definition leads to a significant reduction in manual intervention. This, along with a centralized design process, means that process outcomes are more predictable.

Assessment criteria might include the existence of data management policies, the use of scalable processes, and the consistency of data models and system controls.

• Level 4 Managed: Institutional knowledge gained from growth in Levels 1-3 enables the organization to predict results when approaching new projects and tasks and to begin to manage risks related to data. Data management includes performance metrics. Characteristics of Level 4 include standardized tools for data management from desktop to infrastructure, coupled with a well-formed centralized planning and governance function. Expressions of this level are a measurable increase in data quality and organization-wide capabilities such as end-to-end data audits.

Assessment criteria might include metrics related to project success, operational metrics for systems, and data quality metrics.

• Level 5: Optimization: When data management practices are optimized, they are highly predictable, due to process automation and technology change management. Organizations at this level of Data maturity focus on continuous improvement. At Level 5, tools enable a view data across processes. The proliferation of data is controlled to prevent needless duplication. Well-understood metrics are used to manage and measure data quality and processes.

Data Maturity Models & Assessment criteria might include change management artifacts and metrics on process improvement.

Each Data maturity level covers specific assessment criteria related to the Data management and Governance processes of an organization. Based on the findings, the organization can develop;

• High-value improvement opportunities related to processes, methods, resources, and automation.
• Capabilities that align with business strategy.
• Governance processes for periodic evaluation of organizational progress based on characteristics in the model.

A Data Management and Governance Assessment can be used to evaluate overall, or it can be used to focus on a single process. This can assist in aligning uniformity in views of business and IT on the efficiency and effectiveness of data management practices within the organization. The assessment can set and measure organizational goals and compare the organization to peers as well as industry benchmarks.

Popular and unique Knowledge Graph Use Cases for data analytics

References

Baskarada, Sasa. IQM-CMM: Information Quality Management Capability Maturity Model. Vieweg+Teubner Verlag, 2009.

Data Management Body of Knowledge, DAMA International Technics Publications, Basking Ridge, New Jersey.

IBM Data Governance Council. https://ibm.co/2sUKIng.

INC., M. (n.d.). PRESS RELEASES: Enterprise Data Management Market worth $122.9 billion by 2025. Retrieved from MarketsandMarkets:

https://www.marketsandmarkets.com/PressReleases/enterprisedata-management.asp