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There is a sundry of benefits associated with effective Data Governance including:
- Remaining compliant
- Reducing Risk
- Bettering Inventory Management
- Improving Data Quality
- Increasing Transparency
Nonetheless, these boons (as well as additional complications associated with integrating various technologies for Data Management) can obfuscate what should be the principle objective of Data Governance programs—to further the ends of the business. According to Gartner, “Too many information governance success criteria are focused on intermediate metrics and analytics that don’t relate directly to business outcomes and their improvement.”
The future of Data Governance is one in which governance practices, roles, and responsibilities are organized around attaining business objectives. It is a future in which the various aspects of governance (stewardship, governance councils, metadata) are mastered and benefits are determined by business value. And it involves aligning IT and the business, utilizing Cloud options for governance, facilitating data integration alongside data quality measures, and much more.
Cloud Data Governance
As adoption rates for Cloud Computing and its specific applications are growing, its impact on Data Governance is increasing as well. Most governance programs account for the Cloud as additional sources of data. The most prudent ones take steps to ensure that Cloud data and processes are governed the same way that those from conventional sources are, thereby reducing the possibility of silo culture.
However, there is increasing evidence that indicates that the Cloud can also be a source of governance measures as well. Information Governance as a Service may be the latest of SOA acronyms, but governance SaaS can provide a centralized, top-down approach for policy enforcement while reducing the load (and expenses) of on-premise IT resources. Data quality SaaS tools currently have low adoption rates, but are a demonstrable area in which Cloud applications can assist governance practice.
The majority of this software focuses on customer data and has less influence over other spheres of data quality such as general data cleansing and data profiling. Nonetheless, it can significantly enrich data while reducing inconsistencies and duplications. Such software is also available as part of Platform as a Service (PaaS) offerings. Additional benefits include the fact that these models are expensed as part of operations and require little upfront capital. Thus, they are viable means of supplementing traditional quality measures and should only expand their capabilities across different domains in the coming years.
Business and IT Alignment
More than other facets of the enterprise, Data Governance provides an opportunity for the merging of IT and the business, which need to work together to provision accurate, reliable, and timely data that is accessible to the end user. By viewing governance as just such an opportunity to narrow the traditional divide between these facets of the enterprise, organizations increase the propensity for deploying user-autonomous technologies including mobile, social media, and Cloud applications in a way that is consistent with desired governance practices.
In addition to denoting specific goals for governance, it is the business that determines the metrics and KPIs that IT implements. IT’s role is to function as a sentinel for ensuring that governance principles are followed, and to work in conjunction with Data Stewards (which typically come from business domains) to ensure this goal is met. Therefore, stewardship processes should be ingrained within business practices related to governance; stewards should also operate as a means of facilitating communication between these two sides. The objective is to motivate representatives across business units to align with IT to fulfill governance goals.
Synthesizing Quality and Integration
Data integration has historically presented issues for data quality and governance in general. However, several more recent developments have accounted for this fact and increasingly merged these two pivotal facets of governance. A number of Master Data Management (MDM) solutions are equipped with integration capabilities, while vendors are steadily making a point to provide complementary technologies for their quality and integration solutions.
There is also an emerging trend in which data integration solutions are equipped with quality capabilities to further blur the distinction between what were once two distinct markets. Although the level of integration that these synthesis approaches offer can be improved, organizations and vendors alike are recognizing the fact that integration is virtually meaningless without quality, and that a diversity of sources provides the most quality data. Some of the more frequently used functions of data quality tools include metric visualizations, monitoring, standardization, matching, parsing, and cleansing.
Additional Data Sources/Types
Contemporary and future governance programs will have to come to terms with a substantial increase in the sources and types of data. In particular, programs must contend with the emergence of mobile technologies and the BYOB (Bring Your Own Device (BYOD) phenomenon, social media, and Big Data in a uniform way in which governance policies are adhered to. The degree to which they do so and the most effective means of implementing policy may prove surprising:
- Mobile: The convenience and functionality of tablet devices and smart phones encourages the sort of autonomy which can override governance—without the proper policies implemented. Regardless of location or device used, any data accessed or altered from an enterprise account are subject to governance, as are organizational data accessed from personal accounts. Data quality tools can assist in ensuring that enterprise data has not been compromised by remote use, while stewardship and IT monitoring can help to reduce the usage of policy infringement as well.
- Social Media: Governance concerns for data generated from social media sites are related to both mobile technologies and Big Data, as they are accessible through the former and can constitute facets of the latter. Text, search tools, and semantics technologies can render social media data into forms in which governance policies can be applied, while quality techniques for structured and unstructured data can help ensure conformity as well.
- Big Data: A recent Gartner survey found that “buyers place…little emphasis on support for Big Data issues when selecting data quality tools and vendors; and we predict that, through 2016, 25% of organizations using consumer data will risk damage to their reputations because of their inadequate understanding of information trust issues.” Implementing data quality tools for Big Data Governance is an effective means of augmenting trust strategies which are largely based on sources of Big Data.
Regulatory issues at the industry, local, and even international level can dictate the lifecycle management and retention factors of various Data Governance policies. Policies must mandate who has access to such data and for how long, as well as when it is prudent to shift repository or storage facilities for data. Issues of discovery can complicate this process and should be thoroughly researched by legal teams. Organizations should also determine which metadata are relevant and which can be deleted during different stages of the lifecycle process.
The Future Revealed
The Data Management landscape is changing extremely rapidly, and it is necessary for Data Governance to evolve with it so that organizations can continue to derive value from it and attain competitive advantage by doing so. This latter facet of the implications for governance is probably the most vital to its future – governance policies must be designed around enhancing the business. Once this is so, the specific technologies for accessing and utilizing data – whether they involve the Cloud, Big Data, Small Data, mobile or social media – can be aligned to facilitate that value, along with inherent organizational structuring (synthesizing IT and the business).