TECH | Nov 15, 2016

Is holistic Data Governance possible?

What are the key aspects for applying a holistic strategy of Data Governance in the company?

Data Governance is not just a project related to IT structures but a real business need because its aim is not only to define the responsibilities and competences on corporate data, but above all to understand how to make the most of the entire information heritage and render it remunerative.

In addition to the speed and timeliness of production, information must also comply with implicit quality and certification requirements, now increasingly undermined by the redundancy of data related to multiple pertinent sources and the proliferation of departmental systems for information management. Often, in fact, DCIM (Data Centre Infrastructure Management) platforms and those for IT service management (ITSM) work and operate in isolation, even using different work teams which generate an increase in the possibility of conflicts among human resources. It is therefore essential for companies to be able to implement a structured approach, oriented towards the good governance of information processing and to holistic data management (Data Governance) which operates completely within various information, organisational, architectural and political silos. At the same time, this path requires alignment of people, processes, policy and technology to ensure the delivery of reliable and secure data, in order to meet sector regulations, reduce the cost related to business and increase profits.

Data Governance

Already in 2013, Rand Worldwide’s Data Governance Survey had shown that 96% of the sample of survey participants indicated data governance as one of the strategic assets at business level. Today there are several catalysts that favour Data Governance and in some highly regulated sectors, such as banking and health. It is possible to invest in Data Governance for supporting business growth objectives using data as an asset for obtaining competitive advantage, improving up-selling and cross-selling or increasing customer loyalty. Data Governance is considered on the one hand as a critical opportunity to support the achievement of objectives on gains, cost reductions and improvements of business efficiency, and on the other as an opportunity to optimise the complex process of merger and acquisition under way.

The holistic strategy

For designing a holistic and effective Data Governance strategy, it is necessary to start from an accurate assessment for verifying the state of maturity of the company with respect to data management. In this regard, Informatica has proposed a ten-asset model which, starting from the definition of vision, outlines the wide-ranging strategic goal outreach to then move on to the business case, showing clearly all the specific business opportunities facing decision-makers.

The role played by people involved in the programme appears to be fundamental in this new process: people such as executive sponsors, preferably to be identified from among cross-functional C-level executives; data stewards, those who possess specific knowledge and excellent communicators who must translate the implications of Data Governance onto business processes; the leaders of Data Governance, who coordinate the data stewards and mediate with stakeholders.

Technology, understood as IT tools and architectures, is a third asset and includes all on-premise and cloud systems and applications of the business ecosystem that generate and use information, as well as a shared business glossary and tools for data management throughout the entire life cycle with functionalities of discovery and profiling, metadata management, data equality and security, process modelling, collaboration, etc.

In the proposed model, company policies are an essential component and serve to define a number of aspects, including: responsibility and ownership of data, roles and responsibilities of those involved in the project, standards for data acquisition and validation, rules of access, use and conservation.

The fifth point of the proposed model refers to the process of strategic alignment for regulating work relations among roles, while the sixth concerns measurement of performance on the basis of three very distinct levels: impact on business organisation; effectiveness of policies and validation standards; and returns for business, such as reduction of penalties for non-compliance, improvement of operational efficiency, profit growth and customer satisfaction.

It appears obvious that in this approach, change management – to be supported by adequate training and proper and effective communication – must also be pursued with patience so that Data Governance and the correct use of information bring about real cultural change within the entire organisation. All the interconnected activities of Data Management must be considered in this process: dependent processes upstream (collection, processing and updating of data), stewardship (application of policies and standards), and downstream (analysis, cleaning and protection).

Finally, all the data management activities must have a solid Program Management by management experts for coordinating interactions, training and monitoring, as well as defined processes regarding discovery, or analysis of the status of the data and activities, tools, skills to support data management, definition of business glossaries, standards and policies, application of rules and procedures, and definition of responsibilities and roles, measurement of ROI, as well as regulatory and business requirements compliance.



Source: The ten aspects of Data Governance,


The holistic strategy of data management becomes increasingly necessary with the transition to cloud, the explosion of applications, the growing demand for security and compliance, and anywhere computing.

Data are now at the centre of everything and companies must have the possibility to activate them wherever they reside. N. Robert Hammer, chairman, president and CEO of Commvault, has recently stressed precisely five fundamental aspects that would allow companies to adopt a holistic data management strategy and which may be summarised as follows:

  1. Know data: companies must know where their data are stored in order to guarantee security and know their availability for activities of resetting, disaster recovery, test/dev, reporting and analysis. By adopting a rich, wide, dynamic and scalable index, companies can collect, modify and organise all their information.
  2. Federate data: today the companies have not only one but multiple information silos and often, in fact, do not even know which data they possess or where they are allocated. Federating data means protecting, retrieving, moving, finding and delivering applications and data that reside on different infrastructures, to be able to easily access and use them without having to move them.
  3. Mobilise data: companies must aggregate data, which currently reside in different places, on-premise, in the cloud, in virtual and mobile environments, to do what is necessary to be able to back up such data on these devices to prevent loss and allow their elimination.
  4. Manage data: companies must be compliant with a growing number of regulations, federating data under a single corporate governance and adopting the policies necessary for ensuring compliance.
  5. Protect data: in a world subject to continuing threats to security from malware, ransomware, data breach and other types of internal and external attacks, organisations must be able to protect their data. An effective security strategy must provide for authentication of those who want to access the data, confirmation of access rights and encryption/protection of data with passwords.

In conclusion, it thus appears clear that, regardless of the production or economic sector, Data Governance helps to optimise the business value obtained from data. In fact, the need for Data Governance cannot be ignored if profits, market share and growth of the product portfolio are not to be lost, as well as customer experience optimised and supply chains made more efficient.

Effective Data Governance meets quality and management requirements independently of where data reside and where they are acquired or consumed. Holistic and integrated management makes it possible to define the responsibilities and competences regarding data, but above all to understand how to make the most of the entire range of corporate information assets and make them profitable over time.

Emma Pietrafesa