Why is Data Governance Key for Your Data Business?

Why is Data Governance Key for Your Data Business?

Data governance is a term often used in the context of digital transformation and the drive toward a data-driven organization. It refers to the process of managing the availability, usability, integrity and security of the data used in enterprise systems, and involves a set of internal data procedures and policies that also control data usage. The term is used on both a macro and micro level.

Governance is key to a data-driven business, where it’s essential to have in place a robust operating model in relation to the policies, procedures and standards as they relate to data, specifically the controls around it and the value it can generate. The principal participants in data governance are key stakeholders from across the organization.

Implicit in the need for strong governance is acceptance of the requirement for an organizational culture that understands data work flows and embraces a digital approach to the enterprise. Digital transformation – in essence, Placing data at the heart of the organization – sometimes referred to as digital transformation – is a necessary early step for firms seeking to monetize their data in a sustainable way.

In many organizations, the use of data is often isolated, with little appreciation for the strategic imperatives of the organization as a whole, and little consideration given to data as a supply chain item and the data ‘lifecycle’. In order to fully leverage the value of their data, originators need to embrace a digital-first stance across almost every aspect of operations, from HR to Marketing and Sales to IT. Organizational structures may need to be revised. Roles, responsibilities, policies and procedures need to change, while effective data training programs under the auspices of the digital-first culture need to be introduced.

Establishing a Focus on Data Quality

For more traditional and less agile organizations, this may be challenging. But when it comes to creating monetizable data products, it is an essential first step. Without a data-driven culture that is harmonized with the overall strategic vision of the organization and socialized within all functional areas, it will prove impossible to create data products that can be productized and monetized in their own right.

Companies seeking to offer alternative data products need to put in place an agile, data-driven business strategy and operational methodology if they are to establish a viable data business. At the same time, as they execute their data strategies, organizations must continue to support internal teams in the continuous acquisition of data and the identification of unseen commercial opportunities.

In this way, companies can establish a firm internal focus on data quality. Ensuring data quality is often regarded as an operational headache as it is closely connected to many challenging aspects of digital transformation. As part of the digital transformation process, organizations must assign ownership to data assets, and must take steps to identify and document the various technical, regulatory and strategic requirements for an optimal, bespoke approach to leveraging the data as a saleable commercial asset.

As such, it is essential that any data strategy should put in place active ownership and management of data sources, embodied within a governance program, with the aim of creating sources of consistent, high-quality, normalized data that can then be monetized and launched to the market.

What Does it Mean to Have Effective Data Governance?

Robust data governance is characterized by a number of important elements:

  • Key stakeholders are part of an organized framework for overseeing the organization’s approach to data.
  • A recognized operating model is in place, bringing together the key stakeholders to discuss policies, procedures and standards as they relate to data, the controls around it and the value it can generate.
  • Policies exist describing the approach to data governance, as well as how data is collected, processed, maintained, stored and used. Compliance with such policies is incorporated within the organization’s existing Audit and Risk Management frameworks.
  • The effectiveness of the Data Governance model must be measured, and maintained to ensure it is evolving to support the needs of the organization.

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Is Your Firm Allowed to Sell Data? An Ethical Framework for Data Privacy

Hand-in-hand with strong data governance is the requirement for the creation of an ethical framework that is aligned with the organization’s core values. It is essential that companies tie these principles back to reputational risk and ask themselves what would clients, customers and suppliers think about using this data?

Data-driven companies recognize that frequent data breaches have sapped consumer trust. Policies should be in place to ensure that data is only used for the purpose it has been collected and that the organization is aligned with data-focused regulations such as the CCPA or GDPR, not forgetting that other, industry-specific rules and regulations may apply.

There are three ethical principles that should be key considerations when approaching any data monetization initiative:

  1. Private customer data and identity should remain private. If data transactions occur all efforts need to be made to preserve privacy. This is further to Article 12 of the UN declaration of human rights.
  2. Customers have a right to know how their data is being used or sold. Ideally, they should have easy access to control the usage rights of their data. This means providing a dedicated point of contact for customers to address any data-related concerns, to exercise their rights under data legislation and to comply with the accountability principle that underpins such laws.
  3. Information about privacy and security practices should be easily understandable and accessible.

When referring to data privacy in the context of external monetization of alternative data, use cases often relate to licensing of data to investors, asset managers, insurers and others to evaluate a company, market or investment. In these instances, individual data must be anonymized, pseudonymized or erased, since the object of the exercise is to identify and analyze trends at a broader level. The aim is not for targeted advertising; therefore, individuals should not be impacted by private data being shared to third parties.

Getting your data and market positioning right are essential. But so too are putting in place robust governance and privacy safeguards. The latter two are often overlooked in the race to take advantage of an opportunity to monetize valuable datasets, but it’s worth taking the time to ensure there are no nasty surprises later.

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