Data Governance - where to start?

Having a strategic data governance plan and successfully implementing it are two very different things, both containing many moving pieces that need to work together. As you embark on the data governance journey there are many components that can influence your model and questions that can surface. Here are a few to consider…

Trend Influences

As technologies emerge at an ever quickening pace, organisations trying to keep up with industry trends (and buzzwords) like Cloud, Big Data, Analytics and the Internet of Things (IoT) need to have a strong understanding of their data to leverage and provide value to their customers and investors.

Enforcing a policy to manage and better govern data doesn’t happen overnight, nor should it be dictated by the next big thing. Having one cloud based solution to your data validation needs might solve a problem for one business unit, but how does it integrate with other departments of the business and their needs?

Tip: Consider the applicability of a solution across multiple stakeholders within your business to maximise your return on investment.

Data Specific Challenges

Not all data is created equal. The purpose or reason behind acquiring particular data attributes might be different in different contexts. For example, consider an alcoholic beverage supplier’s CRM and the record of a person’s age. One system might record the age as a flag of being of legal drinking age or not. Another could record the entire date of birth, especially if a proof of age card like a driver’s licence is supplied. Different still is the record of the person’s current age based on the capture date.

Overarching rules on which data source is most reliable and specific rules on when particular data sets would be more appropriate need to be created.

Tip: Data governance policies and rules need to be created considering the intended business use of the data, and the context in which it is meant to be used.


With the realisation that data can bring previously untapped opportunities for operational efficiencies and new revenue streams, comes the desire of multiple stakeholders from across the business to be interested in being involved in setting policy, managing data and processes and improving the insights data can provide. Establishing roles and responsibilities for various aspects of each individual business line, processes to manage creation, discussions on the use and growth of data assets as well as the technologies that enable this is crucial.

Tip: Leaders must ensure there is constructive communication and collaboration between business units and stakeholders with alignment in activities to the same goals.

Project and culture challenges

Legacy systems, neglected data quality, silos of information (or fragmentation), incomplete unified views of customers and important entities all contribute to a compromised data governance function.

To compound this, multiple personalities, internal cultures and processes can prove to be a big challenge. An organisational culture that values data quality and promotes practical methods of data management, including setting clear communication policies and robust evaluation and change processes will help establish a sound governance framework.

Tip: Not everyone can contribute to and make decisions on directions or set the scope for data governance initiatives. Like all other projects, data governance programs require strong project management including an achievable scope, timeline and a realistic budget.

Business requirements and technology

Insights required by various business units might need to be sourced from incompatible data sources. New requirements and directives (such as predictive analytics) may require stable and mature data matching and Single Customer View solutions in place to reduce corporate risk and provide maximum return.

Recognising and understanding technological problems can also guide the project direction and lead to a successful data governance strategy being implemented.

Tip: Create a well thought out technology roadmap with clear milestones and deliverables to ensure periodic “wins” and a clear path to achieve stated business goals.

Start with assessing the current state of affairs in the company and what the future needs of the business are by answering these questions:

  1. Are there piecemeal solutions within the organisation such as point solutions or one-off data quality projects?

    If the drivers for implementing point solutions are for the sole purpose of data quality, consider an integrated data standardisation, validation and enrichment solution, rather than working with several individual, incompatible solutions from different vendors.

  2. Would a Federated Search serve the needs of combining incompatible data sources for related information and help business intelligence analysts and decision makers?

    Investigate Federated Search capabilities which are able to search multiple sources, and present the result in a single aggregated result set. Federated search can work by either replicating information to be used as a search index (faster and more flexible search, but greater setup and maintenance), or it will broadcast the search requests to connected information repositories, and merge the search responses.

  3. How can enabling solutions like a Single Customer View assist in improving business processes, improve customer experience, and provide insight for competitive advantage?

    Having consolidated information at hand when a customer calls can provide benefits to customer experience, save staff time and frustration as well as improving downstream activities such as follow ups or providing a unified experience in multi-channel and multi-device customer touch points. Effective aggregation, data enhancement and segmentation can be achieved with a Single Customer View which leads to the ability to detect activity trends and predict future behaviour of individual and groups of customers. This type of data analytics can help provide a competitive edge.

Answering the “whys” behind a data governance strategy will often result in more clear business requirements and set the scene for a well-defined vision of enabling technical solutions.

Successful data governance relies on a highly capable technological base for the people and processes that drive them to extract the benefits offered by a framework that is meant to unite and empower an organisation. The outcomes are high value and successful decisions.

Article written by Darren Wu.

Darren is a sales manager at Intech Solutions with over 15 years working with data, data systems and solving business issues.

For a confidential discussion on your data governance challenges, goals and technology requirements, call the Intech Solutions team today on 61 2 8305 2100 or email sales and let us help you put the pieces of the jigsaw together.