How organisations can succeed with data governance

In today’s world, an almost incomprehensible amount of data is produced each day.

A recent Raconteur report estimates that data volumes are set to swell even further – in fact, its researchers estimate that by 2025, 463 exabytes of data will be created around the world every day. To put that into relatable context, one exabyte is more than 1,000,000 times larger than the volume of a terabyte hard drive.

The scale of the data created is impressive, and so is the value which can be extracted from it. Organisations of all sizes now use and rely on data in everything they do, but to operate effectively and succeed in using that data it needs to be of high quality. For businesses to have the chance of developing better client relationships, delivering seamless customer experiences, and creating effective sales and marketing campaigns, a robust CRM that is populated with clean, accurate data is essential.

However, owing to the great amount of data that companies are constantly generating in a short space of time, ensuring data is always reliable and of high quality is an unenviable task. The key to success here is data governance.

What is data governance?

To put it simply, data governance consists of a team of people that a business employs to manage its use of data, as well as the processes and technology leveraged. A standard for data needs to be established that matches the organisation’s individual requirements and processes, as well as a plan for implementing, enforcing and supporting that standard.

Although security and governance are distinct matters, a complete and thorough governance policy should include a security review which ensures that the right people have proper access to the right information, including compliance with regulations and storage.

For data governance policy to be efficient and effective, each set of data should, at a minimum, answer the following:

– Where is the data stored?
– Who has access to the data?
– How is the data structured?
– How are key terms and entities defined within the data?
– What are the organisational expectations in terms of data quality?
– What does the organisation want to do, or be able to do, with the data?
– What needs to happen so the data meets these goals?

While data governance can be simply defined, being able to answer the questions above to establish and implement effective governance is indeed a complex challenge.

Who to involve?

A lot of companies relegate data governance as a job for an IT team, but this approach is too limited. IT teams are great at the technical aspects of data management, but they are usually too far removed from the organisation’s business needs and daily operations to be able to develop a comprehensive and effective strategy for delivering data to the people who need it.

Data governance really goes far beyond the scope of an IT team’s capabilities, and instead, businesses should focus on creating a team to develop and oversee their data governance programme. This team should involve people from various departments across the company and anyone who is a producer, collector, user, protector or owner of data.

It all depends on the structure of the organisation, but in addition to IT, the teams to consider typically includes sales, marketing, tech support, professional services, product development, legal, compliance, finance, management and more. Through the involvement of all these representatives, it then becomes possible to guarantee that many perspectives and priorities within a large business are fairly represented. Businesses are also then more likely to be able to create data governance policies and procedures that address the needs of all departments around the company.

How to combat a complex procedure

Although achieving data governance is all about simplifying data access and usage with logical and meaningful standards and processes that are easy to follow, the road to creating this can become very complex. Businesses should remind themselves not to treat data governance as a hurdle to overcome, but as a tool to improve the business outcomes and an ongoing exercise with no end date.

It can be very easy to spiral into a process-building rabbit hole, but to ensure the data governance goals are achieved, there are various tips to follow. The first is to look for quick wins that can keep the team motivated, and then build to more ambitious goals over time. It is also important that businesses prioritise the process by first getting the right team in place, then building the right processes and moving onto defining the technology needs.

Once that is done, clear and measurable goals need to be set so that progress can be easily monitored. Everyone should also be able to identify and understand their role and responsibilities so that they are aware of why they are involved and what is expected of them with data management and governance. Again, as the main goal is to simplify data, the processes should also be simplified wherever possible with the help of automation.

Why tailor technology to work for you?

As soon as a data governance policy is established and there is a clear vision of what a business wants from data and how the users are going to interact with it, the search for appropriate technology solutions suddenly becomes more manageable.

While potential solutions are being evaluated, certain parts of the business’ data policies and processes may be flagged as needing to be tweaked and adjusted. It is completely normal for this to occur when trying to tailor the processes and technologies to complement each other, and the business need can also change over time which can require further modification.

It is important to note that it is highly unlikely to find one technology or solution that can meet all business needs. A range of tools and solutions should be implemented and leveraged within an organisation, and there are some key functionalities to consider:

Data import. A business should be able to retrieve data from a spreadsheet or another source and import into the CRM quickly and easily, in whatever format specified, to then be manipulated, updated, exported and deleted as necessary.

Data verification. This is important to ensure data in the CRM is free of errors, such as invalid email addresses.

Deduplication. Based on the company’s customised definition, any ‘duplicate’ data should be removed from records with a flexible rule creation for both records and individual data fields.

Data reporting and analytics. It is critical to understand the quality of the CRM data through customisable dashboards and alerts that show how data affects pipeline management, campaign performance and customer retention.

Data operations. An organisation can boost data management productivity and operational efficiency by integrating data into configurable views that reveals only the data that is relevant to the task at hand.

Data maintenance. With specialised filters that address common data entry issues, any junk data can easily be found and removed.

Data security. An organisation should set and configure customised rules on who can access the data and who can change it.

Ultimately, businesses need effective data governance. It’s an investment in time, budget and resource, but once the necessary steps are taken, there will be significant returns on an organisation’s data investments. Successfully being able to put in place the right people, processes and technologies will ensure that data assets are available wherever and whenever they are needed so that businesses can rely on quality data to operate and succeed.