Why does your organisation need Data Governance?
Let’s start this blog post with a few facts and figures. I gratefully acknowledge the resources made available by Experian (2018), which conducts a large-scale Data Management (DM) survey every year amongst 1000+ employees doing DM-related work in various types of jobs (including IT, finance, customer service, marketing and risk management).
The most important findings from the latest edition of the Global Data Management Research 2018 are:
• 55% of the survey respondents believe that unreliable data has a very disruptive effect on the organisation;
• 73% of those surveyed agree that it is often difficult to predict when and where the next data-related challenge will emerge;
• 68% of those surveyed feel that it is becoming increasingly difficult to fulfil the legal requirements for the growing volumes of data;
• 69% of those surveyed believe that incorrect data will undermine their ability to deliver an excellent customer experience.
These figures clearly show that when organisations are unable to establish an effective Data Governance structure, it could inevitably have an impact on their competitive position and their ability to do their work in accordance with prevailing laws and regulations (compliance).
Data Governance: how does that work?
The term is used with increasing frequency, but Data Governance has a wide range of different definitions. Essentially, it refers to a framework that an organisation can use to manage its data effectively to ensure that the data meets that organisation’s needs. For example, Data Governance includes policies that determine how an organisation manages its data and which roles and responsibilities are defined for Data Management. Would you like to know more about Data Governance? You may want to check out my blog posts on “What does Data Governance mean and what does it do?” and “The Data Governance Organisation – roles and responsibilities”.
But why does an organisation need Data Governance?
There are a number of business drivers that are actively supported by a solid approach to Data Governance.
Ensuring data is up to date
I cannot state often enough that high-quality data is an exceptionally valuable asset that can give organisations a long-term competitive edge, as long as the data is managed effectively. Given that fact, it’s surprising that organisations often define the business requirements for their data, but then make almost no effort to set up processes designed to maintain and monitor data quality. In such situations of ‘data neglect’, the data will decrease in value over time. Moreover, many organisations do not have access to the right tools to check and improve the quality of their data, especially not long-term. If an end user loses confidence in the quality of the available data, this will lead them to find workarounds to clean up and/or enrich the data. Such solutions not only make the process more time-consuming and expensive, but also lead to a growing patchwork of IT solutions for end users (thus causing extra management costs for organisations).
Laws and regulations
Managing and mitigating compliance risks is becoming harder and more expensive as the scope and diversity of data continues to grow. Many organisations are currently opting for minimum compliance, only meeting the minimum requirements for data management imposed by legislators. This checklist-based approach has a number of downsides. It may make it possible to monitor and manage certain subsets of the data, but it hardly helps to facilitate the cultural change that organisations need in order to adopt a pro-active approach in managing their own data and promoting data quality. In addition, it has become apparent in recent years that legislators have a better understanding of the concept of Data Governance and data quality, so their requirements in these areas are becoming increasingly detailed and broader in scope with each new round of regulations. By now, the more pro-active organisations have realised that embracing Data Governance can give them a competitive edge (see e.g. Informatica (2018)).
Under the heading of ‘Ensuring data is up to date’, I noted that end users resort to workarounds when data quality is uncertain. Ironically, these types of operational inefficiencies are often established practice and are no longer even recognised as deviations from the standard workflow. Check in your own organisation: how much time does your team spend manually solving data quality problems? In practice, when an organisation adopts a holistic approach to Data Governance, they generally spend a lot less time and money on ad-hoc solutions for optimising data which is required to support the business processes (see e.g. Infosys (2018)).
Considering how much money organisations spend on Customer Relationship Management (CRM) systems, you might expect that they would have access to the gold standard in customer information by now. Sadly, the opposite is true: most organisations do not have accurate data on their clients. Many organisations operate within isolated vertical silos, each department managing its own data in departmental systems that often aren’t synchronised with the systems outside the department. Without the right Data Governance measures, databases used for automated marketing and customer support can easily become contaminated with redundant, incomplete, inaccurate or outdated information about your clients. As we all know first-hand, nothing is more frustrating than how that affects the customer experience: having someone call you the wrong name, receiving multiple copies of the same catalogue, or getting the same promotional email in all your inboxes 10 times.
Changing data sets
You’ve definitely heard about Big Data. But do you know about Open Data and Linked Data?
These new forms of data are opening up a whole new world of potential applications… But they also create new challenges in terms of Data Governance and data quality. For that reason, it is important for organisations to quickly design a solid foundation for a Data Governance framework for their existing data, and implement that framework, before worrying about how to handle these new data formats from a Data Governance perspective. After all, you cannot expect to successfully merge massive quantities of divergent new data with your existing data if you don’t even understand the data you already have, let alone manage it effectively.
Finally, the massive quantities of data that are being stored are still growing exponentially and changing constantly. Clearly, organisations should feel a growing sense of urgency to implement a Data Governance framework. Let’s be honest: if you don’t already understand and manage your data, how do you expect to keep up with the current pace of change?
Tentive Solutions offers organisations support in resolving their Data Governance and Data Management issues. Would you like to know more? Feel free to contact our Data Management Team