Using data governance to turn data into a critical business asset by Ritva Aula.
Content updated in May 2021.
Let’s get started with a simple real-life example – our phones. Imagine you are trying to contact your customer. You search your contact list, but the information is incorrect or missing. As a result, you try to reach the wrong person or are unable to make contact at all. And thus, additional effort is needed.
Now let’s expand this scenario to your company – and multiply it by hundreds or even thousands of times when people try to use data and find information from tens or hundreds of different tools. If the data they find is inaccurate or missing, it can lead to incorrect actions and decisions – or a failure to act at all, not to mention wasted effort. Surprisingly enough, this is not just a thought experiment – it is the actual reality in many companies…
In a nutshell, data governance aims to ensure that this scenario does not occur in companies and that data is seen and systematically managed as a strategic asset. It requires management attention, like any other critical function in the company. Simply put, data governance is needed for the company to collect quality data and make better decisions based on it – and to do this in a cost-efficient manner.
Let’s clear up a few basics – as to how data is seen and understood affects how it is approached. The first common issue in companies is that data content and data-related tools often get mixed up.
Hyped terms such as data science, machine learning (ML) and artificial intelligence (AI) are great means for companies to succeed in their business. But they are not silver bullets that alone make any company data-driven. Instead, they are tools and methodologies that utilize data content provided by other tools for e.g. discovering new insights and business opportunities and optimizing business processes. Without sufficient quality data, these tools can’t work to their fullest.
This means ensuring data quality is a must. Data as a source of information presents what is happening or has happened historically, which illuminates the current situation and enables predictions. If data is incorrect, it tells “fake news”. Data governance done right aims to ensure that appropriate and quality data is identified, gathered and made available at different levels within organizations.
Understanding the nature of data helps to solve data quality issues – consider data as water, flowing through pipes of a water plant, and processed by dedicated tools that ensure the water meets quality standards, is stored correctly, and can be safely used. Just like water in this example, data often comes from somewhere other than where it is consumed. Therefore, companies should proactively ensure transparency of where their data is created and what happens to it on its way to data consumers.
The second common misunderstanding is that IT or IT strategy alone will ensure good quality data. This is not the case. As in the water plant example, differentiated roles are needed. Data roles and responsibilities should be clear and communicated across the company. Acknowledging that the business has a key role in defining their data requirements and verifying that data content, makes it easier to set up needed capabilities – both in the business generally and IT specifically.
The third common issue is a lack of data management strategy. Data governance and management also need to be a strategic, enterprise-level initiative to get it up and running as a business-as-usual function. A data management strategy should ensure silos and different data governances do not occur within business units and functions – and should be linked to the overall business strategy, with clearly defined ownership and roles. Lastly, top management support is vital. Without it, any long-lasting and company-wide data governance efforts will probably fail.
Data governance is becoming even more critical: the amounts of data are increasing, and regulations such as the GDPR are bringing new restrictions and policies, underlining the importance of an enterprise-level strategic approach. Additionally, when data-driven initiatives and digitalization activities become more common, companies face unfamiliar challenges. If solid data governance is not in place, many organizations will face algorithms that do not work, leading to increasing resources dedicated to data validation and failed projects.
Newly formed data science teams won’t be able to fulfil their promise if they spend most of their time finding, understanding and validating data. All this will also decrease productivity, increase costs, hamper agility and create employee dissatisfaction. Also, building company-wide competencies and key differentiators, such as customer experience, and business transformation – all of which are data-driven initiatives – will prove difficult without data governance.
There is a clear connection between data governance success and business success. And when a company sets out to define its data governance, it is actually building one of its core competencies.
So, how is your data quality? What is your data governance approach? Have you considered a data management maturity assessment to show you where to start? If you haven’t already asked yourself these questions, now is the time.
Ritva is a true data management professional. She consults customers primarily in data governance, data management maturity assessment and data management strategy. She has a wide experience in leading data governance architecture, design and implementation, as well as master data management, data warehousing, reporting and ERP development. In addition, Ritva can also support customers with change management, training and coaching. She has worked in banking, telecom and manufacturing industries