CBO at QueryPie
March 21, 2022
Businesses today face the challenge of delivering data results quickly while also ensuring data governance practices are followed. As data volumes increase, many companies struggle to balance agility and governance in their data management processes. A key characteristic of agility is the speed and efficiency of delivering work to the market, while governance refers to processes that increase predictability and transparency.
Organizations need to balance agility and governance to minimize risks and maximize productivity. By failing to achieve this balance, companies risk becoming less agile as they cannot keep up with rapid business changes or, worse, wasting money on unproductive initiatives that are poorly executed.
What is Agility?
A company’s agility is defined as their ability to rapidly iterate and make changes in their business processes, systems, or products.
Highly successful agile transformations typically delivered around 30 percent gains in efficiency . In other words, an agile company can adapt quickly in response to changing market conditions and customer demands. With increased levels of digitalization and integration, agility has become more critical than ever for businesses.
Cultural Clash within Enterprise Culture
Across the modern business management chain, there is this constant source of clashes happening with individuals holding positions in the firm.
- Analysts and data engineers are constantly looking for ways to access more data. They are on the edge of data analytics and have to deliver timely analytics to the stakeholders to make mission-critical decisions. Their fast-paced job requires constant iterations, with new data points being introduced constantly.
- Data Stewards and Security/Privacy administrators are part of the company that ensures that no individual with malicious intent mistakenly leaks classified information and provides complete control. They ensure data access and necessary security protocols are implemented at the behest of CISO and stakeholders.
- On the bleeding edge of data, governance is CISOs, who ensure that data compliance protocols are implemented across the management chain. They build the backbone of how data is assimilated, stored, structured, and security protocols alongside it.
Culturally every role has a different philosophy and goals that they need to achieve to keep the business moving. However, the pressures to perform their jobs optimally can lead to solid clashes between the two. The upper management needs to ensure that there is a form of empathy between the different factions in the group. Even if their roles do not align, they are still working towards the same goals.
The Disruption Effect
The parameters for data agility have always been defined by the company’s stakeholders, who set the larger goals. Around the world, there is a new level of pressure to deliver results at all costs. Startups are disrupting established companies’ structures because they are more cost-effective and agile than their counterparts. They do not need effective data governance regulations due to the smaller sizes of their management teams.
Digital transformation requires speed and agility to keep up with the evolving market. With digital transformation, companies compete with other companies and consumers who constantly stay ahead of the curve on how things work. The world has become a 24/7 society where people expect information at their fingertips. Hence, companies need to perform at this level to compete in today’s marketplace.
CTOs and stakeholders have resorted to creating small startups like teams that deliver on agility but lead to data silos being formed. From a longstanding perspective, it is a trend that is not scalable to enterprises and might be more detrimental to overall growth.
The Coexistence of Governance and Agility
The governance and agility paradox are an intricate relationship. How do you ensure that you deliver work efficiently while still meeting your organization’s needs? The answer is to balance these two opposing forces by having a balanced view of the paradox. You may want to think about the following three aspects to help with balancing this paradox:
- Processes– This includes building out processes for greater predictability and transparency.
- People– This includes having a balanced view of your people across agility and governance.
- Culture– This includes having a balanced view of culture between agility and governance.
A Cultural Shift for Data Governance
The idea of Data Governance is in opposition to what agility stands for is a relatively simplistic way of approaching it. There is a philosophy of moving fast and breaking things that have become extremely popular among the tech industry. However, while there is a rush to be at the bleeding edge, it is only possible to take risks if a body provides the stability to take big swings.
Similarly, data governance provides the necessary bedrock to ensure smooth sailing for enterprises. Here are methods that one can employ to ensure a company rethink about data governance-
- Identify the core values– If your core values are about agility and efficiency, use those as a guide for tackling managerial challenges. Afterward, you can decide whether to be more governance-driven or agile-driven.
- Become flexible – When you can adapt quickly to change and move quickly with your decisions, it becomes easier to get over the paradox between governance and agility.
- Commit to priorities – As long as you have clear priorities in mind, it will be easier for decision-makers to make quick choices when needed.
To ensure that the needs of stakeholders are met, enterprises must balance governance and agile data management – bringing in and creating a whole new method to think through data needs as part of agile governance.
A key aspect of agile governance is the ability for people to collaborate across teams and make decisions with self-governance and tools that enable agility at the same time. This way, each team member can see the whole picture and the importance of the larger ecosystem at play.
These structures should be designed with feedback loops in mind and allow for a quick change when necessary while also providing ample time for planning and execution. The best data goals are set when both executions meet a larger goal that promotes the interests of the stakeholder’s instant needs