Systems Thinking for Data Quality

Today, organizations realize that data and analytics provide an essential tool in creating a competitive advantage. Companies that will be able to build the best data capabilities and best data quality today will dominate in the near future, and yet, too many companies embrace data and leave it at just that without changing their core system and operating model. 

Many organizations were able to achieve some quick small wins, but scaling up those wins requires a holistic approach to leverage the data and develop the right capabilities that work together in a cohesive Operating Model.

How does Systems Thinking help develop Data Governance that affects the entire organization?

Data Governance consists of four main building blocks: data policies, data tools, data structures, and the organization’s participants and target operating model.

Good data matters not only for complying with regulatory obligations but also to allow an organization to create value and optimize efficiency to meet and anticipate continually evolving customer needs and expectations through advanced analytics, RPA and Artificial Intelligence (AI.)

Is technology enough to improve data quality? Do we have the right organization model in place? How do we define and measure current data quality?

Why Does Government Need ‘’Adaptive Strategy’’?

Why Does Government Need ‘’Adaptive Strategy’’?

With the rise of complexity and ever-changing citizens' expectations, governments need a new managerial model that enables systems thinking and non-linear thinking when developing strategies. The way Peter Senge puts systems theory to work is by understanding and addressing the whole and examining the interrelationships between the parts. When it comes to analyzing problems, Government tends to address the symptoms of the problems, therefore focusing on the parts rather than the whole, and they fail to see the organization as a dynamic process. How can Government address the original causes of the problem and be able to shape a better future and build Strategies that are up to the demands of the complex and uncertainty of the modern world?

What Government should do:

Encourage adaptation and learn from failure.  Governments need to look at the organization and society as "complex adaptive systems" that can't be controlled. An environment of uncertainties which makes it difficult to make strategic decisions because the problems they are trying to solve are often not known in advance, they continuously evolve. The solution is hard to implement or impose, because of the potential reactions of the wide variety of actors affected. A good strategy should have very clear goals but be flexible about how to deliver them. Government tends to do the opposite, being very rigid about how to execute strategy, but more flexible about what they should do. 

Vision.  One of the biggest challenges in government is the way the vision is crafted, too broad, and often unrealistic. Therefore, it is very hard to identify which goals to pursue and which ones to abandon. Adaptation in gov is imperative, and it starts with some high-level direction that will help a civil servant to know which areas to prioritize.

Uncertainties: How can government prepare for uncertainty? When the environment is constantly evolving and unpredictable, government needs to employ an adaptive approach and readiness to prepare against continuous disruption by continuously experimenting and identifying risks and opportunities more quickly. As Michael Porter described, ‘’Strategy is about making choices, trade-offs; it's about deliberately choosing to be different." 

If you can't predict, adapt. The right approach to strategy in an adaptive complex system is the one that guides and orients the government rather than control everything that its agencies do. Government should continuously try different approaches and iterate more rapidly - what is known as "adaptive learning" along with "generative learning", which will enhance the capacity to create, exploit, and scale up the most promising approach before repeating the cycle.