The Challenge of Creating a Data-Driven Organization
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A data-driven organization bases its operations and decisions on real-time or current data. Building such an organization, however, is a challenge.
Data-driven organizations use data to guide and optimize their business and operational models. A data-driven organization is ideally positioned to benefit from dynamic feedback loops, making it adaptive and responsive.
Organizations exist on a maturity spectrum, with varying levels of advancement in each of these areas:
- Availability of employee skills, specialization, and experience
- Reactivity and scalability of processes
- Sophistication of mission, goals, and objectives
- Effectiveness of decision-making systems
What Is a Mature Organization?
A mature organization is a vibrant ecosystem where employees are highly competent and specialized working units that can be employed and deployed effectively in environments characterized by dynamic and frequently changing processes. This requires a system with a highly defined set of objectives and response behaviours. The biggest challenge to creating a data-driven organization is developing decision-making systems with built-in data components. In lower-maturity organizations, realizing the full benefit of data analysis can be challenging. The power of data analysis relies on the ability to generate high-quality data and to act on it in a timely and useful way.
The following problems are usually at the root of this challenge:
- Employees lack the knowledge, skills, and experience (i.e., data literacy) to deal with data
- Employees wear many hats and are called on to perform or contribute to project tasks or activities in a capacity outside their specialization and experience
- There is resistance to change, a natural tendency that often results in entrenching behaviours and practices such as the proliferation of committees, meetings, and governing structures
- The organization fails to recognize when it needs to undergo substantial or fundamental changes in its business practices
If you want to learn more about this topic, read it here.
Creating a Data-Driven Organization
Addressing this challenge involves orchestrating and communicating data effectively to elicit the engagement and collaboration of various stakeholders. The following practices can help tackle the problems hindering the effective optimization of business analytics benefits.- Build data analytics libraries. Such libraries help foster a learning organization, creating an environment where employees can reference and cite studies. Analytic libraries should include running analytical data, specifically operational data, but can also include research data and analytical tools.
- Build models. Models can be concrete, mathematical, and computational. They’re an innate tool to understand how things work through interdependencies.
- Build experimental labs. There are now accessible tools that enable an organization to experiment with their business parameters and scenarios, allowing users to test and validate solutions and theories.
In short, the unique structure of a data-driven organization supports decision-making systems based on dynamic feedback loops. The challenges to achieving this structure include varying knowledge and skill levels, low specialization levels, and a reluctance to implement self-governing and self-managing decision-making systems.
Business analysis professionals can enhance their organizations by building data analytics libraries, creating accurate models, and establishing experimental business labs.
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About the Author
Mundher Al Alawi is a business intelligence professional with a core emphasis on business analysis. He has leveraged his experiences in various industries—retailing, real estate development, hospitality, mining, and education—and training in programming, statistics, and accounting to support organizations in building business solutions.
