Challenges along the way

1. Challenges along the way

Welcome back!

2. Challenges exist

Data culture is a powerful tool for business success, but creating a thriving data culture is challenging. Companies that fail to establish a data culture may experience operational challenges and, in some cases, business failures. For example, Kodak lost market share to competitors like Fujifilm and Canon because they were slow to recognize the importance of digital photography and did not make strategic decisions based on data. We will explore the key challenges an organization could face when creating a data culture.

3. Classifying challenges

Challenges in building data cultures can arise from various sources, but we can broadly categorize them into people and process challenges. People challenges involve the human aspects of an organization, such as behavior, attitudes, skills, and knowledge. In contrast, process challenges focus on organizations' systems, procedures, and workflows to manage their operations. People challenges often encompass "softer" issues, including data silos, data literacy, and data ethics, while process challenges tend to involve more "hardcore" aspects like data quality, privacy, and security. Let's delve into each of these challenges in greater detail.

4. People - data silos

Data silos, or isolated information within teams or departments, can hamper collaboration and limit organizational understanding. An example is the automobile manufacturer, Ford. They had various departments that were siloed. The engineering team responsible for designing new vehicles could not access data from the sales and marketing team, which was crucial in understanding customer preferences and market trends. Their solutions were a centralized data team and a unified data management system, effectively breaking data silos. Although a robust data culture promotes cross-functional collaboration...

5. People - data silos

...A data-sharing mindset among employees needs to be established for a data culture to be successfully implemented in the first place. Unfortunately, many companies stumble at this initial step.

6. People - data literacy

This leads our discussion to data literacy. Ensuring all employees can interpret and use data effectively is crucial for clear communication. However, human can be reluctant to gain new skills and adapt to the changing data trend. Some organizations also fail to commit long-term to the training programs, eventually preventing data culture from functioning. For instance, some organizations assume that all employees have the same level of data literacy, leaving some non-technical employees needing to catch up in leveling up their skills. Customized data literacy upskilling works better than non-tailored ones. Continuously monitoring the progress and refining the learning program can also yield better outcomes.

7. People - data ethics

Data ethics is another crucial consideration involving responsible, fair, and transparent data usage. As companies build their data cultures, considering the ethical implications of data usage sometimes does not come naturally. One example of ethical concerns is when companies deploy biased algorithms that discriminate against certain groups of people. Even Amazon discovered that their algorithm used for hiring employees was biased against women in 2015. To address these concerns, it's important to establish clear ethical guidelines and foster open discussion on ethical issues. This helps companies remain accountable and responsible in their use of data.

8. Process - data quality

Building a data culture also needs data quality measures. We must ensure that the information we rely on is accurate, reliable, and up-to-date. In 2017, Uber used inaccurate data in its app through a tool called "Greyball," to deceive regulators in several cities worldwide, resulting in fines, legal action, and reputational damage. We can address this challenge by implementing data validation processes and leveraging automated data cleansing tools to maintain high-quality data.

9. Process - data privacy & security

Similarly, data privacy and security should never be an afterthought. Elevated advocation for data usage can cause the overlook of data privacy and security. Although we are utilizing more data to optimize the workflow, protecting sensitive information is vital to maintain customer trust and avoid potential breaches. In 2017, the American credit bureau Equifax experienced a data breach that exposed millions of customer's personal information, leading to a significant loss of trust and legal consequences. Implementing strong access controls, using encryption techniques, and conducting regular security checks are all solutions to this dilemma.

10. Let's practice!

Understanding these hurdles will help us unlock the full potential of data and become more mindful when creating a data culture. Time to practice!

Create Your Free Account

By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.