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🌈 Abstract

The article discusses the challenges and strategies for building a data-first corporate culture, which is key to increasing efficiency, productivity, and customer experience. It highlights issues such as resistance to change, data silos, lack of data literacy, and ethical concerns around data collection and usage. The article provides actionable strategies from Forbes Technology Council members to overcome these hurdles and establish a successful data-first culture.

🙋 Q&A

[01] Controlling Access to Sensitive Data

1. What is the challenge in controlling access to sensitive data, and how can organizations address it?

• The challenge is providing professionals who need to use the data with access, while also controlling access to sensitive information.
• To address this, organizations should incorporate limited access into their data-first culture, where professionals only have access to sample data while getting wide-ranging answers from systems that have access to larger amounts of data. This helps conceal sensitive information.

2. How can blockchain technology help in providing paradata (information on how the data was collected) in addition to accurate metadata?

• Blockchain technology may help in providing paradata alongside corporate data, in addition to metadata, which is important in the era of AI.

[02] Ensuring Data-Driven Decision-Making

1. What is the challenge in ensuring that all employees consistently use data to support their decisions?

• The challenge is ensuring that everyone in the organization, regardless of their position or seniority, consistently uses data to support their decisions.
• To address this, all employees should be knowledgeable about identifying and presenting the data that best demonstrates their outcomes and results.

2. How can organizations combat confirmation bias when interpreting data?

• Confirmation bias, where people tend to agree with data they favor and dismiss contrary data, is a challenge.
• Techniques to combat this include ensuring teams have a diverse range of perspectives, respecting one another's opinions, and working towards building consensus among the group interpreting the data.

[03] Prioritizing the Customer Experience

1. What is the challenge for companies that rely on spreadsheets or shared drives to manage data, and how can they address it?

• Companies that rely on spreadsheets or shared drives to manage data will struggle to provide a differentiated customer experience and reap the benefits of AI, which requires a strong data foundation.
• To address this, companies should consolidate their data into a single system designed around the customer, not the organization.

2. What are the ethical and philosophical concerns around amassing customer data, and how can organizations address them?

• There is an ethical and philosophical debate about the large-scale collection of customer data.
• To alleviate employee and customer concerns, organizations should focus on the intentionality of data use and placement, understanding where specific data fits into the organization's goals.

[04] Overcoming Data Challenges

1. What is the challenge of data "hiding" in cloud computing environments, and how can organizations address it?

• Even if an organization thinks it knows where to find valuable data, there could be copies of it in other places due to the seamless movement of data across the data estate in cloud computing.
• To solve this, organizations must adopt a holistic, cloud-native security strategy that acknowledges their unique security requirements and risks.

2. How can organizations address employee resistance to change when implementing a data-first culture?

• Employee resistance to change is a common challenge.
• To overcome this, organizations should provide comprehensive data literacy training, foster open communication about the benefits of data-driven decision-making, and incentivize employees to embrace data-driven approaches through recognition and rewards.

[05] Making Timely Decisions with Imperfect Data

1. What is the challenge of making timely decisions when data may not be perfect or may conflict?

• Data may not always be perfect, and occasionally, data points may conflict with each other, making it challenging to make timely decisions.
• To address this, organizations should settle on a discrete set of KPIs and metrics to help clarify decision-making, and learn to make decisions without complete or perfect data, as speed can be as important as accuracy in some circumstances.

2. How can manufacturers build trust in data-driven manufacturing when facing operational issues highlighted by initial data?

• In manufacturing, facing the truths revealed by data-driven manufacturing, which may highlight previously unknown operational issues, can be a challenge.
• To build trust in the process, leadership must emphasize that transparency and accuracy in data are essential for long-term improvement.