Making data analytics work: Building a data-driven organization

Summary

The transcript emphasizes the importance of organizational design, people, and processes alongside traditional technology hardware and software for effective big data analytics. It highlights the need for data hygiene, governance, and a service bureau culture to ensure analytics are useful to internal customers. The speaker suggests focusing on creating a service bureau culture before deciding on centralization or decentralization. Key roles such as data scientists, business solution architects, campaign experts, and advanced modelers are crucial for leveraging big data effectively, ensuring models are actionable and results-driven.

Takeaways

• 🛠️ The script emphasizes the importance of traditional capabilities such as technology hardware, software, and applications in building infrastructure for big data and analytics.
• 🏗️ It highlights the need for building capabilities around organizational design, people, and processes to effectively utilize big data and analytics.
• 🧑‍🔬 The role of analysts and data scientists is crucial, and it's important to determine the type of expertise required, whether it's just analysts or more specialized roles like data scientists or business solution architects.
• 🔍 The script stresses the importance of data quality, stating that 'garbage in, garbage out' still applies, and that data hygiene and cleansing are vital for achieving accurate results from analytics models.
• 🗃️ Data governance is essential to ensure that data is clean, accurate, and secure, with clear responsibility assigned for maintaining this standard.
• 🤝 The concept of a 'service bureau' culture within the analytics organization is introduced, where the focus is on providing useful and utilized analytics to internal customers.
• 🌐 The script discusses the balance between centralizing and decentralizing analytics, suggesting that the first question should be about creating a service bureau culture rather than the structure itself.
• 🤔 It suggests that companies should consider the advantages of centralization, such as a culture that rewards and protects advanced data scientists, while ensuring their work is used effectively.
• 💡 The importance of having a framework to help companies decide how much to centralize or decentralize is mentioned, indicating that there is usually a mix of both approaches.
• 📍 The script identifies 'kingpin roles' such as data scientists, business solution architects, campaign experts, and advanced modelers as critical for leveraging big data effectively.
• 🛑 It concludes by emphasizing the need for these roles to ensure that models are not only created but also turned into actionable campaigns that impact consumers or customers.

Q & A

What are the traditional capabilities needed for building big data and analytics infrastructure?

The traditional capabilities include technology hardware and software, as well as the applications that people typically turn to.

Why is it important to build capabilities around organizational design, people, and processes in big data and analytics?

It is important because after building the infrastructure, organizations need to determine who will use the data, how it will be used, and what kind of analysts and roles are required for effective data utilization.

What roles are essential in ensuring that big data and analytics are used effectively within an organization?

Essential roles include data scientists, business solution architects, campaign experts, and advanced modelers, who are responsible for creating models, ensuring data accessibility, and turning models into actionable results.

Why is it crucial to have a service bureau culture within the analytics department?

A service bureau culture ensures that the analytics team focuses on providing useful and used analytics to internal customers, rather than just doing analytics for the sake of it.

What is the significance of data hygiene and data governance in big data analytics?

Data hygiene and governance are crucial because they ensure that the data used in analytics is clean, accurate, and secure, which directly impacts the quality of the results produced by the models.

What are some advantages of centralizing analytics within an organization?

Centralizing analytics can allow for a culture that rewards and protects advanced data scientists, ensuring that their work is used and useful within the organization.

What is the first question an organization should ask itself when considering centralizing or decentralizing analytics?

The first question should be how to ensure that the organization responsible for analytics views their role as a service bureau and provides useful analytics to internal customers.

What are the key roles that are critical for leveraging big data effectively and quickly?

The key roles include data scientists, business solution architects, campaign experts, and advanced modelers, who contribute to the creation, accessibility, and application of big data models.

How does the concept of 'garbage in, garbage out' apply to big data and analytics?

The concept applies as poor quality data input into a model will result in poor quality output, regardless of how sophisticated the model itself is.

What is the role of a business solution architect in big data and analytics?

A business solution architect is responsible for understanding and creating the right big data warehouse, ensuring that the data is accessible and useful in a usable way.

What is the importance of campaign experts in the context of big data and analytics?

Campaign experts are crucial as they ensure that advanced models are turned into actionable campaigns that can effectively engage consumers or customers.