How to Build Better Data Products?

The Future of Data Usability

Think about how seamlessly your smartphone integrates into your daily life. By 2030, data usability will be just as seamless, enhancing productivity and decision-making in organizations.

The Data to Impact Framework

Let’s say a retail company wants to reduce customer churn (the number of customers leaving). They have a lot of data but need to make it useful. Here’s how they can do it:

1. Technology: Set up automated systems and AI to process data.Example: An AI program analyzes customer data to predict who might stop buying.
2. Data Products: Create data assets that are easy to use for specific purposes.Example: A list of customers likely to churn, with details about their purchase history.
3. Humans: Use these data products to make informed decisions.Example: The marketing team uses the churn list to create targeted offers.

Key Takeaway: Combining technology, quality data products, and human decision-making turns raw data into valuable insights.

Enhancing Data Usability

Data usability means making data easy for users to work with. Here’s how:

1. User Understanding: Know who will use the data products.
2. Personas: Define different user types to tailor products.
3. Process & Context: Understand how users work.
4. Impact Goals: Focus on what users want to achieve with the data.
5. Data Product Design: Present data in an easy-to-understand way.
6. Right Tools: Provide tools that help users interact with data easily.

Key Takeaway: Making data easy to use involves understanding user needs and providing the right tools.

Building Better Data Products

Here are some examples of improving data usability:

1. Environmental Social Governance (ESG) Data Lab: Companies focusing on ESG can provide relevant data in user-friendly tools.
2. KPI Reports: Managers need easy-to-use reporting tools.
3. Citizen Data Engineering: Business teams handle some data tasks themselves.
4. AI in Healthcare: AI helps radiologists detect issues in medical scans.

Developing a Data Strategy

Creating effective data products requires teamwork. Here’s how to do it:

1. Adaptive Budget Allocation: Use flexible budgets for resource sharing.
2. Enterprise Architecture Principles: Choose tools that integrate well.
3. Empowered Data Product Ownership: Owners should oversee usage across teams.

Key Takeaway: Collaboration and strategic planning enhance data usability, leading to better data products.Imagine a world where data flows as smoothly as water through a well-oiled machine. By 2025, McKinsey predicts that seamless data interactions will be as commonplace and essential as a corporate balance sheet, driving unparalleled productivity.

The Data to Impact Framework

Let's consider a real-world example: A retail company wants to increase customer retention. They have vast amounts of customer data but struggle to turn it into actionable insights.

1. Technology: The company sets up automated pipelines and AI to process data.

- Example: An AI algorithm analyzes CRM data to identify customers at risk of leaving.

2. Data Products: They create reusable data assets for customer insights.

- Example: A dataset detailing customer purchase history and engagement metrics.

3. Humans: Analysts use these data products to devise retention strategies.

- Example: Marketing teams use the insights to create personalized offers for at-risk customers.

Key Takeaway: Combining technology, high-quality data products, and human analysis turns raw data into impactful actions.

### Enhancing Data Usability

Data usability ensures users can effectively achieve their goals with data. Here's how it can be done:

1. User Understanding: Know your users deeply to create user-friendly data products.

- Example: In a financial firm, analysts need detailed reports while executives prefer high-level summaries.

2. Personas: Create user personas to guide product design.

- Example: For a health app, personas might include tech-savvy users and less experienced seniors.

3. Process & Context: Understand the specific processes and contexts of users.

- Example: In a hospital, doctors need quick access to patient data during emergencies, whereas researchers need comprehensive data for studies.

4. Impact Goals: Focus on what users aim to achieve with the data.

- Example: A teacher might use student performance data to tailor their teaching methods.

5. Data Product Design: Ensure data is presented in a comprehensible manner.

- Example: Sales data could be shown through interactive charts and graphs for easy analysis.

6. Right Tools: Provide tools that match user needs.

- Example: A marketing team might use visualization tools like Tableau, while data scientists use Python notebooks.

Key Takeaway: Improving data usability involves understanding users' needs, contexts, and providing the right tools.

### Building Better Data Products

Let's explore how focusing on usability creates impactful data products through some examples:

1. Environmental Social Governance (ESG) Data Lab: Companies are increasingly focusing on ESG. Providing ESG data in user-friendly tools helps data scientists generate insights.

- Example: A company provides ESG data in Jupyter notebooks, making it easier for scientists to analyze and report findings.

- Takeaway: Streamlining access to specific data enhances productivity.

2. KPI Reports: Managers rely on key performance indicators to make decisions. Easy-to-use reporting tools ensure they have timely, accurate information.

- Example: A dashboard automatically updates with the latest sales figures, allowing managers to make quick, informed decisions.

- Takeaway: Presenting critical metrics in an accessible format enables timely decision-making.

3. Citizen Data Engineering: In the Data Mesh paradigm, business teams manage their own data tasks. Providing user-friendly tools helps them succeed.

- Example: Business users use a visual pipeline tool to transform data without needing to code.

- Takeaway: Empowering non-technical users with intuitive tools fosters a collaborative data culture.

4. AI in Healthcare: Radiologists use AI to detect abnormalities in scans, improving accuracy and efficiency.

- Example: An AI system highlights potential issues in mammograms, helping radiologists quickly identify areas of concern.

- Takeaway: Integrating AI insights into professional tools enhances accuracy and efficiency.

### Developing a Data Strategy

Creating usable data products requires collaboration across teams. Key strategies include:

1. Adaptive Budget Allocation: Flexible budgets allow for resource sharing across teams.

- Example: A company implements dynamic budgeting, enabling teams to allocate resources as needed for data projects.

- Takeaway: Flexible budgeting supports innovation and efficient resource use.

2. Enterprise Architecture Principles: Choose tools that integrate well with existing systems.

- Example: Selecting platforms with robust APIs ensures seamless data flow between tools.

- Takeaway: Integrated tools enhance overall data usability.

3. Empowered Data Product Ownership: Data product owners should oversee usage across teams.

- Example: A data product owner coordinates with marketing, sales, and IT to ensure the product meets all needs.

- Takeaway: Cross-functional ownership promotes comprehensive data usability.

Key Takeaway: A collaborative, strategic approach enhances data usability, leading to better data products.

By focusing on data usability, organizations can transform their data into powerful, actionable insights that drive significant business impact. Understanding user needs, leveraging the right tools, and fostering collaboration are essential for creating effective data products.