Data Monetization Pt.2

Descriptive Analytics

• What happened?

  • E.g., analyzing last year’s student enrollment numbers, average GPA, course pass/fail rates

Predictive Analytics

• What will happen?

  • Using historical data to predict what students will do next (drop out or retake courses)

Prescriptive Analytics

• What should we do?

  • Recommending interventions, such as tutoring programs, offering more sections of high-demand courses, etc.

Data Monetization Model

Improving processes

A hospital uses data analytics to improve its processes (e.g. admission processes making them more efficient and effective)

Wrapping data around products

A bank offers free analytics (reports, alerts) to clients

Insights

A company analyzes patient data and sells the results to hospitals

• Identify patterns that predict hospital readmission for heart failure patients

Action

A company uses insights to directly perform or support operational actions, often as consulting or outsources services

Sources of Value

Data

Data must be unique and have value in the marketplace

• Data value: volume, comprehensiveness, accuracy, diversity of sources, etc.

Data architecture

Distinctive architecture (IT platform) based on open-source technologies, custom programs, innovative design, etc.

Data Science

• Attract, train, and retain a team of bright data scientists

• Foster a data science culture

• University partnerships

  • students have very valuable and new perspectives compared to existing companies

Domain Leadership

Perfect understanding of and expertise in the domain (e.g. finance, healthcare, etc)

• Hiring employees from specific industries

Commitment to Client Action

• Ensure that clients act on the insights gathered from the data to generate business value

• Provide training and support to the client

Process Mastery

• Sells master the business process they are offering to inform

Data governance

Must have norms and policies that ensure data-related activities are compliant and ethical

Data liquidity

The ease of data asset reuse and recombination to enable NEW value creation

• by using tools such as:

  • master data management, metadata management, data integration, data quality management, etc.

Data, information, knowledge, and wisdom

• Know your data and its value

• if you cannot act on insights do not waste time and resources analyzing it

Databases, data warehouses, and data lakes

What data to keep and how much does it costs to keep it?

• Analytics → data warehouses

• Data lakes → data that is not used yet

You do not need necessarily need sophisticated tools

If you can use accessible tools like Excel take advantage of that!

Analytics: Descriptive? Predictive? Prescriptive?

• Which one? Depends on the data and what is being analyzed

• If an insight is actionable, it can “prescribe” an action