isde big data 20140910

Innovation and Strategy in

the Digital Economy

Class 2: Data and “Big Data” in Business

Milan Miric – CBS / INO

(mmi.ino@cbs.dk)

About me

● PhD Student at Department of Innovation & Organizational Economics.

● Area of expertise: – Digital economy, technology platforms, innovation, competitive strategy &

commercialization.

● Academic background: – Undergraduate degree in Mechanical Engineering, MBA.

● Visiting scholar at Bocconi University (2012 – 2013) and London Business School (2013 – 2015).

Positioning of this class

● Class 1: Digital transformation

– Focus: Digital technology is transforming business.

● Class 2: Data & “Big Data”

– Focus: How is “Big Data” specifically is shaping business.

Digital Transformation: Recap

Digital technology transforming a wide number of industries.

● Affecting the way firms do business in brick-and-mortar industries.

● Creating new types of competitors for firms in digital industries.

● Creating entirely new industries that did not previously exist.

Brynjolfsson & Hitt (1996)

Brynjolfsson & Hitt (1996)

Digital transformation & Data● Digital vs. Analog Technology:

– Analog: Information represented by electronic pulses.

– Digital: Information represented by binary (0 or 1) values.

Key distinction: Digital information is easier to collect, store, analyze & less noisy (prone to errors).

So far more data is being collected than ever before!

From Varian (2013)

● A billion hours ago, modern homo sapiens emerged.

● A billion minutes ago, Christianity began.

● A billion seconds ago, the IBM PC was released.

● A billion Google searches ago … was this morning.

Benefits of Data Explosion

● Scope for production efficiencies.

– Ex. Automobile manufacturers.

● Scope for more effective marketing.

– Ex. Airline / Hotel rewards programs.

● Scope to collect, combine and analyze information which was previously not possible.

– Ex. Demographics – behavior - location – revealed preferences.

Challenges of Data Explosion

● Challenges of collecting data.

– Ex. Collecting millions of data points in real time.

● Challenge of storing and managing data.

– Ex. Storing vast amounts of relational information.

● Challenge of analyzing data.

– Ex. Difficult to disentangle causal effects.

– Ex. Difficult to analyze data with such a large amounts of variables / observations.

In this session

● What is the data used for?

● What makes data a valuable asset?

● (How) can firms profit from data?

Hype around

BIG data!

But does this answer the

earlier questions?

NO!

What is data used for?

Data in Brick-and-Mortar Industries

● Firms use data to offer better products and services.

● Winners & Losers:

– What are examples of firms that are doing this well? (AND WHY?)

– What are examples of firms that are doing this poorly (AND WHY?)

● In products? In services? In manufacturing?

● Can “data” be a source of competitive advantage? Why or why not?

Data in Digital Industries

Firms use data to offer better products and services.

● Winners & Losers:

– What are examples of firms that are doing this well? (AND WHY?)

– What are examples of firms that are doing this poorly (AND WHY?)

● In products? In services?

– Can “data” be a source of competitive advantage for digital businesses? Why or why not?

From Chen et al. (2012)

From Chen et al. (2012)

Break

(15 min.)

What is Data Used for?

● Inform decision making?

(evidence based decision making)

● Improve productivity.

● Offer new products / services.

But, What makes data valuable?

What type of data is valuable here?(Examples)

● Inform decision making? (evidence based decision making)

● Improve productivity.

● Offer new products / services.

What is the abstract “value” of data?

How is the value of data measured?

Scientific Question:

How would you measure the actual value of data?(as precisely as possible)

Avg. Revenue Per User: Facebook, Twitter, Google

Does this reflect the market value of data?

Then, can firms profit (develop CA) from

data?

What is nessesary for data to provide CA?

● Valuable.

● Rare (Not everybody has it).

● Inimitable (Difficult to copy).

● Organization Specific

(More valuable to own it than sell it).

CA from Data

Firms can have an advantage over competitors based on data.

● Winners & Losers:

– What are examples of firms that are doing this well?

– What are examples of firms that are doing this poorly?

What are examples of firms that may be better off selling their data than profiting from it themselves.

What does a market for data look like?

Break

(15 min.)

How does the revenue from users translate into valuations?

Source: Forbes 2013.

What explains the size of these valuations?

For example:

Why was WhatsApp valued at $19 Bn?