Seven roads to data-driven value creation

last modified: 2018-05-20

Clément Levallois

EMLyon logo corp

Not a closed list, not a recipe! Rather, these are essential building blocks for a strategy of value creation based on data.

1. Predict

prediction

Examples of companies

  1. Predicting crime predpol

  2. Predicting deals tilkee

  3. Predictive maintenance cat

Obstacles and difficulties

  1. The cold start problem

  2. Risk missing the long tail, algorithmic discrimination, stereotyping

  3. Neglect of novelty

2. Suggest

suggestion

Examples of companies

  1. Amazon’s product recommendation system amazon

  2. Google’s “Related searches…” google

  3. Retailer’s personalized recommendations auchan

Obstacles and difficulties

  1. The cold start problem, managing serendipity and filter bubble effects.

  2. Finding the value proposition which goes beyond the simple “you purchased this, you’ll like that”

3. Curate

curation

Examples of companies

  1. Clarivate Analytics curating metadata from scientific publishing crv logo rgb rev

  2. Nielsen and IRI curating and selling retail data nielsen iri

  3. ImDB curating and selling movie data imdb

Obstacles and difficulties

  1. Slow progress: curation needs human labor to insure high accuracy, it does not scale the way a computerized process would.

  2. Must maintain continuity: missing a single year or month hurts the value of the overall dataset.

  3. Scaling up / right incentives for the workforce: the workforce doing the digital labor of curation should be paid fairly, which is not the case yet.

  4. Quality control

4. Enrich

enrich

Examples of companies

  1. Selling methods and tools to enrich datasets watson

  2. Selling aggregated indicators edf

  3. Selling credit scores

Obstacles and difficulties

  1. Knowing which cocktail of data is valued by the market

  2. Limit duplicability

  3. Establish legitimacy

5. Rank / match / compare

rank

Examples of companies

  1. Search engines ranking results google

  2. Yelp, Tripadvisor, etc… which rank places tripadvisor

  3. Any system that needs to filter out best quality entities among a crowd of candidates

Obstacles and difficulties

  1. Finding emergent, implicit attributes (imagine: if you rank things based on just one public feature: not interesting nor valuable)

  2. Insuring consistency of the ranking (many rankings are less straightforward than they appear)

  3. Avoid gaming of the system by the users (for instance, companies try to play Google’s ranking of search results at their advantage)

6. Segment / classify

muffin

Examples of companies

  1. Tools for discovery / exploratory analysis by segmentation

  2. Diagnostic tools (spam or not? buy, hold or sell? healthy or not?) medimsight

Obstacles and difficulties

  1. Evaluating the quality of the comparison

  2. Dealing with boundary cases

  3. Choosing between a pre-determined number of segments (like in the k-means) or letting the number of segments emerge

7. Generate / synthesize (experimental!)

generate

Examples of companies

  1. Intelligent BI with Aiden aiden

  2. wit.ai, the chatbot by FB wit

  1. Close-to-real-life speech synthesis google

  2. Generating realistic car models from a few parameters by Autodesk: autodesk

A video on the generation of car models by Autodesk:

Obstacles and difficulties

  1. Should not create a failed product / false expectations

  2. Both classic (think of clippy) and frontier science: not sure where it’s going

Combos

Combinations

The end

Find references for this lesson, and other lessons, here.

round portrait mini 150 This course is made by Clement Levallois.

Discover my other courses in data / tech for business: https://www.clementlevallois.net

Or get in touch via Twitter: @seinecle

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