Creating Value from Data: Acquire, Organise, Use and Learn
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Creating Value from Data: Acquire, Organise, Use and Learn
- 1. Creating Value from Data Paul Gathercole, Universal Music Group #m4pt5 Event partners #opendata Music 4.5 is organised by
- 2. Creating Value from Data Paul Gathercole Universal Music Group http://www.linkedin.com/in/paulgathercole
- 4. Creating value ... • Acquire • Organise • (Challenges) • Use • Learn
- 5. 1. ACQUIRE
- 7. “I have an intern who [insert laborious task] every Monday...”
- 10. 2. ORGANISE
- 12. Challenges • lack of standards • holes in data • how to apply it • appropriate query -> appropriate solution • cultural
- 13. 3. USE + LEARN
- 14. Reshape
- 19. 4. What we learned
- 22. too much is as bad as not enough
- 23. use what you have and what you can get
- 24. beware metadata
- 26. Not IT!
- 27. Creating value ... • Acquire • Organise • (Challenges) • Use • Learn
- 28. Creating Value from Data Paul Gathercole Universal Music Group http://www.linkedin.com/in/paulgathercole
- 29. Thank you to our event partners #m4pt5 #opendata Music 4.5 is organised by
Editor's Notes
- difficult where to pitch it majors, indies, live, management, professional service providers. so no theory but a story of real life practice story starts - before “open data - before “big data” - before “data-driven decision making”
- Last summer a Gartner report said i) open data is underused and ii) APIs are key. A lot of commentary is IT people talking about secure transport etc... They also talked about what openness means in this context. Sidestep debate about what is open data... - public: like Open gov data - not siloed: easily accessible - free: are there every cases where one pays for “open data” - license: is it really open? what are the restrictions on use? are you free of privacy concerns? Open data refers to the ability to expose strategic enterprise data assets for use inside or outside of the enterprise. what talk about i) we are still trying to make sense of this digital thing ii) fighting for wallet share (competing with other industries who may be more advanced or have better data access Industry wide Rising tide raises all boats not least because. hope first because easy to get bombarded with this... or this... Touch on most of the definitions but focus on Creating Value...
- What we have been going through at Universal over the past 4 years. Change process broken into these steps.
- Acquire more like prospecting, mining and building a pipeline - monitor: piracy - crawl: YT - then APIs: lastfm - lastfm represents a shift to using the data to power the business a la Amazon
- Acquire - plumbing But more like prospecting, mining and building a pipeline - monitor: piracy - crawl: YT - then APIs: lastfm - lastfm represents a shift to using the data to power the business a la Amazon We can see what the consumer is doing? But story take unexpected course. How make the data work? Put a front end on it but soon...
- - how does this stuff become useful - automate - start tackling real business problems - e.g. supply chain visibility boring, but...
- storechecker image
- one issue/problem leads to another
- - inevitably want to match to internal data - silos - messy metadata - reasons for messy metadata (acquisitions, BP enhancement,
- - Classify and structure - and make accessible -then acquire more - internal and external - metadata augmentation - discogs, google, musicbrainz ...pause... reflect on challenges
- - ontology / DDex /artist identifiers (not waiting for industry initiatives but fix it in a flexible way) - 2 kinds: i) flaky feeds from BPs ii) last click in consumer journey e.g. iTunes (ask business partners for more data) (share analysis when in discussion) - dashboards enable time-saving/reporting/at a glance, but beyond that are bigger problems from the business - ask the right questions and reframe if necessary, i.e. discuss - charts, new charts, timelines, post-mortems on projects
- - democratise - get it on the desktop - everyone should be looking at the data if not for analysis, then reporting or in campaign effectiveness - we built a tool, dashboard, that aggregates sales v streaming v social. End to end inhouse this goes beyond the tool about capabilty . Capability gives us the ability to analyse the data, ask questions of it to find answers - reshape...
- - reshape - think back to the lastfm example, another example would be amazon - businesses built on data - classical recommendation engine (uses real stream data / cut by genre) - straight back to that metadata issue - making new things out of old things - making function emerge from data (this is beyond reporting)
- ReUse - turn a question into a tool (Spector fan listening on Spotify) - APIs on everything - artist lookups - sharing extends to with managers and business partners - the more detail we get from BPs the better we understand consumers - another example, Setify
- Visualise (we are a very visual industry) and explain Explain? Give it context, benchmark,
- Learn in unlikely places Research Academic papers are probably the original open data and if most of your social knowledge comes from vendors trying to sell you something it’s a different take. Popularity (number of people following you) does NOT mean you are more influential. An average twitter user only retweets one in 318 URLs [from Influence and Passivity in Social Media – M Romero, S Asur, W Galuba, B Huberman] 50% of retweets occur within an hour, 75% under a day People only retweet from a small number of people and only a subset of a user's followers actually retweet A retweeted tweet reaches an average of 1,000 no matter the number of followers of the original tweet 77.9% of user pairs with any link between them are connected one-way, and only 22.1% have reciprocal relationships between them. [What is twitter, a social network or a network media? - H Kwak, C Lee, H Park, S Moo
- Try shit - prototype and even throw away FB’s Move fast and break things Google’s everything is a beta
- - helps manage expectations
- - taking features out
- - and then get more
- - what do you do when performing analysis on classical data and you see an instance of The Jam. - the metadata gets altered through the distribution chain - one artist irate that he had personally curated his metadata and Universal had delivered it correctly, but further down the line it was
- - e.g. interns - used to be music business degrees, now CS - they problem solve, they code - creative computing - analysts are not wedded to BI tools they query data
- - but the tech is changing all the time - getting cheaper - more options from google docs to amazon’s aws (problem goes back to bandwidth) - technologies we avoided 3 years ago due to skills shortage in the market are now commonplace
- - start small - look outside/ look inside - success may be incremental rather than big wins - start small projects and numbers of people - one person driven and passionate is worth two who aren’t and just put the hours in! - the technology is no longer the challenge it is the people.
- difficult where to pitch it majors, indies, live, management, professional service providers. so no theory but a story of real life practice story starts - before “open data - before “big data” - before “data-driven decision making”