UK GIAF: Summer 2015
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The Games Industry Analytics Forum returned for its 10th meet-up on Thursday 27th August at Product Madness in London. GIAF is a free event for game analytics practitioners held in both the USA and UK, organised by game analytics & marketing company deltaDNA. Featuring ever-changing presentations, venues and expert panel discussions, it's a unique opportunity for practitioners looking to generate insight and value from big data game analytics; one of the most important trends in games. UK GIAF: Summer 2015 Featured: Peter Warman, CEO and Co-founder at Newzoo What to do with data from 1 billion smart devices in China? Volodymyr (Vlad) Kazantsev, Head of Data Science at Product Madness From Data Science to Data Impact: On many ways to segment your players & more Heather Stark, Analyst at Kinran Limited Trends in game analytics: What’s happening (and why) Interested in speaking at a future event or in finding our more? Visit www.deltadna.com/GIAF
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UK GIAF: Summer 2015
- 2. UPCOMING TALKS What to do with data from 1 billion smart devices in China? Peter Warman - CEO & Co-founder, Newzoo From Data Science to Data Impact: On many ways to segment your players & more Volodymyr (Vlad) Kazantsev - Head of Data Science, Product Madness Trends in game analytics: What’s happening (and why)? Heather Stark - Analyst, Kirnan Limited
- 35. 35 2015 © All rights reserved to From Data Science to Data Impact: On many ways to segment your players Volodymyr (Vlad) Kazantsev Head of Data Science at Product Madness jobs@productmadness.com volodymyrk
- 36. 36 What we do? jobs@productmadness.com volodymyr.kazantsev@productmadness.com volodymyrk
- 37. 37 Heart of Vegas in (public) Numbers iPad US - #13 top grossing iPhone US - #32 top grossing Android - #44 top grossing US (games) Australia iPad - #1 top grossing iPhone - #1 top grossing Android -#3 top grossing jobs@productmadness.com volodymyrk
- 38. 38 Data Impact Team Ad-hoc analytics; dashboards Deep dive analysis; Predictive analytics ETL, R&D jobs@productmadness.com volodymyrk
- 39. 39 Data Impact Team 7 people; 4 in London office jobs@productmadness.com volodymyrk Ad-hoc analytics; dashboards Deep dive analysis; Predictive analytics ETL, R&D
- 40. 40 Ad-hoc analytics; dashboards Deep dive analysis; Predictive analytics ETL, R&D Data Impact Team 7 people; 4 in London office jobs@productmadness.com volodymyrk
- 41. 41 Technology Stack ETL orchestration Transformation & Aggregation SQL Data Products Reports Dashboards +
- 42. 42 few examples .. A B A/B TestsCustomer Lifetime Value days $value Segmentation group 1 group 2 group 3 group 4
- 44. 44 Successful segmentation is the product of a detailed understanding of your market and will therefore take time - Market Segmentation: How to Do it and Profit from it, 4th edition: Malcolm McDonald
- 45. 45 Basics Customers have different needs and means Segmentation can help to understand those differences Which can help to deliver on those needs And drive higher profitability
- 46. 46 What is a Player Segment? A segment is a group of customers who display similarities to each other... Customers move in and out of segments over time
- 47. 47 How many segments are there? There is no one right way to segment (not should there be): Many different approaches and techniques Mix of art, science, common sense, experience and practical knowledge Depends on business needs and availability of data Don’t aim to build one holistic model to meet all needs
- 48. 48 Strategic Management Product Development Marketing Operations Comments Geography /Demographics Loyalty / Length of Relationship Behavioural Needs-based Value Based
- 49. 49 Strategic Management Product Development Marketing Operations Comments Geography /Demographics ✭✭ ✭✭ ✭✭ Separates players by country, city, city-district, distance from land-based casinos. By generational profile: boomers, Gen-Y, Gen-X. Loyalty / Length of Relationship ✭✭✭ ✭ ✭✭✭ New players, on-boarding, engaged, lapsed, re- engaged, cross-promoted. Behavioural ✭ ✭✭✭ ✭✭✭ Based on identifying player’s behaviour characteristics that help to understand why customer behave the way they do Needs-based ✭ ✭✭✭ ✭ Divide customers based on needs which are being fulfilled by playing Online Slots Value Based ✭✭✭ ✭ ✭✭ Based on present and future value of the customer (RFM/CLV)
- 50. 50 Land-based Slots Player segmentation <10% >50%
- 51. 51 Segmentation = building a taxonomy All Players New (<28 days) Established (>28d) Payer Non Payer0-2 days 3-7d 8-27 <30 spins >30 … High V Med V Low V Engaged Casual… VIP Concierge
- 52. 52 ..and simplifying it daily use All Players New (<28 days) Established (>28d) Payer Non Payer0-2 days 3-7d 8-27 <30 spins >30 … High V Med V Low V Casual… New High Value Med Value Low Value Engaged Casual Engaged
- 53. 5 3 How to profit from Segmentation? 2
- 54. 54 Clients of Segmentation ○ Strategy and Finance ○ Product development ○ Marketing operations
- 55. 55 Strategy and Finance This Month high-value med-value low-value super free-rider casual slotter recently lapsed high-value 55.27% 30.06% 4.81% 5.54% 2.00% 2.32% med-value 11.11% 42.50% 25.25% 10.92% 6.20% 4.02% low-value 0.59% 7.72% 36.02% 30.59% 17.12% 7.96% super free-rider 0.04% 0.30% 2.76% 70.50% 22.22% 4.18% casual slotter 0.01% 0.10% 0.96% 8.98% 51.37% 38.58% recently lapsed 0.05% 0.22% 1.01% 8.93% 13.00% n/a New 0.01% 0.08% 0.67% 3.22% 31.05% 64.97% This Month 0.15% 0.54% 2.13% 21.56% 31.22% 23.03% Last Month 0.11% 0.43% 2.03% 21.09% 37.19% 27.20% LastMonth
- 56. 56 Strategy and Finance This Month high-value med-value low-value super free-rider casual slotter recently lapsed high-value 6.80% -0.45% -1.66% -2.39% -1.07% -1.24% med-value 3.09% 2.60% -2.81% -2.12% -0.60% -0.16% low-value 0.11% 0.90% -1.63% 1.99% -0.54% -0.82% super free-rider 0.01% 0.05% -0.05% -2.05% 2.58% -0.54% casual slotter 0.00% 0.02% 0.05% -1.26% 2.71% -1.54% recently lapsed -0.01% -0.05% -0.35% -4.21% -8.43% N/A New 0.01% 0.04% 0.36% 1.59% 16.17% 1.21% Manage transitions, not churn LastMonth
- 57. 57 Product Development New Slot Game Released Coins Spent
- 58. 58 Product Development Geo: Australia Value: Low-value Behaviour: Prefer Medium bet New Slot Game Released Coins Spent
- 59. 59 Marketing Objective Behavioral RFM/CLV geo/demographic Lifecycle Sale Events Monetization campaigns Retention campaigns Re-engagement VIP management
- 60. 6 0 How to actually do segmentation? 3
- 61. 61 Pillars of Successful Segmentation Project Business knowledge Data knowledge Analytical skills People Process Technology ETL Machine Learning Business Intelligence Product Integration Marketing Product Data Services
- 62. 62 Top-down approach to segmentation 1. Define objectives and therefore customer characteristics a. dd 2. Choice method to split users a. d 3. Prioritise segments to target a. d 4. Operationalise segmentation a. s 5. ‘land’ the segmentation within the organization
- 63. 63 Bottom-up approach 360o player view Segmentation Player transitions Tailored interventions Prioritisation and testing ● Build database to provide 360o view of the customer ● Demographic, behavioural, payments, etc. ● Add predictive attributes, such as conversion probability, churn risk, LTV, etc. ● Segment customers by desired attributes: more than one approach ● Use robust statistical techniques for clustering or validation of empirical segmentation ● Ensure segmentation is intuitive for the business and can be used across business functions ● Identify how players are moving from one segment to another (segment transition matrix) ● Determine value levers and identify potential improvement ideas ● Create tailored interventions (CRM, push ..), aimed at moving customers to more valuable segments ● Build predictive models to detect best offer and prevent undesirable transitions ● Prioritise interventions based on expected LTV uplift and ease of implementation ● Test interventions through experimentation
- 64. 64 How to actually do segmentation? Just Look at Data Clustering Decision Trees Player Attributes de-correlate Normalise Scale
- 65. 65 de-correlate and normalise Player 1 more similar to Player 2 ? Player 3 more similar to Player 2 ? Weekly Play Summary
- 66. 66 de-correlate and normalise Weekly Play Summary
- 67. 67 de-correlate and normalise Player 1 more similar to Player 2 !
- 68. 68 de-correlate and normalise Player 1 more similar to Player 2 !
- 69. 69 de-correlate and normalise Player 1 more similar to Player 2, isn’t he?
- 70. 70 de-correlate and normalise Player 3 more similar to Player 2 !
- 71. 71 What now? K-means Hierarchical Clustering Decision Trees .. and many more
- 72. 72 Decision Tree for Clustering All Payers 500 (next month>$100): 4.7% 10000 did not: 95.3% Last_months_dollars <=$2 2 (next month>$100): 0.04% 5000 did not: 99% Last_months_dollars >$2 498 (next month>$100) > $100: 9% 5000 did not: 91% Transactions <=10 243 (next month>$100): 5.5% 4200 did not: 94.5% Transactions > 10 255 (next month>$100): 24% 800 did not: 76%
- 73. 73 Decision Tree for Clustering All Payers 500 (next month>$100): 4.7% 10000 did not: 95.3% Last_months_dollars <=$2 2 (next month>$100): 0.04% 5000 did not: 99% Last_months_dollars >$2 498 (next month>$100) > $100: 9% 5000 did not: 91% Transactions <=10 243 (next month>$100): 5.5% 4200 did not: 94.5% Transactions > 10 255 (next month>$100): 24% 800 did not: 76%
- 74. 7 4 Segmentation at Product Madness 4
- 75. 75 Lifestage Segmentation On-Boarding Disengaged Engaged not played game Churned
- 76. 76 On-boarding segment On-Boarding Disengaged Engaged not played game Churned
- 77. 77 On-boarding segment On-Boarding Disengaged Engaged not played game Churned
- 78. 78 Lifestage Segmentation On-Boarding Disengaged Engaged low risk high risk low risk high risk low risk high risk not played game churned churned Churned
- 79. 79 Behavioural Segmentation Average Bet Gifts per Day Bonuses per Day Machine Stickiness Days Played Spins per Day Preference for New Machines %% of spin on High-Roller machines Big Win Stickiness etc. Hierarchical Clustering
- 81. 81 Infrastructure Data Warehouse Segmentation Engine CRM Email GAME Reporting Ad Hoc Analytics Predictive Analytics
- 82. 82 Segmentation for A/B tests A B
- 83. 83 Segmentation for A/B tests A B
- 84. 84 Bonferroni correction: Bayesian Hierarchical Model Combine stats with Market Intuition! Adjustment for multiple testing 𝛼adjustted = 𝛼desired/M
- 85. 85 2015 © All rights reserved to Thank You! jobs@productmadness.com volodymyr.kazantsev@productmadness.com volodymyrk
- 88. Kinran @HAStark Market forces Tech enablers Frontier zones Failure prevention
- 91. Kinran @HAStark
- 92. Kinran @HAStark ...[according to VB research] most mobile- first companies are trying to pay between $1 and $1.50 for users, but they are only getting quality users at multiples of those numbers... August 12 2015 http://venturebeat.com/2015/08/12/this-service-tells-you-what-supercell-machine-zone-and-other-big- publishers-spend-on-user-acquisition/
- 93. Kinran @HAStark ...[according to AppScotch] Machine Zone is currently spending somewhere around $12 per user with AdColony, InMobi, and Unity Ads, up to $20 per user with Vungle, and between $2 and $30 per user with Chartboost... August 12 2015 http://venturebeat.com/2015/08/12/this-service-tells-you-what-supercell-machine-zone-and-other-big- publishers-spend-on-user-acquisition/
- 96. Kinran @HAStark
- 98. Kinran @HAStark
- 99. Kinran @HAStark Kevin Schmidt and Luis Vicente, Mind Candy Practical real-time approximations using Spark Streaming hyperloglogs to count uniques bloom filters to count revenue stream-summary for top-k (metwally agrawal abbadi 2005) from nucl.ai Data Science track (to be published) earlier version from huguk available now http://www.slideshare.net/huguk/fast-perfect-practical-realtime-approximations-using-spark-streaming
- 100. Kinran @HAStark Image: re-work.co Deep Learning Summit Boston
- 104. Kinran @HAStark
- 105. Kinran @HAStark Miloš Milošević, Nordeus Early Churn Prediction and Personalised Interventions In Top 11 later detection is more accurate - but less useful tried many techniques – logistic regression good! cluster users based on first day gameplay customise messaging based on clusters increased D1 retention (and downstream metrics) from nucl.ai 2015 Data Science track (to be published) writeup available on gamasutra now: http://www.gamasutra.com/blogs/MilosMilosevic/20150811/250913/How_data_scientists_slashed_early_churn_in_Top_Eleven. php
- 108. Kinran @HAStark Meta S. Brown Analytics failure and how to avoid it Analytics programs fail... .... because they lack a viable plan for success Imperial College Data Science Institute 24 June 2015 http://www.slideshare.net/metabrown/analytics-failure-how-to-avoid-it
- 109. Kinran @HAStark Meta S. Brown Analytics failure and how to avoid it Analytics programs fail... .... because they lack a viable plan for success Define success, and who decides on it Imperial College Data Science Institute 24 June 2015 http://www.slideshare.net/metabrown/analytics-failure-how-to-avoid-it
- 110. Kinran @HAStark Meta S. Brown Analytics failure and how to avoid it Start with a business problem a small one understand the business problem really well As you scale up – pay attention to process replicable! replicable! replicable! Imperial College Data Science Institute 24 June 2015 http://www.slideshare.net/metabrown/analytics-failure-how-to-avoid-it
- 111. Kinran @HAStark Meta S. Brown Analytics failure and how to avoid it Use only as much data as you need to The best use case for Big is personalisation Imperial College Data Science Institute 24 June 2015 http://www.slideshare.net/metabrown/analytics-failure-how-to-avoid-it
- 113. JOIN IN THE CONVERSATION PARTICIPATE IN THE NEXT GIAF Analytics for Games www.deltadna.com/giaf events@deltadna.com #UKGIAF
Editor's Notes
- \/ \/ \/ \/ \/ \/ \/ US (games) iPad US - #13 top grossing iPhone US - #32 top grossing Android - #44 top grossing Australia Who cares about Australia. We do iPad - #1 top grossing iPhone - #1 top grossing Android -#3 top grossing Overall, not only games
- Ad-hoc analytics, daily fires, dashboards, Insights Deep dive analysis - reports that take few weeks to complete; Predictive analytics, Machine learning, statistical modelling Data Pipeline, platform for machine learning and modelling
- Insights Data Science Data Engineering 7 people; 4 in London office
- We Are Hiring ! jobs@productmadness.com
- Events are generated on server-side. This way we control data quality. We are processing 350 Million events per day They got ingested into Amazon Cloud to S3, with the help of Python and Spark. And then got copied to Amazon Redshift - Columnar parallel database. Currently we have 12 nodes, with total capacity of 24TB Once the data is in there - we do all heavy aggregations and transformations. We have moved from Hadoop more than a year ago and haven’t looked back since. We perform most of interactive analysis in Python Notebooks. For trivial things we are using re.dash, which is similar to Mode and Periscope. It is Web-based SQL client with integrated plotting and collaboration functionality. You can even create dashboards with re.dash, but for production dashboards we prefer to use Tableau or our own D3.js-based application. All our web applications are using Python backend, Flask framework. We use scikit-learn for machine learning and predictive analytics. As you have probably guessed, we like Python.
- What we do: AB tests bread and butter of data science teams yet controversial and often misunderstood Customer Lifetime Value modelling knowing how much your customer worth, shortly after you acquite them is a holy-grail of User Acqusition can easily spend next 40 minutes talking about customer lifetime value modelling, but .. So - segmentation
- In this presentation I will not be taking in details about classification algorithm, dimensionality reduction or machine learning. Instead, we will be looking at segmentation from Product Marketing perspective.
- Successful segmentation is the product of a detailed understanding of your market and will therefore take time Segmentation is not a two-weeks task you assign to your analytics department
- Customers have different needs and means. Some players play for fun, others got a kick from competition. We all know that players have very different willingness to pay. Most of you know how rare it is to find a Normal Distribution among our players - our games are played by outliers. If you remove outliers from any analysis - you will probably miss the point of it. Segmentation can help to understand those differences Which can help to deliver on those needs And drive higher profitability
- A segment is a group of customers who display similarities to each other... They may react similarly in a product/service offering They may provide comparable values (profitability) to the company They may bear the same needs or behave in alike ways Customers move in and out of segments over time
- There is no one right way to segment (not should there be): Many different approaches and techniques. I will cover few techniques in the following slides. Mix of art, science, common sense, experience and practical knowledge You need to take business needs into account, but also what data is available and can be used, operationally. Don’t aim to build one holistic taxonomy to meet all needs, So what are different types of segmentation? How do you approach a problem like this?
- Multiple way to segment users And there are different use cases for segmentation. You can segment on: geography and basic demographics. In our case - Australian players are very important, and usually behave quite differently from the rest of the world. You can segment based on stage in a player’s Lifecycle - new players behave differently to someone who have been playing your game for the last two years. Also, knowing users who are showing signs of disengagement is very important. You can also segment on Behaviour, Needs (if you can identify them, possibly based on observed behaviour) and, of course, based on Player Value. But different parts of the business are interested in different segmentations.
- Product Managers and Marketing teams might be very interested in Behavioural Segments. But CEO may be more interested to track retention metric for your most valuable players (whales).
- The actual segmentation might be hybrid. This is the segmentation of the Land-based Slot players. First layer - by frequency of play, e.g. engagement Second layers - bahavioural
- But of course, it is important to understand why segmentation is useful for a business. What decisions can it help to make? And how it can affect daily operations and possibly product?
- Clients of Segmentation Strategy and Finance Product development Marketing operations
- Strategy and Finance
- When we looked at data after launch, amount of coints spent has actually dropped on the day of the launch! But was it even a real drop?
- But for a specific segment, that day was very positive.
- Business knowledge: - high-level segments goals - product/marketing strategy Data knowledge: - how to access 360 view? - what are segments definitions? Stats/Analytical skills - how to profile various segments? ETL - recalculated daily or real-time - regular reviews Integration with back-office and game - segmentation engine + ETL Dashboards Reporting Marketing: - day-2-day campaigns for segments - reporting (monthly and daily) Product - review feature success for segment Analytics and data engineering - ongoing support and refinement
- What are business objectives and therefore customer characteristics we should use to profile the market? What approach should we take to ensure segments accurately represent the market and actionable? What criteria should we use to prioritise segments and select targets? How can we ensure segmentation is operational and can be deployed? How to do ‘land’ the segmentation within the organization and ensure it gains traction?
- K-means Hierarchical Clustering Decision Trees .. and many more
- I’m going to take a look at what’s going on in game analytics. And why.
- I’m going to look at four aspects – what’s going on in the market we serve – in the games industry itself – and how that’s shaping what gets done in analytics. And I’m going to look at changes in the tools and tech we have at our disposal, to do the work we do. I’m going to look at frontier zones – areas what gets done is evolving. Liminal zones where the ocean and fog meet. Finally, I’m going to look at failure prevention in games analytics. (I didn’t want to leave you sad by titling it failure.) This is a glass half full take on things. I’ve only picked out a few things under each heading, and there might be more that you think of. Tell me in question time, or afterwards in the bar.
- You’re probably wondering. How does she know all this stuff? Has she wired my studio up for telemetry? Or is she making it all up? Well it’s neither. I do what you do – talk to people, read trade press, listen in on social media, look at changes in vendors’ service offerings, go to events, and use this info as tell-tales to see which way the wind is blowing. Last month I curated and chaired the Data Science and Analytics track at the games AI conference in Vienna, and I’m bringing back a few shiny snippets from that to share with you. Apparently crows don’t actually like shiny things, which I found out after researching this cool picture, but I do.
- It makes sense that what gets done in games analytics would be influenced by what’s going on in the games industry itself. So what’s going on in games? It depends a lot on what facet of the market you live in. The market as a whole is still growing. Could be worse. But there are two trends that make life difficult. They have to do with distribution – both on mobile appstores and on steam. One trend is the increasing number of games on the market. The other is the stickiness of the top 10 lists. Visibility in the face of competition, both long-tail and top 10, is a huge challenge. Best sellers tend to hang about like low cloud over England in summer. This isn’t by accident. It’s to do with store managers wanting to optimise their returns, and giving successful titles visibility via multiple internal channels. Also also, on mobile appstores, it’s about sophisticated use of the advertising ecosystem by top sellers. Also - the game needs to be good. But that isn’t enough.
- What’s happening as a result? It’s making people pay even more serious attention to distribution and visibility. Some are choosing to go with publishers rather than self-publish.
- Ouch.
- Really. Ouch. It’s bleeping expensive. This isn’t the kind of spend to do casually.
- This means having a good grip on your i/o for acquisition. This can get complicated. But the key point is that different players come in from different sources, which have different costs. You need to balance that view of your costs, with predictions about likely revenue. These predictions will become more accurate the longer a run of real data you have, but by that time your media buying window may have closed.
- Since it’s so nailbitingly pricey to acquire players, there is an increasing focus on understanding how to keep them. This has always been of interest. But the truism that usually cheaper to retain a customer than to get a new one is being taken more seriously, now that competition for attention is fiercer than ever.
- The areas that service providers are focussing on is often directional. App Annie has recently begun to offer competitor intelligence on how players interact with other games. After offering to integrating player metrics with their store performance data, for free.
- There’s lots to say about tech enablers. I’m only going to give a light kick to two aspects of it here – but ask me other things in the question time, or afterwards.
- One thing I’m seeing is the need for speed, for taking certain decisions. And with that an interest in streaming architectures and algorithms, particularly Spark streaming. There’s a good piece of work from nucl.ai on this, from one of our London games firms.
- I’d like to give a shout out to some open sourced work by Mind Candy, on using probabilistic data structures for stream-based metrics. This is very like the material covered in Ilya Katsov’s ‘Highly scalable’ blog, but it includes links to the source code. Most of these approaches use hashes to enable constant-space scalability, at the expense of perfect accuracy. Agrawal from Berkeley is the author to watch here, if this is your bag. He’s got a whole book on it.
- Also on the tech enablers front, there’s something almost unbearably hot in the machining learning world, that hasn’t yet become standard operating procedure in game analytics: deep learning.
- Actually this is a better picture. There’s huge excitement about deep learning as it enables the system to learn the features which are important – and not only that – learn a hierarchy of features, with lower level features being more general, and higher level features being category specific. This has resulted in big progress in speech recognition, and visual processing. The visual processing work is particularly interesting as it dovetails well with work on neurology of visual processing, and on mathematical modelling of processing channels.
- I have heard of people using it in analysis of play data, for player segmentation, but it’s been more along the lines of ‘I’ve tried everything but the kitchen sink and here’s this cool thing I will try too - I’m not quite sure what it’s good for but hey why not’. I’d say adoption is at the garage tinkering stage. Hence the picture of the messy machine shop. Which looks a bit like my desk. I’m not sure what problems in game analystics are the right shape, and have a strong analogy to signal processing. I think that’s tbd.
- By frontier zone, I mean places where practice is evolving, interesting, and not settled. You could I guess count the use of deep learning as a discovery technique as a frontier zone. Here are a few more.
- I’m not talking about Dark Side of the Moon, as the final frontier. But about being able to segment players (and game elements) in a way that informs design. There’s a particularly good piece of work in this direction from nucl.ai
- There has been a fair amount published on churn prediction detection – game play gives off a number of signals that can be useful in this respect. There was some interesting work at CIG2014 on that last summer, from Wooga in collaboration with University of Lausanne. What there’s been less of, is work that integrates churn detection with churn prevention. Here’s a really good piece of work from the track I chaired. What’s good about it combines analysis for prediction, for analysis to guide interventions. And it worked really well for them.
- Another frontier zone I’d wave a hand to is a meeting of minds between games user research and quantitive product management. Games user research is a discipline with its own set of conferences. I went to one in July, in London, and there’s going to be a big one, ChiPlay in London in October. In the whole day there was only one talk that had any numbers in it. It may not be obvious but these guys could make a great music together if they’d only learn each other’s languages.
- Finally here’s something everyone cares about. My poster child for this once again comes from someone else’s work: Meta Brown. She wrote Data Mining for Dummies. She’s not a dummy though. She has an advanced degree in nuclear engineering from MIT. She’s done lots of big ticket analytics project work, and consulting. She gave a talk earlier this summer at the Imperial College Data Science Institute. I’ve never laughed so hard in a data science talk. I can’t begin to imitate her dry midwestern sense of humour, but there was some good stuff she said that I think bears repetition.
- This is like the kind of koan you can chant while walking around whacking yourself on the head with a board. It’s that good.
- If you don’t get clear agreement about what success looks like – and to whom – success is going to be elusive. Hugely so. I’m not talking here about collecting basic metrics, but about going offroad, or on a deep dive in search of treasure.
- The key message here is to agree the business problem – and understand it as well as possible – before touching a drop of data. I do see people enjoying just diving in to see what’s there, and that’s fun for a side proejct, but being prepped property makes it easier to explore further. And as you get more complicated – you need more process support. That means Crisp-DM, or some cousin. According to Meta.
- Don’t go mad for big data when small data will do. Use it for what it’s good for.
- Here I’ve talked about what I think’s going on at the moment. As to what next, let’s take it to the bar.