Data Analytics Time to Grow Up
•Download as PPTX, PDF•
1 like•552 views
This presentation was given at the festival of marketing 2014. How grown up is your analytics? This slide deck will help you understand what you need to achieve optimum business benefit from your data analytics.
Recommended
Recommended
More Related Content
What's hot
What's hot (20)
Similar to Data Analytics Time to Grow Up
Similar to Data Analytics Time to Grow Up (20)
More from Lynchpin Analytics Consultancy
More from Lynchpin Analytics Consultancy (6)
Recently uploaded
Recently uploaded (20)
Data Analytics Time to Grow Up
- 1. Analytics Time to Grow Up
- 2. The Data Party 2 2012 http://hbr.org/2012/10/data-scientist-the-sexiest-job-of-the-21st-century/
- 3. The Data Party is Over 3 2012 2014 http://hbr.org/2012/10/data-scientist-the-sexiest-job-of-the-21st-century/ https://gigaom.com/2014/03/19/the-sexy-era-of-big-data-is-over-as-vcs-turn-to-fund-unexpected-industries/
- 4. What Does Good Look Like? Insight 4 Data 01 Action 02 03 Turning data into insight and action? Transforming data into actionable insight?
- 5. The Reality: Lots of Moving Parts 5 One size does not fit all Pricing Customers Competitors Business Plan People Skills Tools Platforms KPIs Data Structure
- 6. Maturity Is Not One-Dimensional 6 Data Reporting Analysis Optimisation Personalisation
- 7. Risk of Box Ticking 7 Data Reporting Analysis Optimisation Personalisation Got some data? Is the data in a report? Hired an analyst? Done a test? Switched on personalisation?
- 8. Risk of Oversimplification 8 Data Optimisation requires granular data Reporting Analysis Optimisation Personalisation Reporting requires high-level data
- 9. MVT Visualisation Analytics Maturity 9 Time to Grow Up Modelling Big Data Predictive Business Intelligence Test & Learn DMP Streaming AVOIDING THE HYPE SPEED, SCALE & “SHINY” ACHIEVING BALANCE DATA VERSUS DECISIONS 01 02 03 PRIORITISING SKILLS, TOOLS AND PROCESSES
- 10. MVT Visualisation Analytics Maturity 10 Time to Grow Up Modelling Big Data Predictive Business Intelligence Test & Learn DMP Streaming AVOIDING THE HYPE SPEED, SCALE & “SHINY” ACHIEVING BALANCE DATA VERSUS DECISIONS 01 02 03 PRIORITISING SKILLS, TOOLS AND PROCESSES
- 11. Avoid The Hype 1: Speed 11 Speed is not in itself an outcome. Humans rarely make (good) decisions in real-time. Speed often arises only from automation, which comes after analysis. The need for speed becomes a barrier to investing in longer term analytics with much higher ROI.
- 12. Avoid The Hype 2: Scale 12 Rubbish In = Rubbish Out Applies at every size of data Critical success factors are the same regardless of size: Massive data Small Data Relevancy Accuracy Coverage Structure Rubbish Medium data Big Data
- 13. Avoid The Hype 3: The Shiny 13 Useful Shiny
- 14. MVT Visualisation Analytics Maturity 14 Time to Grow Up Modelling Big Data Predictive Business Intelligence Test & Learn DMP Streaming AVOIDING THE HYPE SPEED, SCALE & “SHINY” ACHIEVING BALANCE DATA VERSUS DECISIONS 01 02 03 PRIORITISING SKILLS, TOOLS AND PROCESSES
- 15. Balance is Key Analytics maturity is two-dimensional Constant trade-off between: Sophistication and availability of data Capacity to make effective decisions 15 Data Maturity Decisions
- 16. Data Famine Trying to run sophisticated models and make extremely granular automated decisions… … without investing in getting decent data to feed the process. 16 Data Maturity Decisions
- 17. Drowning in Data Overinvestment in data capture and systems integration… …lack of capacity or understanding to respond to the data flows 17 Data Maturity Decisions
- 18. Maturity Model 18 Understanding Benchmarking Educated Guesswork Competitive Advantage Optimisation Data Decisions
- 19. Maturity Model 19 Understanding Benchmarking Educated Guesswork Competitive Advantage Optimisation Data Decisions Ad Hoc Capture Measurement Design Real-Time Flows
- 20. Maturity Model 20 Understanding Benchmarking Educated Guesswork Competitive Advantage Optimisation Ad Hoc Capture Measurement Design Real-Time Flows Automation Test & Learn Statistical Analysis Business Intelligence Basic Reporting Data Decisions
- 21. Maturity Model Example Measurement Design 21 Statistical Analysis Data Decisions Understanding early behavioural triggers to purchase e.g. a key signal of being in the market for a holiday is performing multiple searches with different dates on travel sites
- 22. Maturity Model Example Optimisation with critical link between analytics (segmentation) and testing (variant) data e.g. quickly understanding how a test performs for different audience/customer segments or varies across devices before it is too late 22 Real-Time Flows Test & Learn Data Decisions
- 23. MVT Visualisation Analytics Maturity 23 Time to Grow Up Modelling Big Data Predictive Business Intelligence Test & Learn DMP Streaming AVOIDING THE HYPE SPEED, SCALE & “SHINY” ACHIEVING BALANCE DATA VERSUS DECISIONS 01 02 03 PRIORITISING SKILLS, TOOLS AND PROCESSES
- 24. People Process Technology 10 5 0 I q II q III q IV q Resourcing Debate 24
- 25. Analytics Nirvana? What do you think is the optimal balance between technology, people and process to achieve analytics nirvana? 2014 Econsultancy/Lynchpin Measurement & Analytics Survey 25
- 26. Refocusing the Debate 26 35% 45% ? 20% What are the best processes to invest in? What are the right technologies to buy? Who are the right people to hire?
- 27. Technology Digital is increasingly Just Another Data Source for visualisation and modelling tools Success not always about having the best tool More often about using the right tool for the right job 27 Technology 10 5 0 I q II q III q IV q
- 28. Investing in Process? 28 PROCESS = ROI
- 29. Process Determines ROI on Analytics What are the objectives? What is the most appropriate methodology? Is there a genuine balance between: Improving data quality/relevancy/accuracy The capacity to respond to that data? 29
- 30. ROI Comparison 30 Digital Analytics Tool Re-platform return 0% Rare to break-even in first year once cost of change is taken into account Customer Retention Model return +25-30% Average 25% - 35% performance improvement from targeting customers that are likely to lapse Attribution Project return 3-6% Average 3 - 6% performance improvement from evidence based reporting and some budget reallocation
- 31. People: Critical Skills • Strategy – Objective setting is 20% of the time but 80% of the importance of most analytics projects. 31 • Engineering – Measurement, data flows and databases needs to be designed, not hacked together. • Analysis – Understanding the business model is as important as understanding a statistical model.
- 32. Critical Skills Analysis Strategy Ad Hoc Capture Measurement Design Real-Time Flows 32 Automation Test & Learn Statistical Analysis Business Intelligence Basic Reporting Engineering
- 33. Analysis (is not) Magic 33 Experience plays a massive part in the process Finding new avenues of exploration Knowing how far to go down an avenue before pulling back Fitting data to purpose and questions Preparing and manipulating data so it’s in the right shape
- 34. 5 Take Home Points 02 03 04 05 Beware false dawns of speed, scale and shininess 34 Analytics Maturity 01 Process determines ROI: choose the right analytics projects wisely Maturity is not one-dimensional: one size does not fit all Balance between data and decisions is vital for analytics success Invest in the critical engineering, analysis and consulting skills
- 35. 35 Understanding Benchmarking Educated Guesswork Competitive Advantage Optimisation Ad Hoc Capture Measurement Design Real-Time Flows Automation Test & Learn Statistical Analysis Business Intelligence Basic Reporting Data Decisions
Editor's Notes
- This is the purest analytics presentation you’ll see all day: it’s essentially an entire presentation about one graph. But, first, to get a sense of the room: how many people here have analytics as the primary focus of their role?
- And how many people got into analytics because of this article? Well: bad news.
- Apparently the data being sexy phase has lasted less than 2 years. Poor Tulisa and Amy Willerton have had to abort their data science careers in favour of the X Factor and I’m a Celebrity Get Me out of Here.
- So if – tragically - sex appeal is no longer a benchmark for analytics and we can’t do a line-up of beautiful data scientists, what does “good” analytics look like? The classic definitions are solid, but fairly open to interpretation. “Turn” and “transform” are fairly broad metaphor here for a lot of things. Over the past 10 years we’ve seen some good and bad transforming!
- And the reason there’s not a straightforward benchmark is that even within industries, e.g. if I think about all the different financial services organisations we work with, the business stage/strategy/objectives and context around teams, tools and platforms is highly diverse.
- So the classic maturity model looks great; but is really hard to actually use (not very actionable). The words are very open to interpretation. Is there anyone that doesn’t do some reporting? Would any business really ‘fess up to having never done any analysis?
- Think of the best decision you made. Was it the fastest? Now thing about the decision you most regret? You get the picture. Speed is often really about automation. Not about feeding real-time dashboards into the brains of superhumans.
- I’m hoping I’m allowed to say that it really doesn’t matter what size the data is. Structure is a critical success factor. Big data is technically unstructured.
- There are lots of shiny buzzwords in analytics. Based on The Digitals last night you could have composed a pretty good awards entry by stringing all of these together with a decent twitter hashtag… but that doesn’t automatically make them the most useful analytical tools or techniques.
- So let’s acknowledge that it’s more complex than a simple one-dimensional path. But it’s not that complex, and the key unacknowledged issue is that data is tricky to manage, analytics is tricky to resource and there’s a balance required here.
- So, here’s the graph. Constant balance. More sophisticated the analysis, the more sophisticated data you often have to feed it.
- Decent data is not about it existing. E.g. ASOS have had decent data from scratch, but before they had a critical mass of customers that wasn’t in itself an asset. Major banks have loads of customer data, but it’s not necessarily all easy to access.
- Often a classic symptom of this is lack of ROI from technology solutions.
- So, the graph.
- Ad Hoc: It’s there. Or it’s not. And it might be right. Or it might not. Good place to be if you like surprises! Measurement Design: examples, mobile app, customer service calls (cancellations). Real-Time Flows: it happens in one system and is automatically available in another one as fast as you need to make the decision (real-time is often an aspiration rather than a requirement).