People are losing it over AI. From analyst predictions, to industry events, to earnings calls, artificial intelligence and machine learning are all the rage. And dollars are flowing in response: the number of AI start-ups getting funding has spiked 4x from 2012 to 2016 (from 160 deals to 658 deals, according to CB Insights) and a recent Forrester study projects a 300% increase in corporate investments from 2016 to 2017. Yet, we’ve seen this hype before. Especially those of us who were at the front lines of AI back in the early days (or at least the mid-90’s!).
Yes, AI is super-exciting. Yes, machine learning (a sub-set of AI, but sometimes not) offers to revolutionize the way we monitor, and model and pattern match and interpret all the big and small data that is swirling around all of us. But no, it’s not all going to happen overnight. And (especially) no, many businesses and consumers still don’t have a clue about the best ways to select, apply and monetize AI for practical, everyday stuff. The following rules should help in this regard – but mainly are envisioned as a (time-tested) sanity check for surviving the latest edition of the AI hype machine.
Rule #1: Scope Matters. Creating a general-purpose thinking machine is hard. Creating an intelligent agent (or bot) that automates a single or small set of everyday, repetitive, “standard” tasks is a lot more tractable. Just as the key to early AI – and the Knowledge Management movement that followed – was finding narrow but high value applications like streamlining problem resolution in call centers or processing loan applications, the same type of “think global, act local” approach applied to today’s AI is equally important. For the same reason, starting with small-ish data vs. super-large big data sets can make sense when applying analytical techniques to many non-scientific business applications (more on this next).
Rule #2: Machine Learning is Not Magic. And it’s not easy (for most) to get right the first time either. Experimentation with a tool like RStudio is key, and there are many algorithms to choose from (Bayes, decision trees, regressions, and once-again popular neural network models aka “deep learning”). Of course training deep learning models can be both an art and a science. However, the good news is that an excellent recent article by Andrew Beam shows that you don’t need Google-scale data to use deep learning. I saw this in some of my graduate work when I was training neural nets to do simple pattern recognition, and it’s great to see this type of small data approach continuing to stand the test of time!
you don’t need Google-scale data to use deep learning”
Rule #3: Data is King. Getting close to customers, understanding their journey, tailoring their experience, and selecting just the right offer are all outcomes that are enabled by insights powered by big and small data. Generating these insights in a timeframe and cost that make them readily available to front line teams (and consumers themselves) is where advanced analytics and techniques like deep learning need to go. But as mentioned above, what you get out is very much a function of what (data) you put in. Where will your training data come from? How will you prepare it? Who will test the performance? These are questions as important as what tool or algorithm you’ll use.
Rule #4: People Matter. Even as AI systems become more skilled at complex decision-making, and take over some “back of house” functions previously performed by humans, we are a long way from creating virtual human beings, a point that Om Malik made in his excellent piece for The New Yorker last summer. Which is why some of the best, most impactful use cases will continue to augment rather than replace human workers, such as the AI voice analysis and feedback system from Boston-based Cogito that gives real-time guidance to employees as they engage customers on the phone, or the “davis” AI powered virtual assistant for IT ops from APM pioneer Dynatrace.
Rule #5: Consumers Don’t Care About Your Technology. Data nerds want to know what flavor of machine learning you are using. If you are selling to them or other techies, than skip to Rule #6. For everyone else, take note that it’s more important to focus on the “why” than the “how” when selling the value of your AI initiative to internal or external stakeholders. Why is the problem interesting? Why is it hard to solve with traditional (non-AI) approaches? Why is this repeatable/scalable vs. one-off solution? Even more so, what unique value is AI providing to your initiative or app? And how will you show ROI going forward?
Rule #6: Embedding AI Drives Adoption. Back in the day, the old joke among AI researchers was that when something in AI become successful, it wasn’t called AI any more. Today, many successful AI powered apps and services have AI “in them” but the technology is not apparent to the end-user. And that’s the point really – embedding AI drives adoption. Fortunately there are a growing number of tools to add AI or machine learning or other intelligent capabilities. These include open source development frameworks and engines like Apache Lucene (NLP) and Mahout (ML), Eclipse BIRT (Developed by Actuate – now part of OpenText) for embedded analytics and visualization, and RapidMiner for machine learning; embedded analytics specialists like Izenda and Sisense; developer platforms like IBM Watson APIs (conversation, speech, vision) and Microsoft Cognitive Services (decision rules, search, vision); and even custom hardware like Nvidia’s Jetson TX2 card.
Rule #7: Focus on Improving Everyday Work. Much of my research and writing over the past few years has focused on turning small and big data insights into everyday value. For marketers there are established use cases for data-driven marketing (see some of them in the piece I did for DMN a few years back), and there’s also a helpful framework for considering which marketing processes are mostly likely to be disrupted by AI from the folks at TopRight Partners. And for others looking at the bigger picture, there’s a very cool study (and poster!) on the overall potential of automation in the workplace – “Where machines could replace humans” – from McKinsey that is worth checking out.
I had the chance to talk small data and business performance for a recent episode of MSNBC’s Your Business. Check out the clip below.
At the beginning of the year it looked like 2014 was shaping up to be a year when Small Data moved out of the shadows and into the mainstream (or maybe even THE Year of Small Data as I predicted in this op-ed for ZDNet). Judging by the volume of articles, social chatter, and casual conversation I’ve had at numerous industry events, Small Data has arrived. But it still means different things to different people. And that’s cool, since there’s a lot we’ve learned over the past couple years about why it’s important – not only as an alternative to Big Data – but also as a design philosophy and movement that shifts the conversation from processing power…to people!
So if you’re new to the topic, or just curious how Small Data has grown up, I’ve collected my “Top 10” articles for sampling the early concepts as well as the latest thinking. There are many more pieces I’ve left off, but this might serve as a core reading list. And yes, I’ve included just 2 of my own articles, but if you like them you can find many more on this blog or on the Actuate buzz page.
The Early Days – Foundations
Forget Big Data, Small Data is the Real Revolution – Rufus Pollock’s (@rufuspollock) original post on the power of Small Data, collaboration and “decentralized data wrangling” from April 2013. Consistently one of the top 5 most cited pieces on the topic, it’s a great companion to my Forbes piece below.
These Smart, Social Apps Bring Big Data Down to Size – my piece with Mark Fidelman (@markfidelman) from October 2012 that introduced the pillars of small data design: make it simple, be smart, think mobile (be responsive) and predicted a new wave of user-centric business intelligence.
What happens when each patient becomes their own “universe” of unique medical data? – Prof. Deborah Estrin (of @cornell_tech) talk at TEDMed 2013 has key ideas around managing our own personal Small Data. Especially relevant to not only privacy discussions but also those pondering the next wave of wearables and fitness tracking devices.
Data-driven Marketing – 3 Perspectives
Small Data Can Help Businesses Be More Human – great post on the need for “human scale” in marketing by Brand Networks CEO Jamie Tedford (@jamietedford).
What the “Small Data” Revolution Means for Marketers – it’s all about delivering more targeted, more personalized and all-around more intelligent marketing campaigns says CommandIQ (@CommandIQ) CEO Noah Jessop. Right on!
What’s The Big Deal About ‘Small Data’? – recent roundup of perspectives on the marketing front (yup, including my own) by my friends at CMO.com.
Is Small the New Big? – Mainstream Thinking and New Voices
Focus on data value, not its ‘bigness’ – one of my recent op-eds on data value, decentralization and the new CRM. Good overview piece if you want to get a high level perspective and stir up thinking within your own organization.
In Praise of ‘Small Data’: How Targeted Analytics — Not ‘Big Data’ — Are Transforming Education Today – new post by Brian Kibby (@BrianKibby), President of McGraw-Hill’s Higher Education Group.
Your Sushi May Be Getting Smarter – new piece in The Atlantic by Tech Editor Adrienne LaFrance (@AdrienneLaF) on “smart objects,” food safety and the power of Small Data (and IoT).
How To Create Incredible Customer Service Through The ‘Small Data’ Advantage – new piece by customer service speaker and author Micah Solomon (@micahsolomon). While a lot of my focus to-date has been on marketing use cases, I think there are some great ones in customer service/loyalty as well as this post suggests.
What articles did I miss? What would you add if I made it a “top 20” list instead?
The small data movement continues to gain followers. In fact, just in the past couple weeks we’ve seen articles in a wide range of publication including Ad Age, CITEWorld (smart data!), InformationWeek India, RetailWeek, and the SmartData Collective, as well as new opinion pieces on the problems with big data in the New York Times and FT magazine. Meanwhile one of the more popular sessions at Adobe’s marketing summit tackled the topic and featured my former colleague Scott Liewehr who reviewed findings from the study we did at DCG last summer (see nice recap of the session by Trueffect here).
As I’ve discussed over the past 2 years, many of the benefits of thinking small focus on making personalized insights more pervasive via new, smarter devices, tools, and apps. Bringing big data down to size helps both decision makers and “everyday” business users or consumers find (just the right) information/content they need, in a format that makes it super clear and easy to take action. This to me is the essence of what it means to be data-driven, and in many ways is powering the excitement around wearable technology like Pebble, Fitbit, and Google Glass.
So, for consumers, small data continues to be about connecting (with data and peers), convenience, and portability. Yet for businesses looking to tap the power of big data for everyday decision making, it’s also about speed. In fact, one could argue it’s mostly about speed and being faster to market and more responsive to customer needs. How so?
As brands strive to be more data driven in their product development, marketing campaigns, and experience design, thinking fast means getting actionable insights in minutes and hours vs. days and weeks as we’ve become used to in “traditional” BI and Big Data (thanks to having to wait for IT resources or our neighborhood data scientist to pay us a visit!).
In today’s business environment, it’s increasingly tough to justify these bottlenecks. Especially when many business users are already part of the way there with tools like Excel or Tableau, and others have seen the potential of a small data approach via their experiences with consumer apps and Websites.
The bottom line? When social and mobile are the rule, and data increasingly drives competitive advantage, speed, and being more responsive, is the new “big.” For business users, this starts with being ruthlessly focused.
Staying Focused to Move Fast
Moving fast, failing fast, even being an agile fast follower, are all proven business strategies. This is the case for organizations in growth industries, and especially for leaders who need to deal with external competitors as well as internal adversaries like budget cycles or organizational inertia. But to go fast, you need to have focus. And think small (and fast), when it comes to data access, discovery, and delivery.
In business, being focused is critical to bringing benefits of big and small data to the broadest set of users. And often requires that we reduce scope to just the essential questions/algorithms/visuals needed to help business analysts and their peers understand the essence of their data and optimize campaigns and experiences in near real-time.
Building on our small data design principles, this means striving for simplicity in our tools, being smart “enough” to handle role-specific tasks, being responsive in terms of portability AND delivering immediate value, and making it easy to compare notes and socialize findings.
Customer Analytics for the Rest of Us
Digging deeper, if we review our definition for small data, the last part (be accessible, understandable, and actionable, for everyday tasks) effectively becomes our checklist for designing and evaluating prospective customer analytics tools that are geared to the non-data scientist and everyday sales, marketing, and service tasks (like Actuate’s BIRT Analytics), as follows:
- Is it fast and simple to access all the data we need – from CRM, ERP, Web, campaign tools, social channels, and other sources – to create a true picture of customer and market needs (and easy to clean the data if needed)?
- Can non-technical users explore, enrich, and understand this multisource data in one place – creating an analytics “sand box” as Boris Evelson from Forrester has called it?
- It is easy for business analysts to create visualizations, share findings, and apply insights in new campaigns and experiences (via workflow and connectors) without needing expert assistance?
- Are common “everyday” tasks/use cases like segmentation, cross-sell campaigns, or social/Web targeting supported – and are there pre-build algorithms tied to these use cases?
Moving fast shortens the time to value, and allows more time for socializing insights and field-testing new campaigns and offers (as a type of “rapid prototyping” perhaps). Doing so requires simpler and smarter tools, and removing IT bottlenecks. But most importantly it means putting the power of advanced analytics in the hands of everyday business users.
Big data has never been bigger. On the heals of a new study (reported here) that shows that investors have pumped $3.6 billion into big data startups this year, and Gartner’s latest Hype Cycle which shows big data quickly approaching the top of the hype “cliff” (along with Consumer 3D Printing and Gamification), one is tempted to see more of the same in 2014. Yet all the steam coming out of the big data hype machine seems to be obscuring our view of the big picture: in many cases big data is overkill. And in most cases big data is useful only if we can do something with it in our everyday jobs.
Which is why against the backdrop of more hype (and more confusion) about the real role for big data, the case for thinking small looks more and more attractive. In fact, I recently penned an opinion piece for ZDNet in Paul Greenberg’s excellent column on Social CRM on 10 Reasons 2014 will be the Year of Small Data. You can read the full piece here, and here’s the Cliffs Notes version of the first 5 reasons:
- Big data is hard. Doing it at scale takes time, especially when the tech guys own it (I used to be a data scientist, so I can say this!).
- Small data is all around us. Social and mobile signals can help us understand customer needs – today.
- Small data powers the new CRM. Small data is central to the rich profiles essential to targeting offers and experiences.
- ROI is the thing. To realize the full value of data-driven apps they must be accessible, understandable, and actionable for everyday work (remember our definition).
- Data-driven marketing is the next wave. As more consumers want to access, and consume and even wear useful data, there’s an unprecedented opportunity for savvy marketers and platform providers like Adobe, HubSpot, and Salesforce, along with data specialists and tool providers like Actuate, Localytics, TrueLens, and Visible to power the next wave of apps, experiences and devices.
What do you think? Are you finalizing your big and small data strategy for 2014? If you’d like DCG’s perspectives as you formulate your plans I’ll be hosting a special Webinar on Dec 12 at 1:00pm ET to share our outlook and 2014 research agenda. Hope to see you there!
UPDATE: missed my Webinar? You can view the replay by clicking here.
In the 4 months since I started to research my last piece on small data for Forbes.com, I’ve had a LOT of conversations about why it’s time to bring the power of big data to the masses, who is doing something about it, and where to focus first. I’ve taken a lot of notes, and I’ve also had the good fortune to run my emerging ‘small data manifesto’ by some real smart folks including Nobby Akiha, Bill Blundon, Jeff Boehm, Dorie Clark, Mike Gualtieri, Esteban Kolsky, Mitch Lieberman, Richard Pasewark, and my colleague Scott Liewehr (I’m sure I’m leaving out some others).
What have I learned? First off, the small data concept is resonating with a lot of the folks I’ve met. And looking at the volume of new posts and articles since the start of the year – in places like Forbes, Inc, Xconomy, and a number of marketing blogs – there seems to be a groundswell building that points to the value of thinking small.
Second, I’ve refined my ‘watch list’ of vendors that are powering this movement and ‘get’ the value of a creating/enabling simple, smart, responsive, socially aware tools and solutions. I’ve intentionally tried to focus on specialty tool providers vs the IBMs and SAPs. And I know that I’ve just scratched the surface – these are 10 vendors you’ll want to know, but not the only vendors in the space! So here’s my list, in alphabetical order, with a few comments on why they fit the bill.
10 Vendors Worth Watching
Actuate – One of the dashboard and reporting pioneers and founder of the Eclipse BIRT open source project, Actuate’s track record promoting ‘BI for the masses’ is well established (disclaimer: I actually helped with some of this back in the day). Actuate’s recent purchase of Quiterian gives the company a leg up over some of its peers when it comes to combining big data analytics and small data delivery.
Attivio – On the heals of a $34M investment, Attivio is poised for big things with its next-gen database that pulls together data from multiple sources and offers to bridge the worlds of big and small data. I love the focus on correlation and breaking down silos, and making it easy to see both the big and small picture.
GoodData – Driven by a $25M series C found in mid-2012, GoodData has become one of the leaders in bringing big data to life for all types of businesses. How? There’s a lot of small data thinking at work, as a quick tour of the company’s blog illustrates. One of the poster-children for why small data will be a big business.
Google – Starting with search, I’d argue that Google was the original small data company. Simple? Check. Smart? Oh yeah. Mobile? Yup. Social. Ah…getting there! With it’s purchase of Wildfire and improvements in Google+ and YouTube, plus resources second to none, Google could be to small (and big) data what Microsoft was to PCs. Seriously.
NetBase – I love how NetBase (former client) has created its Brand Passion Index to make its high-end analytics (using NLP and text analytics and other cool stuff) approachable and fun. Plus another $9M in funding this past January and a key partnership with SAP is bringing its tools to the wider enterprise market. Great strategy.
Nimble – one of the pure-play social CRM vendors founded by GoldMine CRM founder Jon Ferrara, Nimble is all about simple, smart apps and tools that users will want to use. It’s clear these guys understand that if you drive adoption by focusing on the end-user experience, ROI will follow (Saleforce gets this too!).
QlikTech – In terms of powering simple, mobile, contextual apps QlikTech is very much aligned with the small data vision, and one of the more complete offerings in the space. Also announced partnership with Attivio in January.
Tableau – Driven by the goals of powering fast analytics for ‘everyone’ and storytelling on the Web, Tableau’s positioning is lock-step with the vision and opportunity of using small data to bring big data to the masses. I also really dig the company’s messaging and overall creative. Nicely done.
Twitter – By nature of enforcing a small view of messaging and communication, Twitter should be in the small data hall of fame. But it’s really the company’s recent purchase of Bluefin Labs that moves these guys to the head of the class. Brilliant move.
Visible Technologies – Coming at small data from a social analytics perspective, Visible has a super-intuitive dashboard product, and a great handle on making data highly consumable. It’s clear the management team gets the small data potential, and for good measure the company was just named one of 9 Twitter Certified Products partners.
So who would you add to my watch list?