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.
As I approach 5 years of writing, speaking, applying and tracking the growth of small data, it seemed like a good time to compile an updated resource guide with the apps, research centers, agencies, influencers, and vendors who have advanced the state of the art and continue to create what is now a vibrant marketplace of tools and ideas. This exercise has provided an opportunity to revisit some early contributors to the small data community, and discover many new players who have embraced some/all of the small data philosophy.
It has also reminded me that there is a real need to continue evolving the notion of business intelligence, even as AI seems to have replaced big data at the top of the hype curve. And for companies to make better use of all the data (and content) they have already collected – not just to inform decision making, but also to share with customers and even monetize via new, derivative data assets.
Meanwhile, the race among brands to provide more personalized, omni-channel, and ultimately immersive consumer experiences will require new data-driven profile management and tracking approaches. While the expanding Internet of Things (IoT) and wearables adoption offer to shift the focus of analytics further to embedded use cases and small data, and to create even more devices that both create and consume (more high fidelity) local information.
What remains constant is the diversity of views and opinions about what constitutes small data – where it comes from, who owns it, how much is “small” and why it matters. My approach all along was to recognize each new view as is comes along, but also attempt to mash them up into one workable definition:
Small data connects people with timely, meaningful insights (derived from big data and/or “local” sources), organized and packaged – often visually – to be accessible, understandable, and actionable for everyday tasks.
Of course this framing was actually created back in the summer of 2013 and unveiled in this post. Overall, I’d say the definition has stood the test of time, and works well along side more recent functional definitions such as Martin Lindstrom’s for brand marketers (“seemingly insignificant behavioral observations…pointing towards an unmet customer need”).
In parallel, I’ve also spent some time over the years looking at how data impacts the customer journey, and how smart apps and products could influence our design thinking. Readers may want to see my talk from the 2016 SPARK Boston event, which has sections on both of these topics.
With this as background, the remainder of this post outlines the first Small Data 100 – 100 of the most noteworthy apps (10), research centers and agencies (10), influencers (40) and vendors/tools (40) that are fueling a new era of data-driven innovation. Of course there are many others who didn’t make the cut, so feel free to nominate additions to my list in the comments below. Thanks for reading!
The first article I published in 2012 (with Mark Fidelman) on the topic of small data focused on the idea of consumer-style and self-service “smart” apps that would bring the power of big data to the masses. And today, some of the best examples of data in action are consumer apps and websites that embrace one or more small data goals (simple, smart, responsive, social). Here are 10 that are especially noteworthy:
Amazon – sure, the site and the company’s mobile apps have fueled many case studies for using data to recommend products and the power of reviews, and there’s Alexa of course, but the move into physical stores is Amazon’s true “gateway to small data” says IMD Switzerland professor Howard Yu in a recent piece in Forbes.
Credit Karma – the freemium credit score app which now claims over 60M users, is a great example of starting with a “killer use case” (get your free credit score), and is evolving into a more full featured personal finance app while continuing to hit on 3 out of the 4 core principles of small data: be simple, smart and mobile.
Fitbit – early on the Nike Fuelband was my poster child for the power of small data, and now that it’s retired, the Fitbit has taken its place as a mass market example of how to deliver real-time, contextual (and personalized) insights, all in a highly intuitive package.
Gilt Groupe – now owned by Hudson’s Bay Company (Saks Fifth Avenue), Gilt was one of the early pioneers in retail flash sales, and has been a testbed for predictive modeling, social engagement and “best offer” style data-driven apps.
Groupon – the daily deal (once) high-flyer is continuing to reinvent itself via analytics acquisitions (Venuelabs) and has mastered how to provide an app experience users want. In fact it topped the latest user sentiment ranking of retail apps by the ARC at Applause.
Kayak – the travel site’s “When to Book” tool (also called price predictor) remains an excellent example of filtering down complex big data into a highly consumable, visual small data, including a recommended action: wait or buy now.
Runkeeper – sitting at the intersection of big data (from a 50M strong community) and small data, this type of “self-quantification” app offer a unique glimpse at the future of engagement and ownership of personal data.
Shopkick – now owned by Korea’s SK Telecom, Shopkick is a widely used product discovery and rewards app that uses beacons (already installed in 14000 stores) to integrate local small data on shoppers’ activity with other insights from 15M users.
Tinder – the (in)famous dating app has been in the news (no not for that) as an example of using simple card-swiping interfaces to gather user-generated small data and set up “anticipatory computing” opportunities – see this piece in Medium.
Yelp – the review site is certainly a great model for tapping the power of user-generated content (TripAdvisor is another one), and has long used big and small data to improve their recommendations – see highlights of their approach here.
Research Centers and Programs
While a lot of small data innovation comes from the commercial tech community, several academic and non-profit centers have been critical to the growth of the field as well. Here are 5 of them, followed by 5 agencies that are on the front lines of small data:
Open Knowledge International – Co-founder Rufus Pollock was one of the first to promote the idea of small data as a way to “decentralize data wrangling” back in 2013.
The Wharton School – the school’s online business journal, Knowledge@Wharton, has been a frequent publisher of articles on moving beyond big data.
The Small Data Lab at Cornell Tech – founded by professor Deborah Estrin (of TEDMed fame), the group has expanded to a number of researchers, advisors and grad students working on new apps and infrastructure for all things small data.
MIT Living Lab – Multi-disciplinary center exploring technical society impacts of data, including development of a data management platform for collecting and integrating personal small data from smart phones, activity trackers and wearables, campus data, and external sources like social, weather and city data.
United Nations University Small Data Lab – initiatives include those looking at small data and real-time information tools (with Vodaphone and Microsoft as corporate partners) to improve local decision making and sustainable development.
Agencies and Consultancies
Jack Morton – the brand agency jumped into the small data pool with a splash early last year, as it announced a partnership with Martin Lindstrom with the modest goal of “leading the small-data revolution” (they are creative guys after all) – see here.
SapientRazorfish – the newly merged firm has been doubling down on its data and AI chops and has some unique perspectives on the role of data in powering the buyer’s journey – see news of the firm’s Microsoft partnership here.
The small data movement has been shaped by a growing number of institutions and individuals. Representing several disciplines ranging from BI and analytics (my focus when I was at Digital Clarity Group and OpenText) to product design, branding and customer experience strategy, here are 40 key influencers I’ve learned from and in some cases collaborated with over the past 5+ years. A number are fellow analysts or marketers, while others are technologists, editors or authors. All are worth following and getting to know:
Lisa Arthur – @lisaarthur – CMO at e-discovery vendor Kcura, former CMO of Teradata Applications, and author of “Big Data Marketing”
Kirk Borne @KirkDBorne – Principal Data Scientist at BoozAllen and prolific contributor to the analytics Twittersphere
Joe Chernov @jchernov – content marketing maven (was VP of content at HubSpot) and now head of marketing at InsightSquared
Rob Ciampa @robciampa – digital strategy and analytics guy, former CMO at Pixability, and author of “YouTube Channels for Dummies”
Dorie Clark @dorieclark – Duke professor, contributor to Forbes and HBR, and author of “Stand Out”
Erica Dhawan @edhawan – former Harvard researcher, expert on collaboration and connectional intelligence, and author of “Get Big Things Done”
Boris Evelson @bevelson – Forrester VP and Principal Analyst who has been a key proponent of democratizing big data via his “Systems of Insight” model
Laura Fitton @pistachio – founder of oneforty.com, co-author of “Twitter for Dummies” and HubSpot’s inbound marketing evangelist
Daniel Gutierrez @AMULETAnalytics Managing Editor at insideBIGDATA.com, lecturer and author of 4 books on databases and data science technology
Claudia Imhoff @Claudia_Imhoff – author and big data analyst, and founder of the Boulder BI Brain Trust
Esteban Kolsky @ekolsky – customer strategist, enterprise feedback pioneer, Gartner alum, and valued sounding board for my early small data perspectives
Suzanne LaBarre @suzannelabarre FastCo.Design editor
Charlene Li @charleneli – Principal Analyst at Altimeter and co-author of “Groundswell”
Mitch Lieberman @mjayliebs – former CRM industry executive and now director of research at G2 Crowd, focusing on business modeling and software research
Scott Liewehr @sliewehr – head of Digital Clarity Group, long-time analyst and consultant, and collaborator for my small data research while I was at DCG
Martin Lindstrom @MartinLindstrom– branding consultant, speaker, and author of the bestselling “Small Data: The Tiny Clues That Uncover Huge Trends”
Maribel Lopez @MaribelLopez – forward-looking tech analyst (formerly at Forrester and IDC) and author of “Right-Time Experiences: Driving Revenue with Mobile and Big Data”
Loie Maxwell @loiemaxwell – former creative executive at CVS and Starbucks, and now VP of Creative at Cartoon Network; Loie is co-author of the work I’ve done around “Designed Serendipity” (see our Forbes piece here)
Ron Miller @ron_miller – TechCrunch enterprise reporter, Contributing Editor at EContent Magazine, and contributor to SocMediaNews
Debbie Millman @debbiemillman – brand consultant, host of Brand Thinking, and author of “Look Both Ways”
Margaret Molloy – @MargaretMolloy branding expert and CMO at Siegel+Gale
Mark Morley @MarkMorley – marketing technologist/IoT guy at OpenText
Don Norman @jnd1er – noted design thinker at UCSD, former VP at Apple, and author of the “The Design of Everyday Things”
Annie Pettit @LoveStats – market researcher and guru of questionnaire design
Gergory Piatetsky @kdnuggets – AI, data mining and big data veteran (alum of GTE where I worked back in my AI days) who manages KDnuggets.com
Augie Ray @augieray – well-known customer experience and loyalty researcher at Gartner and former head of social media at USAA
Jenny Rooney @jenny_rooney – Editor of the CMO Network at Forbes
Lisa Joy Rosner @lisajoyrosner – former CMO at Neustar and before then NetBase, where she launched the company’s Brand Passion Index
Nate Silver @NateSilver538 – besides pushing statistics into the mainstream, Nate is all about using big (and small) data to tell stories; author, “The Signal and the Noise”
Aaron Strout @AaronStrout – CMO at agency W2O Group, long time social media guy, and author of “Location-Based Marketing for Dummies”
Edward Tufte @EdwardTufte – statistician and information design pioneer; his books are a must read for those looking to visualize any type of data
Tim Walters @tim_walters – Digital Clarity Group co-founder, x-Forrester, and former colleague who is one of my go-to guys RE digital experiences + disruption
Ray Wang @rwang0 – prolific tweeter, futurist and CEO of Constellation Research
When selecting the 40 vendors to include in this resource guide, I started with some of the tools that I first identified when I launched this blog in 2012 as well as those I covered over the years, and then broadened my research to look at new entrants since that time, along with established SaaS and infrastructure providers who have signaled alignment with the small data movement (either explicitly or via support for one of the key tenets).
From my definition above – and the taxonomy we created for my 2013 study at Digital Clarity Group, tools that were considered work with social, transactional or other online data, are focused on “everyday” business users and tasks, and generally support one or more of the following functions:
- They make multi-source data and content more accessible by collecting and processing it (e.g. social monitoring, mobile analytics, DIY survey tools, data blending tools),
- They allow users to explore, generate and share insights, so data is more understandable by more people (e.g. add-on reports and simple dashboards, visualizers and profiling tools), and
- They make insights more actionable for everyday tasks – or trigger actions automatically (e.g. DIY workflow/applet builders, campaign tools, usability testing solutions)
Adobe – an early supplier/advocate of data-driven marketing, via its Marketing and Analytics Cloud (also sponsored my 2013 study on the topic at Digital Clarity Group).
Applause – the original crowdsourced software testing pioneer, Applause also offers usability studies, and a growing range of competitive and product research services; the company closed a $35M Series F round in September 2016.
Attivio – a leader in “cognitive search” for bringing structured and unstructured data together and making insights more accessible to business users (one of my 2013 Vendors Worth Watching); the company raised $31M in March 2016.
Bison Analytics – great example of a (small data) add-on to a broadly used platform, in this case tailored analytics and reporting for QuickBooks.
Brand Networks – social advertising platform using triggers based on real-world (small data) inputs like weather, foot traffic, media etc plus predictive measurement.
Brandwatch – leading social intelligence and market research company with some impressive real-time reputation monitoring.
Cision – Media intelligence platform that acquired Visible Technologies in Sept 2014 (social monitoring), which was one of my small data companies to watch in 2013; the company went public in March 2017.
ClearStory Data – Apache Spark-based next-gen BI player with some slick data discovery, prep, blending and contextual “storyboards” to deliver self-serve insights; the company raised another $10.5M in the summer of 2016.
comScore – the media measurement and analytics provider has long been versed in making complex, cross-platform data accessible and actionable by marketing users.
Ensighten – an omni-channel customer data platform, including solutions for data collection, profiling and data privacy – acquired Anametrix in 2014; the company raised a $53M Series C round in Oct 2015.
Geckoboard – one of the best ways to easily access and visualize KPIs from various transactional data sources (we were a happy user at Placester).
GoodData – one of the early movers bringing the power of big data to the masses, now focused on transforming business data into new revenue generating assets (on my list of the Vendors Worth Watching from Feb 2013).
Google Analytics – still one of the easiest/best ways to track website traffic and turn it into simple insights.
Grow – simple BI reporting and dashboard software for SMBs, the company launched at the end of 2014 to focus on small data and finding actionable insights; the company raised a $9M Series A round in July 2016.
Hortonworks (Onyara) – one of the leaders in open source big data, moved into the IoT and small data arena when it purchased Onyara in 2015.
HubSpot – the company known for Inbound Marketing is also obsessively data-driven, with tools like its free CRM that are consistently simple, smart and social (former client).
IFTTT – Web-base automation service that enables everyday users to create simple “recipes” (applets) that link 2 services together with an action (like automatically adding all tweets with a conference hashtag to a tracking Google spreadsheet).
InsightSquared – provider of business analytics and reporting tool for sales teams, and strong proponent of empowering SMBs to become more data-driven.
Izenda – next-gen BI provider focused on embedding analytics to turn everyday users into citizen data scientists.
Localytics – mobile analytics provider with new offerings for in-app marketing and optimization tools; the company raised another $10M in Sept 2016.
Microsoft – starting with Excel and now its Power BI freemium self-service cloud service, Microsoft has arguably been the first to bring analytics to the masses and continues to promote the value of “thinking small” – see here, here and here
Moz – an all in one SEO and local marketing platform, the company’s analytics tools feature highly actionable visuals and recommendations for everyday marketers; the company raised a $10M Series C round in Jan 2016.
NetBase – NLP-based social analytics tool, their Brand Passion Index/Report is a brilliant example of how to visualize brand relationships (former client).
Nimble – the first pure-play social CRM vendor, the company has always been about simple, smart, data-driven apps that users will actually want to use (one of my 2013 Vendors Worth Watching); the company raised a $9M Series A round in March 2017.
OpenText – global enterprise information management leader with a range of tools for both “big content” and small and big data analytics via its Actuate and Recommind acquisitions.
Optimizely – data-driven experimentation and personalization company for optimizing experiences across web, mobile and connected devices; the company first started promoting small data in 2014 in this post.
QlikTech – While the company has gone more mainstream, it’s still about powering simple, mobile, contextual apps (one of my 2013 Vendors Worth Watching); the company was acquired by Thoma Bravo in June 2016.
SurveyMonkey – leading freemium online survey platform, with goal of making the way people give and take feedback “accessible, easy and affordable.”
Tableau – mass market leader for storytelling with data via easy to use data blending, visualization and analytics.
Talech – a simple retail and restaurant POS system focused on the “small data opportunity” to provide merchants with analytics to run their business better; the company raised a $15M Series B round in Nov 2015.
TIBCO – the analytics and event processing company has made a number of acquisitions (Spotfire, Jaspersoft) that broaden its predictive/embedded capabilities and has been an early proponent of small data – see here.
Trueffect – first-party media and measurement platform focused on using small data to better target and engage customers.
Velocidi – multi-source marketing analytics firm focused on helping marketers bring their campaign data together and “organize and derive insights from it” – see coverage here from siliconANGLE; raised $12M Series A round in Nov 2016.
WordStream – paid search and social campaign platform, with a widely used free PPC tool – CEO Ralph Folz is another GTE alum.
Yesware – sales productivity and analytics tools for Outlook and Office 365.
Zapier – simple, event-based automation tool for connecting everyday web apps and streamlining repetitive tasks (a super-charged IFTTT)
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?
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.
One of the best parts of my research over the past several months as I worked on DCG’s “Bringing the Power of Big Data to the Masses” study (report now available here) has been getting to sample both current viewpoints as well as some of the literature and developments from the past 20+ years on the topic. In fact, big data and today’s analytical tools are very much a product of the “generations” of data and data processing that came before them.
Here’s a cool illustration we created for the report that highlights some of the key developments, deals, and publications related to big data, analytics, and small data since the invention of the web in 1989. We had to leave out a number (many) of potential items, so I’d love to hear what milestones would you have included – enjoy!
With a flurry of new articles on small data in various publications recently – including Wired, Technorati, and AllThingsD – there’s a growing number of voices contributing to the small data movement (a great thing!). But with these new perspectives, I feel like it’s a good time to re-ground the conversation in an actual definition of what we are talking about when we refer to “small data” – or at least what I’m talking about!
So in this post I wanted to share my first cut at a proper definition. Yes, I’ve framed the pillars for small data in many other places, going back to my first guest piece in Forbes on the topic a year ago (!), and more recently I’ve embraced the idea of describing small data as “the last mile of big data.” But these were descriptions or principles vs. definitions for the most part.
So, after spending the last couple month working on Digital Clarity Group‘s new multi-client study on “Bringing the Power of Big Data to the Masses” (sponsored by my friends at Adobe, Actuate, HubSpot, and Visible), seeing how marketers are looking to make analytics more accessible and actionable – and creating some new starter use cases, an interesting thing happened: a definition emerged!
In fact, while our final report is still a week or so away from being available from DCG and our sponsors, I shared the definition with our audience at the Digital Pulse Summit this past week during my panel on small data, and given the response, I wanted to provide it here as well.
A New Definition for Small Data
Small data connects people with timely, meaningful insights (derived from big data and/or “local” sources), organized and packaged – often visually – to be accessible, understandable, and actionable for everyday tasks.
Note, as we describe in our report, this definition applies to the data we have, as well as the end-user apps and analyst workbenches for turning big data set into actionable small data. The key “action” words here are connect, organize, and package, and the “value” (the 4th V of big data) is rooted in making insights available to all (accessible), easy to apply (understandable), and focused on the task at hand (actionable).
In fact, I hope it’s as much a mission statement, as it is a definition. What do you think? Did we nail it?
As I prepared my presentation on small data for the recent DCG Insights day in New York, I did a quick review of some of the latest coverage of the topic, and discovered a number of new articles worth checking out. This list just scratches the surface, so I’ll add to it in subsequent posts, but wanted to share this first batch as a reading list for those interested in the topic. Enjoy!
5. Inc.com – Why Small Data May Be Bigger Than Big Data – in this piece which came out in April, x-McKinsey consultant and loyalty expert Victor Ho argues that small data is the key to solving the “data divide” for local businesses.
4. ITworld – Big Data Benefits with Small Data – tech journalist Brian Proffitt questions the necessity of big data for non government agencies or businesses that don’t have giga-scale ecommerce sites, and references the notion of personal data stores – a distinct small data use case. His closing point is a good one:
“Thanks to big data, many businesses recognize the value of data analysis. But there may be several new paths that will open up to help them achive the benefits of data decision making.”
3. MediaPost – Does Big Data Require a Big Rethink? – in this commentary Michael Hemsey of Kobie Marketing urges brands to focus on “the little details,” calls out his recommendations for thinking small when it comes to data assets, and talks about the age of mini-measurement – which is a cool way to put it!
“I don’t want to have a relationship with a marketing department. I don’t want to be your friend. I don’t want to engage in conversation with you. I feel no loyalty towards you. When I say I like you I’m not entirely sincere.
And yet I chose to share an enormous amount of my life with you. The detail I’m able and happy to share has grown big. Really, really big. But understand this: my reason for sharing this data is entirely motivated by self-interest. You see, I know as much about you as you do about me. I know how valuable my data can be to you. So I expect you to use this data for my benefit. Because you can be damn sure I will be.”
His conclusion is that small data is specific and concrete, which makes it easier to to “make good use of it.” And more so, requires us to understand our customers, their lives and where we fit within them. This is exactly the connection I’m looking to make between the worlds of CRM and small data, in the research I’m ramping up at Digital Clarity Group.
1. The Guardian (UK) – Forget Big Data, Small Data is the Real Revolution – one of the most widely referenced pieces of the last few months, Open Knowledge Foundation founder Rufus Pollock frames big data as the latest “centralization fad” and notes:
“the real opportunity is not big data, but small data. Not centralized “big iron”, but decentralized data wrangling. Not “one ring to rule them all” but “small pieces loosely joined.”
I love the point about decentralization, which ties nicely into the social aspect of small data. In fact Pollock ends with a great framing of small data as part of a larger movement driven by digital disruption and democratization of IT:
“This next decade belongs to distributed models not centralized ones, to collaboration not control, and to small data not big data.”
In terms of older articles, check out my guest post in Forbes from last October, and the early piece by Patrick Gray in TechRepublic that argues for a practical, consolidated approach to data and reporting.
What articles did I miss? What’s on your “must read” list?