While Big data is being continuously talked about, it is Open data that seems to be more revolutionary in nature. Open data is a movement where more & more data is being brought into the public space. Actors in the public, private, and development sectors are beginning to recognise the mutual benefits of creating and maintaining a “data commons” in which this information benefits society as a whole.
So is Personal data becoming a new economic “asset class”, a valuable resource for the 21st century that will touch all aspects of society. On an average day, users globally send around 47 billion (non-spam) emails and submit 95 million “tweets” on Twitter. Each month, users share about 30 billion pieces of content on Facebook. The impact of this “empowered individual” is just beginning to be felt. Here is a snippet from an interesting World Economic forum report:
The types, quantity and value of personal data being collected are vast: our profiles and demographic data from bank accounts to medical records to employment data. Our Web searches and sites visited, including our likes and dislikes and purchase histories. Our tweets, texts, emails, phone calls, photos and videos as well as the coordinates of our real-world locations. The list continues to grow. Firms collect and use this data to support individualised service-delivery business models that can be monetised. Governments employ personal data to provide critical public services more efficiently and effectively. Researchers accelerate the development of new drugs and treatment protocols. End users benefit from free, personalised consumer experiences such as Internet search, social networking or buying recommendations.
And that is just the beginning. Increasing the control that individuals have over the manner in which their personal data is collected, managed and shared will spur a host of new services and applications. As some put it, personal data will be the new “oil” – a valuable resource of the 21stcentury. It will emerge as a new asset class touching all aspects of society
Data is actively collected from individuals who provide it in traditional ways (by filling out forms, surveys, registrations and so on). They are also passively collected as a by-product of other activities (for example Web browsing, location information from phones and credit card purchases). The increasing use of machine-to-machine transactions, which do not involve human interaction, is generating significant amounts of data about individuals. All of this data is further analysed and mashed together to create inferred data.
In addition, individuals are no longer merely the subjects of data – they are also being recognized as “producers” of data. For example, digital personal-health devices such as Fitbit and Nike+ Fuelband measure daily physical activities. They provide a new way of capturing a rich data set about an individual. These devices present an opportunity to combine and commingle intimate,high-resolution, activity-based health data with other data sets to provide a daily health dashboard for individuals. It helps them set wellness targets, measure progress and more effectively engage in achieving healthier lifestyles
So how do you motivate consumers to share personal data in ways that are innovative? How do you begin to capture customer behaviour that allows you to market more effectively to consumers.
Can Marketers come up with innovative concepts that make it easy for consumers to share their personal data?
Here is a great example from the San Francisco-based StreetOwl. This interesting company has figured out a way to make data sharing a "win-win" for youngsters too. The company uses an age-old tactic: bribery.
Its RefuelMe iPhone app tracks driving behavior, earning points for proper speed, acceleration, braking and cornering (see below).
The app measures how teenagers drive, including factors such as speeding, accelerating, breaking or cornering. Then the app gives them rewards from their parents for safe driving. Parents meanwhile can view a web dashboard to see how their teens are driving. The company plans to make money by providing lead generation to insurance companies..
Young drivers earn awards established by their parents. In the example below, you can see that the driver is about 1% of their way to earning a $25 Chevron card
What does this mean for marketers?
- It would be interesting to ask that if given the appropriate checks and balances, would consumers like to be given the option to make some of their data public?And can this public data be used in anyway by others to make better goverment policy- Reducing mobile recharges in a district could alert the goverment about lower seasonal income.
- How can banks & financial institutions construct Reward & loyalty programs for younger consumers?
- This can also lead to interesting "community loyalty" concepts-eg:where a neighbourhood becomes "safer" by a collective change in behaviour.
- This has huge implications on further Customer Intelligence & data mining.
- And of course, this has many implications regarding data privacy& ethical issues of data sharing.Marketers will need to learn to deal with this reality.
In his new book, How to Create a Mind, technologist Ray Kurzweil estimates that a human brain can recognize 100,000 patterns. But consumers are producing a huge amount of information by the minute & our minds may just not be fast enough. “Big data” is what they call this data deluge & it has become the sexiest word in business in a very short time.
I wrote about this earlier & made the point that the current data situation for most companies is like having sections of a jigsaw puzzle in different rooms, but the puzzle keeps growing without a “puzzle master” integrating all this. The analyst, like the “ring master”, is really the “puzzle master” here & she needs to think very differently to do this. We don’t need more data; we need the correct interrelationships between data to be established & then we need “Big execution commitment” to make the data matter, by bringing decisions closer to the front end of every business.
You can read my earlier post here:Big data is puzzling
But clearly Big data is creating a discontinuity in the market place! Estimates suggest that more than a zettabyte (that’s a 1 followed by 21 zeroes) of information now circulates around the internet. Most minds will need help before they can analyse this massive stream of information, likened to drinking from a firehose! Darian Shirazi, founder of Radius Intelligence Inc., calls this a problem of "haystacks without needles." Companies too often "don't know what they're looking for, because they think big data will solve the problem," he says. So Analysts will have to treat "Big data" differently & overcome a set of issues before being able to leverage it.
And of course there is also a Dark side to "Big Data". Some of the initiatives out of Big data are downright scary:
1. If I keep my meeting schedule in a Google calendar & get an external feed of traffic conditions, shouldn’t my phone tell me when to leave for my next meeting? (Check! – get Google Now).
2. Progressive, a insurance company, is one example of a company using big data projects to transform their business. Using detailed information about customers' driving habits, the insurer has created a usage-based model that defines a policy's price down to the individual. Progressive gets the data through a device a driver plugs into a car's diagnostic port, according to its website. It can track how often customers slam on their brakes, drive late at night and other possibly risky driving habits. If the data shows a customer is driving safely, they can get significant discounts on their insurance.
Big data is so sexy & the hype of inflated expectations is so high that its about time we started seeing results. I am planning to look at Big data across a range of industries...here is the first one-Big data & the media world!
Media companies have such a huge treasure trove of data. Imagine if Times of India, Forbes or The Wall street Journal were to put together a “one view” of its readers & actually get to know the households that it delivers to each morning at a personal level. Some 80 percent of what can be considered Big data is unstructured or semi-structured information. This is where Media companies have masses of information & they can use their information to make more effective decisions in their business of journalism.
Kenneth Neil Cukier is the Data Editor of The Economist. He argues that having access to vast amounts of data will soon overwhelm our natural human tendency to look for correlation and causality where there is none. In the near future, we’ll be able to rely on much larger pools of “messy” data rather than small pools of “clean” data to get more accurate answers to our questions.
Cukier says something very interesting: “When we teach journalism in the future, we’re not just going to teach people the fundamentals of how to do an interview, or what a lede paragraph is. We’re going to tell people how to interview databases. And also, just as we train journalists by telling them that sometimes people that we interview are unfaithful and lie, we’re going to have to teach them to be suspicious of the data, because sometimes the data lies, too. You have to bring the same scrutiny as in the analog world — talking to people and observing — to the data as well.”
The Financial Times have been an interesting case study in the paywall debate & they are huge users of data. They've had their metered paywall approach for several years, but they are often regarded as an exception because of the specialist nature of the information they communicate. Estimates for Feb 2013 suggest: 286,000 print subscribers and 316,000 digital subscribers — the first newspaper to see digital surpass print. They are probably amongst a few Media companies that boast of a large analytically savvy team. The data team has about 30 people, organized into three groups: Data Analytics & Campaigns, Data Product Development, and Data Technology
The FT, by requiring sign-in to access even the free content, has had years to build up a massive database of users – and any free user is a potential subscriber. Using analytics to target the tipping point where people might begin to pay for the product is a smart move. Since enforcing on-site registration, the FT has gathered not only a vast amount of data about who its readers are and how to sell subscriptions and ads to them — the paper also knows a good deal about what they read and when, as well as the kinds of editorial products that appeal to them.
Read more about FT at Data led marketing at Financial Times
We know that channels can be used & abused! Telecalling has already reached that pinnacle in India. Email is already there with the amount of spam we get. Marketers need to be conscious of this & treat this medium responsibly. The biggest bane is that most marketers treat Email as a mass marketing medium. Email is not Mass marketing & the only way you can make it work is if you make it relevant & create a Relationship marketing paradigm!
I spoke about this at a conference a few days ago organised by the IAMAI. Nishad from Cequity was my co-creater for the presentation & you can access our presentation here: Email marketing: Used & Abused
The total number of worldwide email accounts is expected to increase from 3.3 billion accounts in 2012 to over 4.3 billion accounts by year-end 2016. This represents an average annual growth rate of 6% over the next four years. Nearly half of worldwide email users are in the Asia Pacific region.
Radicati Group, a technology market research firm, estimates that more than 2.8 million emails are sent every second and about 90 trillion emails are sent per year.(estimates vary by source ...but billions of mails for sure!)
Around 90% of these millions and trillions of message are but spam and viruses.
I spoke at the conferance about the need to relook at how we do Email marketing.
The 6 takeaways that I spoke about at the IAMAI conference
- Most used. Most abused: Email is hugely misused & we as marketers are responsible for using this medium responsibly. Marketers need to create a community effort to work on this malaise. I don’t see too many marketing associations taking this up. I do know that IAMAI is active in this space but more participation is definitely required.I am willing to join other marketers in this journey , so lets talk!
- Relationship Vs Mass Marketing: Email is not mass marketing period!Thinking 1to1 marketing needs a change in mindset.
- Desktop to Mobile: Large movement towards opening emails in mobile phones & marketers need to design emails for this & also think through how to change the "calls for action" given this reality. In 2013, India is supposed to get to a 45 million smart phone mark! (Nielsen estimate). That allows for a lot of creative thinking & email can integrate with this so beautifully.
- Integrated thinking: Email does not stand alone but needs to be part of a larger Integrated marketing plan. How many CMO's even spend time on this medium..or is it ignored & abused within the marketing fraternity!
- Email + Social: Make email work harder for you by integrating it with social. Commercial TV took 13 years to reach 50 million households; it took Facebook just a year to hit the 50 million user mark. It took Twitter 9 months to touch 50 million users. More than a billion people now log into Facebook everyday. To date, only two national states have breached this barrier: China and India.Mastering this new animal (Social) requires a different breed of people and process due to its real-time nature. Marketers need to marry real-time interactions with traditional marketing campaigns & analytics. Silos need to be broken so that marketers don’t think Brand, digital & campaign differently but rather run a “strategic thread” & integrate the customer engagement.
- Creating a “single source of truth”: Bring all of the data that we as marketers have into what I call a “digital hub” which allows marketing to have a “single source of truth”. And once you have this, improve your analytics by not looking only at Email open & click through rates but connect that data with Social data & to the CRM (actual buyer behaviour) that is with you.
Commercial TV took 13 years to reach 50 million households; it took Facebook just a year to hit the 50 million user mark. It took Twitter 9 months to touch 50 million users. More than a billion people now log into Facebook everyday. To date, only two national states have breached this barrier: China and India
The number of worldwide email accounts is projected to increase from over 2.9 billion in 2010, to over 3.8 billion by 2014. However, Social Networking currently represents the fastest growing communication technology among both consumers and business users which are projected to grow to over 3.6 billion accounts by 2014.
And yet Email marketing is far from dead!!
One of the reasons Email marketing is not going away too soon, is just that it is more profitable! According to a 2011 study by the Direct Marketing Association (DMA), e-mail marketing yields a return of $40.56 per dollar spent, compared with $22.24 for search, $12.71 for social networking and $10.51 for mobile. In fact, the DMA’s 2016 estimate pegs e-mail marketing’s ROI at $35.02 per dollar compared with social’s $13.43. (Source: article written by Vineet Manghani,Cognizant)
Unveiling the full potential of Social requires an “integrative” mindset. It demands marketers to think in a very 1 to 1 way across both mass & personalised media. And this requires marketing to create a “Campaign backbone” that leverages the rich data that consumers are producing as they wade through their “social life”.
Over the last few years,the Facebooks, Googles and Amazons of the world have leveraged “Big data” to create such a “campaign backbone” . This allows these companies to improve their decision-making based on the infrastructure technology and analytics-related software they have been developing & talk to consumers in a far more “real time” fashion.
Every year organizations collect more and more data on customer “touch points”. Technology advances are allowing for the storage and analysis of data that just 5 years ago wouldn’t have been possible
Companies need to look at Social & traditional email as a part of a “more integrated” data based Marketing strategy.
So how does this impact the good “old email” as a campaign?
In his book Permission Marketing, Seth Godin referred to email marketing as “the most personal advertising medium in history". That was 1999. Maybe we need to listen to that message to reinvent Email marketing.
In my view, that re invention is about how data can form a central part of how marketing campaigns are designed. And in that transformation how emails strategies can leverage a plethora of data-the humble “customer check ins” for something like Four Square being an example!
Social media check-ins provides marketers an unprecedented view into the lives of their customers and prospects. Knowing location, and understanding actual behaviours, creates opportunities to captivate an audience with greater relevance during moments when they are most receptive.
Here are a few examples of how Social instead of killing emails makes them a far more powerful medium:
- Use check-ins as triggers
Right timing a message always gets you far better response. The intent and the timing are clearly signalled by the check in. Check-ins also create opportunities to communicate with customers when they’re not in your store, but nearby, which is a great time to send a special offer.
- Improve segmentation with behavioural data
Check-ins is a window into your customers’ lives, enabling a deeper understanding of where they go and what they do. By analysing check-in data you can categorise & segment customers based on who shops at department stores & who is a high end fashion consumer.. But this requires marketers to create a “big data” environment which can process & analyse this data at speed
- Extend the definition of check-in
Typically, check-ins are an explicit act of using a location-based application to identify where you are. The term "soft check-in," which is an "implied" check-in from social media such as Twitter and Facebook, is now emerging.
For example, if someone tweets they just finished eating a "Mc Burger", it’s a pretty good assumption they are dining at the fast food chain. And, of course, there are the more obvious Facebook or Twitter posts such as "Standing in line to see Sky Fall", the popular James Bond movie !
Using a natural language filtering algorithm that extracts presence extends the number of check-ins marketers can exploit to engage with people and acquire customer information.
So to summarize:
- Mastering this new animal (Social) requires a different breed of people and process due to its real-time nature. Marketers need to marry real-time interactions with traditional marketing campaigns & analytics. Silos need to be broken so that marketers don’t think Brand, digital & campaign differently but rather run a “strategic thread” & integrate the customer engagement.
- Email needs to leverage customer intelligence & drive highly relevant communications to customers. Big data allows you to do that at scale & speed.
- Email needs to be integrated with Social & a larger brand strategy to maximise impact
Recently a client asked me an interesting question: How would you start analytics in an organization?
This also seems to depend on the industry. While Banking has greater maturity in the use of Analytics, FMCG companies may have different challenges given the lack of end customer data. Also it takes time to build analytical maturity in a company. And it takes a certain unique mix of people- a combination of left & right brain talent!
The question was interesting from many perspectives:
- What exactly is analytics and does the name describe the function?
- How should one go about starting doing the work that analysts are supposed to do?
- Where should the Analytics team report-is it part of a marketing team or somewhere else?
- What kind of issues should analytics try and solve?
- How much money needs to be invested to really make Analytics work?
My experience across both Retail & Retail banking has been that it is best to start small, very small! A lot of analytics can be done on an excel sheet and does not require a PhD in statistics to do. The simpler the analysis the “lesser” is the barrier in implementing the call for action that emanates from it. So my first suggestion to anyone starting out this kind of work is to follow the well know “KISS principle”(Keep it simple stupid). The most important next step from here is to choose the business area where you want to make an impact.
I would go for the counter intuitive bit here
and try to make your analysis work for a business unit that is not doing so well
. Businesses doing very well, have a lot of competing ideas clamouring for a share of the credit. It’s in the businesses that need help, that you will find maximum support. And finally I would say that choose business themes that are close to the CFO’s heart! The CFO’s support for analytics is probably the most critical part of what you would do-this forms the building blocks on which you can scale up your efforts in the years to come!
I have often come across situations where organization seem to believe that investing in top end statistical resources and buying high end technology is enough to extract value from analytics. The truth is vastly different and I strongly believe that embedding simple ideas and focussing far more on execution is critical for an organization to succeed in analytics based strategy.
Laura McLellan, a research VP at Gartner, caused quite a stir at the beginning of this year when she boldly predicted that by 2017, CMOs will spend more on IT than CIOs.
Marketing departments should sell the idea of a marketing technology office (MTO) to CIOs, who would have to relinquish control of certain customer-facing technologies and hand responsibility for the function over to the Chief Marketing Officer (CMO).
This is the opinion of Forrester analyst Suresh Vittal
Vittal used Rubbermaid, a U.S.-based manufacturer, as an example of an enterprise that has decided to create such a function.
"Rubbermaid's MTO has to help its brands make a value shift from thinking about how their customers could fit into the product lines, to putting the customer at the centre of operations and thinking about what products they could design to suit their customer base," said Vittal.
"It also has to help Rubbermaid's brands think about how they share product information in different stages. How do they present content to the consumer when the consumer is researching the product? Or when the consumer is learning to use the product?"
He added: "The third area of responsibility was to identify innovations that happen at the brand level, then take those innovations and wrap them up so other brands can learn from them."
Rubbermaid's MTO function reports to the CMO, not the CIO, and the only relationship the CIO has with the department is to ensure that it is complying with the company's defined IT standards and processes.
Employees in the division include a strategist, database administrators, web developers, application programmers and 'marketing scientists'.
Vittal said: "As a rule of thumb I like to think infrastructure technologies, storage systems, authentication systems - these would all be centrally owned. However, channel and customer facing technologies would be owned by the MTO."
Here is my reaction to this:
- I am sure that CMO’s don’t want to add to their head count & grow an army of Technology folk. CMO’s need to partner their CIO/CTO’s & understand more of technology & communicate more about “marketing” to make the partnership work.
- Technology is changing fast & Marketing has enough catching up to do with a changing consumer. I don’t see CMO’s becoming passionately involved in the changes that are driving Technology.
- There is too much uncertainty in technology investments. Successful outcomes from technology investments depend on a number of factors which are out of the CMO’s control.
- Having a strong technology person who actually drives 1to1 marketing operations is a more valuable addition to the CMO
The three Vs — volume, velocity and variety — are the essential characteristics of big data. But isn’t it amazing that “big data” suddenly seems to have happened overnight & is conveniently a great marketing ploy for Technology companies to sell their wares! But do companies need “more data” or “less”. What you actually need is “Big commitment” to data period!
The current data situation for most companies is like having sections of a jigsaw puzzle in different rooms, but the puzzle keeps growing without a “puzzle master” integrating all this. The analyst, like the “ring master”, is really the “puzzle master” here & she needs to think very differently to do this. We don’t need more data; we need the correct interrelationships between data to be established & then we need “Big execution commitment” to make the data matter, by bringing decisions closer to the front end of every business.
I am constantly hearing that Data analysis is becoming a more important component to many businesses. IDC estimates enterprises will spend more than US$120 billion by 2015 on analysis systems. IBM estimates that it will reap $16 billion in business analytics revenue by 2015
There is so much talk about shortage of analytics skills. Research from McKinsey & Co suggests that US organizations are facing a shortage of 200,000 IT staffers with deep analytics skills. I wonder whether we really have a shortage or are we looking for the wrong people?
Do we need more “specialists” who really cannot “integrate” the many parts of a puzzle or do we need people from all sorts of backgrounds who bring fresh perspectives to the data that we already have not some mythical “big data”!!
Here is something interesting: Watching how people put together picture puzzles can reveal "a lot of profound effects that we could bring to big data" analysis, said Jeff Jonas, IBM's chief scientist for entity analytics
Joab Jackson has this very interesting take in the CIO magazine:
Puzzles are about assembling small bits of discrete data into larger pictures. In many ways, this is the goal of data analysis as well, namely finding ways of assembling data such that it reveals a bigger pattern.
A lot of organizations make the mistake of practicing "pixel analytics," Jonas said, in which they try to gather too much information from a single data point. The problem is that if too much analysis is done too soon, "you don't have enough context" to make sense of the data, he said.
Context, Jonas explained, means looking at what is around the bit of data, in addition to the data itself. By doing too much stripping and filtering of seemingly useless data, one can lose valuable context. When you see the word "bat," you look at the surrounding data to see what kind of bat it is, be it a baseball bat, a bat of the eyelids or a nocturnal creature, he said.
"Low-quality data can be your friend. You'll be glad you didn't over-clean it," Jonas said. Google, for instance, reaps the benefits of this approach. Sloppy typers will often get a "did you mean this?" suggestion after entering into the search engine a misspelled word. Google provides results to what it surmises are the correct word. Google guesses the correct word using a backlog of incorrectly typed queries.
Read more about this here:
I wrote earlier about this huge gap between talking about analytics & being able to actually make a large impact on business. I also wrote earlier about Analytics being a sexy & much hyped area but it needs a lot of "hard work" to make it happen on the ground.
I wonder how analytics professionals can help in transforming this. How can analytics play a bigger role? I clearly see analytics as the heart, but definitely a part of a bigger CRM engagement within companies. Unless companies take the insight & begin to take action on it, there will never be impact!
Even more critically, it needs marketing leaders to start articulating this vision with the CEO & gain support at that level.
Christine Moorman is the Director of The CMO Survey and the T. Austin Finch, Sr. Professor of Business Administration, The Fuqua School of Business, Duke University. She runs an annual CMO survey to guage Marketer trends. To evaluate the impact of marketing analytics, the 2012 CMO Survey asked top marketers to answer this question:
“In what percent of your projects does your company use available or requested market analytics before a decision is made?” The average score was 37.2%. This means that 62.8% of the times, managers are not using marketing analytics! By this measure, marketing analytics must do more. If not, its funders will place bets on other strategic weapons they believe will allow the company to serve customers better and to pull ahead of competitors”
I looked at another metric. I am convinced that analytics can make a difference only when it starts getting embedded into the Operational IT layer within the company. Unless the algorithm helps make a decision its true impact will never be felt. In fact, Gartner analyst Laura McLellan recently predicted that by 2017, CMOs will spend more on IT than their counterpart CIOs.
But this does require stronger conversations between IT & marketing. Both these functions don’t often talk & don’t understand each other’s point of view well enough. Strangely both these functions should be natural allies, because they struggle to show business impact of the large $ investments they both make. In 2003, for example, Nicholas Carr, a leading commentator on technology and business, argued that “IT doesn’t matter” because the strategic and competitive advantages of IT spending quickly dissipate.
Here is an extract from a McKinsey report that I found useful:
From 2006 to 2010, Amazon spent 5.6 percent of its sales revenue on IT, while rivals Target and Best Buy spent 1.3% and 0.5%, respectively. The results of this spending and focus include:
- Sophisticated recommendation engines that deliver over 35 percent of all sales
- Automated e-mail/customer service systems (90 percent are automated, versus 44 percent for the average retailer) that deliver best-in-class customer satisfaction (Amazon’s American Customer Satisfaction score is 87)
- A sophisticated and highly efficient supply chain that has reduced Amazon’s cost of goods sold by 3 to 4 percent
- Dynamic pricing systems that crawl the Web and react to competitor pricing and stock levels by altering prices on Amazon.com, in some cases every 15 seconds
The CMO Survey results indicate that companies currently spend an average of 5.7% of their marketing budgets on marketing analytics and that this number is expected to grow to 9.1% in the next three years. This 60% increase represents a large shift. Also when one connects this to enterprise IT investments that Analytics can generate-it becomes sizeable. Of course this data is from North America & the situation for India will be vastly different.
This does require a range of different skills within Marketing. It evens needs IT people to be staffed within the Marketing function. It needs analysts with a different mindset. Analytics as a function needs to be transformed!
Is analytics yet another fad? Is there much more talk about it than real solid action. It does seem so when you look around you as a consumer.
Marketers still don’t care, as much, about being relevant to you. You get that umpteenth credit card solicitation from the bank which has already sold you a card. And nothing about a physical Retailer shopping experience makes it personal for you!
And yet your online persona seems to be treated differently & when you go to Amazon & other sites you do get a feeling of getting offers being recommended for you. And as a consumer you flit between your online & offline avatars & this becomes more & more obvious.
What’s the difference? Is there a category of organization which is able to leverage “data” far more effectively?
Gartner says that only 20% of enterprise will use more than 50% of the total data they collect to gain competitive advantage.
This is what software architect Grady Booch had in mind when he uttered that famous phrase: "A fool with a tool is still a fool."
Google-executive-turned-Yahoo-CEO-thought-leader Marissa Mayer declares "data is apolitical" and that her old company succeeds because it is so data-driven: "It all comes down to data. Run a 1% test [on 1% of the audience] and whichever design does best against the user-happiness metrics over a two-week period is the one we launch. We have a very academic environment where we're looking at data all the time. We probably have somewhere between 50 and 100 experiments running on live traffic, everything from the default number of results to underlined links to how big an arrow should be. We're trying all those different things."
A recent Ad Age article carried this comment:
British Airways spent almost a decade corralling passenger data from 200 sources into one database. It built infrastructure to support the number crunching, but perhaps the harder piece, said Simon Talling-Smith, exec VP-Americas, is getting in-flight personnel to use the technology and data to create better consumer experiences. And BA introduced onboard iPads to send in-flight crews passenger-specific information, but Talling-Smith said encouraging staff to use them is still a challenge. "Probably half of the messages don't even get delivered," he said.
Maybe there are some learning’s here:
- Analytics doesn’t need you to solve a technical problem but a “business & social” problem. And most Business analysts have not spent much time in business roles. They are super specialised number crunchers without a sufficient exposure to business reality. Even if the managers have some exposure to business through experience across a variety of analytics projects, is it enough? Does this bring the analytics career into some jeopardy? Would analysts be able to grow in companies beyond a level or is it a parallel consulting stream only?
- Analyts need to “Story tell” to embed analytics into the fabric of the company. But analysts are too one-dimensional & not embracing the intersection of “technology, statistics & business”. So analysts struggle to tell stories. Often I see journalists do a far better job with infographics in media. But information journalists are not wanting a career in analytics & so there is a gap in “story telling”.
- Analytics is too theoretical. Not enough integration with systems has happened to push decisions to the point at which consumers interact with the business. This is far easier to do in new Online businesses which have built their systems around this capability. CIOs & technology teams in large existing offline businesses don’t see this as important.
- Average age of employees in online business is far lower. Younger people are adopting analytics far faster. They are getting exposed to it in their education & they are consuming it through their “digital avatars”. They see this often as a “no brainer”. Older executives are harder to convert to this line of thinking.
Geoff Tracy couldn’t clone himself. But as his restaurant empire grew he did the next-best thing, creating a complex, data-driven system to ensure that his employees always do everything—from plating a salad to setting the dishwasher temperature—the Chef Geoff’s way.
I have not heard of a restaurant that used data effectively to run its business & so Chef Geoff was a huge surprise. Isn’t it amazing how large companies often hesitate to use data effectively & end up setting up a central analytics function which needs to fight turf wars to even enable them to get their hands on data? While at the same time a restaurant like Chef Goeff’s could so effectively use the data to enable its business.
Finally it boils down to belief, are you ready to make changes in Operations basis what your data tells you. And it is always a Catch 22, unless you make the changes that data is telling you to, you can never prove what is more effective.
There is this interesting "Hype cycle" from Gartner which seems to suggest that Analytics is one of those "happening" trends. And yet it takes time to adopt it in large companies while we see this interesting application in a much smaller enterprise!
I read this interesting statement from D J Patil from Linked In
Data Jujitsu: the art of using multiple data elements in clever ways to solve iterative problems that, when combined, solve a data problem that might otherwise be intractable. It’s related to Wikipedia’s definition of the ancient martial art of jujitsu: “the art or technique of manipulating the opponent’s force against himself rather than confronting it with one’s own force.”
I have seen that the strongest data-driven organizations all live by the motto “if you can’t measure it, you can’t fix it”. And in such companies the “data or analytics people” are embedded in the operations! Analytics may not even be a separate function; rather it is the way business is done!
Read this interesting story about Geoff Tracy & how he is building his restaurant business on data steroids!! Also remember that now with Social data streaming in, the possibilities are endless. If Geoff Tracy was to claim his business on Four Square he would also get access too a whole bunch of Analytics dashboards from Foursquare telling him about his daily check ins.