Have we entered an era of Artificial Intelligence industrialization for enterprises? What is needed to embark on AI industrialization journey?

We sure have entered an era of AI Industrialization! I can say it emphatically because I am helping a Fortune 15 company execute its AI Industrialization Strategy. I  believe that most of the Fortune 500 companies will follow suit sooner rather than later.

So, what does AI Industrialization really mean? What are the business and technical drivers that lead any company to embark on this journey?  Also, why are they calling it Industrialization of AI? What are the prerequisites needed for companies to start on this journey, to set a solid foundation and reap all the benefits?

Let’s revisit the concept of industrialization by looking at history to understand the different phases of industrialization.  If we just look at first phase of US Industrialization from (1820 -1910), it went through phases of industrialization: cotton plantations, canals and boats, railroads, immigration, banking, steel mills, automobiles, and telephones.  If we analyze it further, we realize that the key characteristics of this industrialization process were:

1.          Inventions and Innovations

2.          Entrepreneurship

3.          Funding

4.          Improved Productivity

5.          Mass Production

6.          Cheaper Production

7.          Repeatability of Processes

8.          Critical Mass of people producing goods

9.          Critical Mass of people consuming goods

10.      Improved Standards of Living

11.      Better Experiences

12.      Cheaper to Buy

13.      Increase in Real Incomes and Return on Investment

 

Now let’s try to draw parallels with AI timeline:

1956:    John McCarthy coined the term AI

1997:    IBM's Deep Blue beat Gary Kasparov

1980s:   Neural network is popularized

2010s:   Amazing breakthroughs with Deep Learning technology

2011:    IBM’s Watson beats the two best human performers on Jeopardy

2015:    Google DeepMind’s AlphaGo beats human champion

2017:    Google announces AI First strategy

Last 3-4 years: Hundreds of new AI startups have been funded from Silicon Valley to Boston to Tel Aviv to Bangalore.

What have we really achieved in AI if we compare it against all 13 characteristics of industrialization listed above? The reality is that from an enterprise perspective, we have only achieved only the first 3 out of the 13. The majority of AI accomplishments in the enterprise context have been limited thus far to inventions, entrepreneurship, and funding. Of course, I am not including what pure play Web companies like Google, Amazon, Netflix, Uber etc. have accomplished. When it comes to mass production, faster production, cheaper production, as well as achieving the ROI, AI in enterprises falls far short of true industrialization. There are always few use cases to boast about, but it doesn’t mean that AI industrialization has truly happened. It means there is tremendous opportunity to build the right AI strategy for AI industrialization.

Traditionally, AI has been a hand-crafted, POC-driven and expensive initiative but the hype and the promise must now deliver at scale. It is high time; we have to start scaling AI initiatives across the enterprise in a cost effective and consumable manner so that it is truly democratized and is accessible to everyone. The operational efficiencies by AI across the board should never be questioned. The core value proposition of AI Industrialization is to take thousands of models in lab to enable and empower LOBs by embedding AI in business processes and in digital transformation journey.

What is missing today is an enterprise enablement view of AI. Let’s take a snapshot of sample of AI opportunities in a one of largest Fortune 15 company today.

SAMPLE OF AI USE CASES FOR A LARGE ENTEPRISE

 

These use cases are typically led by different lines of business who face many challenges in scaling AI. Typical challenges in implementing and deploying AI use cases include the use of different technologies or versions, difficulty governing the process, lack of repeatability and automation, and complications with collaboration and transfer of knowledge between AI engineers.

Another challenge is that you don’t want every LOB to worry about building their own AI COE, AI infrastructure, end-to-end AI platform, building data pipelines, machine learning pipelines, machine learning operations and catalogue, experimentation platform, design their own feature stores, data ops strategy, data quality, model decay, develop their AI cloud strategy, worry about bias, compliance, and AI ethics. You want them to focus on building AI use cases only and rest of the AI infrastructure is provided by centralized AI COE team chartered to develop the AI industrialization strategy as well as governance process to enable LOBs.

The reference architecture for AI Industrialization looks something like this:

Best practices around intelligent enrichment of data and methodology to deploy data pipelines need to be developed? What is the best way to build collaboration ecosystem between engineering, data analysts and data scientists? How do you figure out where manual intervention is needed versus complete automation? How do you create a map of where data starts, how it changes and where it is viewed from cloud as well on-premise perspective? How do you measure error rate decline in production? How to build features at scale from raw data for training? How do you combine features into training data? Calculating and serving features in production? How do you know which sources are biased? What are the best practices for model deployment, retraining and monitoring? How do you design a model workflow from approval perspective in a large enterprise? How do you decouple your data pipelines from your model development and governance? How do you democratize model building so that even data analysts and power business users can start contributing? How do you shorten the life cycle of model creation from deployment to deployment for hundreds of models? What AI components should you leverage from each cloud provider? How do you create a mix of on-premise and hybrid AI cloud strategy? How do you build standardization and repeatability across the board? Who is accountable when things go wrong?

There are hundreds of questions and answers that have to be figured out and best practices needs to be developed and communicated. AI Industrialization is a necessary journey for this era. But it is extremely important that it is setup for success for the long run by team of experts who understand what it takes to build this end-to-end.

Will Crowdsourcing have an impact on Artificial Intelligence adaption by Enterprises in future?

I think so.

Crowdsourcing is not a new concept! It was year 1858. The Philological Society in London formally adopts the idea of developing a new dictionary with the assistance of volunteers to read books and catalogue words. Nearly 30 years and 800 volunteers later, the first version of the Oxford English Dictionary was published.

But what has really changed in last decade is the means as well as the speed to crowdsource to create very interesting outcomes. Thanks to digital platforms like Kaggle (now owned by Google), Topcoder (owned by Wipro), Innocentive, KickStarter (crowd funding), GoFundme (raising money for individuals) QMarkets (Innovation Management) and the list has been growing exponentially in last decade for various domains.

Taxi hailing services like Uber and Lyft are also Intelligent crowdsourcing platforms. They have done phenomenal job in connecting providers with consumers.  They have been able to scale the platform from both revenue, adaption and subscribers’ perspective.

I believe it is easier to scale when the services offered on the platform can be commoditized as in case of Uber and Lyft but it is harder to scale when you are looking for experts. Expert solvers, who are indispensable, are not interchangeable.

Let’s talk more about Machine Learning and Data Science crowdsourcing platforms.

Kaggle is probably the most well-known data science platform with significantly increased membership after Google’s acquisition. Not it has 2.7 million data members (active approx. 300k) with 15k datasets and 200k scripts/notebooks.  

Kaggle has two revenue streams:

1. Revenues it generates from a fee it charges customers to license its platform
2. Revenues it generates from fees it charges for problem setup consulting.

There are few more variations of Kaggle that have cropped up in last few years. Like we have Drivendata (Data science for social causes), Numerai (Hedge fund focus), Crowdanalytix, Codalab, Datascience Challenge, Analytics Vidya and today even Alibaba has its own data science crowdsourcing platform, called Tianchi, focused more on Chinese market. The field is becoming very crowded. 

Despite the strong vision about Gig Economy and open R&D, many of these data science crowdsourcing platforms have faced challenges like: 

1.        Running a challenge was best for small on-the-spot issues but it has not worked so well for systematic enterprise innovation so far.
2.       Lack of focus on verticals. You need to have dedicated staff to understand issues in say financial services, retail, Insurance, Manufacturing, healthcare, Telecom etc., who can conduct client workshops, define problems, get it solved by crowdsourcing and then productize and roll it out. Measure the effectiveness. Rinse and Repeat.
3.       The focus has been more on creating technologies communities on platform and solving few hard problems but not on scaling it at enterprise level. What I mean here is that you have to do it for one customer at a time and develop a focused strategy.
4.      You also have to develop methodology to crowdsource certain aspects of the work for the enterprises. Not everything can be or should be crowdsourced. How do you break down the problem and crowdsource few pieces and then integrate it back? Also, how do you go about educating the community on enterprise standards? Many of these things are still not understood even after more than a decade of existence of many of these platforms!

There is no doubt that these data science competitions have been useful as a playground to test ideas, for inspiration, learning, visibility and branding but the time has come to go for crowdsourcing version 2.0 for these crowdsourcing platforms. Enteprises are ready for AI adaption but they don't have all the right talent.  Crowdsourcing offers a very good option. 

It will be easier to attract more talent on a crowdsourcing platform if the incentives are good and you can generate work in a repeatable manner. Just like most of the Uber drivers switch and become Lyft drivers if there is business.

Google has already started taking concrete steps by integrating Kaggle with Google cloud. After the Google’s acquisition, Kagglers can visualize key insights with Datastudio dashboard, use Big Query and do collaboration with Google Sheets. The adaption of Google Cloud is bound to increase which will results in more share of the cloud where it is lagging behind AWS and Azure. Very few companies know better than Google to turn their acquired assets into strategic advantage and they are likely to do it again with Kaggle.

Topcoder, with almost 450k data science members, is also in a very unique and strategic position to take the advantage of huge client base of Wipro Technologies - A 8.5 billion-dollar technology consulting and outsourcing company founded by their visionary founder Azim Premji.

I was just fascinated by the recent work done by a team of researchers from the Dana-Farber Cancer Institute, Brigham and Women's Hospital and Harvard in collaboration with Topcoder. They developed an AI-based solution to address the critical and resource intensive task of tumor segmentation. You can find more information here. Such is the power of crowdsourcing!

I believe that the future of Artificial Intelligence and Machine Learning lies not only in the cloud but it also lies in who can crowdsource it effectively. The Gig economy is going to be more real than many of us realize.

Marketing in a Machine Learning World: Simplicity is the key!

I was really inspired to write this post after talking to a senior marketing executive of a Fortune 20 company. So if you work for a Marketing or E-commerce organization then you should read it.

Perhaps, it is one of the most exciting times to be marketing in this digital world because marketing is increasingly becoming more high profile job than ever in most of the organizations! But that also means that marketer’s life has become more complicated despite having more tools at her disposal. The reality is that the marketers have to worry more about problems like how to break through the noise in this hyper competitive era; how to drive loyalty; shift from product-centric to customer-centric model; deal with the saturation in social media – everyone is a content producer nowadays; changing demographics; understanding customer’s context; how to justify ROI internally; having consensus for creative assets; the confusion around attribution model and spend; the changing relationship between brands and consumers; keeping pace with technology; sentiment analysis; omni-channel customer journeys; rise of mobile; figuring out millennial; why so many shoppers are dropping out of their marketing funnel; lack of alignment with sales; lack of trust in the customer database and so on. The list is very long and it will continue to evolve. In the marketing world, what needs to be done is usually clear. But what isn't always clear is how to do it in an optimized way. 

Is there anything different Marketers can do to deal with these problems?

Yes, some of the answers are hidden in machine learning! It's time you start thinking about it seriously! 

As we move from hypothesis driven world to data driven world, we might realize that  we don’t need more theories – we need to rely on data to help us make practical decisions. Access to hundreds of data reports and interpreting it yourself is Not what I am talking about. I am talking about data recommending you a course of concrete actions that is possible only through Machine learning (ML). If companies are betting on building driver-less cars using power of machine learning then I am sure ML can help you in solving few of your problems also. Machine Learning in marketing is considered by many as one of the biggest game changing opportunity for marketers just because we have now more data than ever and it is no longer humanly possible to make sense of the data without the help of machine learning. Machine Learning can't solve all your problems but it does help you in giving a logical path forward to deal with many problems in marketing world.

So, Why is everyone talking about Machine Learning Now?

Machine Learning (ML), a branch of artificial intelligence, in simple terms focuses on the development of computer programs that can teach themselves to grow and change when exposed to new data. Machine learning is almost like an intelligent assistant that draws from fields like Artificial intelligence, Statistics, data mining and optimization.

The reality is that Machine Learning technology has been there for decades but two trends have contributed significantly to phenomenal rise of Machine Learning:

1. Big Data – You have more data than ever. Machine Learning becomes better and relevant with more data.  A lot has been written about big data so I won’t go into too much detail.
2. Affordability - Till few years back, the machine learning technology wasn’t accessible easily to marketers – the cost to setup infrastructure and build specialized team was just very high. In the past, successful use of machine learning algorithms required made-to-order algorithms and huge R&D budgets, but all that is changing. IBM Watson, Microsoft Azure, Google and Amazon have launched turnkey cloud-based machine learning solutions. At the same time startups like Idibon, MetaMind, Dato and MonkeyLearn have built machine learning products that companies can take advantage of.  I have used some of these libraries myself and found it very simple to use but very powerful. Again, these models aren't perfect, but they're very useful.

    So, What is happening in Industry with respect to Machine Learning?

     There have been notable acquisitions in the machine learning startups in advertising, sales and marketing: Oracle acquired Crosswire for $50 million; Twitter acquired TellApart for more than $530m, Google acquired marketing-management startup Granata Decsion Systems, and Israeli Unicorn Ironsource merged with in-app advertising startup Supersonic, to name a few. Machine learning startups in marketing space like Appier (cross device marketing), Databerries (in store traffic), Drawbridge (cross device advertising), Emarsys (content personalization), Lattice Engines (predictive scoring for leads), Oculus360 (marketing Intelligence Paltform) and Personali (Uses ML to form emotional connection) have been funded with tens of millions of dollars receently. 

      Deep learning - a cutting edge branch of machine learning inspired by the architecture of human brain -is the hottest thing happening in machine learning if you consider the recent acquisitions by Amazon (Orbeus), Facebook (Wit.AI), Google (Dark Blue Labs, Deep Mind, DNNresearch), IBM (alchemyAPI) and Microsoft (SwiftKey). Deep Learning was the underlying technology which was used by the Google’s DeepMind AI who beat Lee Sedol, a legendary Go player. There is a big race among major software players for technical superiority

      Even Salesforce.com has come out with their machine learning product called Einstein and Adobe has made announcement to embed machine learning in their  offerings.

 Is anybody in your organization looking at all the innovations happening in the  machine learning world; doing gap analysis and making recommendations about  your machine learning roadmap?

What can Machine Learning really do for a Marketer?

     Anticipating customer needs is not a new phenomenon but what is truly new is the ability to respond to customer needs automatically, in real time and at scale with the help of machine learning. The most common use cases of ML in marketing are primarily:

• Finding and predicting best and least valuable customers from lifetime value standpoint
• Building personas based on customer clusters and building appropriate creative, content and services for them
• Recommending new products and content based on who you are which prospects are most likely to buy;
• Tagging content with right keywords
• Testing countless paths consumers may take through content
• Programmatic ad buying
• Optimizing moments of interests by personalization of content
• Predictive lead scoring

There will be many more use cases in your organization if you explore and go deeper.

What is Missing? Why Everyone in my Organization is not using it? 

Despite having so much data and access to machine learning technologies, the rate of adaption should be better across the board in any marketing organization. There are two major reasons: Lack of training and Lack of Agility of Machine Learning Implementations. Let me explain in more detail:

• Democratization, Simplification and Training of Machine Learning concepts - I believe what is lacking is bare minimum machine learning training at conceptual level for marketers – from executives to marketers who  are in trenches - so that they can start having the right conversations.  The whole concept of using machine learning needs to be simplified and they should know how to make it actionable. Customized machine learning training should be developed for marketing and e-commerce organizations. No, marketers don’t need to become data scientists for that. They just should be able to exploit the machine learning technologies already existing either in their organizations or outside in the cloud. The idea is to develop a primer for them so that they can start having meaningful conversations with data scientists.There are some very good books out there for machine learning but they are either too technical or very high-level to be actionable. Marketers just need to articulate the problem in a better way with some understanding of ML! Defining the machine learning problem precisely is the hardest part and it should be initiated by marketers – not data scientists.

Lets take a look at one of the most popular machine learning  algorithms and see how a marketer needs to articulate a question to a data scientist:

Example of Marketer’s Question	Machine Learning Algorithm by Data Scientist
How much should I spend on advertising to achieve certain sales volume?	Linear Regression
What are the chances that the customer will a) stay a customer b) re-purchase a product or c) respond to a direct mail?	Logistic Regression
Can the machine help me discover segments or clusters in my customer base by using already known factors in my customer base?	K-means
How can I match my customers’ interests with the description and attributes of my products?	Support Vector Means
How can I predict customer retention and profitability?	Random Forests
How can I identify the right audience to target on social platforms and what should I say based on what data tells us?	Deep Learning/Neural Networks
How can I develop Amazon-like recommendations on my website?	Collaborative Filtering

Of course, you will have to dig deeper and iterate over each of these questions. But asking the right question is the first step to start meaningful conversation with data scientists and technologists in your organization. This will also relieve too much burden from data scientists as they are generally overwhelmed with work and the complexity of their work is never understood properly.

• Agility and workflow of Machine Learning Implementations – You can have a great team of machine learning engineers and data scientists but the process of deploying applications based on machine learning is usually so slow. How do you deploy models in 2 weeks versus 3-6 months? So there is a disconnect between what marketers are being asked to do versus what is produced by data scientists from timing standpoint. It becomes harder if you are dealing with millions of customers and large data. You will probably get two different viewpoints if you ask a marketer versus a data scientist in your organization! Agility in machine learning deployment models will not be exactly like agile software methodologies but there are many common elements. The big difference is that ML agile methodologies will be data driven. To solve that problem you need to customize good agile project management practices and apply them in ML context for your organization. But again you can’t do much about it unless you are trained, as mentioned in point above, in basic concepts. Machine learning in production is becoming less about algorithms and becoming more focused on the data workflows surrounding them. Data Flow is about all the steps required to train machine-learning models in the lab, deploy those models into production, monitor and evaluate their performance, and iteratively improve those models. If your data flows are long, expensive or manual than you have a huge problem. You need to rethink about strategy and consider alternate solutions to simplify it. The last mile problem of machine learning is well known. Most data scientists will not know deeply about marketing or/and marketing systems to embed predictions into the daily routine of marketers. 

In the end, it doesn't matter whether you own marketing strategy in your organization or have an operational role, you need to start thinking about taking concrete steps to integrate machine learning in the DNA of your marketing organization.  It really helps you in developing a solid long-term marketing strategy and an optimized operational model. The best time is now! Simplicity is the  key to success in this context.

Insights from Adobe Digital Marketing Summit

I read a profound quote recently in context of marketing from Marc Mathieu, SVP of marketing of Unilever. According to him, “The marketing industry is still putting too much emphasis on digital as a separate category, rather than marketing to people in a digital world.” Not sure, if everyone agrees to it or not but advertising and marketing are in an unprecedented time of growth and invention. According to IDC, from 2014 to 2018, marketing technology spending will reach $130B for the 5 year period. There’s been over $21.8 billion of venture capital and private equity invested in marketing technology companies recently – it doesn’t include any money raised from public offerings. It is a common knowledge now that the real innovation in marketing is a shift from producing communications to delivering customer experiences - basically, it is just not just art and copy, but also code and data. Also, the line is really blurring between sales, marketing, CRM and media in the digital world.

The digital marketing summit organized by Adobe is viewed as one of the premier events in the digital marketing landscape. It was held in March 2015 at Salt Lake City convention center and had 7000+ attendees from more than 44 countries. In a nutshell, it revolved around Adobe’s marketing cloud, its value proposition and also a peek into what will be coming in the next twelve to eighteen months. Before we dive into the details of what Adobe is saying, let’s explore the concept of marketing cloud and its positioning and significance now.

Why Marketing Cloud?

Basically, in theory, marketing cloud is a concept of one stop solution, offered as a “Software-as-a-Service” model, for all your marketing solutions need.  The space is very new and evolving and it has some basic components like multi-channel marketing automation, content management tools, social media tools, testing tools, analytics platforms, media optimizer solutions etc.

Today the big players in the marketing cloud are Adobe, IBM, Salesforce.com, Oracle and HP. These vendors are still defining what marketing cloud can/should be based on market dynamics. Since these players can't offer all the solutions at once, each company has a head start in few areas mainly because of historical acquisitions.  Adobe is playing to its historical strength of content creation and data; Salesforce.com is all about social and CRM integration; Oracle has great platforms for multi-channel marketing and ecommerce; IBM has overall dominance in commerce; and HP is placing its bets on Big Data.

Today, these vendors just solve only few aspects of the marketing technology landscape.  Either they miss many important pieces or they lack integration because they have acquired many disparate products in a very short span of time! Adobe lacks integration with the sales side of the business as it lacks the CRM piece. Salesforce.com lacks the content creation piece. Oracle lacks a web analytics platform and an ad tech solution. IBM is trying to catch up and still runs all new acquisitions as separate businesses. Both IBM and Oracle have a strong e-commerce solution which both Adobe and Salesforce.com lack.

What’s New in Adobe Marketing Cloud?
Lets talk about few significant offerings and how it can potentially matter.

• A New Data Management Platform - Adobe audience manager (formerly Demdex) is a new data management platform that helps build unique audience profiles that can identify your most valuable segments and use them across any digital channel. In the past, we could define an “audience” in Adobe Analytics for analytical purposes, but to recreate that same segment to, say, send an email from Adobe Campaign or to personalize content on the website or mobile app, required manual step-by-step recreation. Adobe has made strides to automate this process which is a significant step.

• Streaming and Monetizing Videos across devices - Adobe Primetime delivers TV to every IP-connected screen. It gives programmers and operators modular capabilities to stream and monetize video across desktops and devices. Media companies can now deliver personalized ads across platforms, and ensure that a single user with more than one device doesn’t have to watch the same ad with more than the desired number of exposures. It is used by NBC Sports, Comcast, Turner Broadcasting, Time Warner Cable and others.

• Programmatic ad buying continues to be a challenge for today’s advertisers, with too much focus placed on display ad bidding and multiple data vendors providing different buying methods and billing practices. Adobe announced a solution, combining a new algorithmic engine and key advancements to Audience Core Services to unify audience targeting, buying, data and billing in one platform. The solution integrates audience and behavior data from a broad range of sources (including Web, mobile app and CRM systems), automates the execution of paid media campaigns through Adobe Media Optimizer (formerly Efficient Frontier – a demand side platform) and lets marketers use the same audience segments across earned and owned campaigns to deliver consistent experiences. Today only 5% of advertising is bought programmatically but that number is growing 50% year over year

• New Campaign Tool - Adobe Campaign (Formerly Neolane) allows marketers to create and manage sophisticated email campaigns across devices. Oracle, IBM etc. already have products and it allows Adobe to have a similar offering in its marketing stack.

• Better integration of Adobe Creative with Adobe Marketing – Adobe announced the 25th anniversary of Photoshop which reminds you that creative is a bigger business than marketing for Adobe. Historically, the creative business (based out of SFO) and marketing business (based out of Salt Lake city) have operated separately but you can see steps in trying to integrate them seamlessly in some of the products like AEM.

• Significant Improvements in Adobe Experience Manager – Adobe 6.x (formerly CQ) has some significant architectural, HTML5 compliant templates and DAM (Digital Asset Management) improvements. It also offers Assets on Demand option which is SAAS version with many features of DAM and Scene 7 combined. The new DAM will help us to manage our digital assets in a much better and collaborative way.

• Contribution analysis – Digital Analysts spend countless hours searching for explanations to change in metrics. They have had to carry out this time- consuming analysis on their own by importing large amounts of data in data warehouse. Until now. Contribution Analysis scans all variables (conversion traffic etc.) to explain changes in metrics and identify what contributes most to an anomaly.


• Mobile app development - Adobe announced a new mobile app framework that gives companies an end-to-end workflow to manage the complete mobile app lifecycle — from app development and user acquisition to app analytics and user engagement. Also, half a dozen mobile app technology providers are integrating their tools into Adobe Marketing Cloud.

• Marketing extends to IoT (Internet-of-Things) - Adobe Marketing cloud enables brands to extend the impact of marketing across more touch points including wearables and IoT devices. Adobe Experience Manager Screens and Adobe Target now bring personalized experiences to physical spaces like retail stores and hotel rooms and enable marketers to optimize content across any IoT device. The new IoT SDK lets brands measure and analyze consumer engagement across any of those devices. And new Intelligent Location capabilities allow companies to use GPS and iBeacon data to optimize their physical brand presence.

Key Observations

It gets reinforced in a summit like this that while the growth rate of overall marketing spend will probably not change in a big way, its composition will change dramatically and technology will command a much larger share in the coming years.

Is it a good idea to standardize on One Marketing Cloud?

When it comes to standardizing on one marketing cloud, the reality is that very few companies have a green field when it comes to their marketing stack. Almost everyone has different legacy components, across different business units. For instance, CRM uses its own set of technologies and the ecommerce team might be using say its Oracle/Endeca stack.  Basically, it is a heterogeneous marketing technology world. One marketing cloud vendor will not be able to satisfy all requests because marketing in Fortune 100 companies is just too big for that. Also, we are in an era of unprecedented innovation, especially as it applies to digital marketing, and companies who lock themselves completely into one marketing cloud are at risk of missing out on hugely disruptive technologies both now and in the future.

Having a Holistic view from Implementation Standpoint

Marketing cloud concept is pioneered by software vendors – probably not by organizations like yours. So let’s ignore what the vendors are saying for a moment and try to understand what it really means for us? Many organizations have implemented many different pieces of Adobe stack already. So you need to have better clarity about what new technologies and incremental changes in the existing technologies will mean for you. You need to understand it holistically and have an integrated view of all the pieces of marketing cloud. Technology management is all about deciding which changes are adapted – also when and how those changes are adapted.

Connecting the Dots  

Technology and new initiatives are always changing and impacting the whole digital ecosystem. You have to tirelessly connect the dots and use data as the glue which binds different pieces together. Related bits of data are worth more when they’re combined. In order to be successful in a digital world, you still have to go to the basics and spend more time to understand your changing requirements. No vendor or a marketing cloud can really help us in that. In the end, data, insights, our vision, processes and governance are the true differentiator. If all of your competitors also use the same pieces of technology in the marketing cloud then how can we truly differentiate?

Do you want to know about a Blind Girl’s Online Retail Experience?

I am talking about Amy here – not any fictional character. She likes to call herself a blind chick because she is young and blind. She is actually getting married in a week to Dave who is also visually impaired.  Amy is funny, full of life, always smiling and talks with lot of passion about her online retail experience which is definitely not great.

Online Shopping gives her privacy, dignity and makes her feel more independent.  The Online retail experience is a very broken experience for her today but she remains hopeful.  

There is just no bitterness in her about it. Amy was recently stuck in Boston during a snowstorm for many days and wanted to do most of her wedding shopping online but had a hard time doing it. According to her, “Most of the time it is almost like going to a car dealership when you don’t know how to drive.”  Unfortunately, many times she is forced to talk to customer service representatives! Sometimes she is asked if she could find someone else to do her online shopping! How fair is that?

Amy also gives examples of websites like lush.com and jetblue.com who provide a great experience to people with disabilities.

There are millions of people in USA and all over the world who have blindness or very low vision; are hearing impaired; have mobility-dexterity challenges; have speech difficulties or have various cognitive disorders.  

Due to modern miracles of medicines impacting longevity, we have an aging population who are likely to develop many of these symptoms. The Web is almost 25 years old and there is a whole generation of us, including millions of people with disabilities, who grew up with World Wide Web. Sir Tim Berner Lee, the inventor of the web, always envisioned a web that is truly for EVERYONE and is accessible to all and one that empowers all of us to achieve our dignity, rights and potential as humans. He also felt that it was very important to keep the balance between commercial and social needs of the web.

So where did we go wrong? How could we have such a big miss?

Web accessibility is an area that needs serious work by all of us. The laws like Section 508, American Disability Act, Section 255 and others are not very clear and are interpreted differently by companies. Government/Federal, Non-profit and University websites are more compliant than the commercial organizations because they have to be section 508 compliant in order to exist – basically they have no choice. Many people consider WCAG 2.0 standard by W3C very hard to implement and also  very blind centric. Some of the commercial companies have begun becoming accessible but most have a long way to go. Lawsuits in the web accessibility space have increasingly become more prevalent. But in addition to legal concerns, the focus on user experience is equally important from accessibility perspective.

The ecosystem for web accessibility has developed in last decade but we are just not there yet. Today, you have screen readers like JAWS, NVDA and Apple voiceover to help out visually impaired people. Wordspace from Deque Systems can help you in auditing your website. Accessibility management tools like SSB Bart, Audioeye, Amaze Deque and IBM Browse Out Loud help in management aspects of web accessibility.  According to web accessibility practitioners, eighty percent of the responsibility still lies with website operators even if you buy any of the assistive technologies or related tools. Website owners need to create environment that is more conducive to their content authors, developers, designers, testers, project managers as well as agencies.

Most of the online retailers are unaware about the number of disabled people visiting their website or the type of disability they have. Besides the impact to their conscience, they may be missing financial opportunities. People with disabilities have increasingly become very web savvy, and they love their smart devices like all of us. According to Webaim.org, iphones are more popular than Androids devices among people who describe themselves as disabled.  If you are interested in more survey results then please go to http://webaim.org/projects/practitionersurvey/.

Companies like Apple, Google, Amazon, IBM and Salesforce.com have taken web accessibility very seriously and are ahead of the curve than others. BBC is considered a gold standard in the web accessibility area. IBM has even appointed a Chief Accessibility Officer recently.  Over the last few years, AT&T  increased focus in the web accessibility and are leading in the telecom space. Target, after settling an accessibility lawsuit, has the most interesting turnaround in this area. They have ramped up their team  and  are very proud of what they have achieved so far. In the end, it is not that hard and like any successful initiative in any organization, it needs executive sponsorship and commitment at all levels.

If you are an online retailer then the first step is to recognize your shortcomings in this area and make a very serious and focused effort to fix web accessibility. Enabling accessibility on your website is not about building a feature but is more about right processes, culture, training, tools and discipline.

To be successful, you will need to build a team and culture to embrace accessibility. You will have to increase awareness and do training  to make it part of your processes. Automation is key to accessibility so you will have to start thinking in terms of accessible components and issue automatic test failures where possible.

Winston Churchill, rightly said, "We make a living by what we get, but we make a life by what we give.”