Category Archives: Data Governance
The latest North American B2C e-commerce market report is out now. For my followers I took the freedom to summarize some “Magnificent Seven Facts on B2C eCommerce in North America” in a short blog. The report covers United States, Canada and Mexico, but as well comparisons to Europe and Asia. According to this report, North American B2C e-commerce market is expected to reach $494.0 billion in 2014.
The Magnificent Seven Facts
- 122.5 million households in North America
- 336 million internet users in North America
- North America makes up 29.2% of the total global online sales ($1,552.0bn) in 2013.
- In terms of global B2C e-commerce, North America ranked third in 2013, behind Asia-Pacific and Europe
- North American consumers spent on average$2,116 online in2013. This is significantly above the global average of €1,280.
- With an average spending per e-shopper of $2,216, American consumers spent most online in2013. Canadians ranked second with an average spending of $1,577, while Mexican e-shoppers on average spent $1,133 online in2013.
- Canadians are more likely to shop mobile
Mobile Commerce: Canada Leads the Pack
Within North America, mobile commerce is most popular in Canada, with more than half of the online purchases per week being made through a mobile device. At 38.2%, Northern Americans still make their mobile purchases in the safe surroundings of their homes.
What are the barriers preventing mobile purchasing?
Free downloads available now
Would you like to find out more about global e-commerce? The free light versions of our Regional/Continental Reports can be downloaded here.
The thing that resonates today, in the odd context of big data, is that we may all need to look in the mirror, hold a thumb drive full of information in our hands, and concede once and for all It’s not the data… it’s us.
Many organizations have a hard time making something useful from the ever-expanding universe of big-data, but the problem doesn’t lie with the data: It’s a people problem.
The contention is that big-data is falling short of the hype because people are:
- too unwilling to create cultures that value standardized, efficient, and repeatable information, and
- too complex to be reduced to “thin data” created from digital traces.
Evan Stubbs describes poor data quality as the data analyst’s single greatest problem.
About the only satisfying thing about having bad data is the schadenfreude that goes along with it. There’s cold solace in knowing that regardless of how poor your data is, everyone else’s is equally as bad. The thing is poor quality data doesn’t just appear from the ether. It’s created. Leave the dirty dishes for long enough and you’ll end up with cockroaches and cholera. Ignore data quality and eventually you’ll have black holes of untrustworthy information. Here’s the hard truth: we’re the reason bad data exists.
I will tell you that most data teams make “large efforts” to scrub their data. Those “infrequent” big cleanups however only treat the symptom, not the cause – and ultimately lead to inefficiency, cost, and even more frustration.
It’s intuitive and natural to think that data quality is a technological problem. It’s not; it’s a cultural problem. The real answer is that you need to create a culture that values standardized, efficient, and repeatable information.
If you do that, then you’ll be able to create data that is re-usable, efficient, and high quality. Rather than trying to manage a shanty of half-baked source tables, effective teams put the effort into designing, maintaining, and documenting their data. Instead of being a one-off activity, it becomes part of business as usual, something that’s simply part of daily life.
However, even if that data is the best it can possibly be, is it even capable of delivering on the big-data promise of greater insights about things like the habits, needs, and desires of customers?
Despite the enormous growth of data and the success of a few companies like Amazon and Netflix, “the reality is that deeper insights for most organizations remain elusive,” write Mikkel Rasmussen and Christian Madsbjerg in a Bloomberg Businessweek blog post that argues “big-data gets people wrong.”
Big-data delivers thin data. In the social sciences, we distinguish between two types of human behavior data. The first – thin data – is from digital traces: He wears a size 8, has blue eyes, and drinks pinot noir. The second – rich data – delivers an understanding of how people actually experience the world: He could smell the grass after the rain, he looked at her in that special way, and the new running shoes made him look faster. Big-data focuses solely on correlation, paying no attention to causality. What good is thin “information” when there is no insight into what your consumers actually think and feel?
Accenture reported only 20 percent of the companies it profiled had found a proven causal link between “what they measure and the outcomes they are intending to drive.”
Now, I can contend they keys to transforming big-data to strategic value are critical thinking skills.
Where do we get such skills? People, it seems, are both the problem and the solution. Are we failing on two fronts: failing to create the right data-driven cultures, and failing to interpret the data we collect?
Working with executives in retail, distribution and CPG has always been a passion for me and our team. Our MDM in NYC (February 24) is dedicated the theme of “Driving Value from Business Critical Information” and comes with special break out room from 10.30 am – 5.00 pm focussing on “Omnichannel & Product Information Management”.
Customer speakers include:
- How product information in ecommerce improved Geiger’s ability to promote and sell promotional products (Triple Award Winner) – Speaker: Mike Plourde, IT Director of Data and Analytics
- Harrods: Improving Customer Experience with Product Information – Speaker: Peter Rush, Head of Governance Planning
Informatica & Management Forum present:
Executive Tour – Retail Innovation in NYC
This time, I am proud to have a special partnership in place which allows you to visit an attractive list of retail stores in Manhattan: The list includes Bloomingdale’s, Target, Glossybox, This is Store, Indochino and much more. Did you know, re-inventing the store, was one of the hot topics at NRF, retailers big show early January.
Business partners of Informatica will get a discount for this Executive Tour and will also get free access to Informatica’s MDM Day. If you are interested in the store-tour using the discount for Informatica, please drop me an email.
This blog post initially appeared on CMSwire.com and is reblogged here with their consent.
Friends of mine were remodeling their master bath. After searching for a claw foot tub in stores and online, they found the perfect one that fit their space. It was only available for purchase on the retailer’s e-commerce site, they bought it online.
When it arrived, the tub was too big. The dimensions online were incorrect. They went to return it to the closest store, but were told they couldn’t — because it was purchased online, they had to ship it back.
The retailer didn’t have a total customer relationship view or a single view of product information or inventory across channels and touch points. This left the customer representative working with a system that was a silo of limited information. She didn’t have access to a rich customer profile. She didn’t know that Joe and his wife spent almost $10,000 with the brand in the last year. She couldn’t see the products they bought online and in stores. Without this information, she couldn’t deliver a great customer experience.
It was a terrible customer experience. My friends share it with everyone who asks about their remodel. They name the retailer when they tell the story. And, they don’t shop there anymore. This terrible customer experience is negatively impacting the retailer’s revenue and brand reputation.
Bad customer experiences happen a lot. Companies in the US lose an estimated $83 billion each year due to defections and abandoned purchases as a direct result of a poor experience, according to a Datamonitor/Ovum report.
Customer Experience is the New Marketing
Gartner believes that by 2016, companies will compete primarily on the customer experiences they deliver. So who should own customer experience?
Twenty-five percent of CMOs say that their CEOs expect them to lead customer experience. What’s their definition of customer experience? “The practice of centralizing customer data in an effort to provide customers with the best possible interactions with every part of the company, from marketing to sales and even finance.”
Mercedes Benz USA President and CEO, Steve Cannon said, “Customer experience is the new marketing.”
The Gap Between Customer Expectations + Your Ability to Deliver
My previous post, 3 Barriers to Delivering Omnichannel Experiences, explained how omnichannel is all about seeing your business through the eyes of your customer. Customers don’t think in terms of channels and touch points, they just expect a seamless, integrated and consistent customer experience. It’s one brand to the customer. But there’s a gap between customer expectations and what most businesses can deliver today.
Most companies who sell through multiple channels operate in silos. They are channel-centric rather than customer-centric. This business model doesn’t empower employees to deliver seamless, integrated and consistent customer experiences across channels and touch points. Different leaders manage each channel and are held accountable to their own P&L. In most cases, there’s no incentive for leaders to collaborate.
Old Navy’s CMO, Ivan Wicksteed got it right when he said,
“Seventy percent of searches for Old Navy are on a mobile device. Consumers look at the product online and often want to touch it in the store. The end goal is not to get them to buy in the store. The end goal is to get them to buy.”
The end goal is what incentives should be based on.
Executives at most organizations I’ve spoken with admit they are at the very beginning stages of their journey to becoming omnichannel retailers. They recognize that empowering employees with a total customer relationship view and a single view of product information and inventory across channels are critical success factors.
Becoming an omnichannel business is not an easy transition. It forces executives to rethink their definition of customer-centricity and whether their business model supports it. “Now that we need to deliver seamless, integrated and consistent customer experiences across channels and touch points, we realized we’re not as customer-centric as we thought we were,” admitted an SVP of marketing at a financial services company.
You Have to Transform Your Business
“We’re going through a transformation to empower our employees to deliver great customer experiences at every stage of the customer journey,” said Chris Brogan, SVP of Strategy and Analytics at Hyatt Hotels & Resorts. “Our competitive differentiation comes from knowing our customers better than our competitors. We manage our customer data like a strategic asset so we can use that information to serve customers better and build loyalty for our brand.”
Hyatt uses data integration, data quality and master data management (MDM) technology to connect the numerous applications that contain fragmented customer data including sales, marketing, e-commerce, customer service and finance. It brings the core customer profiles together into a single, trusted location, where they are continually managed. Now its customer profiles are clean, de-duplicated, enriched and validated. Members of a household as well as the connections between corporate hierarchies are now visible. Business and analytics applications are fueled with this clean, consistent and connected information so customer-facing teams can do their jobs more effectively.
When he first joined Hyatt, Brogan did a search for his name in the central customer database and found 13 different versions of himself. This included the single Chris Brogan who lived across the street from Wrigley Field with his buddies in his 20s and the Chris Brogan who lives in the suburbs with his wife and two children. “I can guarantee those two guys want something very different from a hotel stay,” he joked. Those guest profiles have now been successfully consolidated.
According to Brogan,
“Successful marketing, sales and customer experience initiatives need to be built on a solid customer data foundation. It’s much harder to execute effectively and continually improve if your customer data is a mess.”
Improving How You Manage, Use and Analyze Data is More Important Than Ever
Some companies lack a single view of product information across channels and touch points. About 60 percent of retail managers believe that shoppers are better connected to product information than in-store associates. That’s a problem. The same challenges exist for product information as customer information. How many different systems contain valuable product information?
Harrods overcame this challenge. The retailer has a strategic initiative to transform from a single iconic store to an omnichannel business. In the past, Harrods’ merchants managed information for about 500,000 products for the store point of sale system and a few catalogs. Now they are using product information management technology (PIM) to effectively manage and merchandise 1.7 million products in the store and online.
Because they are managing product information centrally, they can fuel the ERP system and e-commerce platform with full, searchable multimedia product information. Harrods has also reduced the time it takes to introduce new products and generate revenue from them. In less than one hour, buyers complete the process from sourcing to market readiness.
It Ends with Satisfied Customers
By 2016, you will need to be ready to compete primarily on the customer experiences you deliver across channels and touch points. This means really knowing who your customers are so you can serve them better. Many businesses will transform from a channel-centric business model to a truly customer-centric business model. They will no longer tolerate messy data. They will recognize the importance of arming marketing, sales, e-commerce and customer service teams with the clean, consistent and connected customer, product and inventory information they need to deliver seamless, integrated and consistent experiences across touch points. And all of us will be more satisfied customers.
The verdict is in. Data is now broadly perceived as a source of competitive advantage. We all feel the heat to deliver good data. It is no wonder organizations view Analytics initiatives as highly strategic. But the big question is, can you really trust your data? Or are you just creating pretty visualizations on top of bad data?
We also know there is a shift towards self-service Analytics. But did you know that according to Gartner, “through 2016, less than 10% of self-service BI initiatives will be governed sufficiently to prevent inconsistencies that adversely affect the business”?1 This means that you may actually show up at your next big meeting and have data that contradicts your colleague’s data. Perhaps you are not working off of the same version of the truth. Maybe you have siloed data on different systems and they are not working in concert? Or is your definition of ‘revenue’ or ‘leads’ different from that of your colleague’s?
So are we taking our data for granted? Are we just assuming that it’s all available, clean, complete, integrated and consistent? As we work with organizations to support their Analytics journey, we often find that the harsh realities of data are quite different from perceptions. Let’s further investigate this perception gap.
For one, people may assume they can easily access all data. In reality, if data connectivity is not managed effectively, we often need to beg borrow and steal to get the right data from the right person. If we are lucky. In less fortunate scenarios, we may need to settle for partial data or a cheap substitute for the data we really wanted. And you know what they say, the only thing worse than no data is bad data. Right?
Another common misperception is: “Our data is clean. We have no data quality issues”. Wrong again. When we work with organizations to profile their data, they are often quite surprised to learn that their data is full of errors and gaps. One company recently discovered within one minute of starting their data profiling exercise, that millions of their customer records contained the company’s own address instead of the customers’ addresses… Oops.
Another myth is that all data is integrated. In reality, your data may reside in multiple locations: in the cloud, on premise, in Hadoop and on mainframe and anything in between. Integrating data from all these disparate and heterogeneous data sources is not a trivial task, unless you have the right tools.
And here is one more consideration to mull over. Do you find yourself manually hunting down and combining data to reproduce the same ad hoc report over and over again? Perhaps you often find yourself doing this in the wee hours of the night? Why reinvent the wheel? It would be more productive to automate the process of data ingestion and integration for reusable and shareable reports and Analytics.
Simply put, you need great data for great Analytics. We are excited to host Philip Russom of TDWI in a webinar to discuss how data management best practices can enable successful Analytics initiatives.
And how about you? Can you trust your data? Please join us for this webinar to learn more about building a trust-relationship with your data!
- Gartner Report, ‘Predicts 2015: Power Shift in Business Intelligence and Analytics Will Fuel Disruption’; Authors: Josh Parenteau, Neil Chandler, Rita L. Sallam, Douglas Laney, Alan D. Duncan; Nov 21 2014
Reinventing the store was one of the key topics at NRF. Over the last three to four years we have been seeing a lot push and invest for ecommerce innovation and replatforming ecommerce strategies. Now the retail, CPG and brand manufacturers are working on a renaissance of the store and show room, driven by digital. And there is still way to go.
Incremental part of the omnichannel strategy of our PIM customer Murdoch’s Ranch and Home Supply is digital signage for in-store product promotions. This selfie was shot with my dear colleague Thomas Kasemir (VP RnD PIM & Procurement) at the NRF booth of Four Winds Interactive.
Four Winds serves about 5,000 companies worldwide and I would consider them as one of the market leaders. Alison Rank and her team did show case how static product promotions work and how dynamic personalized product promotions can look like, when John Doe enters the store.
John Doe’s Personalized Purchase Journey
John Doe and his wife are out and about in the city; with the advice from his son, John has created a pro-file on Facebook and Foursquare with his new generation smartphone enabling him to receive any special offers in his vicinity. Mr. Doe has voluntarily agreed to share his data for the specific purpose of allowing retailers to call to his attention any special offers in the area. As both of them have interest in visiting the store they respond to the offer.
At the entrance to the store he is advised to start up the special store app and is promised a “personalized shopping” experience. As John Doe enters the store, a friendly greeting appears on his digital signage screen: “Welcome Mr. Doe, the men’s suits are on the 3rd floor and we have the following offers for you.” Upon reaching the 3rd floor, the salesperson is already standing there with the right suit. The suit is one size smaller than usual, but it fits John Doe. After the fitting, the salesperson even points out the new women’s hat collection in the women’s department. Satisfied with their purchases, Mr. and Mrs. Doe leave the store.
For me it is clear assuming that the future of shopping will look something like this, due to the fact that all of these technologies are already available. But what has taken place? The reason why John Doe receives location-based offers has already been explained above; the point that needs to be made is that there is now the ability to link personal and statistical data to customers. By means of the app, the store already knows whom they are dealing with as soon as they enter the store. Or can messaging services be used to send an alert to a shop assistant that a A-Customer with high value shopping carts has just entered the store.
To this point, stores can leverage both personal information as well as location-based information to generate a personal greeting for the customer.
- What did he buy? In which department was he and for how long?
- When did he purchase his last suit(s)?
- What sizes were these?
- Does he have an online profile?
- What does he order online and does he finish the transaction?
All of this analytical data can be stored and retrieved behind the scenes.
Catch Me if I Want
The targeted sales approach at the point of interest (POI) and point of sale (POS) is considered to be increasingly important. This type of communication is becoming dynamic and is taking precedent over traditional forms of advertising.
When entering the store today, customers are for the most part undecided. Based on this assumption, they can be influenced by ads and targeted product placement. Customers are now willing to disclose their location data and personal information provided there is added value for them to do so.
Example from Vapiano Restaurant
A good example is the Vapiano restaurant chain. Vapiano restaurants take an extra step further than the tradi-tional loyalty card by utilizing a special smartphone app where the customer can not only choose the nearest restau-rant along with special offers and menu, but also receive a kind of credit after payment via barcode. After collecting 10 credits, the restaurant guest receives a main course for free on the 11th visit. Sound good? It sure does, and from the company’s perspective this is a win-win situation. These obvious benefits move the customer to disclose his or her eating habits and personal data. The restaurant chain now has access to their birth dates, which is rewarded as well. This data aggregation is definitely recommendable, since it requires the guest’s explicit consent and assumes a certain degree of active participation from the guest to be eligible for the rewards offered by the restaurant.
If John Doe allowed my as brand manufacturer in my showroom or as a retailer to catch him, companies will need to ensure that they are really able to identity John Doe wit this all channel customer profile to come up with a personalized offer on digital signage. But this needs to be covered in an additional blogs…
I live in a very small town in Maine. I don’t spend a lot of time thinking about my privacy. Some would say that by living in a small town, you give up your right to privacy because everyone knows what everyone else is doing. Living here is a choice – for me to improve my family’s quality of life. Sharing all of the details of my life – not so much.
When I go to my doctor (who also happens to be a parent from my daughter’s school), I fully expect that any sort of information that I share with him, or that he obtains as a result of lab tests or interviews, or care that he provides is not available for anyone to view. On the flip side, I want researchers to be able to take my lab information combined with my health history in order to do research on the effectiveness of certain medications or treatment plans.
As a result of this dichotomy, Congress (in 1996) started to address governance regarding the transmission of this type of data. The Health Insurance Portability and Accountability Act of 1996 (HIPAA) is a Federal law that sets national standards for how health care plans, health care clearinghouses, and most health care providers protect the privacy of a patient’s health information. With certain exceptions, the Privacy Rule protects a subset of individually identifiable health information, known as protected health information or PHI, that is held or maintained by covered entities or their business associates acting for the covered entity. PHI is any information held by a covered entity which concerns health status, provision of health care, or payment for health care that can be linked to an individual.
Many payers have this type of data in their systems (perhaps in a Claims Administration system), and have the need to share data between organizational entities. Do you know if PHI data is being shared outside of the originating system? Do you know if PHI is available to resources that have no necessity to access this information? Do you know if PHI data is being shared outside your organization?
If you can answer yes to each of these questions – fantastic. You are well ahead of the curve. If not – you need to start considering solutions that can
- Identify PHI in all of your data streams
- Monitor and track the flow of this data throughout your organization and
- Mask this data if it is being shared with resources that don’t need to be able to identify the individual.
I want to researchers to have access to medically relevant data so they can find the cures to some horrific diseases. I want to feel comfortable sharing health information with my doctor. I want to feel comfortable that my health insurance company is respecting my privacy. Now to get my kids to stop oversharing.
Several months ago, I was talking to some CIOs about their business problems. During these conversations, I asked them about their interest in Big Data. One sophisticated CIO recoiled almost immediately saying that he believes most vendors are really having a problem discussing “Big Data” with customers like him. It would just be so much easier if you guys would talk to me about helping my company with our structured data and unstructured data. At the same time, Gartner has found that 64% of enterprises surveyed say they’re deploying or planning to deploy a Big Data project. The problem is that 56% of those surveyed by Gartner are still struggling to determine how to get value out of big data projects and 23% are struggling with the definition of what is Big Data and what is not Big Data.
Clearly, this says the term does not work with market and industry participants. To me this raises a question about the continued efficacy of the term. And now, Thomas Davenport, the author of “Competing on Analytics”, has suggested that we retire the term all together. Tom says that in his research “nobody likes the term”. He claims in particular that executives yearn for a better way to communicate what they are doing with data and analytics.
Tom suggests in particular that “Big Data” has five significant flaws:
1) Big is relative. What is big today will not be so large tomorrow. Will we have to tall call the future version Big Big Data?
2) Big is only one aspect of what is distinctive about the data in big data. Like my CIO friend said it is not as much about the size of data as it is about the nature of the data. Tom says bigness demands more powerful services, but a lack of structure demands different approaches to process the data.
3) Big data is defined as having volume, variety, and velocity. But what do you call data that has variety and velocity but the data set is not “big”.
4) What do you call the opposite of big data? Is it small data? Nobody likes this term either.
5) Too many people are using “big data” incorrectly to mean any use of analytics, reporting, or conventional business intelligence.
Tom goes onto say, “I saw recently, over 80 percent of the executives surveyed thought the term was overstated, confusing, or misleading”. So Tom asks why don’t we just stop using it. In the end, Tom struggles with ceasing his use of the term because the world noticed the name Big Data unlike other technological terms. Tom has even written a book on the subject—“Big Data at Work”. The question I have is do we in the IT industry want to really lose all the attention. It feels great to be in the cool crowd. However, CIOs that I have talked to say they are really worried about what will happen if their teams oversell Big Data and do not deliver tangible business outcomes. The reality Tom says it would be more helpful than saying, we are cool and we are working on big data to instead say instead we’re extracting customer transaction data from our log files in order to help marketing understand the factors leading to customer attrition”. I tend to agree with this thought but I would like to hear what you think? Should we as an industry retire the term Big Data?
Author Twitter: @MylesSuer
Not so long ago, Google created a Web site to figure out just how many people had influenza. How they did this was by tracking “flu-related search queries”, “location of the query,” and applied it to an estimation algorithm. According to the website, at the flu season’s peak in January, nearly 11 percent of the United States population may have influenza. This means that nearly 44 million of us will have had the flu or flu-like symptoms. In its weekly report the Centers for Disease Control and Prevention put this at 5.6%, which means that less than 23 million of us actually went to the doctor’s office to be tested for flu or to get a flu-shot.
Now, imagine if I were a drug manufacturer. There is a theory about what went wrong. The problems may be due to widespread media coverage of this year’s flu season. Then add social media, which helped news of the flu spread quicker than the virus itself. In other words, the algorithm is looking only at the numbers, not at the context of the search results.
In today’s digitally connected world, data is everywhere: in our phones, search queries, friendships, dating profiles, cars, food, and reading habits. Almost everything we touch is part of a larger data set. The people and companies that interpret the data may fail to apply background and outside conditions to the numbers they capture.
Now, while we build our big data repositories, we have to spend some time to explain how we collected the data and under what context.
In my previous blog, I talked about how a business-led approach can displace technology-led projects. Historically IT-led projects have invested significant capital while returning minimal business value. It further talks about how transformation roadmap execution is sustainable because the business is driving the effort where initiative investments are directly traceable to priority business goals.
For example, an insurance company wants to improve the overall customer experience. Mature business architecture will perform an assessment to highlight all customer touch points. It requires a detailed capability map, fully formed, customer-triggered value streams, value stream/ capability cross-mappings and stakeholder/ value stream cross-mappings. These business blueprints allow architects and analysts to pinpoint customer trigger points, customer interaction points and participating stakeholders engaged in value delivery.
One must understand that value streams and capabilities are not tied to business unit or other structural boundaries. This means that while the analysis performed in our customer experience example may have been initiated by a given business unit, the analysis may be universally applied to all business units, product lines and customer segments. Using the business architecture to provide a representative cross-business perspective requires incorporating organization mapping into the mix.
Incorporating the application architecture into the analysis and proposed solution is simply an extension of business architecture mapping that incorporates the IT architecture. Robust business architecture is readily mapped to the application architecture, highlighting enterprise software solutions that automate various capabilities, which in turn enable value delivery. Bear in mind, however, that many of the issues highlighted through a business architecture assessment may not have corresponding software deployments since significant interactions across the business tend to be manual or desktop-enabled. This opens the door to new automation opportunities and new ways to think about business design solutions.
Building and prioritizing the transformation strategy and roadmap is dramatically simplified once all business perspectives needed to enhance customer experience are fully exposed. For example, if customer service is a top priority, then that value stream becomes the number one target, with each stage prioritized based on business value and return on investment. Stakeholder mapping further refines design approaches for optimizing stakeholder engagement, particularly where work is sub-optimized and lacks automation.
Capability mapping to underlying application systems and services provides the basis for establishing a corresponding IT deployment program, where the creation and reuse of standardized services becomes a focal point. In certain cases, a comprehensive application and data architecture transformation becomes a consideration, but in all cases, any action taken will be business and not technology driven.
Once this occurs, everyone will focus on achieving the same goals, tied to the same business perspectives, regardless of the technology involved.