Category Archives: Data Services
Strata 2015 – Making Data Work for Everyone with Cloud Integration, Cloud Data Management and Cloud Machine Learning
Are you ready to answer “Yes” to the questions:
a) “Are you Cloud Ready?”
b) “Are you Machine Learning Ready?”
I meet with hundreds of Informatica Cloud customers and prospects every year. While they are investing in Cloud, and seeing the benefits, they also know that there is more innovation out there. They’re asking me, what’s next for Cloud? And specifically, what’s next for Informatica in regards to Cloud Data Integration and Cloud Data Management? I’ll share more about my response throughout this blog post.
The spotlight will be on Big Data and Cloud at the Strata + Hadoop World conference taking place in Silicon Valley from February 17-20 with the theme “Make Data Work”. I want to focus this blog post on two topics related to making data work and business insights:
- How existing cloud technologies, innovations and partnerships can help you get ready for the new era in cloud analytics.
- How you can make data work in new and advanced ways for every user in your company.
Today, Informatica is announcing the availability of its Cloud Integration Secure Agent on Microsoft Azure and Linux Virtual Machines as well as an Informatica Cloud Connector for Microsoft Azure Storage. Users of Azure data services such as Azure HDInsight, Azure Machine Learning and Azure Data Factory can make their data work with access to the broadest set of data sources including on-premises applications, databases, cloud applications and social data. Read more from Microsoft about their news at Strata, including their relationship with Informatica, here.
“Informatica, a leader in data integration, provides a key solution with its Cloud Integration Secure Agent on Azure,” said Joseph Sirosh, Corporate Vice President, Machine Learning, Microsoft. “Today’s companies are looking to gain a competitive advantage by deriving key business insights from their largest and most complex data sets. With this collaboration, Microsoft Azure and Informatica Cloud provide a comprehensive portfolio of data services that deliver a broad set of advanced cloud analytics use cases for businesses in every industry.”
Even more exciting is how quickly any user can deploy a broad spectrum of data services for cloud analytics projects. The fully-managed cloud service for building predictive analytics solutions from Azure and the wizard-based, self-service cloud integration and data management user experience of Informatica Cloud helps overcome the challenges most users have in making their data work effectively and efficiently for analytics use cases.
The new solution enables companies to bring in data from multiple sources for use in Azure data services including Azure HDInsight, Azure Machine Learning, Azure Data Factory and others – for advanced analytics.
The broad availability of Azure data services, and Azure Machine Learning in particular, is a game changer for startups and large enterprises. Startups can now access cloud-based advanced analytics with minimal cost and complexity and large businesses can use scalable cloud analytics and machine learning models to generate faster and more accurate insights from their Big Data sources.
Success in using machine learning requires not only great analytics models, but also an end-to-end cloud integration and data management capability that brings in a wide breadth of data sources, ensures that data quality and data views match the requirements for machine learning modeling, and an ease of use that facilitates speed of iteration while providing high-performance and scalable data processing.
For example, the Informatica Cloud solution on Azure is designed to deliver on these critical requirements in a complementary approach and support advanced analytics and machine learning use cases that provide customers with key business insights from their largest and most complex data sets.
Using the Informatica Cloud solution on Azure connector with Informatica Cloud Data Integration enables optimized read-write capabilities for data to blobs in Azure Storage. Customers can use Azure Storage objects as sources, lookups, and targets in data synchronization tasks and advanced mapping configuration tasks for efficient data management using Informatica’s industry leading cloud integration solution.
As Informatica fulfills the promise of “making great data ready to use” to our 5,500 customers globally, we continue to form strategic partnerships and develop next-generation solutions to stay one step ahead of the market with our Cloud offerings.
My goal in 2015 is to help each of our customers say that they are Cloud Ready! And collaborating with solutions such as Azure ensures that our joint customers are also Machine Learning Ready!
To learn more, try our free Informatica Cloud trial for Microsoft Azure data services.
How do you know if you have found ‘true love’?
Biologists and psychologists tell us that when we are struck by cupid’s arrow, our body is reacting to a set of chemicals that are released in the brain that evoke emotions and feelings of lust, attraction and attachment. When those chemicals are released, our bodies respond. Our hearts race, blood pumps through our veins, faces flush, body temperatures rise. Some say it feels like electricity is conducting all over the skin. It releases a flood of emotions that may cloud our judgment and may even cause us to make a choice considered unreasonable to others. Sound familiar?
But what causes our brains to react to one person and not another? Are we predisposed to how certain people look or smell? Do our genes play a role in determining an affinity toward a body type or shape?
Pheromone research has shown how sensors in our nose can smell whether or not someone’s immune system compliments our own based on the scent of urine and sweat. Meaning, if someone has a similar immune deficiency, that individual won’t smell good to us. We are more likely to prefer the smell of someone who has an immune system that is different. Is our genetic code programming our instincts to preselect who we should mate with so our offspring has a higher chance of surviving?
It is probably not surprising that most men are attracted to women with symmetrical faces and hourglass figures. Genetic research hints that men’s predispositions are also based on a genetic code. There is a correlation between asymmetric facial characteristics and genetic disorders as well as between waist to hip ratios and fertility. Depending on where you are in your stage in life, these characteristics could have a weighting factor in how your brain responds to the smell of the perfect pheromone and how someone appears. And, some argue it is all influenced by body language, voice tone and actual words used in dialogue.
Psychologists report it takes only two to four minutes to decide if you are falling in love with someone. Even if you dismiss some or accept all of the possibilities I am presenting, experiencing love is impacted by a variety and intensity of senses, interpretations and emotions combined together in a short period of time. If you are a data nerd like myself, variety, volume and velocity of ‘signals’ begins to sound like a Big Data marketing pitch. This really is an application of predictive analytics using different data types, large volumes of data and real-time decision making algorithms. But, I’m actually more interested in how affective computing, wearable devices and analytics could help determine whether or not what you feel is actually ’true love’ or just a bad case of indigestion.
Affective computing, according to researcher Rosalind Picard, gives a computer the ability to recognize and express emotions, develop that ability and enable it to regulate and utilize emotions. When applied to wearable devices that can listen to how you talk, measure blood pressure, detect changes in heart and respiration rate and even measure electro-dermal responses, is it possible that technology could sense when your body is responding to the chemicals of love?
What about mood rings, you may ask? Mood rings, the original form of an affective wearable device that grew in popularity in the 1970s changed color based on your mood. Unfortunately, mood rings only change based on body temperature. Through data collection and research, researchers have shown that physiology patterns cannot be determined by body temperature alone. In order to truly differentiate emotion of, let’s say ‘true love,’ you need to be able to collect multiple physiological signals and detect a pattern using multi-variant pattern recognition algorithms. And, if you only have 2-4 minutes, it pretty much needs to calculate chances of ‘true love’ in real-time to prevent making a life-altering mistake.
The evolution of wearables technology has reached medical grade, allowing parents to detect when their children are about to have an epileptic seizure or are experiencing acute levels of stress. When tuned to love-seekers’ queues, is it possible that this same technology could send an audio or visual signal to your smart phone alerting you as to whether or not this person is a ‘true love’ candidate? Or glow red if you are in the proximity of someone who is experiencing similar physiological changes? Maybe this is the next application for match-making companies such as eHarmony or Match.com?
The reality is this. Assuming that the data is clean and accurate, safe from violating any data privacy concerns and truly connected to your physiological signals, wearable device technology that could detect close proximity of ‘true love’ is probably five years out. It is more likely to show up in a popular science fiction film than at an Apple store in the near term. But, when it does, think about how the signal on your smart phone device tells you the proximity of a potential candidate, where a local flower shop is, integrated with facial recognition and Facebook photos and ‘status’ (assuming it is true), with an iTunes recommendation of ‘Love Is In The Air’ by John Paul Young, ‘True Love’ is only 2-4 minutes away.
 R. Picard. Affective Computing. Pages 227-239, MIT Press, 2000
 Cacioppa and Tassinary (1990)
You might think this year’s Valentine’s Day is no different than any other. But make no mistake – Valentine’s Day has never been more powered by technology or more fueled by data.
It doesn’t get a lot of press coverage, but did you know the online dating industry is now a billion dollar industry globally? As technology empowers us all to live and work in nearly any place, we can no longer rely on geographic colocation to find our friends and soul mates. So, it’s no surprise that online dating and social networking grew in popularity as the smartphone revolution happened over the last eight years. Dating and networking are no longer just about running into people at the local bar on a Friday night. People are connecting with one another on every consumer device they have available, from their computers to their tablets to their phones. This mass consumerization of devices and the online social applications we run on them have fundamentally changed how we all connect with one another in the offline world.
There’s a lot of talk about big data in the tech industry, but there isn’t a lot of understanding of how big data actually affects the real world. Online dating serves as a fantastic example here. Did you know about 1 out of 6 people in the United States is single and that about 1 of 3 of them are members of an online dating site? With tens of millions of members just in the United States, the opportunity to meet new people online has never been more compelling. But the challenge is helping people sift through a “big data” store of millions of profiles. The solution to this has been the use of predictive recommendation engines. We are all familiar with recommendation engines on e-commerce sites that give us suggestions for new items to buy. The exact same analytics are now being applied to people and their preferences to help them find new friends and companions. Big data analytics is not just some fancy technology used by retailers and Wall Street. The proof is in the math: 1 of 18 people in the United States is using big data analytics today to fulfill the most human needs of all in finding companionship.
However, this everyday revolution of big data analytics is not just limited to online dating. US News and World Report estimates that a whopping $19 billion dollars will be spent around Valentine’s Day this year. This spending includes gifts, flowers, candy, jewelry and other forms of celebration. The online sites that sell these products are no foreigners to big data either. Organizations with e-commerce sites, many of whom are Informatica customers, are collecting real-time weblog and clickstream information to dynamically offer the best possible customer experiences. Big data is not only helping to build relationships between consumers, but is also building them between enterprises and consumers.
In an increasingly data-driven world, you cannot connect people unless you can connect data. The billion dollar online dating industry and the $19 billion dollar Valentine’s Day industry would not exist if they were not fueled by the ability to quickly derive meaning out of data assets and turn them into compelling analytical outcomes. Informatica is powering this data-driven world of connected data and connected people by making all types of data ready for analytics. Our leading technologies have helped customers collect data quickly, re-direct workloads easily, and perfect datasets completely. So on this Valentine’s Day, I invite you to connect with your customers and help them to connect with one another by first connecting with your data. There is no better way to help get ready for love than by getting big data ready for analytics!
This blog post was originally featured on Business.com here: Lovenomics: The Price of Love This Valentine’s Day.
After the Blue Cross sales that dominate January, Valentine’s Day offers welcome relief to the high street. Valentine’s Day marks the end of Christmas sales and the first of the year’s seasonal hooks providing retailers with an opportunity to upsell. According to the National Retail Federation’s Valentine’s Day Consumer Spending Survey, American consumers plan to spend a total of $4.8 billion on jewelry and a survey high of nearly $2 billion on clothing this year. However, to successfully capture customers, retailers need to develop an omni-channel strategy designed to sell the right product.
Target the indecisive
For the most part, the majority of Valentine’s Day shoppers will be undecided when they begin their purchasing journey. Based on this assumption, a targeted sales approach at the point of interest (POI) and point of sale (POS) will be increasingly important. Not only do retailers need to track and understand the purchasing decision of every customer as they move between channels, but they also need to have a real-time view of the product lines, pricing and content that the competition is using. Once armed with this information, retailers can concentrate on delivering personalized ads or timely product placements that drive consumers to the checkout as they move across different channels.
Related Article: 11 Cheeky Business Valentine’s Day Cards for the BFF In Your Office
Start with search
Consumers will start their shopping journey with a search engine and will rarely scroll past the first page. So brands need to be prepared by turning Valentine’s Day product lines into searchable content. To capture a greater share of online traffic, retailers should concentrate on making relevant products easy to find by managing meta-information, optimizing media assets with the keywords that consumers are using, deploying rich text and automatically sending products to search engines.
Next generation loyalty
Retailers and restaurants can now integrate loyalty schemes into specialized smartphone apps, or maybe integrate customer communication to automatically deliver personalized ads (e.g., offers for last minute gifts for those who forget). However, to ensure success, brands need to know as much about their customers as consumers know about their products. By being able to monitor customers’ behavior, the information that they are looking at and the channels that they are using to interact with brands, loyalty programs can be used to deliver timely special offers or information at the right moment.
Valentine’s Day represents an opportunity to reinvent the in-store experience. By introducing digital signage for special product promotions, retailers can showcase a wide range of eclectic merchandise to showroom consumers. This could be done by targeting any smartphone consumers (who have allowed geo-located ads on their phones) with a personalized text message when they enter the store. Use this message to direct them to the most relevant areas for Valentine’s Day gifts or present them with a customized offer based on previous buying history.
Related Article: Small Business Marketing Tips for Valentine’s Day
supermarkets have become established as the one-stop shop for lovers in a rush. Last year, Tesco, a British multinational grocery and general merchandise retailer, revealed that 85 percent of all Valentine’s Day bouquets were bought on the day itself, with three-quarters of all Valentine’s Day chocolates sold on February 14.
To tap into the last-minute attitude of panicked couples searching for a gift, retailers should have a dedicated Valentine’s Day section online and provide timely offers that come with the promise of delivery in time for Valentine’s Day. For example, BCBGMAXAZRIA is using data quality services to ensure its email list is clean and updated, keeping its sender reputation high so that when they need to reach customers during critical times like Valentine’s Day, they have confidence in their data.
Alternatively, retailers can help customers by closely managing local inventory levels to offer same-day click-and-collect initiatives or showing consumers the number of items that are currently in-stock and in-store across all channels.
Valentine’s Day may seem like a minor holiday after Christmas, but for retailers it generates billions of dollars in annual spending and presents a tremendous opportunity to boost their customer base. With these tips, retailers will hopefully be able to sweeten their sales by effectively targeting customers looking for the perfect gift for their special someone.
Informatica users leveraging HDP are now able to see a complete end-to-end visual data lineage map of everything done through the Informatica platform. In this blog post, Scott Hedrick, director Big Data Partnerships at Informatica, tells us more about end-to-end visual data lineage.
Hadoop adoption continues to accelerate within mainstream enterprise IT and, as always, organizations need the ability to govern their end-to-end data pipelines for compliance and visibility purposes. Working with Hortonworks, Informatica has extended the metadata management capabilities in Informatica Big Data Governance Edition to include data lineage visibility of data movement, transformation and cleansing beyond traditional systems to cover Apache Hadoop.
Informatica users are now able to see a complete end-to-end visual data lineage map of everything done through Informatica, which includes sources outside Hortonworks Data Platform (HDP) being loaded into HDP, all data integration, parsing and data quality transformation running on Hortonworks and then loading of curated data sets onto data warehouses, analytics tools and operational systems outside Hadoop.
Regulated industries such as banking, insurance and healthcare are required to have detailed histories of data management for audit purposes. Without tools to provide data lineage, compliance with regulations and gathering the required information for audits can prove challenging.
With Informatica, the data scientist and analyst can now visualize data lineage and detailed history of data transformations providing unprecedented transparency into their data analysis. They can be more confident in their findings based on this visibility into the origins and quality of the data they are working with to create valuable insights for their organizations. Web-based access to visual data lineage for analysts also facilitates team collaboration on challenging and evolving data analytics and operational system projects.
The Informatica and Hortonworks partnership brings together leading enterprise data governance tools with open source Hadoop leadership to extend governance to this new platform. Deploying Informatica for data integration, parsing, data quality and data lineage on Hortonworks reduces risk to deployment schedules.
A demo of Informatica’s end-to-end metadata management capabilities on Hadoop and beyond is available here:
- A free trial of Informatica Big Data Edition in the Hortonworks Sandbox is available here .
The Ponemon Institute stated that the biggest concern for security professionals is that they do not know where sensitive data resides. Informatica’s Intelligent Data Platform provides data security professionals with the technology required to discover, profile, classify and assess the risk of confidential and sensitive data.
Last year, we began significant investments in data security R&D support the initiative. This year, we continue the commitment by organizing around the vision. I am thrilled to be leading the Informatica Data Security Group, a newly-formed business unit comprised of a team dedicated to data security innovation. The business unit includes the former Application ILM business unit which consists of data masking, test data management and data archive technologies from previous acquisitions, including Applimation, ActiveBase, and TierData.
By having a dedicated business unit and engineering resources applying Informatica’s Intelligent Data Platform technology to a security problem, we believe we can make a significant difference addressing a serious challenge for enterprises across the globe. The newly formed Data Security Group will focus on new innovations in the data security intelligence market, while continuing to invest and enhance our existing data-centric security solutions such as data masking, data archiving and information lifecycle management solutions.
The world of data is transforming around us and we are committed to transforming the data security industry to keep our customer’s data clean, safe and connected.
For more details regarding how these changes will be reflected in our products, message and support, please refer to the FAQs listed below:
Q: What is the Data Security Group (DSG)?
A: Informatica has created a newly formed business unit, the Informatica Data Security Group, as a dedicated team focusing on data security innovation to meet the needs of our customers while leveraging the Informatica Intelligent Data Platform
Q: Why did Informatica create a dedicated Data Security Group business unit?
A: Reducing Risk is among the top 3 business initiatives for our customers in 2015. Data Security is a top IT and business initiative for just about every industry and organization that store sensitive, private, regulated or confidential data. Data Security is a Board room topic. By building upon our success with the Application ILM product portfolio and the Intelligent Data Platform, we can address more pressing issues while solving mission-critical challenges that matter to most of our customers.
Q: Is this the same as the Application ILM Business Unit?
A: The Informatica Data Security Group is a business unit that includes the former Application ILM business unit products comprised of data masking, data archive and test data management products from previous acquisitions, including Applimation, ActiveBase, and TierData, and additional resources developing and supporting Informatica’s data security products GTM, such as Secure@Source.
Q: How big is the Data Security market opportunity?
A: Data Security software market is estimated to be a $3B market in 2015 according to Gartner. Total information security spending will grow a further 8.2 percent in 2015 to reach $76.9 billion.
Q: Who would be most interested in this announcement and why?
A: All leaders are impacted when a data breach occurs. Understanding the risk of sensitive data is a board room topic. Informatica is investing and committing to securing and safeguarding sensitive, private and confidential data. If you are an existing customer, you will be able to leverage your existing skills on the Informatica platform to address a challenge facing every team who manages or handles sensitive or confidential data.
Q: How does this announcement impact the Application ILM products – Data Masking, Data Archive and Test Data Management?
A: The existing Application ILM products are foundational to the Data Security Group product portfolio. These products will continue to be invested in, supported and updated. We are building upon our success with the Data Masking, Data Archive and Test Data Management products.
Q: How will this change impact my customer experience?
A: The Informatica product website will reflect this new organization by listing the Data Masking, Data Archive, and Test Data Management products under the Data Security product category. The customer support portal will reference Data Security as the top level product category. Older versions of the product and corresponding documentation will not be updated and will continue to reflect Application ILM nomenclature and messaging.
I have always loved making connections: between people, between a product and its message, between partner companies and their messages. Coming from a creative agency background where I worked with our clients, created messaging, found images, and wrote copy all day, what I did not love was cold hard data. In fact, I’m embarrassed to admit I rarely thought about it. I handed off my creative work and let the client worry about the boring details. As long as they kept coming back, all was well.
Enter year 2008. I decided to go in-house for an IaaS provider, with a laser focus on SaaS companies. In many ways it was an easy transition, with one glaring difference – METRICS. I used every trick in my book to escape tracking and reporting. I was “too busy” with “more important” things. Needless to say, this did not go over well. But my background, along with our non-compatible (how I saw it at the time) systems, had me up nights worrying about the reports I should be doing. In truth, I was too busy to spend an extra several hours pulling information from three systems, sifting through and manually mashing it together in a spreadsheet to get the report I needed. And by the end I always had a huge headache and wasn’t even sure my information was correct. But none of that got me out of doing the work I hated.
Then came the first time I was able to prove a program’s worth; there was a spark of excitement – an awakening to the power of data. For the next several years, through both start-up and enterprise environments, I had a love / hate relationship with data. No company I worked for had integrated SaaS/software systems, and reporting took hours of manual work for me, and my teams. The desire was there, even the occasional win – but it was laden with bitter feelings, from the pain of wasted time and uncertain results.
Everything changed last year when I joined Informatica. For the first time, my marketing automation was integrated with my CRM, which was integrated with my… you know the rest. And reporting? Even that was now easy. For a company that lives and breathes data integration, obviously this makes sense. As a person who’s never experienced this before, I had no idea what a relief it would be until I lived it.
Now imagine unlocking this ease of use not only for your employees (very important), but also for your customers (maybe even more important). I’d like to invite you to Informatica’s first SaaS ecosystem event where data-driven executives from Salesforce.com, AWS, Tableau, Marketo, AppDynamics, D&B, Adobe, NewRelic, and more will share their stories around data and the difference it’s made in their competitive differentiation.
Data Mania is a private event for SaaS leaders, March 4, in San Francisco. Right now, it’s the stealth version of Dreamforce or Oracle Openworld. And like any A-list after party, it’s drawing a who’s-who of SaaS & data industry insiders. It is the event to attend if you are a product management, engineering, professional services or customer success executive at a SaaS company and want to know the data story behind some of the most successful companies in your space.
Planned sessions and panels include something for everyone.
For customer success management, we offer the chance to learn firsthand how native connectors quickly onboard new customers, improve business processes and establish connectivity with other best-of-breed applications.
Engineers and developers – and anyone involved with R&D – will hear how their peers have figured out a way to refocus their attention on developing new products and enhanced features while still providing the data integration required for mass adoption.
And, finally, for product management, we offer freedom — to consider all the potential opportunities and applications that open up when you quit worrying about how “to make the data work and scale” and instead focus on “all the ways data can make your product better” and provide your customers with greater insights and value.
Leading up to Data Mania, we’re also holding Connect-a-thon, a hackathon-like event to get you connected to hundreds of your customers’ cloud and on-premises apps. Connect-a-thon will give your dev team direct access to Informatica Cloud R&D resources – at no cost – to help them develop connectors and custom mappings to make these connections. And if your company is under $5M in annual bookings, and you choose to embed Informatica Cloud, we have a very special offer* for you (think free software and services). Then come to the show for advice on the next steps from your peers and data-driven leaders.
In the end, if you think Salesforce, Adobe, Amazon Web Services, Tableau, Qlik, Dun & Bradstreet and Informatica have something to say about connection and data — and the role they play helping to create the customer-driven enterprise – then you want to be at Data Mania to hear it.
I’m proud of the event we’ve put together and I know you won’t be disappointed. Conceiving and producing Data Mania with a small team here has been my chance to come full circle back to my love of making connections in the SaaS community, using my creative background AND working with the data and metrics I’ve learned to love. I’m counting down the days to the event on March 4th, and I hope you’ll join me. I’ll be the Data Maniac with the biggest smile.
*Offer applies to the first 25 participants
I think I may have gone to too many conferences in 2014 in which the potential of big data was discussed. After a while all the stories blurred into two main themes:
- Companies have gone bankrupt at a time when demand for their core products increased.
- Data from mobile phones, cars and other machines house a gold mine of value – we should all be using it.
My main take away from 2014 conferences was that no amount of data is a substitute for poor strategy, or lack of organisational agility to adapt business processes in times of disruption. However, I still feel as an industry our stories are stuck in the phase of ‘Big Data Hype’, but most organisations are beyond the hype and need practicalities, guidance and inspiration to turn their big data projects into a success. This is possibly due to a limited number of big data projects in production, or perhaps it is too early to measure the long term results of existing projects. Another possibility is that the projects are delivering significant competitive advantage, so the stories will remain under wraps for the time being.
However, towards the end of 2014 I stumbled across a big data success story in an unexpected place. It did (literally) provide competitive advantage, and since it has been running for a number of years the results are plain to see. It started with a book recommendation from a friend. ‘Faster’ by Michael Hutchinson is written as a self-propelled investigation as to the difference between world champion and world class althletes. It promised to satisfy my slightly geeky tendency to enjoy facts, numerical details and statistics. It did this – but it really struck me as a ‘how-to’ guide for big data projects.
Mr Hutchinson’s book is an excellent read as an insight into professional cycling by a professional cyclist. It is stacked with interesting facts and well-written anecdotes, and I highly recommend the reading the book. Since the big-data aspect was a sub-plot, I will pull out the highlights without distracting from the main story.
Here are the five steps I extracted for big data project success:
1. Have a clear vision and goal for your project
The Sydney Olympics in 2000 had only produced 4 medals across all cycling disciplines for British cyclists. With a home Olympics set for 2012, British Cycling desperately wanted to improve this performance. Specific targets were clearly set across all disciplines stated in times that an athlete needed to achieve in order to win a race.
2. Determine data the required to support these goals
Unlike many big data projects which start with a data set and then wonder what to do with it, British Cycling did this the other way around. They worked out what they needed to measure in order to establish the influencers on their goal (track time) and set about gathering this information. In their case this involved gathering wind tunnel data to compare & contrast equipment, as well as physiological data from athletes and all information from cycling activities.
3. Experiment in order to establish causality
Most big data projects involve experimentation by changing the environment whilst gathering a sub-set of data points. The number of variables to adjust in cycling is large, but all were embraced. Data (including video) was gathered on the effects of small changes in each component: Bike, Clothing, Athlete (training and nutrition).
4. Guide your employees on how to use the results of the data
Like many employees, cyclists and coaches were convinced of the ‘best way’ to achieve results based on their own personal experience. Analysis of data in some cases showed that the perceived best way, was in fact not the best way. Coaching staff trusted the data, and convinced the athletes to change aspects of both training and nutrition. This was not necessarily easy to do, as it could mean fundamental changes in the athlete’s lifestyle.
5. Embrace innovation
Cycling is a very conservative sport by nature, with many of the key innovations coming from adjacent sports such as triathlon. Data however, is not steeped in tradition and does not have pre-conceived ideas as to what equipment should look like, or what constitutes an excellent recovery drink. What made British Cycling’s big data initiatives successful is that they allowed themselves to be guided by the data and put the recommendations into practice. Plastic finished skin suits are probably not the most obvious choice for clothing, but they proved to be the biggest advantage cyclist could get. Far more than tinkering with the bike. (In fact they produced so much advantage they were banned shortly after the 2008 Olympics.)
The results: British Cycling won 4 Olympic medals in 2000, one of which was gold. In 2012 they grabbed 8 gold, 2 silver and 2 bronze medals. A quick glance at their website shows that it is not just Olympic medals they are wining – but medals won across all world championship events has increased since 2000.
To me, this is one of the best big data stories, as it directly shows how to be successful using big data strategies in a completely analogue world. I think it is more insightful that the mere fact that we are producing ever-increasing volumes of data. The real value of big data is in understanding what portion of all avaiable data will constribute to you acieving your goals, and then embracing the use the results of analysis to make constructive changes in daily activities.
But then again, I may just like the story because it involves geeky facts, statistics and fast bicycles.
The holidays that just passed weren’t the only thing to celebrate, according to historical trends. As we moved from December into 2015, how many of you were seeing a lot more engagement announcements on Facebook, or even became engaged yourself?
December is the most popular month to get engaged (according to wedding website TheKnot.com), so it’s likely many of us gearing up for the typical spring/summer calendar full of weekend weddings.
While December is not known as a big month for weddings, it is a big time for jewelers, including the months leading up to it. Diamonds, gold, and other fine jewelry become very popular purchases at this time.
Fine jewelry is an emotional buying decision, which you can see from the jewelry store commercials that evoke sentiment for our loved ones.
But that emotional pull to purchase diamonds, gold and precious stones could be changing significantly.
Diamond sales are down this year – but what is up? Technology-related gifts, including smart phones, tablets, and other functional devices. To understand why, all you have to do is think about the ages of people getting engaged: 18-34 year-olds.
People in that age range who are getting engaged right now just aren’t drawn in by the emotional purchase of fine jewelry anymore, if they ever were. They value technology purchases.
But it’s not just function over form. The emotional motivation behind a purchase (whether technology or fine jewelry or any high-dollar item) is always there.
“Status for this generation isn’t about money — it’s about attention,” said psychology professor Kit Yarrow in a recent Pacific Standard magazine article. Therefore, a smart phone is considered a better gift (and better use for the money) than fine jewelry, since it allows you to share your life and stay connected much more than a gold and diamond ring can do.
As the article notes, using technology to create “an everlasting Facebook album from that scuba diving trip in Bali says so much more than one lone photo of a pave diamond necklace.”
WHAT FUELS YOUR BUSINESS DECISIONS?
The average decision process for a consumer making a purchase is estimated at 80% emotional and 20% rational, according to an annual customer loyalty report from Brand Keys.
It’s interesting to think that the car in your garage, or the shoes on your feet, could have ultimately been something you felt you wanted (80%), and then justified the need for later (20%). Brands, especially in the luxury market, depend on this ratio.
This realization brings us to your business planning as we begin 2015. What guides your business decisions as a data-fueled marketer: emotions, or rationale?
How do brands make decisions about how to operate, what customers to market to, where to locate stores, what marketing campaigns to do, and many more strategic plans? It needs to be much more in the “rational” category – but how do you do that as a data-fueled marketer?
As consumers, we are emotional creatures without even realizing it. That can be a habit we bring to other things in our lives as well, including decisions at work.
Since your customers still have emotional reasons for making a purchase or using a service, the only thing that should be emotional is your messaging to your customers; not your planning. Creating customer profiles and making decisions from them should never be solely a ‘gut feeling’ or only based on your professional instincts.
At the same time, we all know that in our work, over time we develop good instincts about what we do. We learn to trust our sense of what will work or won’t work in the market, or in the supply chain, or within product development – whatever it is you do. You can never ignore that, because no one can completely predict the future with total accuracy. You have to trust your experience and knowledge to lead you.
Turn the 80/20 ratio on its head, and instead focus 20% on emotional thinking and 80% on rational thinking. Make your brand’s business decisions and planning based on good data.
Who are your customers? Where do they live? What do they do and what are their preferences? Basing the answers to these questions only on what has worked in the past, or what you think your customers should want, will only lead to bad business decisions.
The first step, however, is to know that your customer data is valid and complete. Gartner estimates that 40% of failed business initiatives are due to bad data. Validate, correct, and enrich your customer data before you use it. Then as a truly data-fueled marketer, you can use the 20/80 ratio properly and steer your brand to a great 2015.
What do all marketers have in common? Marketing guru Seth Godin famously said that all marketers are storytellers. Stories, not features and benefits, sell.
Anyone who buys a slightly more expensive brand of laundry detergent because it’s “better” proves this. Godin wrote that if someone buys shoes because he or she wants to be associated with a brand that is “cool,” that brand successfully told its story to the right market.
A story has heroes we identify with. It has a conflict, which the heroes try to overcome. A good story’s DNA is an ordinary person in unusual circumstances. When is the last time you had an unusual result from your marketing campaigns? Perhaps a pay-per-click ad does poorly in your A/B testing. Or, there’s a high bounce rate from your latest email campaign.
Many marketers aren’t data scientists. But savvy marketers know they have to deal with big data, since it has become a hot topic central to many businesses. Marketers simply want to do their jobs better — and big data should be seen as an opportunity, not a hindrance.
When you have big data that could unlock great insight into your business, look beyond complexity and start with your strength as a marketer: Storytelling.
To get you started, I took the needs of marketers and applied them to these “who, what, why and how” principles from a recent article in the Harvard Business Review by the author of Big Data at Work, Tom Davenport:
Who is your hero? He or she is likely your prospective or existing customer.
What problem did the hero have? This is the action of the story. Here’s a real-life example from the Harvard Business Review article: Your hero visits your website, and adds items to the shopping cart. However, when you look at your analytics dashboard, you notice he or she never finishes the transaction.
Why do you care about the hero’s problem? Identifying with the hero is important for a story’s audience. It creates tension, and gives you and other stakeholders the incentive you need to dig into your data for a resolution.
How do you resolve the problem? Now you see what big data can do — it solves marketing problems and gives you better results. In the abandoned shopping cart example, the company found that people in Ireland were not checking out. The resolution came from the discovery that the check-out process asked for a postal code. Some areas of Ireland have no postal codes, so visitors would give up.
Remember it’s possible that the data itself is the problem. If you have bad contact data, you can’t reach your customers. Find the source of your bad data, and then you can return to your marketing efforts with confidence.
While big data may sound complicated or messy, if you have a storytelling path like this to take, you can find the motivation you need to uncover the powerful information required to better engage with your audience.
Engaging your audience starts with having accurate, validated information about your audience. Marketers can use data to fuel their campaigns and make better decisions on strategy and planning. Learn more about data quality management in this white paper.