78% of British businesses rely on spreadsheets for key financial decisions

A report from F1F9 in conjunction with YouGov has revealed that UK businesses are facing ‘looming financial disasters’ through the misuse of spreadsheets within their organisations.

Due to the fact that spreadsheets are prone to human error, and in complex spreadsheets with thousands of formulas, errors can be very difficult to identify, the report highlights that billions of pounds are being put at risk daily due to spreadsheet inaccuracies.

Spreadsheets are predominantly used within businesses, according to the research, for the following reasons:

• Account preparation
• Pricing decisions
• Investment decisions
• Budgeting and forecasting

All of these financial processes are central to a business, hence why errors in spreadsheets can result in large losses or disastrous consequences for businesses.

Delft University analysed over 15,000 spreadsheets following Enron’s demise and found that 24% of all spreadsheet formulas contained errors.  755 spreadsheets had more than 100 errors in each spreadsheet.  This is a staggering figure, and shows the compounding effect that one error can have when the incorrect formula is utilised repeatedly.

Within UK businesses, 16% of companies have admitted to finding inaccurate information within their spreadsheets more than 10 times during 2014.  Who knows how many millions of pounds or margin percentages this could have affected for UK businesses?

The report, which was also featured in the Telegraph, cites a lack of standardisation as to how spreadsheets are used within a business, and the severe lack in training on spreadsheets as the main culprits.

Many businesses assume that all staff are competent in using Excel spreadsheets however competencies range drastically, and as spreadsheets are prone to being changed, saved and then sent out, it is very hard to instigate rigorous control over versions.

At C24, we regularly speak to customers who are struggling to manage hundreds of different spreadsheets that their businesses run on.  These spreadsheets exist in many different versions, and are saved by different users, with little knowledge over who changed what part of who is responsible for which spreadsheet.  This leads to inertia and confusion, and before long causes an results in an error that impacts the entire organisation.

We speak to our customers about instilling better data governance practices, through more controlled access to reporting; making it easy to view reports but putting in place standard processes to change and manipulate data to avoid errors.

Without a critical review of all of the different spreadsheets being used within your organisation, it is only a matter of time before data is lost, deleted or incorrect.  The report from F1F9 says that nearly one in five large businesses have suffered financial loss as a result of spreadsheet errors, and our increasing ability to share files quickly and easily will result in more widespread and less tangible user access across each organisation.  Without standard processes and best practices in place, this reliance on spreadsheets can quickly spiral out of control.

 

Image courtesy of R. Nial Bradshaw.

Big Data Analytics – Acquire, Grow and Retain Customers

Start of this year in Jan 2013, I had discussed in my blog Is Customer the King? In Retail, Analytics Say “Yes” about how Retail industry can leverage big data insights to optimize and personalize customer interactions, improve customer lifetime value, improve customer retention and satisfaction, improve accuracy and response to marketing campaigns. In an article by The Wall Street Journal last year, WSJ said that Big Data refers to the idea that companies can extract value from collecting, processing and analyzing vast quantities of data about their customer experience. Businesses that can get a better handle on these data will be more likely to outperform their competitors who do not. Kimberly Collins, Gartner Research vice-president stated that big data, will be the next major “disruptive technology” to affect the way businesses interact with customers.

In this new era of big data, companies need to create team of customer relationship management experts that can understand the psychology and buying behavior of their customers, apply their strong analytical skills to internal and external data and provide a personalized and individualized experience to their customers. In addition, companies will also need to apply futuristic insights using predictive and prescriptive models that will help steer innovation in the industry. Steve Jobs and his company created a need. Nobody knew they needed an iPhone or iPad but today it’s a need for millions of users. Companies need to reorient themselves to 21st century thinking, which unequivocally involves applying big data analytics to their customers (clients, employees and other stakeholders).

Today, companies have access to data unlike they have ever had before from internal systems and external media. This includes all structured data and unstructured data. And now companies have access to advanced modeling and visualization tools that can provide the insight to understand customers and even more powerfully, predict and prescribe behaviors.

Ironically – athough the retail industry is under tremendous pressure to stay competitive – the industry as a whole lags behind other industries in its use of big data analytics. A report from Ventana Research suggests that only 34% of retail companies are satisfied with the processes they use to create analytics. According to a recent infographic from marketing optimization company Monetate, 32% of retailers don’t know how much data their company store. And more than 75% don’t know how much of their data is unstructured data like call center notes, online forum comments and other information-rich customer data that can’t be analyzed in a database.

In one of the recent industry case study, CMO of a retail company convened a group of marketing and product development experts to analyze their leading competitor’s practices, and what they had found was the competitor had made massive investments in its ability to collect, integrate, and analyze data from each store and every sales unit and had used this ability to run myriad real-world experiments testing their hypothesis before implementing them in real world. At the same time, it had linked this information to suppliers’ databases, making it possible to adjust prices in real time, to reorder hot-selling items automatically, and to shift items from store to store easily. By constantly testing, bundling, synthesizing, and making information instantly available across the organization—from the store floor to the CFO’s office—the rival company had become a different, far nimbler type of business. What this customer had witnessed was the fierce market competition with effects of big data.

Retailers that are taking advantage of Big Data’s potential are reaping the rewards.  They’re able to use data to effectively reach consumers through the correct channels and with messages that resonate to a highly targeted audience.  Smart retailers are using advanced revenue attribution and customer-level response modeling to optimize their marketing spends Although there are obvious benefits, many retailers are surprisingly still failing to act on these trends. This delay is largely due to a dependence on siloed information, lack of executive involvement and a general trend among marketers to fail to understand analytics. Without advancing internal structures, gaining executive support or educating internally, jumping on these Big Data trends is nearly impossible.

The new IBM/Kantar Retail Global CPG Study of over 350 top CPG executives revealed that 74 percent of leading CPGs use data analytics to improve decision making in sales compared to just 37 percent of lower performing CPGs. By the same token, the new IBM study of 325 senior retail merchandising executives, conducted by IBM Center for Applied Insights in conjunction with Planet Retail, reports that 65 percent of leading retail merchandisers feel big data analytics is critical to their business compared to just 38 percent of other retail companies.

The two independently developed studies found interesting trends:

• Sixty-three percent of top retail merchandisers have the data they need to conduct meaningful analytics while 33 percent of other retailers do not.
• Thirty-seven percent of leading CPG companies make decisions predominately on data and sophisticated analytics versus 9 percent of lower performing CPG companies.
• Eighty-three percent of leading retail merchandisers are focusing more on the consumer, compared to just 47 percent of lower performing retailers.
• Forty-three percent of leading CPG company’s sales organizations are highly focused on the consumer versus 28 percent of others.
• Sixty-nine percent of the marketing departments of top retail merchandisers are highly collaborative vs. 39 percent of other retailers.
• Forty-four percent of leading CPG companies report a “robust partnership” between marketing, sales and IT versus only 20 percent of their competitors.

For retailers like Macys, the big data revolution is seen as a key competitive advantage that can bolster razor-thin margins, streamline operations and move more goods off shelves. Kroger CEO David Dillon has called big data analytics his “secret weapon” in fending off other grocery competitors. Retailers are moving quickly into big data, according to Jeff Kelly, lead big data analyst at Wikibon. Big retail chains such as Sears and Target have already invested heavily in reacting to market demand in real time, he said. That means goods can be priced dynamically as they become hot, or not. Similar products can be cross-sold within seconds to a customer paying at the cash register. Data analysis also allows for tighter control of inventory so items aren’t overstocked.

To stay competitive, retailers must understand not only current consumer behavior, but must also be able to predict future consumer behavior. Accurate prediction and an understanding of customer behavior can help retailers keep customers, improve sales, and extend the relationship with their customers. In addition to standard business analytics, retailers need to perform churn analysis to estimate the number of customers in danger of being lost, market analysis to show how customers are distributed between high and low value segments, and market basket analysis to determine those products that customers are more likely to buy together.

Retail Banks such as Wells Fargo has gathered electronic data on its customers for decades, but it is only in the past few years that the fourth-largest U.S. bank has learned how to put all that information to work. JPMorgan Chase, Bank of America, Citigroup and Capital One are also taking advantage of the big data opportunity. Big banks are embracing data analysis as a means to pinpoint customer preferences and, as a result, also uncover incremental sources of revenue in a period of stalled revenue growth. Smarter banks will increasingly invest in customer analytics to gain new customer insights and effectively segment their clients. This will help them determine pricing, new products and services, the right customer approaches and marketing methods, which channels customers are most likely to use and how likely customers are to change providers or have more than one provider.

Banks, Retailers and CPG companies that are applying big data analytics to better understand consumers and adjust to their needs are outperforming their competitors who don’t, according to a pair of studies released by IBM. Advanced Big Data analytical applications leverage a range of techniques to enable deeper dives into customer data, as well as layering this customer data with sales and product information to help retailers segment and market to customers in the ways they find most compelling and relevant. Historically, retailers have only scratched the surface when it comes to making use of the piles of customer data they already possess. Add social media sentiment to the mix, and they can access a virtual treasure trove of insights into customer behaviors and intentions. The timing couldn’t be better, because these days’ consumers award their tightly held dollars to retailers that best cater to their need for customized offers and better value. The ability to offer just what customers want, when they want it, in the way they want to buy it requires robust customer analytics. The opportunity is now: It’s critical that retailers step up their customer analytics capabilities as they transition to an all-channel approach to business.

http://thebigdatainstitute.wordpress.com/2013/09/20/big-data-analytics-acquire-grow-and-retain-customers/

 

Infographic: The 10 Key Findings returns on Big Data

http://sites.tcs.com/big-data-study/big-data-infographic-3/

10 Greatest Challenges Preventing Businesses From Capitalizing On Big Data – Infographic

A research from Tata Consultancy Services among 1.217 companies about how companies invest in big data and derive results from it also revealed the 10 greatest challenges businesses face when implementing a big data strategy. The research revealed that big data is clearly paying off for some companies, and big time in some cases. There are however also a lot of companies that face difficult challenges moving ahead with big data. The below infographic lists the 10 most important challenges. Interesting fact is that most of the challenges are cultural challenges and not so much the technical difficulties of big data.

Thanks to Big Data Startups

Understanding Your Business With Descriptive, Predictive And Prescriptive Analytics

Great blog from http://www.bigdata-startups.com/understanding-business-descriptive-predictive-prescriptive-analytics/

Companies have long been involved in the analysis of how a company performed over time. As the history of big datashows, already for many years we try to understand how the organisations or the world around us behaves by analysing the available data. In the past this used to be merely descriptive analytics. This answers the question “what happened in the past with the business?” With the availability of big data we entered the new area of predictive analytics, which focuses on answering the question: “what is probably going to happen in the future?” However, the real advantage of analytics comes with the final stage of analytics: prescriptive analytics. This type of analytics tries to answer the question: “Now what?” or “so what?” It tries to give a recommendation for key decisions based on future outcomes. What’s the difference between these three ‘…tives’ and how do they affect your organisation?

First of all, these three types of analytics should co-exist. One is not better than the other, they are just different, but all of them are necessary to obtain a complete overview of your organisation. In fact they are more consecutive and all of them contribute to the objective of improved decision-making.

Descriptive analytics is about the past

Descriptive analytics helps organisations understand what happened in the past. The past in this context can be from one minute ago to a few years back. Descriptive analytics help to understand the relationship between customers and products and the objective is to gain an understanding of what approach to take in the future: learn from past behaviour to influence future outcomes.

Common examples of descriptive analytics are management reports providing information regarding sales, customers, operations, finance and to find correlations between the various variables. Netflix for example uses descriptive analytics to find correlations among different movies that subscribers rent and to improve their recommendation engine they used historic sales and customer data.

Descriptive analysis is therefore an important source to determine what to do next and with predictive analytics such data can be turned into information regarding the likely future outcome of an event.

Predictive analytics is about the future

Predictive analytics provides organisations with actionable insights based on data. It provides an estimation regarding the likelihood of a future outcome. In order to do this, a variety of techniques are used, such as machine learning, data mining, modelling and game theory. Predictive analytics can for example help to identify any risks or opportunities in the future.

Predictive analytics can be used in all departments, from predicting customer behaviour in sales and marketing, to forecasting demand for operations or determining risk profiles for finance. A very well-know application of predictive analytics is credit scoring used by financial services to determine the likelihood of customers making future credit payments on time. Determining such a risk profile requires a vast amount of data, including pubic and social data.

Another example of predictive analytics is forecasting the demand for a certain region or customer segment and to adjust production based on the forecast. This is quite a common analysis and it takes into account many different data sets, from open, weather, data for example, to sales data and social media data.

Historical and transactional data are used to identify patterns and statistical models and algorithms are used to capture relationships in various data sets. Predictive analytics has really taken of in the big data era and there are many tools available for organisations to predict future outcomes. With predictive analytics it is important to have as much data as possible. More data means better predictions.

Prescriptive analytics provides advice based on predictions

Prescriptive analytics is the final stage in understanding your business, but it is still in its infancy. In this year’s Hype Cycle of Emerging Technologies by Gartner, prescriptive analytics was mentioned as an “Innovation Trigger” that takes another 5-10 years to reach the plateau of productivity. Prescriptive analytics not only foresees what will happen and when it will happen, but also why it will happen and provides recommendations how to act upon it in order to take advantage of the predictions.

It uses a combination of many different techniques and tools such asmathematical sciences, business rule algorithms, machine learning and computational modelling techniques as well as many different data sets ranging from historical and transactional data to public and social data sets. Prescriptive analytics tries to see what the effect of future decisions will be in order to adjust the decisions before they are actually made. This will improve decision-making a lot as future outcomes are taken into consideration in the prediction.

As prescriptive analytics is so new, it is only around since 2003, and so complex there are very little best practices on the market. Only 3% of the companies use this technique, and still with a lot of errors in it. One of the best examples is the self-driving car of Google that makes decisions based on various predictions and future outcomes. These cars need to anticipate on what’s coming and what the effect of a possible decision will be before they make that decision in order to prevent an accident.

Prescriptive analytics could have a very large impact on business and how decisions are made and it can impact any industry and any organisation and help them becoming more effective and efficient. For example, prescriptive analytics can optimize your scheduling, production, inventory and supply chain design to deliver the right products in the right amount in the most optimized way for the right customers on time.

With descriptive, predictive and prescriptive analytics understanding your business will become easier and better-informed decisions can be made that take into account future outcomes. There are not many big data startups currently that can take advantage of prescriptive analytics, but one well-know big data company is Ayata. Prescriptive analytics is the future and IBM already called it “the final phase” in business analytics in below video.

10 Tips For A Successful Predictive Analytics Project

in Blog, Organisational advice / by Koen Havlik

In his book “Predictive Analytics” Eric Siegel calls predictive analytics “the power to predict who will click, buy, lie, or die”. You can apply this to both people and machines.

With the increase of data-generating devices, sensors, and software, the amount of data in organizations is growing exponentially. But more data doesn’t automatically translate into information for man and machine until you can extract actionable information. Unfortunately, the capacity of most organizations to analyze this data has not increased at the same pace as the available data. In order to replace gut feeling based on experience with a data-driven approach we need to enhance this capacity by introducing predictive analytics.

Predictive analytics trains a computer model to automatically learn from large amounts of data to find the complex, hidden patterns that can optimize your inventory; predict fraud, maintenance, or customer retention; recommend the products that customers actually need; or even diagnose Alzheimer’s disease. Predictive analytics has gotten a lot of attention recently through the success ofKaggle. Kaggle is a web platform where organizations like General Electric, Pfizer and Facebook host predictive modeling competitions in which data scientists can win up to $3M.

Based on our experience with customer projects and Kaggle competitions, we atAlgoritmica want to share some of the misunderstandings on this topic, and provide some lessons and takeaways that will benefit your predictive analytics projects.

1. Start with the end in mind

Big data might be the new oil, but don’t take this analogy too far. Domain knowledge (i.e. understanding your own product and customers) is essential at the beginning in establishing boundary conditions from business and IT. Certain questions need to be answered first, like: How are we going to make money with this? How will the model be deployed? What does the data look like? How fast do the predictions need to be? How often do we need to update the model? How will the new output be used? How will we introduce change in the process?

2. No treasure hunting

Treasure hunting is rarely useful for predictive analytics. Your company has to identify, with some help, which processes are worth optimizing. Once the relevant data sets and process are identified, the business opportunity can be reduced to a data problem. Handing over a data set in the hopes that someone can find a pot of gold is not the way to go.

3. Don’t get lost in translation

The data-driven approach is garnering a lot of traction and success. In most companies however, processes are still heavily designed and optimized around people. When introducing predictive analytics into a process, you’re changing it. The adoption and best result can only be achieved if everyone is on board and there’s a plan to introduce this change. The lack of a data-driven culture is the biggest hurdle for most predictive analytics projects.

4. Use modeling experts

Domain knowledge is important in every predictive analytics project. However, once the project is reduced to a data problem you need people that can do modeling really well. Kaggle shows that people that are proficient at predictive modeling can solve a problem for an insurance company and electronics company equally well. For this phase, focus on analytical ability not industry knowledge.

5. Design your data collection

Data is collected because decisions were made on where to put sensors, what sensor frequency to set, what data to aggregate, or how to design an app. Most decisions were made without any consideration of how to analyze that data. For some applications there’s a need for longitudinal data or data aggregated with a certain frequency. In other cases any missing data represents information that you want to include in your model. Imputing these cases can destroy that information. To avoid delays and design bias, it’s good practice to involve an analyst in this process as early as possible.

6. Compete on analytics

There was a time when there wasn’t a lot of data and analytics basically consisted of pie charts for management. Occasionally, someone would query a database. Today, most companies are becoming a software and data company. Look at Google and Amazon. They are leaders in their respective verticals. Google’s algorithms are in direct competition with Microsoft’s. Google is creating whole new business models by leveraging their data using smart algorithms. To be the Google of your industry, compete on your analytical capability. In this light, human capital is crucial. In order to attract the right talent, establishing the right ‘data centric’ culture is key.

7. Presentation matters

A well-published result is that the presentation is just as important as predictive accuracy for recommender systems. Sometimes the output of a predictive model forms the input of a larger optimization chain. This doesn’t mean that supervised learning should stop there. You can test how to present different thresholds or colors to different groups. Spend the effort to create a visually compelling story out of your data insights.

8. Beware of data leakage

Data leakage is the phenomenon where you inadvertently design your predictive modeling pipeline to include information about the future that you wouldn’t normally have. You’ll get very good results when back-testing your model, but these good results won’t be reproducible in the real world. This type of mistake can sometimes be very subtle, but it will have a major impact on the usefulness of your results.

9. Take model training out of the database

Most analytics within companies takes place in the land of SQL. Traditional relational databases and SQL are great for storing, managing, and performing simple analytics jobs. Predictive modeling automatically trains a model by looking at examples. The algorithms used are often both data and computation intensive. The latter makes databases too slow to do training. SQL is also not expressive enough for predictive modeling. Hadoop offers a solution if the work can be divided into pieces and a lot of data is needed for training. Hadoop limits the complexity of the algorithms you can use and the magic still happens on-disc. Rarely will you need to use all of the raw data to distill a model, though. With smart sampling techniques you can end up with a small data set without losing hardly any predictive accuracy. Data sets on Kaggle never exceed the order of a gigabyte and most modeling in the world happens in-memory.

10. Privacy is no afterthought

You have the potential to upset your customers and the media if you design a predictive analytics project without considering its privacy impact. People are put off by the idea of a company magically ‘knowing’ something about their private lives, even if that knowledge is obtained using only public data. On the other hand, customers want you to make their data work for them and playing it too safe will stifle your innovation. Each company’s situation is unique and requires special attention to how knowledge of the customer will be perceived.

You can see predictive analytics as the special sauce that adds value to your data and gives you a competitive edge over the competition. There are great opportunities for organizations at the intersection of data and algorithms. It’s an exciting time to be working with data and predictive analytics. Good luck with your predictive analytics endeavors.

For more information and to visit a great blog please visit http://www.bigdata-startups.com/10-tips-successful-predictive-analytics-project/

Where Technology Can Take Healthcare

Great thought-provoking video about technology and its potential in health care

Big Data Promotes a Culture of Data-Informed Decision Making and Adaptive Marketing – Antony Young-Mindshare

Big Data is quickly being catapulted to the top of Marketing’s agenda, but it remains a challenge for many companies in preparing for this shift. According to a survey conducted by IBM, less than half of CMO’s feel prepared to cope with this increasing amount of marketing data over the next 5 years, with the data explosion cited as their #1 headache. The problem isn’t obtaining data, it’s figuring out how to turn it into marketing magic. I’m seeing a growing list of exceptional cases of marketer’s shifting their organizations to adopt a higher level of data-informed decision making, often with astonishing results.

It’s not so much big data, but smart data used at scale

Last week, I had dinner with Joe Rospars, founding partner at Blue State Digital, who served as Obama’s Chief Digital Strategist for his 2008 and 2012 campaigns, and asked him about big data. He responded, their approach “wasn’t so much big data, but smart data used at scale.” To win this election, they needed to get very granular in their targeting. By extracting voter files and collecting information via the tens of thousands of polling calls made to homes every night, they were able to identify by household individual voter likelihood, and then determine the communications they needed to deliver.

The Obama campaign expertly targeted via online advertising, email, door to door and phone canvassing very personalized messaging. They cleverly extended this strategy via social media. Nearly a million supporters that ‘liked’ the Obama 2012 page also allowed access to their profile data via Facebook Connect. This enabled Obama’s people to identify their Facebook friends in battleground States, cross tabulate with their own databases, which they then asked supporters to email or even personally call their friends that fit likely Obama voter profiles, to remind them to register or vote early.

Data is the engine for Adaptive Marketing

Data is allowing brands to move quicker and more decisively to gain a market advantage by dynamically informing their messaging and media.

Samsung a big investor in data, worked with insights firm Networked Insights, to use real-time social listening to help them keep a finger on the pulse of consumer sentiment and adjust their communications to capitalize on the web discussion about brands.

Within a couple of hours of Apple’s Tim Cook revealing their iPhone 5, Samsung reading the reaction in social channels, drafted new print, digital, and TV ads. The following week as the iPhone hit the stores, they aired TV ads mocking Apple customers queuing up for the new phone and some of its less flattering features. The commercial was a hit, and received more than 70 million views online.

They also used social listening as a real time guide to evaluate how effective their ads were with consumers by measuring what people are saying about them and what effect they’ve having on competitors’ brands. Stressing the importance of data in informing their marketing, Brian Wallace, the former VP of Marketing at Samsung, (who recently moved to Motorola to a global marketing role) said, “The data guys lead these conversations. Not the creative guys. Not the sale guys. And it’s not just analytics — it’s analysis.” He added, “[data] does not crush the art of advertising. It simply informs it — and ultimately improves it.” Samsung’s shift to a strategy of employing social data at the center was one of the key factors that assisted them to move from the number 4 mobile device manufacturer to pass the mighty Apple.

Creating a more personalized customer experience

I’m seeing a focus on data enabling marketers to create smarter, more engaged customer experiences.

I recently chaired a panel which included Sandra Zoratti, co-author of the book Precision Marketing. She cited Caesar’s Entertainment as a marketer that centralized data to better formulate its approach to marketing. They identified 0.15% of their customers that contributed to 12% of their casino revenues. This led to them employing Good Luck Ambassadors to monitor these customers. If they weren’t having a good night on the tables, they offered complimentary tickets to a show or dinner based on their known preferences to ensure they left their casinos with a positive experience.

Building a fluid organization that can capitalize on the data

Shifting to a fast moving data marketing organization isn’t just about software and strategy. It requires a shift in how the agency and clients teams work.

The Obama campaign quadrupled their data team from the previous election campaign, adding data technologists, behavioral scientists and mathematicians to crunch the data and help interpret them into actionable marketing insights.

According to Rospars, to improve speed of activation, they established a persona playbook on how the brand should speak, to allow them to delegate decision making down.

Personally, I love this shift to data-informed decision making. It is creating more adaptive, more relevant and more commercial marketing programs. We are barely scratching the surface, but it’s clear that going forward, data will be an enabler of more potent marketing.

Thanks to Brand Media Strategy

Big data and the future of health care

Humanizing Big Data

HUMAN FACE OF BIG DATA
Some App Results

In less than two months, more than 3 million share and compare questions have been answered, in more than 100 countries, through “The Human Face of Big Data” smartphone survey app.

By collating and analyzing these 3 million+ responses we gained some insightful conclusions related to the attitudes and approaches to life from men and women, young and old, all over the world. Here are just a few of the most interesting findings…

In asking the question “What is most important for good health – diet, exercise, environment or genes?” we discovered that Americans are more likely to believe that good health is in their hands, choosing diet and exercise, while Europeans seem to believe their health is predetermined or out of their control, predominantly selecting either genes or environment

In response to the question “What do you do to help cope with stress most?” we learned that as we get older work and prayer tend to replace friends or the arts as our primary means of stress relief, indicating that older generations prefer to bury themselves in work or deal with stress on their own, rather than by seeking entertainment or distraction
When asked “If I could alter the DNA of my unborn child I would improve their: lifespan, intelligence, immunity or appearance” the findings showed that Americans are most concerned about their children’s education and job prospects, while Europeans worry most about their children’s health, perhaps reflecting the current unemployment rates and standards of available healthcare in these two nations.

While these findings give only a brief snapshot of the world around us, the goal of this app was to encourage people to embrace the subject of big data and to consider its potential to help us shape and change our daily lives. Hundreds of striking examples of ways this is already happening are illustrated in the photographs, infographics and essays within the Human Face of Big Data book.

The anonymous data complied from the app will be made available for educators, data scientists, researchers and the general public to access as a valuable research tool, in order to conduct further in-depth sifting and sorting of the results, that may one day be considered an invaluable snapshot of human history.