What does the number 37 mean to you? It may mean something different to me. Enter the concept of Data Harmony.

How We Can Turn Piles of Raw Data Into Usable Knowledge? How do you know you’re comparing apples to apples? Scientists from every discipline have more data than ever, but it’s only as useful as the meaning behind it. Enter the concept of Data Harmony.

Every bit of information is only explained by the context in which it was gathered, and often in the context in which it is used. “There is no such thing as raw data,” says Bill Anderson of the School of Information at the University of Texas at Austin and associate editor of the CODATA Data Science Journal.

Take the number 37, Anderson says. Other than stating a numerical order, it means little on its own. But with some more information — 37 degrees Celsius, for instance — it can take on more meaning. Now give it some context: 37 degrees C is normal body temperature. Now 37 represents something useful, something a doctor or researcher could use, and it becomes a piece of knowledge that could comfort a patient or answer a question. (my own favorite 37 is NY Yankee, Casey Stengel, who famously said “All right everyone, line up alphabetically according to your height.”)

A scientist may think he’s gathering data for one experiment, but increasingly, the records will be used by many more people than just his research team — it will be parsed and re-parsed, dumped into databases and models, and scrutinized by several different teams in different disciplines. Without proper context and record-keeping, it can be difficult for others to use data in new ways, but better data husbandry can help. That way, scientists can be assured their data is congruent and they’re comparing apples to apples, or normal body temperature to high temperature, as it were.

Why Advertising? 15 billion connected Devices by 2015…that’s why.

At Sociocast we are applying Human Mobility theory to big unstructured data sets to make the data more useful. One of the first ways that we applying that power is through marketing and advertising services. Why?

Because it represents a huge challenge.

There is so much raw unstructured data available about web usage – there are currently 4 billion devices, like computers, phones, televisions, and even refrigerators connected to the internet, growing to 15 billion connected and addressable devices by 2015. Each one of these devices creates usage data as they perform their various tasks. Many of these devices are also “addressable” allowing for communications/messages to be directed back at selected devices. But which messages should go to which devices? How do we reduce the “signal to noise” ratio for incoming messages to a device. We see that as huge and interesting problem to solve…and a lucrative problem as well. Marketers, Advertisers, and Advertising Agencies covet technologies that can place their messages in front of relevant consumers.

Listen as Vice President of Intel’s Architecture Group Kirk Skaugen explains the massive scale of the internet to an audience at the Web 2.0 Summit in San Francisco.

Big Data is Empowering…

Big Data is empowering and not just to Marketers. Whether you are in the fashion industry trying to target a niche group of buyers or a politician trying to run a re-election campaign Big Data has a significant role to play.

Almost every industry or field out there can be empowered by Big Data. It’s no longer a question of if but when people start to utilize this. We have already seen people using similar data sets to predict revolutions, help sequence the human DNA code, predict the next pandemic, and understand how well children are learning across schools.

This emerging industry is not just a marketers dream it’s a tool for almost any researcher in any field. The future I see is not one where information is something that only the few have and are able to capitalize on. It will be one where information is transparent, open and free. The game will be how well people are able to, in real time, process and analyzes the unstructured data that is being produced in epic proportions these day.

Big Data has infinite possibilities and applications it’s time we get real about it.

Using Data to Deliver Advertising Campaign Nirvana

This short and simple post exemplifies three of the more important ways Data should be used to deliver advertising campaign nirvana. There is a common theme behind all successful display Campaigns. Truly great media/advertising happens when smart data strategies are applied across three different ideals: Finding the right customer, show them the right ad, measure and optimize in real time.

Find the right customer. Reaching the right audience is the most important part of any successful campaign. Marketers are saying the single biggest driver of increased display budgets today is the availability of better targeting technologies. This year, nearly all (97%) display advertisers will use audience targeting, according to DIGIDAY. From re-marketing strategies, to interest categories that show interested users your message, to social targeting strategies have all shown demonstrated positive results for marketers. Data is the single biggest contributor to finding the right people to show an advertising campaign.

Show the customers the right ad. Finding the right audience is the first step, but showing the customers the right creative is equally important? With display, marketers have the ability to connect with consumers to drive the results they care about. And an effective ad can come in any format. In fact, many brands today used Standard IAB display ad units but with compelling and attractive creative that are relevant with the people that see the advertisement. Effective uses of data helps marketers create the best advertisement, choose the right advertisement to put in front of the right person, or even deliver creative that are customized to the user.

Measure and optimize in real time. Advertisers have always benefited from transparency into exactly where ads run, their costs, and the right metrics to measure campaign performance, providing actionable insights to optimize their campaigns. By using data marketers can see a complete picture of the chain of impressions and clicks that drives campaign performance.

Leveraging Big Data will gain your Business 60% in Operating Margin

By leveraging Big Data your business will gain 60% in your operating margin, this according to a new study by McKinsey Global Institute. Successfully using Big Data will impact 5 different functions within your company like Supply Chain, Operations, New Business Models, Merchandising and Marketing.

Let’s focus on the last function, Marketing, an area where Sociocast data intelligence has much experience.

From the report: Some of the most effective ways to leverage data within the marketing flow include:

Cross-selling. State-of-the-art cross-selling uses all the data that can be known about a customer, including the customer’s demographics, purchase history, preferences, real-time locations, and other facts to increase the average purchase size. For example, Amazon.com employs collaborative filtering to generate “you might also want” prompts for each product bought or visited. At one point, Amazon reported that 30 percent of sales were due to its recommendation engine. Another example of this lever is using big data analyses to optimize in-store promotions that link complementary items and bundled products.

Location-based marketing. Location-based marketing relies on the growing adoption of smartphones and other personal location data-enabled mobile devices. It targets consumers who are close to stores or already in them. For instance, as a consumer approaches an apparel store, that store may send a special offer on a sweater to the customer’s smartphone. Nearly 50 percent of smartphone owners use or plan to use their phones for mobile shopping.

In-store behavior analysis. Analyzing data on in-store behavior can help improve store layout, product mix, and shelf positioning. Recent innovations have enabled retailers to track customers’ shopping patterns (e.g., footpath and time spent in different parts of a store), drawing real-time location data from smartphone applications, shopping cart transponders, or passively monitoring the location of mobile phones within a retail environment.

Customer micro-segmentation. The next marketing-related big data lever is customer micro-segmentation. Although this is a familiar idea in retail, big data has enabled tremendous innovation in recent years. The amount of data available for segmentation has exploded, and the increasing sophistication in analytic tools has enabled the division into ever more granular micro-segments—to the point at which some retailers can claim to be engaged in personalization, rather than simply segmentation. In addition to using traditional market-research data and data on historical purchases, retailers can now track and leverage data on the behavior of individual customers—including clickstream data from the Web. Retailers can now update this increasingly granular data in near real time to adjust to customer changes.

Sentiment analysis. Sentiment analysis leverages the voluminous streams of data generated by consumers in the various forms of social media to help inform a variety of business decisions. For example, retailers can use sentiment analysis to gauge the real-time response to marketing campaigns and adjust course accordingly. The evolving field of social media data analysis plays a key role because consumers are relying increasingly on peer sentiment and recommendations to make purchasing decisions. A variety of tools has emerged for the real-time monitoring and response to Web-based consumer behavior and choices.

Enhancing the multichannel consumer experience. Enhancing the multichannel experience for consumers can be a powerful driver of sales, customer satisfaction, and loyalty. Retailers can use big data to integrate promotions and pricing for shoppers seamlessly, whether those consumers are online, in-store, or perusing a catalog. Williams-Sonoma, for example, has integrated customer databases with information on some 60 million households, tracking such things as their income, housing values, and number of children. Targeted e-mails based on this information obtain ten to 18 times the response rate of e-mails that are not targeted, and the company is able to create different versions of its catalogs attuned to the behavior and preferences of different groups of customers.

Click here to download the entire report from McKinsey

Real-Time Audience Amplification

Enabling advertisers to purchase “lookalikes” is a key trend in online advertising these days. In fact, Quantcast has recently changed their website to highlight this opportunity! And more and more companies are being added to the mix.

This trend will continue to be accelerated by the unbundling of media from data and the rapid growth of real-time bidding (RTB) platforms. Overall, we are witnessing the ever-increasing value that audience data brings to the marketplace (great info-graphic below):

(JEGI Estimates)

It can be argued that (high-performance) audience data will be a key ingredient in the ad value chain, and this will continue to put pressures on other players in the space.

From a lookalike modeling perspective, advertisers can now amplify their audience buys through companies that find other users are related to their core audience in some way. Interestingly, Media6 does this by looking at the social graph for related users. Their premise is that “birds of a feather flock together”, and that homophily is a pervasive characteristic of friendship networks. Other companies look at profile-centric relations, like overlapping interests or segments, where similarity between users is based on correlation over some apriori characteristics or features. These techniques tend to be very monotone and binary, either a relationship exists or it doesn’t. In some cases, they look at reciprocity and frequency. However, they tend to ignore the temporal nature of behavior to the degree that (a) a consumer’s current state affects his/her future movements and (b) that a decay function should regulate the saliency of more recent events.

To increase the performance and the future potential of audience amplification, we hold to the following:

There are a myriad of ways in which consumers can be connected, both explicitly (defined friendship or interest) and implicitly (behaviorally). The context of the relationship matters. To Sociocast, context means interests and intents.

Amplification is the flip-side of influence and communication flow. In networks we use individuals as proxies for others, thereby increasing the size of particular characteristics. That is, there needs to be a social network centric view on how audiences are amplified, but this network needs to be contextual and influence based.

This is the Sociocast Hypergraph.