The following is a guest contributed post from Dr. Mona Yousry, Chief Data Scientist, Sabio Mobile.

The amount of data that people generate simply by living their lives can be used to tell stories filled with context and insights. This information, combined with digital data, enriches our understanding of consumers and, by extension, the physical world. Big data, mobile phones, social media and behavior analytics each play a vital role in teasing out this understanding; as a result, they allow companies to gain a competitive edge, and are the future of business intelligence.

But what does data intelligence really mean? To put it simply, data intelligence is the ability to identify and analyze relationships, and to visualize the data associated with them. That is to say, business intelligence unifies sources of unstructured data together with up-to-date visualizations in order to access information on trends and experiences quickly.

Most people will already be familiar with the term “big data”. But what many people don’t realize is that the number of data sources that big data can draw from is increasing at a rapid clip, which in turn affects the quality of the insights that business applications will be able to generate. Having more – and better – data also allows for better AI, because the more high-quality data you have to train your systems, the better your AI algorithms will be.

Data opportunities and challenges

Implementing big data successfully is often a challenge for businesses, in part because of the oft-cited “Vs” of big data. Gartner’s 3Vs, variety, velocity, and volume, have traditionally been the way that most practitioners understand big data.

However, in some ways, the traditional way of thinking about big data ignores the larger picture – that is to say, how to interpret such massive amounts of data. As a result, we need to look beyond the original Vs, to what I call the 6V Wheel. In addition to variety, velocity, and volume, organizations also need to be thinking about validity, verification, and visualization.

Let’s start with the traditional Vs. Variety refers to the fact that not all data is created equally. No longer is it a set of observations and measurements that we can apply regression or time series to; instead, we think of data as a set of patterns that can drive amazing insights, particularly when made up of information from multiple sources. In the near past, a retailer might have relied on information drawn from e-commerce sites and social media – but now, with advances in location technology, one can look at data gathered from physical retail locations and points of sale, and subsequently gain more insight into shopping trends and behaviors. As mobile apps and devices have become smarter and more responsive to their environments, the variety of data available has exponentially increased. Most of this information is already in the hands of organizations, yet few are using it to drive value.

Velocity is a characteristic of data, frequently equated to real-time analytics. It is the rate of changes – about linking data sets that are coming with different speeds and bursts of activities. When the spatio-temporal relationship between two or more data sets changes, then everything else changes, even the definition of a “data event.”

Volume refers simply to the amount of data. Keep in mind that simply having more information than your competitor is not a guarantor of success; rather, it comes down to how effectively that information is being used to drive business performance.

Now for the three new Vs:

Validity refers to a question that our AI and machine learning intelligence is continuously addressing: how effective are these methods? For us, it is the accuracy and  how pervasive is our model. We look at many combinations to relate interactions, habits and behaviors, as only then can we start to understand audience needs (with their privacy still intact). For businesses, the power  is in the insights that allow them to measure performance, create value, drive awareness for new offerings, improve loyalty, increase sales, and countless other actions that are beneficial for their organizations. All of this is a validation of big data.

Verification is the process of establishing the accuracy of something; in other words, the establishment by empirical means of the validity of a proposition. Can a business verify that a customer responds well to a specific notification? How do you know if a particular ad was the reason someone visited a store? How good are your campaigns at driving attribution? How relevant are your ads to people? With AI, and the new types of data that are being collected, all of these questions can now be answered.

Finally, Visualization refers to how we dynamically see the data – and the intelligent decisions we make as a result of understanding those insights. Understanding data can be a complicated matter, and it needs to be easily digestible so that marketers and management can quickly assess what is and isn’t working, and make the right decisions.

While Gartner’s 3Vs provide a good baseline, this new 6Vs Vision creates a more holistic and comprehensive way to view data. This is especially vital as the importance of app science, geolocation, and mobile continue to grow exponentially, and as big data continues to create value for organizations.

The post 3Vs are Good, But 6Vs Are Better: A New Way of Understanding Big Data appeared first on Mobile Marketing Watch.

According to a statement emailed to MMW, leveraging advanced algorithms and machine learning, the DDA modeling and MTA scoring extend the marketing intelligence technology behind Journey by CAKE, an enterprise cloud-based platform that collects and analyzes data in real-time.

With the new Journey capabilities, marketers gain a more holistic view of the customer journey by evaluating a broader range of key data points to determine true marketing performance and deliver maximum return on advertising spend.

MTA is a method marketers use to understand the importance their digital marketing efforts have on the customer journey by assigning fractional value to individual touchpoints along the path to conversion. Enabling further expansion beyond pre-defined, rules-based attribution, CAKE’s DDA algorithm produces an MTA score for each touchpoint. The MTA scores feed into the Journey Insights dashboard that provides advanced visualizations to illustrate the efficacy of marketing campaigns and channels.

“Our goal is to redefine the boundaries of innovation and expand the possibilities of marketing intelligence technology,” said Santi Pierini, CAKE President and Chief Operating Officer of Accelerize. “Marketers need to move away from making decisions based on attribution models that are fundamentally flawed and lack clarity of the entire customer journey. Distinct from other solutions, Journey delivers insights with a superior level of integrity and allows marketers to make decisions derived from more complete, accurate, real-time data.”

The post CAKE by Accelerize Rolls Out Data-Driven Attribution Technology appeared first on Mobile Marketing Watch.

These apps, which openly acknowledge requesting various types of user data for advertising purposes, were found in more than 70% of enterprise environments. However, this is just the tip of the iceberg as there is a much larger number of apps lurking in the enterprise that collect user data such as calendar, Bluetooth and photos—and are not upfront about their intentions.

Of the more than 2 million iOS apps scanned by Appthority, the 24,000 flagged were just the ones that openly ask users for access permission to deeper device functionality for advertising purposes. In fact, over 98% of enterprises have apps in their environments that display ads. These results suggest that data leakage from ad-supported apps is a much bigger problem than most enterprises realize.

“As a pioneer in the mobile security space, Appthority has long known that advertising within apps like Facebook is common and comes with risks, such as the leaking of users’ Personally Identifiable Information (PII),” said Seth Hardy at Appthority. “However, the Cambridge Analytica exposure made us wonder how many of these apps are directly accessing and using personal information for advertising.”

The reality is that apps that access data for advertising pose additional risks to enterprises and users compared to apps that access data solely for in-app functions. For example, ad-supported apps typically include third-party advertising libraries, which are not managed by the original app that employees trust and install. Therefore, information accessed by these advertising providers is usually not monitored or regulated by the original apps, users or by enterprises.

What’s more, ad-supported apps often access data without any real functional justification. When accessing data, mobile apps have to state a reason for wanting the access. Accessing data for in-app functions is a justifiable reason, but the iOS apps found were accessing data specifically for advertising purposes. This practice poses an important question about data access in enterprise environments: does the benefit of using the app outweigh the cost of losing control of user or enterprise data?

Because the app economy is heavily supported by ads, eliminating all apps that collect and use data for advertising from a device or enterprise environment is often not possible. But, the report also provides recommendations to users and enterprises to safeguard their data including, among others, being selective about granting permission to access data and deploying a Mobile Threat Defense solution to ensure visibility into and remediation of ad-supported and other app risks.

Register to download the full report here.

The post New Appthority Report Finds Tens of Thousands of Ad-Supported Apps Are Collecting Excessive Data appeared first on Mobile Marketing Watch.

Tealium AudienceStream is the market-leading Customer Data Platform (CDP), combining robust audience management and data enrichment capabilities, resulting in unified customer profiles and the ability to take immediate, relevant action.

Talpa Network’s use of Tealium’s AudienceStream is a key part in standardising their back-end technology platform and data layer, enabling them to scale into any market in a short amount of time. Talpa Network will also have the capacity to convert mass media into one-to-one communications with its viewers and listeners across the globe by improving the effectiveness of content personalisation, direct marketing campaigns, and targeted advertising.

The company has defined a clear strategy and an infrastructure that continues to incorporate Tealium’s iQ tag management, and now AudienceStream.

Hylke Sprangers, CTO at Talpa Network, commented: “The main objective of our Smart Data Platform is to engage our consumers via deep content personalisation and to improve direct marketing campaigns and targeted advertising for our commercial partners. Working with Tealium gives us the ability to create a single 360 customer view across all our brands and channels. As a continued customer of Tealium, we’re very happy with the partnership and look forward to the future.”

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The following is a guest contributed post by Keith Petri, Screen6, Chief Strategy Officer

At Screen6, we have long believed that the solutions in place for cross-device tracking require a fresh set of eyes. It is important that the entire industry that has become reliant on cross-device identification, from buyers to sellers to data processors, understand exactly how any cross-device vendor’s graph is built. This will assure that they can have total confidence in their claims regarding the strength of connections across consumers and devices.

We also believe that marketers need to keep asking questions about the effectiveness of the solutions in market and the quality of the data. Question your current cross-device vendor and question new potential partners, but do not limit yourself to predetermined questions with predefined acceptable answers. In short, asking the right questions will result in better cross-device solutions.

Match rates measure the overall effectiveness of the ID synchronization process – determining that cookie A within a pool belongs to the same person as cookie B from another pool. High match rates between cookies suggest that a particular graph will be more effective and provide access to a larger online audience than a graph with low match rates. Today, match rates between asynchronous cookie pools are what the industry relies on to determine the effectiveness of most cross-device ID vendors. Not all vendors work this way, but some providers still depend on syncing disparate cookie pools.

Marketers use these match rates as a method of validation and comparison among these types of master device graph providers. To truly understand match rates, marketers need to be asking about the types of data that go into a graph, how the match rate claim is calculated and who is vetting the claim. Most importantly we need to consider how fresh the data is and how frequently a graph is updated and sent to clients.

Currently, most cross-device providers build and send their ID graphs to clients once every seven to ten days. This timeline and overall approach to providing actionable data is a core issue that impacts the match rates in cross-device identification, if they define match rates correctly. How big this problem of data freshness and cookie decay rates poses is something we have unique insight into.

We studied trillions of server-to-server events to conduct this analysis. Every day we process each of our clients’ datasets for not only the past 24 hours, but looking back over a variable amount of time. As such, we can identify the cookie (ID) depreciation rate. While some vendors have cookies with significantly higher longevity in lifespan (i.e. >14 days), our analysis shows that the average half-life of cookies across the billions we see daily and trillions we see monthly is 6 to 8 days.

With the average half-life sitting around 1-week, cookies depreciate at the same rate which the average cross-device vendor refreshes its graph associations. If you see a new user on Monday, and a cross-device vendor who only refreshes its graph weekly, on Sundays, returns an updated mapping file the next day (Monday, one-week later) – then what percentage of the graph is actually viable to be leveraged for targeting?

Marketers today need to think both in real-time and in 24-hour increments, reconsidering how often they review and manage data and their campaigns. The chain of old and bad data will have a negative impact on campaign results. We encourage clients to look into their ID graphs and examine the percentage of the graph which matches not just to inventory which they have seen, but inventory which they see after the delivery date of the graph. This is an analysis that most platforms find surprising.

Advertising technology prides itself on advancing the field; everything from profiling and segment creation to ad serving and tracking. Our job is to create new solutions that meet the new demands of the market and the job of marketers is to ask the questions that cut through false claims about bad data, old data and match rates and dig into the core attributes of a cross-device graph.

The post Data Freshness is at the Core of Cross-Device Accuracy appeared first on Mobile Marketing Watch.

Companies are shifting to hybrid and multi-Cloud architectures to improve IT efficiencies, access state-of-the-art applications and technology, and increase flexibility to meet changing business and user demands.

A new report from the Business Performance Innovation (BPI) Network underscores that despite significant progress, companies are early in this transition to the Cloud and face major challenges, including the need for new Cloud skills, concerns about data geo-sovereignty and corporate policy compliance, security and the challenges of implementing a flexible multi-Cloud strategy where data can be moved freely to the Cloud of choice.

The report, entitled “Gain the Ability for Cloud Agility: Assessing Enterprise Capacity to Embrace a Multi-Cloud Strategy,” incorporates in-depth interviews with top IT executives from corporations like Viacom, The Coca Cola Company, DHL, Embraer and others. DBM Cloud Systems, a rapidly emerging leader in intelligent management of Cloud data, sponsored the study.

“Most companies are moving to multi-Cloud and hybrid-Cloud environments that mix and match private and public Clouds with on-premise systems depending on specific needs,” said Dave Murray, head of thought leadership for the BPI Network. “But these mixed models come with challenges. Our study underscores the need for enterprises to improve their agility to move data across Clouds to increase cost-efficiencies, drive better data analytics and performance, and ensure compliance.”

To download the study, click here.

The post Report: Shift to Multi-Cloud Architectures Requires New Data Management Approaches appeared first on Mobile Marketing Watch.

According to teh company, reporting and analytics continue to be top priorities for B2B marketers evaluating marketing automation vendors.

In a recent study Act-On commissioned with Econsultancy, more than half of the marketers surveyed cited data management as a key challenge.

When using Engagement Insights in conjunction with Google Sheets, companies never have to worry about version control or outdated CSV files. The live feed from Act-On to Google Sheets ensures that all key stakeholders always have the most up-to-date campaign performance data at their fingertips.

“Engagement Insights makes it easy for me, my team, and other Education at Work executives to evaluate our marketing efforts and measure ROI,” said Tanya Gantzer, Senior Vice President of Client, University and Marketing Development at Education at Work. “The reports help us better understand our audience and identify ways we can deliver greater value.”

To learn more about Engagement Insights, click here.

The post First Look: Act-On Aims to Make Data-Driven Marketing Easy appeared first on Mobile Marketing Watch.

So how does it work?

By aggregating deterministic data from app publishers and validated third-party data from the extensive list of adsquare’s data partners, advertisers are now able to tap accurate audience data at scale.

In addition to its open and private marketplaces, the company says that this strategic move puts adsquare in the role of a curator and trusted source. The audience segments are available via adsquare’s self-service Audience Management Platform as well as directly through leading DSPs and DMPs.

For Tom Laband, CEO and Co-Founder at adsquare, the launch is an important strategic move.

“With an abundance of data in the industry being scattered through data silos, it’s becoming increasingly difficult for advertisers to employ 1-to-1, people-based advertising at scale,” Laband says. “The adsquare Data Alliance stands for both accurate data and reach and gives advertisers the freedom of choice.”

The post adsquare Launches New Data Alliance appeared first on Mobile Marketing Watch.

Speaking on behalf of the location intelligence innovator, Dave Heinzinger — Vice President of Communications at inMarket — shared the details in a company blog entry.

“At inMarket, we’ve harnessed the power of location data in advertising since 2010,” Heinzinger writes. “From our early in-store campaigns to the sophisticated, online-to-offline programs we run today, one thing has remained consistent: We’ve always managed those campaigns in-house.”

A pioneer long at the forefront of delivering contextual content to consumers in the retail space, inMarket now contends that the demand for accuracy in programmatic “has become too great.”

“Today, we’re extending our hyper-accurate Audiences segmenting product to programmatic trading desks,” Heinzinger explains. “Brands and agencies can now access our walled garden of first-party location data previously only accessible via high-ROI managed campaigns.”

So how does it work?

Audiences will provide trading desks with unprecedented accuracy via our location database, which uses full cycle GPS, WiFi and the world’s largest beacon platform in a waterfall to continually validate data points. Opt-in consumer location data paired with our vast point-of-interest marketing platform reveals consumers’ journeys between POIs — while personas derived from machine learning across billions of consumers data points make targeting more precise than ever.

“Audiences also enables new first party location precision that dramatically reduces crossover contamination in dense markets — for example, when a laundromat is next store to a restaurant,” Heinzinger concludes. “This unlocks superior targeting for brands. The same first party dataset enables inMarket to predict major business trends across America — like customer loyalty — via inMarket inSights.”

Anyone wanting to know more is encouraged to contact inMarket here.

The post inMarket Location Data Hits Programmatic Trading Desks appeared first on Mobile Marketing Watch.

Drawbridge announced this week the addition of unique new data segments to its cross-device advertising platform from PushSpring, the first and largest independent app-based mobile audience platform.

“PushSpring’s data is derived solely from mobile apps, delivering truly differentiated audience inputs and segments for Drawbridge’s self-service and managed-service platform customers,” according to the company.

In fact — because consumers now spend five hours per day or more on mobile devices — the mobile app environment is “a data goldmine.”

PushSpring specializes in mobile audience data sourced from more than 200 million mobile devices. That information is crunched down into more than 250 audience segments ranging from demographic and interest to lifestyle-based categories. The upshot? Data users have a new ability to quickly create custom segments.

“The audience data landscape is extremely cluttered, and most of the available data in the marketplace is very similar, if not duplicative, across vendors,” said Dini Mehta, Drawbridge’s VP of Sales for North America. “Using mobile app ownership data as a targeting parameter combined with our cross-device graph ​is incredibly differentiated.​“

Mehta said advertisers need and want to reach consumers based on where they are spending majority of their time.

“This allows us to be truly device-agnostic from a targeting and activation standpoint,” noted Mehta.

The post Drawbridge Boosts Targeting with PushSpring Mobile App Data appeared first on Mobile Marketing Watch.