That tape over your webcam might not be enough — the hackers are watching; it might be the right time to install another privacy shutter.
In a report just published in Science Advances, researchers at the Massachusetts Institute of Technology (MIT) emphasized the risks to imaging privacy that ambient light sensors can offer. Users of devices worried about security may find solace in software permissions that limit webcam use and hardware solutions like shutters. Nonetheless, studies have demonstrated that one of the typical ambient light sensors used in a variety of devices can be used to collect visual data. These tiny sensors are normally permission-free on a device level and aren’t closed or deactivated by users.
MIT researchers utilized the Samsung Galaxy View 2 in their investigations. The ambient light sensor on this relatively dated and huge (17.3-inch) consumer tablet is located close to the front-facing (selfie) camera — which is still a pretty popular arrangement.
Manufacturers of devices classify ambient light sensors as low-risk since software (or malware) may frequently access them directly without requiring any authorization or privileges. However, prior research has demonstrated that in roughly 80% of cases — even a basic sensor can yield sufficient information to deduce keystrokes from a keyboard and steal a device’s authorizations and passwords. The latest study demonstrates the potential of an ambient light sensor in conjunction with the device’s screen, which serves as an active light source.
Some devices are more susceptible to these ambient light sensor espionage techniques.
Some devices will be more susceptible to this ambient light sensor espionage technique than others because every device has a different light sensor speed and measurement bit depth, screen brightness, and light sensor precision (see image above). As you can see from the source article numbers, some of the tablet device’s image captures took several minutes. However, ambient light sensor imaging spy technology is verifiably accurate and has room for improvement.
The MIT researchers pointed out that the light sensors are “quite useful,” and we need and want them. The MIT researchers said to adjust the following to stop your peeping-cyber-toms.
Reposition the sensor so it doesn’t face the user.
Hopefully, when manufacturers become better aware of the ambient light sensor issues, they will implement a few changes to prevent the “snooping tech” from finding more victims.
Deanna is an editor at ReadWrite. Previously she worked as the Editor in Chief for Startup Grind, Editor in Chief for Calendar, editor at Entrepreneur media, and has over 20+ years of experience in content management and content development.
As a marketing professor, a common question I receive concerns what has changed in the modern era of marketing.
Defining the modern era can be tricky, especially since some accounts date the origins of marketing to be as far back as 1500 BCE. For simplicity, one can take a narrower scope and inquire about the last decade. If one takes such a lens, channels have certainly changed; platforms like Instagram, TikTok, and Twitch were either in their early stages, lacked an advertising capability, or did not even exist.
One of the most significant changes is the rise of informational currency. Marketers now possess the ability to rapidly build, access, and process massive repositories of data. With so much data available, shifts have occurred in the marketing function. It has become more action-oriented; marketers take the data they have access to—market share by geographic region, consumer purchase habits, changes in website traffic, consumer comments in forums— and generate an action or response. For example, if sales are down in a region, marketers might react by pushing advertising toward that region. If a TikTok influencer receives a lot of likes, a brand might seek to sponsor the user. If a consumer lingered on a website with items in their cart, but did not purchase, the brand might send an email promotion. A staple of what we term an action-oriented approach is that marketers collect data in droves and react to it.
This might all seem well and good, but as I have recently written with my co-author[1], Aparna Labroo, we view this action-oriented thinking as leading to potential short-term thinking, data tunnel vision, and precarious decision making. For example, focusing on the data that is coming in offers no guarantee that one is using the most important or the most relevant data. Indeed, we have found ourselves perplexed at brands that favour data because it is cheap or accessible, even though it provides little relevance to a problem at hand. Even when brands have good data—for example, clean data that establishes a decline in online sales— the presence of good data does not necessarily mean the appropriate action is taken. Knowing sales is down might tell you a problem of some form exists, but it does not tell you whether the solution require a change in the product, the advertising channels, the message, the creative work, or some other element.
What is the alternative? As we train the strategists we develop at Kellogg and in practice, rather than adopt a reactionary, action-oriented approach, marketers can instead develop and execute what we term a process-oriented approach. A process-oriented approach eschews a reactionary measure to data. Instead, it uses the data as a starting point to ask why a problem is occurring and what additional data will confirm or reject the hypothesis. A process-oriented leader uses the power of hypotheses to cut to the heart of a problem in a manner than leads to the development of successful long-term solutions as opposed to immediate (and potentially ineffective) short-term ones. A way to contrast the action-oriented and process-oriented approach is as follows: an action-oriented approach sees a problem and begins pulling levers in response to the problem; the hope is that one of the levers will do the trick. In contrast, a process-oriented approach sees a problem and steps back to ask what lever to pull and why.
How does one generate hypotheses, you ask? Although that’s a longer story for another time; it begins with applying what we call our INSIGHT framework. More specifically, marketers must understand the (I)ndidvidual consumer, the (N)etworks they belong to, and the (S)ituation that surrounds the consumer. Each of these involve curating the right data. Next, marketers can explore the (I)mportance of elements within each of these prior factors to understand the weight they carry. Notice, each of these steps involves seeking to understand the data as opposed to immediately reacting to it. Next, the fun part begins. With these elements in place, it becomes possible to (G)enerate (H)ypotheses and engage in (T)esting of these hypotheses. Essentially, via investing in developing and understanding insight, brands can direct their efforts to both gather better data and test ideas to avoid roads to ruin and identify how to pave paths to success.
In the modern era of marketing, it is all too easy to fixate on data in a way that ignores the real problems brand face and, as a result, leads to reactionary efforts that fail to solve the problems. Marketers need to take back the reigns of strategy by adopting a process-oriented approach that places a greater emphasis on understanding the root that underlies a problem as opposed to the rotten fruit on the branches.
I am a professor of marketing at the Kellogg School of Management. For over a decade, I have researched, taught, and consulted on the topics of advertising and persuasion. My endeavors have led to numerous academic publications, a textbook on advertising strategy, and cases written on effective advertising. In addition, I co-lead an annual review of Super Bowl advertising with Kellogg MBA students. Beyond my expertise, I am driven by a passion to better understand the human mind to allow marketers to create, execute, and evaluate advertising in a more effective manner. I hate to see ineffective advertising, and I want to do my part to make it better.
According to Gartner research, just over a quarter of all marketing budgets go toward paid media, with 56% of that spent on digital channels. Proving return on ad spend is already difficult for digital marketing leaders, and changes to cookies and walled gardens strengthening their own walls will make it even more challenging.
Third-party cookie data fuelled two decades of digital media and data-driven performance advertising. It’s no wonder cookie deprecation and restrictions on third-party data are transforming the way marketers target, buy and measure digital media.
In addition to the immediate impacts of cookie loss, the increased regulatory pressures on walled gardens is creating an environment of more black box algorithms and fewer data points with which to measure and independently verify results. Of the three privacy scenarios proposed by my colleague Andrew Frank, we are quickly moving to a walled garden world. And the biggest among them, Google, is at the front of the pack.
Aside from the (continually delayed) deprecation of the cookie, Google has another fast-approaching deadline that will impact almost every marketer with a website: the sunsetting of Universal Analytics.
As my colleague Lizzy Foo Kune shared with The Drum last year, the migration to Google Analytics 4 (GA4) entails an urgent overhaul to long-standing marketing data collection, measurement baselines and operational approaches — and deeper ties to Google’s ad ecosystem. GA4 highlights the data usage and consent gaps between acquisition-oriented advertising and retention-and-growth marketing but provides bridging mechanisms such as lookalike modelling, retargeting, pathing and attribution.
Digital advertising is vital for the success of modern brands, for driving both top-of-funnel awareness and bottom-of-funnel consideration and sales. Key to their success is access to data about their prospects’ and customers’ online behaviour, which helps marketers target and personalize their campaign efforts. Regulations on the collection and sharing of consumer data is prompting major data providers and adtech alike to change how their platforms collect, store and share this data with advertisers.
To maintain their digital media effectiveness, marketers need to build resilience and evaluate existing digital partners for cookie and walled garden alternatives.
Build a cookieless and walled garden risk profile
Purchasing display ads indirectly indicates a high reliance on third-party cookies. Brands with campaigns that rely heavily on indirect impressions could be highly susceptible to disruption from additional privacy changes from walled gardens and limits on third-party tracking from regulatory bodies.
Brands should ensure that their website and digital media campaigns – and the data collected and used to target ads – are both privacy compliant and effective in the face of those challenges by:
Owning, assigning someone on the team or finding a trusted partner to keep up with the latest news on privacy changes, cookies and third-party data regulation and their impact on the brand’s business.
Building first-party data assets by homing in on core customers and building direct-buying relationships with strategically important media partners.
Working across the organization to ensure compliance across user data collection and digital media activation.
Partnering with media companies, as well as established and emerging technology firms, to test novel targeting strategies (e.g., Google’s Topics, contextual targeting, data clean rooms) that reduce the eventual impact of the loss of cookies on existing media strategies.
Once an organization understands how its digital media is purchased, either with an internal analysis or a report from its agency partners, determine the risk exposure to the company’s marketing programs. Privacy changes and cookie deprecation’s impact on advertising depend on two factors: sales strategy (direct or third-party sales) and media strategy (brand versus performance marketing).
Source: Gartner (May 2023)
Sales strategy and the proximity to the final sale are indicative of the relationship with the customer, including the receiving consent to use their data for retargeting and other conversion-oriented digital advertising tactics. Media strategy indicates the number of existing relationships brands have with their target audiences to deploy in a privacy-safe way and their reliance on advertising partners.
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Limit exposure to changes in the long run
After determining the organization’s cookie risk profile and building overall resilience to disruption, follow some of these next steps, tailored for each profile, to limit exposure to changes:
Conversion-seeking brands should focus on the core value of their products to consumers and continue to build upon that niche in addition to investing in performance media partners. Work on increasing the loyalty of existing customers and growing through the network effects of word-of-mouth on social media and outside of digital.
Legacy wholesale brands should maintain mind share through their broad brand advertising strategies while leveraging emerging channels like retail media networks. These channels can help fill any potential gaps in performance advertising left by changes to walled gardens and third-party data.
Direct-to-consumer and mono-brand retail brands should leverage their consent-based first-party data and close relationships with customers to focus their ad spend across trusted sites and apps. With Universal Analytics’ sunset imminent, it is imperative for digital marketing leaders to start collecting data with both UA and GA4 now in order to test for data compatibility and source appropriate alternatives for signal loss.
Platform and multichannel retail brands must continue to innovate on their existing sites, apps and product suite to stay at the forefront of customer needs. If the brand is a Google Analytics site, it’s important to prepare for fewer granular data points on site visitors in exchange for more targeting options within the Google media properties. In addition, work with marketing technology providers to expand revenue opportunities by leveraging audience and conversion data for brands in high-risk, legacy and direct profiles.
Mike Froggatt is senior director, analyst in Gartner’s marketing practice. To read more from The Drum’s latest Deep Dive, where we’ll be demystifying data & privacy for marketers in 2023, head over to our special hub.
Mike Froggatt is senior director, analyst in Gartner’s marketing practice. To read more from The Drum’s latest Deep Dive, where we’ll be demystifying data & privacy for marketers in 2023, head over to our special hub.
Looking back on the past six years, the headlines may have pivoted to cloud, AI, and the continuing saga of open source. But peer under the covers, and this shift in spotlight has not been away from data, but because of it.
t’s been a wild ride over the past six years as ZDNet gave us the opportunity to chronicle how, in the data world, bleeding edge has become the norm. In 2016, Big Data was still considered the thing of early adopters. Machine learning was confined to a relative handful of Global 2000 organizations, because they were the only ones who could afford to recruit teams from the limited pool of data scientists. The notion that combing through hundreds of terabytes or more of structured and variably structured data would become routine was a pipedream. When we began our part of Big on Data, Snowflake, which cracked open the door to the elastic cloud data warehouse that could also handle JSON, was barely a couple years post stealth.
In a short piece, it’s going to be impossible to compress all the highlights of the last few years, but we’ll make a valiant try.
The Industry Landscape: A Tale of Two Cities
When we began our stint at ZDNet, we’d already been tracking the data landscape for over 20 years. So at that point, it was all too fitting that our very first ZDNet post on July 6, 2016, looked at the journey of what became one of the decade’s biggest success stories. We posed the question, “What should MongoDB be when it grows up?” Yes, we spoke of the trials and tribulations of MongoDB, pursuing what cofounder and then-CTO Elliot Horowitz prophesized, that the document form of data was not only a more natural form of representing data, but would become the default go-to for enterprise systems.
MongoDB got past early performance hurdles with an extensible 2.0 storage engine that overcame a lot of the platform’s show-stoppers. Mongo also began grudging coexistence with features like the BI Connector that allowed it to work with the Tableaus of the world. Yet today, even with relational database veteran Mark Porter taking the tech lead helm, they are still drinking the same Kool Aid that document is becoming the ultimate end state for core enterprise databases.
We might not agree with Porter, but Mongo’s journey revealed a couple core themes that drove the most successful growth companies. First, don’t be afraid to ditch the 1.0 technology before your installed base gets entrenched, but try keeping API compatibility to ease the transition. Secondly, build a great cloud experience. Today, MongoDB is a public company that is on track to exceed $1 billion in revenues (not valuation), with more than half of its business coming from the cloud.
We’ve also seen other hot start-ups not handle the 2.0 transition as smoothly. InfluxDB, a time series database, was a developer favourite, just like Mongo. But Influx Data, the company, frittered away early momentum because it got to a point where its engineers couldn’t say “No.” Like Mongo, they also embraced a second generation architecture. Actually, they embraced several of them. Are you starting to see a disconnect here? Unlike MongoDB, InfluxDB’s NextGen storage engine and development environments were not compatible with the 1.0 installed base, and surprise, surprise, a lot of customers didn’t bother with the transition. While MongoDB is now a billion dollar public company, Influx Data has barely drawn $120 million in funding to date, and for a company of its modest size, is saddled with a product portfolio that grew far too complex.
It’s no longer Big Data
It shouldn’t be surprising that the early days of this column were driven by Big Data, a term that we used to capitalize because it required unique skills and platforms that weren’t terribly easy to set up and use. The emphasis has shifted to “data” thanks, not only to the equivalent of Moore’s Law for networking and storage, but more importantly, because of the operational simplicity and elasticity of the cloud. Start with volume: You can analyse pretty large multi-terabyte data sets on Snowflake. And in the cloud, there are now many paths to analysing the rest of The Three V’s of big data; Hadoop is no longer the sole path and is now considered a legacy platform. Today, Spark, data lakehouses, federated query, and ad hoc query to data lakes (a.k.a., cloud storage) can readily handle all the V’s. But as we stated last year, Hadoop’s legacy is not that of historical footnote, but instead a spark (pun intended) that accelerated a virtuous wave of innovation that got enterprises over their fear of data, and lots of it.
Over the past few years, the headlines have pivoted to cloud, AI, and of course, the continuing saga of open source. But peer under the covers, and this shift in spotlight was not away from data, but because of it. Cloud provided economical storage in many forms; AI requires good data and lots of it, and a large chunk of open source activity has been in databases, integration, and processing frameworks. It’s still there, but we can hardly take it for granted.
Hybrid cloud is the next frontier for enterprise data
The operational simplicity and the scale of the cloud control plane rendered the idea of marshalling your own clusters and taming the zoo animals obsolete. Five years ago, we forecast that the majority of new big data workloads would be in the cloud by 2019; in retrospect, our prediction proved too conservative. A couple years ago, we forecast the emergence of what we termed The Hybrid Default, pointing to legacy enterprise applications as the last frontier for cloud deployment, and that the vast majority of it would stay on-premises.
That’s prompted a wave of hybrid cloud platform introductions, and newer options from AWS, Oracle and others to accommodate the needs of legacy workloads that otherwise don’t translate easily to the cloud. For many of those hybrid platforms, data was often the very first service to get bundled in. And we’re also now seeing cloud database as a service (DBaaS) providers introduce new custom options to capture many of those same legacy workloads where customers require more access and control over operating system, database configurations, and update cycles compared to existing vanilla DBaaS options. Those legacy applications, with all their customization and data gravity, are the last frontier for cloud adoption, and most of it will be hybrid.
The cloud has to become easier
The data cloud may be a victim of its own success if we don’t make using it any easier. It was a core point in our parting shot in this year’s outlook. Organizations that are adopting cloud database services are likely also consuming related analytic and AI services, and in many cases, may be utilizing multiple cloud database platforms. In a managed DBaaS or SaaS service, the cloud provider may handle the housekeeping, but for the most part, the burden is on the customer’s shoulders to integrate use of the different services. More than a debate between specialized vs. multimodel or converged databases, it’s also the need to either bundle related data, integration, analytics, and ML tools end-to-end, or to at least make these services more plug and play. In our Data 2022 outlook, we called on cloud providers to start “making the cloud easier” by relieving the customer of some of this integration work.
One place to start? Unify operational analytics and streaming. We’re starting to see it Azure Synapse bundling in data pipelines and Spark processing; SAP Data Warehouse Cloud incorporating data visualization; while AWS, Google, and Teradata bring in machine learning (ML) inference workloads inside the database. But folks, this is all just a start.
And what about AI?
While our prime focus in this space has been on data, it is virtually impossible to separate the consumption and management of data from AI, and more specifically, machine learning (ML). It’s several things: using ML to help run databases; using data as the oxygen for training and running ML models; and increasingly, being able to process those models inside the database.
And in many ways, the growing accessibility of ML, especially through AutoML tools that automate or simplify putting the pieces of a model together or the embedding of ML into analytics is reminiscent of the disruption that Tableau brought to the analytics space, making self-service visualization table stakes. But ML will only be as strong as its weakest data link, a point that was emphasized to us when we in-depth surveyed a baker’s dozen of chief data and analytics officers a few years back. No matter how much self-service technology you have, it turns out that in many organizations, data engineers will remain a more precious resource than data scientists.
Open source remains the lifeblood of databases
Just as AI/ML has been a key tentpole in the data landscape, open source has enabled this Cambrian explosion of data platforms that, depending on your perspective, is blessing or curse. We’ve seen a lot of cool modest open source projects that could, from Kafka to Flink, Arrow, Grafana, and GraphQL take off from practically nowhere.
We’ve also seen petty family squabbles. When we began this column, the Hadoop open source community saw lots of competing overlapping projects. The Presto folks didn’t learn Hadoop’s lesson. The folks at Facebook who threw hissy fits when the lead developers of Presto, which originated there, left to form their own company. The result was stupid branding wars that resulted in Pyric victory: the Facebook folks who had little to do with Presto kept the trademark, but not the key contributors. The result fractured the community, knee-capping their own spinoff. Meanwhile, the top five contributors joined Starburst, the company that was exiled from the community, whose valuation has grown to 3.35 billion.
One of our earliest columns back in 2016 posed the question on whether open source software has become the default enterprise software business model. Those were innocent days; in the next few years, shots started firing over licensing. The trigger was concern that cloud providers were, as MariaDB CEO Michael Howard put it, strip mining open source (Howard was referring to AWS). We subsequently ventured the question of whether open core could be the salve for open source’s growing pains. In spite of all the catcalls, open core is very much alive in what players like Redis and Apollo GraphQL are doing.
MongoDB fired the first shot with SSPL, followed by Confluent, CockroachDB, Elastic, MariaDB, Redis and others. Our take is that these players had valid points, but we grew concerned about the sheer variation of quasi open source licenses du jour that kept popping up.
Open source to this day remains a topic that gets many folks, on both sides of the argument, very defensive. The piece that drew the most flame tweets was our 2018 post on DataStax attempting to reconcile with the Apache Cassandra community, and it’s notable today that the company is bending over backwards not to throw its weight around in the community.
So it’s not surprising that over the past six years, one of our most popular posts posed the question, Are Open Source Databases Dead? Our conclusion from the whole experience is that open source has been an incredible incubator of innovation – just ask anybody in the PostgreSQL community. It’s also one where no single open source strategy will ever be able to satisfy all of the people all of the time. But maybe this is all academic. Regardless of whether the database provider has a permissive or restrictive open source license, in this era where DBaaS is becoming the preferred mode for new database deployments, it’s the cloud experience that counts. And that experience is not something you can license.
Don’t forget data management
As we’ve noted, looking ahead is the great reckoning on how to deal with all of the data that is landing in our data lakes, or being generated by all sorts of polyglot sources, inside and outside the firewall. The connectivity promised by 5G promises to bring the edge closer than ever. It’s in large part fueled the emerging debate over data meshes, data lakehouses, and data fabrics. It’s a discussion that will consume much of the oxygen this year.
It’s been a great run at ZDNet but it’s time to move on. Big on Data is moving. Big on Data bro Andrew Brust and myself are moving our coverage under a new banner, The Data Pipeline, and we hope you’ll join us for the next chapter of the journey.
Do you need a better strategy? Wondering how to use data to gauge when to start, stop, or scale your marketing efforts?
In this article, you’ll discover why data is important, what data to look at, and how to use data to inform your strategy.
Why Data Is Crucial to Your Marketing Strategy
It’s important to understand that data is for everyone, whether you’re a new business owner or have been in business for a long time and already know your ideal customer very well. Without data, as you’re building your strategy, you’re flying blind with little more than a feeling and some hope that you’re right.
We tend to develop our strategies focusing on things we like, things we’ve been told, and ideas that we see others finding success in, and put blinders on to whether these things are actually working. Are your time, money, and effort being returned to you in the form of conversions moving toward your goal?
Data removes those blinders.
Additionally, data makes it easier to connect each of your efforts to dollars. Whether you’re a marketer or business owner running all of your own marketing, understanding exactly how each point within your marketing strategy connects to that revenue helps identify the value of that point in the big picture. This is one of the ways in which data becomes so powerful in the realm of social media marketing because it lets you see exactly where you’re leaking money.
Pro Tip: As a best practice, you should start collecting the data even before you need it. If you’re starting a brand-new business and you don’t need the data right away, track it anyway. Because once you need the data, you need the data. As with anything else, data takes a while to collect, and Google Analytics doesn’t work retroactively. So while you may not look at the data right away, having it collected as early in the business as possible will only help you in the long run.
Experts say the privacy promise—ubiquitous in online services and apps—obscures the truth about how companies use personal data
You’ve likely run into this claim from tech giants before: “We do not sell your personal data.”
Companies from Facebook to Google to Twitter repeat versions of this statement in their privacy policies, public statements, and congressional testimony. And when taken very literally, the promise is true: Despite gathering masses of personal data on their users and converting that data into billions of dollars in profits, these tech giants do not directly sell their users’ information the same way data brokers directly sell data in bulk to advertisers.
But the disclaimers are also a distraction from all the other ways tech giants use personal data for profit and, in the process, put users’ privacy at risk, experts say.
[Companies] saying they don’t sell data to third parties is like a yogurt company saying they’re gluten-free…. It’s a misdirection.
Ari Ezra Waldman, Northeastern University School of Law
Lawmakers, watchdog organizations, and privacy advocates have all pointed out ways that advertisers can still pay for access to data from companies like Facebook, Google, and Twitter without directly purchasing it. (Facebook spokesperson Emil Vazquez declined to comment and Twitter spokesperson Laura Pacas referred us to Twitter’s privacy policy. Google did not respond to requests for comment.)
And focusing on the term “sell” is essentially a sleight of hand by tech giants, said Ari Ezra Waldman, a professor of law and computer science at Northeastern University.
“[Their] saying that they don’t sell data to third parties is like a yogurt company saying they’re gluten-free. Yogurt is naturally gluten-free,” Waldman said. “It’s a misdirection from all the other ways that may be more subtle but still are deep and profound invasions of privacy.”
Those other ways include everything from data collected from real-time bidding streams (more on that later), to targeted ads directing traffic to websites that collect data, to companies using the data internally.
How Is My Data at Risk if It’s Not Being Sold?
Even though companies like Facebook and Google aren’t directly selling your data, they are using it for targeted advertising, which creates plenty of opportunities for advertisers to pay and get your personal information in return.
The simplest way is through an ad that links to a website with its own trackers embedded, which can gather information on visitors including their IP address and their device IDs.
Advertising companies are quick to point out that they sell ads, not data, but don’t disclose that clicking on these ads often results in a website collecting personal data. In other words, you can easily give away your information to companies that have paid to get an ad in front of you.
If the ad is targeted toward a certain demographic, then advertisers would also be able to infer personal information about visitors who came from that ad, Bennett Cyphers, a staff technologist at the Electronic Frontier Foundation, said.
For example, if there’s an ad targeted at expectant mothers on Facebook, the advertiser can infer that everyone who came from that link is someone Facebook believes is expecting a child. Once a person clicks on that link, the website could collect device IDs and an IP address, which can be used to identify a person. Personal information like “expecting parent” could become associated with that IP address.
“You can say, ‘Hey, Google, I want a list of people ages 18–35 who watched the Super Bowl last year.’ They won’t give you that list, but they will let you serve ads to all those people,” Cyphers said. “Some of those people will click on those ads, and you can pretty easily figure out who those people are. You can buy data, in a sense, that way.”
Then there’s the complicated but much more common way that advertisers can pay for data without it being considered a sale, through a process known as “real-time bidding.”
Often, when an ad appears on your screen, it wasn’t already there waiting for you to show up. Digital auctions are happening in milliseconds before the ads load, where websites are selling screen real estate to the highest bidder in an automated process.
Visiting a page kicks off a bidding process where hundreds of advertisers are simultaneously sent data like an IP address, a device ID, the visitor’s interests, demographics, and location. The advertisers use this data to determine how much they’d like to pay to show an ad to that visitor, but even if they don’t make the winning bid, they have already captured what may be a lot of personal information.
With Google ads, for instance, the Google Ad Exchange sends data associated with your Google account during this ad auction process, which can include information like your age, location, and interests.
The advertisers aren’t paying for that data, per se; they’re paying for the right to show an advertisement on a page you visited. But they still get the data as part of the bidding process, and some advertisers compile that information and sell it, privacy advocates said.
In May, a group of Google users filed a federal class action lawsuit against Google in the U.S. District Court for the Northern District of California alleging the company is violating its claims to not sell personal information by operating its real-time bidding service.
The lawsuit argues that even though Google wasn’t directly handing over your personal data in exchange for money, its advertising services allowed hundreds of third parties to essentially pay and get access to information on millions of people. The case is ongoing.
“We never sell people’s personal information and we have strict policies specifically prohibiting personalized ads based on sensitive categories,” Google spokesperson José Castañeda told the San Francisco Chronicle in May.
Real-time bidding has also drawn scrutiny from lawmakers and watchdog organizations for its privacy implications.
In January, Simon McDougall, deputy commissioner of the United Kingdom’s Information Commissioner’s Office, announced in a statement that the agency was continuing its investigation of real-time bidding (RTB), which if not properly disclosed, may violate the European Union’s General Data Protection Regulation.
“The complex system of RTB can use people’s sensitive personal data to serve adverts and requires people’s explicit consent, which is not happening right now,” McDougall said. “Sharing people’s data with potentially hundreds of companies, without properly assessing and addressing the risk of these counterparties, also raises questions around the security and retention of this data.”
Few Americans realize that some auction participants are siphoning off and storing ‘bidstream’ data to compile exhaustive dossiers about them.
Letter to ad tech companies from six U.S. senators
And in April, a bipartisan group of U.S. senators sent a letter to ad tech companies involved in real-time bidding, including Google. Their main concern: foreign companies and governments potentially capturing massive amounts of personal data about Americans.
“Few Americans realize that some auction participants are siphoning off and storing ‘bidstream’ data to compile exhaustive dossiers about them,” the letter said. “In turn, these dossiers are being openly sold to anyone with a credit card, including to hedge funds, political campaigns, and even to governments.”
On May 4, Google responded to the letter, telling lawmakers that it doesn’t share personally identifiable information in bid requests and doesn’t share demographic information during the process.
“We never sell people’s personal information and all ad buyers using our systems are subject to stringent policies and standards, including restrictions on the use and retention of information they receive,” Mark Isakowitz, Google’s vice president of government affairs and public policy, said in the letter.
What Does It Mean to “Sell” Data?
Advocates have been trying to expand the definition of “sell” beyond a straightforward transaction.
The California Consumer Privacy Act, which went into effect in January 2020, attempted to cast a wide net when defining “sale,” beyond just exchanging data for money. The law considers it a sale if personal information is sold, rented, released, shared, transferred, or communicated (either orally or in writing) from one business to another for “monetary or other valuable consideration.”
If you are a social media company and you’re providing advertising and people pay you a lot of money, you are selling access to them.
Mary Stone Ross, a co-author of the California Consumer Privacy Act
And companies that sell such data are required to disclose that they’re doing so and allow consumers to opt out.
“We wrote the law trying to reflect how the data economy actually works, where most of the time, unless you’re a data broker, you’re not actually selling a person’s personal information,” said Mary Stone Ross, chief privacy officer at OSOM Products and a co-author of the law. “But you essentially are. If you are a social media company and you’re providing advertising and people pay you a lot of money, you are selling access to them.”
But that doesn’t mean it’s always obvious what sorts of personal data a company collects and sells.
In T-Mobile’s privacy policy, for instance, the company says it sells compiled data in bulk, which it calls “audience segments.” The policy states that audience segment data for sale doesn’t contain identifiers like your name and address but does include your mobile advertising ID.
Nevertheless, T-Mobile’s privacy policy says the company does “not sell information that directly identifies customers.”
T-Mobile spokesperson Taylor Prewitt didn’t provide an answer to why the company doesn’t consider advertising IDs to be personal information but said customers have the right to opt out of that data being sold.
So What Should I Be Looking for in a Privacy Policy?
The next time you look at a privacy policy, which few people ever really do, don’t just focus on whether or not the company says it sells your data. That’s not necessarily the best way to assess how your information is traveling and being used.
And even if a privacy policy says that it doesn’t share private information beyond company walls, the data collected can still be used for purposes you might feel uncomfortable with, like training internal algorithms and machine learning models. (See Facebook’s use of one billion pictures from Instagram, which it owns, to improve its image recognition capability.)
Consumers should look for deletion and retention policies instead, said Lindsey Barrett, a privacy expert and until recently a fellow at Georgetown Law. These are policies that spell out how long companies keep data, and how to get it removed.
She noted that these statements hold a lot more weight than companies promising not to sell your data.
“People don’t have any meaningful transparency into what companies are doing with their data, and too often, there are too few limits on what they can do with it,” Barrett said. “The whole ‘We don’t sell your data’ doesn’t say anything about what the company is doing behind closed doors.”
How to become a data-driven organization and why is this key for future business success.
A global pandemic has played a big part in elevating the data literacy of the ‘everyday man’, essentially demystifying data science for many of us. There now seems to be a much clearer connection between ‘what the data says’ and ‘what action we’ll take’. It’s a good example of data-led decision-making to drive the best possible outcomes.
In the same way, businesses can also drive better outcomes if they understand this connection and use it to transform their business model to one that is driven by data. Building a data-driven future is what will give businesses their competitive edge at a time when ’survival of the fittest’ counts the most.
With this in mind, many organizations are currently investing in data projects of one kind or another. Whether it’s data analytics, big data, AI, machine learning, data science or any other area of focus, the interest and investment in efforts to become ‘data-driven’ has been given significant extra impetus by the experiences of the last 12 months.
Whether the objective is to drive efficiencies, create competitive advantage or improve decision-making processes, it’s important to remember that isolated or independent data projects do not make you data-driven. Instead, the race to create a data-driven business infrastructure should be seen as a strategic journey, where organizations position data so that it empowers and delivers on business objectives. Success depends on transforming business models so that the whole is greater than the sum of its parts.
So, what does it take to become a data-driven organization? Here are a series of core principles that together, can help build a solid foundation, focus and measurable progress for using data as a strategic asset:
The entire process rests on effective leadership, where a top-down perspective aligns the business with data strategy. Without this approach, it can prove impossible to instigate the culture shift required to truly become data-driven and ensure that initiatives are given the right emphasis, support and representation, as well as driving the education required at a leadership level.
Skills
Next, it’s important to evaluate the relevant skills – and the gaps – within existing teams. For instance, it’s not unusual for analytics skills to be spread across departments, but in creating the right focus, business leaders need to transition to a core, centralized practice to ensure consistency. This does not necessarily mean that teams have to be changed, but organizations must create best practice processes to focus their efforts. Ultimately, building a community of data professionals who share knowledge and work together can be hugely beneficial, even if they don’t work in the same teams on a daily basis.
Best practice
Looking more closely at best practice, the objective should be to move from sporadic and isolated data driven initiatives siloed in each department to an approach which ensures consistency of approach across the organization. This should always be based on a common understanding of how to deliver value from data effectively.
Governance
As skills and best practice processes become integrated into a data driven culture, it becomes more important to ensure governance increases. Indeed, establishing best-in-class governance and frameworks is essential to ongoing data-driven transformation, because it enables leadership to track progress against goals. In practical terms, leaders need to work with data practitioners to ensure that initiatives meet business objectives, that there is consistency in delivery and prioritization, as well as in the platforms and technologies used. At the same time, every organization must meet their data compliance obligations, especially relating to sensitive or personal information.
Increasing the impact of a data-driven strategy is not just a matter of bringing the specialists together. Educating the business at large about the possibilities of analytics is an important part of the process so the whole business can share a common language around analytics and dispel preconceptions of what analytics can and can’t achieve.
Prioritization
As the impact of education efforts take effect, and business interest and knowledge of the potential of data driven decisions grows, many organizations find they are presented with a wide range of potential initiatives. Clearly, prioritization then becomes important, and key questions about each idea and option should include: will an initiative add significant, measurable value? Is the organization ready to implement data driven initiatives that may deliver meaningful results? Is the right data and platform available to make it work, and is the organization in a position to adopt the new practices each initiative will require?
Measurement
With priorities determined and actively being implemented, the process requires a structure to measure success in a consistent way so that all stakeholders can see the data driven program at work, rather than isolated instances of innovation. This is often pivotal for organizations in their efforts to move away from a series of data science projects to being a truly data-driven company.
There’s no doubt that investing time and resources in developing a data-driven culture can radically improve insight and decision making. In today’s rapidly changing business environment, spotting new opportunities and challenges, improving processes and working with greater insight into the variables that affect business success is vital. By adopting a rounded process that addresses these critical areas, businesses have the best chance of succeeding in their mission not just to become data-driven, but in their wider digital transformation strategy.
The pandemic forced D&A leaders to step up research and analysis to respond effectively to change and uncertainty, the firm says.
While much of the loudest buzz surrounding the impact of COVID-19 was focused on the dramatic shift from on premises to remote work, the pandemic further affected every aspect of the enterprise, which includes data and analytics technology. The uncertainty of what the tech industry would face forced D&A leadership to quickly find tools and processes — and put them in place — so they could identify key trends and prioritize to the company’s best advantage, said Rita Sallam, research vice president at Gartner, in the company’s recently released information.
Gartner has now identified 10 trends as “mission-critical investments that accelerate capabilities to anticipate, shift and respond.” It recommended that D&A leaders review these trends and consider and apply as necessary. Following is a summary from Gartner of the trends:
Trend 1: Smarter, responsible, scalable AI
Artificial intelligence and machine learning are key factors. Businesses must apply new techniques for smarter, less data-hungry, ethically responsible and more resilient AI solutions. When smarter, more responsible, scalable AI is applied, organizations will be able to “leverage learning algorithms and interpretable systems into shorter time to value and higher business impact,” Gartner’s report said.
Trend 2: Composable data and analytics
Composable data and analytics leverages components from multiple data, analytics and AI solutions to quickly build flexible and user-friendly intelligent applications to help D&A leaders make the correlation between the discovered insights to actions they must execute. Open, containerized analytics architectures make analytics capabilities more composable.
Public or private, data is unquestionably moving to the cloud and composable data, rendering analytics “a more agile way to build analytics applications enabled by cloud marketplaces and low-code and no-code solutions.”
Trend 3: Data fabric is the foundation
D&A leaders use data fabric to help address “higher levels of diversity, distribution, scale and complexity in their organizations’ data assets,” as a result of increased digitization and “more emancipated” consumers.
Data fabric applies analytics in order to constantly monitor data pipelines; data fabric “uses continuous analytics of data assets to support the design, deployment and utilization of diverse data to reduce time for integration by 30%, deployment by 30% and maintenance by 70%.”
Trend 4: From big to small and wide data
Using historical data for ML and AI models was rendered irrelevant, once changes based on the pandemic had an extreme effect on business. D&A leaders need a greater variety of data for better situational awareness because human and AI decision making grows more complex and demanding.
Therefore, D&A leaders need to choose analytical techniques that can use available data more effectively and they can with more insight that now requires less data.
“Small and wide data approaches provide robust analytics and AI, while reducing organizations’ large data set dependency,” Sallam said in a press release. “Using wide data, organizations attain a richer, more complete situational awareness or 360-degree view, enabling them to apply analytics for better decision making.”
Trend 5: XOps
DataOps, MLOps, ModelOps and PlatformOps, which comprise XOps, are necessary to achieve efficiencies and economies of scale through DevOps and using best practices of reliability, reusability and repeatability. This also reduces duplication of technology and processes and enabling automation.
Operationalization must be addressed initially and not as an afterthought because the latter is why most analytics and AI projects fail. The report said, “If D&A leaders operationalize at scale using XOps, they will enable the reproducibility, traceability, integrity and integrability of analytics and AI assets.”
Trend 6: Engineering decision intelligence
D&A leaders can make engineering decisions more accurate, repeatable, transparent and traceable, as decisions grow more automated and augmented. Gartner refers to “engineering decision intelligence,” which applies to a series of decisions of business processes as well as grouped emergent decisions and consequences.
Trend 7: Data and analytics as a core business function
D&A is now making the shift into a core business function, rather than a secondary activity. D&A now is a shared business asset aligned to business results. Gartner noted that D&A silos break down because of better collaboration between central and federated D&A teams.
Trend 8: Graph relates everything
Graphs form the foundation of most modern data and analytics capabilities and are reliant on the foundation to find relationships between people, places, things, events and locations across a wide variety of data assets. D&A leaders rely on graphs as quick answers to complex business questions, which require contextual awareness and an understanding of the nature of connections and strengths across multiple entities.
Gartner predicts that by 2025, graph technologies will be used in 80% of data and analytics innovations, up from 10% in 2021, facilitating rapid decision making across the organization.
Trend 9: The rise of the augmented consumer
Today, most business users use predefined dashboards and manual data exploration, but this can lead to incorrect conclusions and flawed decisions and actions. Time spent in predefined dashboards will progressively be replaced when users’ needs can be delivered with automated, conversational, mobile and dynamically generated insights customized through a predefined dashboard.
“This will shift the analytical power to the information consumer, the augmented consumer, giving them capabilities previously only available to analysts and citizen data scientists,” Sallam said.
Trend 10: Data and analytics at the edge
Support for data, analytics and other technologies are found in edge computing environments, closer to assets in the physical world and outside IT’s purview. Gartner predicts that by 2023, over 50% of the primary responsibility of data and analytics leaders will comprise data created, managed and analyzed in edge environments.
Gartner concluded: “D&A leaders can use this trend to enable greater data management flexibility, speed, governance, and resilience. A diversity of use cases is driving the interest in edge capabilities for D&A, ranging from supporting real-time event analytics to enabling autonomous behavior of things.”
Gartner Data and analytics summit
Gartner analysts offer more analysis on data and analytics trends at the Gartner Data & Analytics Summits 2021, taking place virtually May 4-6 in the Americas, May 18-20 in EMEA, June 8-9 in APAC, June 23-24 in India, and July 12-13 in Japan. Follow news and updates from the conferences on Twitter using #GartnerDA.
Some agency clients aren’t able to address important questions their marketing partners need answered in order to devise the best strategy to meet their needs. Luckily, analytics tools can help agencies uncover illuminating data points that clients can’t provide up front.
The key to informing a strategy that will achieve a client’s marketing goals is to identify which specific types of data you’re looking for before diving into the analysis. Below, experts from Forbes Agency Council share 11 of the most valuable pieces of information you can glean by analysing your clients’ Google Analytics.
1. What Attracts Versus Repels
As communications experts, we love reviewing Google Analytics to better understand how customers are engaging with a brand and what’s attracting them versus repelling them. This establishes information that allows us to develop more compelling content strategies. You’re able to see the level of leads coming from media relations and placed articles, which is a strong indicator of campaign success. – Kathleen Lucente, Red Fan Communications
2. The Client’s Audience
At the end of the day, the most valuable element of successful marketing is understanding the consumer. Google Analytics can provide some insight into a client’s audience. Combining this with other data sets and marrying the research with strategic analysis can inform an insight-driven marketing strategy. This can inspire consumer targeting, creative, media and more. – Marc Becker, The Tangent Agency
3. ROI On Marketing Investments
No matter what, you want to make sure that you are getting ROI on any marketing investment. Even if your Google Analytics are telling a positive story, if you aren’t getting actual ROI, there is data that either is not accurate or needs to be looked at holistically. There should always be a system of checks and balances, and all touch points should be telling the same story. – Jessica Hawthorne-Castro, Hawthorne LLC
4. Return On Ad Spend Performance
The most important piece of data you can glean from Google Analytics is the ROAS performance of your clients’ media buying across the various websites they are advertising on. By tracking where the users are coming from and tracking their activity on your clients’ sites, you can determine their ROAS. You can then shift media investment to the top-performing websites. – Dennis Cook, Gamut. Smart Media from Cox.
5. The Source Of Relevant Traffic
Analysing their clients’ Google Analytics allows agencies to see where relevant traffic is coming from, identify trends and target opportunities. Additionally, optimizing your campaigns based on the data feedback will lead to higher conversion rates. – Jordan Edelson, Appetizer Mobile LLC
6. Time On Page
Time on page is the most important Google Analytics statistic. Once you get traffic to your site, do they stay? What content do they consume? How much mindshare do they give you? What pages are sticky and not transactional? Time on page tells you what prospects value and where they give your ideas credence. Know this, and you’ll know your audience. – Randy Shattuck, The Shattuck Group
7. Where Viewers Leave The Website
The pages where viewers are leaving the client’s website at abnormally high rates is where to focus. By finding out what pages are causing website viewers to drop off the most, clients can analyze these pages and make necessary adjustments to better grab the attention of future visitors. – Stefan Pollack, The Pollack Group
8. Behaviour Flow
Behaviour Flow is still my favourite feature offered by Google Analytics. Studying the flow of the visitors and the path they take while interacting with a website helps business owners understand what a page means to the customer. This information helps business owners understand how to prioritize and optimize pages to offer visitors a better user experience. – Ahmad Kareh, Twistlab Marketing
9. Goal Conversion Data
Google Analytics can be overwhelming, so a great place to start is by looking at a client’s goal conversions (the number of visitors that took the action your client intended for them to take). This one area can give quick insight into how and why a website was built, as well as whether or not the site is performing the way it’s meant to. If goals have not yet been set up, this is a great opportunity to start a conversation with your client about short- and long-term objectives. – Carey Kirkpatrick, CKP
10. The Most Popular Content
Simply looking at your website’s most popular content can tell you if that website really serves your target customer. All too often, content serves another purpose or user. My agency’s example is that the bio I wrote for our vice president was the most popular piece of content, which proved that web visitors came to copy that bio rather than to hire our agency. – Jim Caruso, M1PR, Inc. d/b/a MediaFirst PR – Atlanta
11. Device Usage
One often overlooked piece of data in Google Analytics is device usage. All clients basically have two websites: a desktop site and a mobile site. Understanding what visitors are doing on both sites is critical, especially when it comes to advertising and landing pages. – T. Maxwell, eMaximize
The most interesting part of a study from Sidecar not shared on Monday in Search & Performance Marketing Daily points to the percentage that marketers rely on data vs. instinct to make marketing and advertising decisions.
Sidecar surveyed 146 marketing professionals in the retail industry. The majority of respondents were based in the U.S., with the remainder in Canada. All reported that they contribute to ecommerce marketing efforts at their company. The study was fielded between September and October of 2020.
When marketers were asked whether their team makes decisions based on data versus experience and instinct, the balanced response was 50% data and 50% instinct, with 24% of respondents reporting this way.
From here the findings become quite unbalanced. Only 1% of participants in the survey said they base their decisions on 100% instinct and zero percent data, and 1% base their decisions on 90% instinct and 10% data. Some 7% base their decisions on 80% instinct and 20% data, and 18% base their decisions on 70% instinct and 30% data.
When flipping the percentages, the findings are a bit surprising. It turns out that none base their decisions on 0% instinct and 100% data. It does get better, however. Only 4% base their decisions on 10% instinct and 90% data, while 10% base their decisions on 10% instinct and 80% data, and 16% base their decisions on 30% instinct and 70% data.
Some 62% of ecommerce marketing teams are making half or more of their decisions based on instinct rather than data, indicating significant headroom to become more data-driven.
In this new year marketers need to think differently to drive growth and connect with consumers. Thinking differently has important implications for marketers in terms of hiring.
Automation will find a home in more companies this year, from ad testing to keyword analysis, and audience segments and performance trend analysis. Among C-level executives, 82% want to automate bid adjustments, while 59% want to automate ad testing, 53% want to automate retargeting, 47% want to automate bid analysis, and 41% want to automate performance trend analysis.
What will marketing teams look like in 2021 as they reach consumers? Ecommerce marketers, for example, plan to grow their internal and extended teams. Some 66% plan to hire vendors and 67% plan to hire in-house talent.
Enterprise and small businesses plan to hire marketers with affiliate marketing and SEO experience.
Enterprise companies plan to hire content marketing to round out the top three, whereas small companies plan to hire those with video production experience.
Midsize companies are looking for specialists with experience in social media, video production and data analytics.
