The use of gig data, often, is a digital marketer’s ideal solution. Like most resources, however, data has downfalls. Internet information certainly isn’t difficult to come by—especially in 2019. As the big data analytics market is likely to reach $103 billion by 2023, 2019 is expected to see another 20 percent of growth before the end of December.
Enormous data sets garnered from numerous sources makes a brand’s journey to meaningful consumer engagement much easier: Between e-commerce portals thriving on custom-tailored selling strategies and ongoing webpage evolutions driven by click-based metrics analysis, the modern decision maker’s resource base is packed with powerful tools.
Due to big data’s quantity and complexity, however, meaningful data sets can’t be gathered, stored or processed using conventional tools. This is why we have analytics tools—those invaluable platforms capable of absorbing data from incredibly versatile sources. In the past, it was nearly impossible to discern meaningful information from white noise when examining web services, digital media, machine log data, and business apps. Today, however, each source of data is but another stream of information to be studied by their relation to one another.
Metrics aren’t infallible, however. Quite the contrary, numerous estimates, and predictions can bog down a digital marketing campaign to the point of immobility. Even worse: The misapplication, or even misuse, of data can spell disaster for an otherwise thriving campaign.
Fortunately, it’s possible to identify these pitfalls before they stop your digital journey’s progression dead in its tracks.
The Growth of Big Data: Metrics as a Necessity
What makes data analysis so important, anyway? Once analyzed, data can detect consumer trends before they even happen. In the banking world, metrics analysis can even recognize illicit activities based upon mere financial patterns. Healthcare, meanwhile, benefits from early-stage disease detection via in-depth health history analysis.
Today’s most valuable metrics rely on the digestion of valuable consumer characteristics—characteristics derived from variance and standard deviation. Informally, these measurements simply describe how much data sets are expected to deviate from one another and how they vary.
By utilizing formal properties to describe informal processes, one can do a number of nifty things—such as:
-Measuring risk and volatility in finance.
-Understanding stock portfolio stability before investment.
-Predicting budget expenditures in an upcoming marketing campaign.
Understandably, a National Institute of Standards and Technology study defines big data as a massive collection of datasets, each studied for their volume, velocity, and variability. Effective use of these datasets requires the employment of scalable architectures capable of storing, analyzing and manipulating numbers within. This is no simple process, of course, as many marketers themselves define big data as a data amount exceeding one petabyte—or one million gigabytes.
The largest slice of big data revenue is presumed to stem from services spending, which accounted for 40 percent of 2017’s overall market. Modern heavyweight providers of metrics analysis services are thusly memorable: Dell, IBM, Oracle, Accenture, and Splunk.
Metrics analysis isn’t a proactive approach to high-quality customer engagement, however. As immense opportunities spawn from invaluable information buried deep within expansive data sets, the big data software market has accrued a worth of nearly 14 billion U.S. dollars. As for the above-mentioned financial sector: Banking is expected to have produced 13.9 percent of business analysis revenue by the end of 2019.
Marketing teams integrating big data into their brand’s culture, infrastructure, campaign insights and execution benefit from one of the most integrated cross-market resources in the world. Seemingly, employing data analysis isn’t a mere decision-making tool anymore—it’s a necessity.
The Cornerstones of Metrics Analysis, However, Pose Several Problems
Again, big data begets a few problems for marketers. The flaws of valuable metrics aren’t necessarily derived from the growing inadequacy of data volume, however. Instead, they’re inherited from big data’s myriad of sources like:
-Social media posts
-Smartphone usage behaviors
-Electronic consumer records
The same buried information existing in all data sources isn’t only difficult to unearth—it’s difficult to refine for competitive advantage. Harnessing the power of data can both fulfill promises to consumers and boost marketing campaigns to new levels of effectiveness. While big data volume does have several inherent pitfalls—the digital world is expected to reach 180 zettabytes of information by 2025—data storage and analysis tools are keeping pace quite well.
Still, even the keenest of marketers fall into the pitfalls of metrics analysis. Before examining these pitfalls, though, it’s important to discover their roots not within big data itself—but within the modern cornerstones of its usage.
The Use of Big Data Volume
The big data boom has granted marketers a wealthy environment of numerous data sets—and each can be exhaustively measured for relevancy. As these data sets grow in number, so too will the modern marketer’s analysis tools.
The sheer volume of big data is an inseparable quality of the resource pool—and it’s an undeniable consideration when discerning valuable information from relatively meaningless extrapolations.
The Use of Big Data Variety
Data is pulled from different resource basis, existing in many forms. Structured data, for example, can be defined by database columns, existing as easily accessed, queried and analyzed. Unstructured data, meanwhile, is a little more difficult to extract value from. This type of data includes social media posts, emails, word content, digital photos, web page layouts, online videos and more.
Marketers already understand the risks associated with examining quantitative data alongside qualitative data—yet they’re becoming better observers of discrepancies between the two. As such, data variety is another preemptive consideration when conducting metrics analysis.
The Use of Big Data Velocity
Check out these statistics:
-Every minute, Google gets about 3.5 billion search queries.
-Every minute, Internet users watch over four million YouTube videos
-Every day, 1.5 billion Internet users are active on Facebook
-Between 2016 and 2018, 90 percent of the world’s data was created.
Data creation is constantly accelerating, and data scientists are constantly coming up with new ways to collect, analyze and use this data. Speed has become a necessary quality of top-tier metrics analysis platforms—having increasing importance as a core quality as the months fly by.
Deeply Rooted Risks
While these data analysis cornerstones are inseparable from every digital marketer’s approach, they suffer a fault of overreliance. More or less: Metrics analysts, sometimes, can’t see the forest for the trees.
Recently, in fact, big-data practitioners have adopted new cornerstones to offset this overreliance. One new defining big data cornerstone is veracity—or the quality of gathered data. If a data set’s source doesn’t meet the task-at-hand’s needs, analysis is useless. Automated decision-making may be imperative, but computers are being scrutinized for mistakes of their own more than ever.
Another new defining cornerstone is data value—or the way a brand can use numbers to maximize decision-making quality. Remember the healthcare industry’s earlier reference? According to a McKinsey article discussing big data’s impact on the U.S.’s healthcare world, metrics analysis might account for $450 billion in reduced spending—about 17 percent of healthcare’s $2.6 trillion billion in overall healthcare costs.
Data’s visualization, too, has become an important consideration; metrics need to be understood by a brand’s nontechnical decision-makers—as digital marketing is more intertwined with business culture than ever before. To transform data into information—and information into insight—comprehensive visual representations need to be prioritized.
After pinning down today’s evolved world of big data, marketers need to abide by the needs presented by big data’s constant shifts. No matter how many cornerstones a data analyst prefers, the big data environment won’t stop changing. To understand what metrics analysis means to your brand, you’ll need to identify—and avoid—several pitfalls.
Pitfall One: The Use of Shallow Data for Deep Learning
Deep learning models, like neural nets, are becoming increasingly popular as computing power grows. Modern marketers can run frighteningly complex algorithms to analyze data sets at break-neck speed. These models are a direct result of data velocity’s proposed flaws—and they’re quite effective at balancing speed and quality.
Sometimes, however, advanced deep learning models are too complex to digest available data—which creates an “over-fitting” problem. Deep learning might create strong results in estimated samples, but it can be misleading when applied to real-world problems.
Using such a sophisticated methodology regardless of a data set’s volume—and definitely its quality—more or less, results in wrong predictions. To avoid this pitfall, any deep learning tools your brand uses should prioritize the separation of your data’s signal from its noise. That is to say: It needs to identify underlying data from dataset relevance. It’s a historical approach, yes, but it’s one constantly left by the wayside when utilizing 2019’s leading deep learning tools.
Pitfall Two: A Lack of Event Tracking
As per Google’s definition, events are content interactions analyzed independent of screen loads. They’re independent—often offering deep insights into the relationships between form inputs, downloads and pop-up window views side-by-side with user interactions.
Many digital marketers fall into this pitfall by overemphasizing conversion rate optimization. Unfortunately, this often foregoes in-depth event tracking to identify solutions quickly. This isn’t necessarily a bad thing—as it’s certainly a sound strategy based upon well-structured priorities.
As a result, however, the same digital marketers haven’t moved active event tracking higher on their list—examining it before cause-and-effect truths validated by correlations between things like click-through and shopping cart abandonment rates.
To navigate this pitfall successfully, blend your website analytics with cross-channel user engagement evaluation. In-depth analysis is an especially potent tactic when focusing on conversion rate optimization, too. This said, there’s a growing need for the discovery of website bugs and user experience design. You don’t need to alter your strategy entirely—but you should place a little more emphasis on problem aversion, here.
Pitfall Three: The Incorrect Use of Out-of-Sample Testing
Another metrics analysis pitfall is one of the traditional testing practices. When using an open-source neural net (or any up-to-date statistical model, for that matter) analysts typically test the model on data sets which aren’t normally tested within the said model. These approaches set aside current test data to focus on the random selection of available data en masse.
Doing so normally avoids the pitfall of a lacking metrics analysis platform, but deep learning methods can only go so far—sometimes resulting in incorrect outputs, even though the time-tested-and-true practice of random selection is often successful.
Instead of relying on such random selection blindly, run many simulations of holdout and sample data sets. Use different training and test set mixes to discern any analysis flaws. Doing so will better determine any elusive inconsistencies lurking within tricky data environments metastasizing from the ever-growing faults of data stratification—the ever-present quality of data populations impacted by unavoidable volume increases.
Pitfall Four: The Use of Information Silos
Speaking of data stratification, data silos—or data divisions based on cross-channel and marketing mix relevancies—are also creating hidden analysis problems. Using data silos makes sense, of course: Split up your data by relevancy. Here are several data silo practices:
-Analyzing web traffic stats separately.
-Analyzing pay-per-click metrics separately from social media.
-Analyzing SEO separately from email and social media, both.
If you don’t interweave your data sets, or integrate resources derived from different sources, you’ll miss a major requirement of successful metrics analysis. Monitoring data sets separately restricts you from seeing deeper insights—which means you won’t see what’s happening through a holistic lens. Examining demand generation, social engagement and website traffic, for example, can offer a better analysis of press coverage data.
Pitfall Five: Unactionable Data
Even today, one of the biggest digital marketing hurdles is not taking action when wielding valuable insights. For a lot of marketing teams, the volume of information derived from the volume of data, still, might be daunting. It’s understandable: What’s riskier than uprooting an entirely data-based marketing campaign? So, what can you do if new data insights don’t align with your current strategy? More importantly: What can you do if your current strategy generates information when respecting the first three pitfalls?
Fortunately, marketing campaigns in 2019 needn’t be uprooted when new conclusions are made. Digital marketing approaches are surprisingly modular, and new information, often, is incremental. Unfortunately, not enough marketers consider this quality—freezing up with new information before ever considering that they can still make impactful, strategic, decisions with slight navigational changes.
Over time, these preventative strategies should steer your campaign’s ship in a better direction—avoiding the lofty, blustery winds of elusively chaotic data lost in the incredible heights of computer processing power. Never forego data quality for quantity—and, as always, always keep the consumer’s user experience in mind.