From analyzation to action: The transformative shift for the modern data manager

Empowering data managers to move past outdated acquisition and analytics into a more actionable future


In the age of IoT, where consumers have access to myriad options at the click of a button, remaining relevant and engaging is an online retailer's impetus and primarily aim. As such, this movement has given rise to increasingly sophisticated and multi-faceted marketing campaigns aimed at better understanding target audiences and appealing to their individual interests. Though more laborious than traditional approaches, the intent is a valuable one. Studies reveal that 44% of shoppers reported they would return to a retailer if the service they received there was customized and targeted to their needs.

Yet, alongside the race to acquire new customers, e-commerce mainstays are also looking to retain and cater to their current client base. The challenge becomes, then, how to understand constantly evolving needs to ensure customers are satisfied throughout their buyer journey. This is the critical touchpoint where big data typically comes in.

Though the approach is a well-intended one, the reality is that unless it is appropriately managed and organized, this data can turn into a tangled and complicated hodgepodge of fragmented, disparate insights that, standing alone, can be difficult to discern. In fact, Forrester research shows that though big data programs and procedures are implemented in most major corporations around the world, as well as within SMEs, up to 73% of this information goes unused. At the receiving end of the spectrum are marketers, who need these insights more than ever before, but are rendered virtually incapable of using them without advanced assistance.

The challenge of the traditional big data journey

At the crux of the issue is the difficulty that lies in locating and securing solid big data sources. Regardless of size, any entity looking for a specific data set today will run into hurdles along the way. Chief among them is locating a set that not only meets its target audience, but is cost-effective and has a transfer system that is compliant with the most recent privacy standards. While this aim is ambitious and often time-consuming and resource-depleting, it is a necessary one. Taking pains to ensure a quality data set can help reduce duplication issues and ensure the overall quality of the information. Still, the pursuit of clean and actionable data can often render SMEs incapable of entering the data race due to resource scarcity.

Yet, once data sets are captured, the journey does not end there, nor do the complications. Historically, there has been doubt and uncertainty around allowing an outside, third party to store and manage a company's data cards and associated collateral. Finding a storage environment that is free of neglect and abuse while remaining completely transparent is a top aim, as the potential for such malintent is rapid in most environments. In the effort to make sense of this data and access it as safely as possible, even the most capable business leaders have lost speed in their push toward the top. In fact, most business leaders are even wary about storing their data in the cloud, with only 23% of organizations reporting that they do so and completely trust them to keep their information safe. So, what can we do differently.

Visit innovation Enterprise's Chief Data Officer Summit, part of the DATAx New York Festival on December 12–13, 2018

Making the shift from data mechanics to value

Though no one may have all of the answers just yet, one way to help solve these important issues and move modern marketing forward might lie in one transformative mindset shift: Data managers must focus equally on the actionable next steps to be taken after information retrieval as they do on the mechanics behind it. Put simply, for data to be the effective marketing tool that it has the potential to be, a company’s strategy shouldn’t be so laser-focused on mining it as it is using it.

Especially as big data continues to work in tandem with AI and ML, it's no wonder why marketers and advertising executives are up to their elbows in reports, forecasts, email lists, and customer contact information. There is so much time devoted to sorting through and understanding the technical, mechanical and financial aspects of customer acquisition and retention that the real question: "How can we use this to strengthen our outreach efforts and add value to our organization?" remains unanswered.

For so long, the push has been to collect all of the insights while you can and focus on sorting it all out down the road. While this tactic might have worked in the short-term, it ultimately snowballed into a seemingly irreversible mountain of data that no one has the tools to break into. However, unless it can be used to propel campaigns forward, even the most wide-scale data lake isn't up to speed.

The issue with the collect now/act later approach

So, where do companies go from here? Are leaders to leverage the data at their fingertips to automate every task in an effort to reduce manual work and conserve resources? Or, are they to analyze it to discover and create new business models altogether? The answer is to apply a little bit of both approaches, finding ways to fine-tune existing models while also incorporating automation where necessary and appropriate. Yet, before companies can even get to this point, they must first understand how to act on the data that is before them.

At the crux of the issue is the understanding that, standing alone, data points have no intrinsic value. An email address or survey response is just a fact unless it's put into context. Thus, for the millions of data endpoints available to any given company to be of any real help to C-suite leaders, they must be put into action. Herein lies a key separation: The consumer is not the value creator. He or she is the data creator. The business on the other end of the e-commerce transaction, web browsing experience or brick-and-mortar interaction assigns a value to the information gathered.

Leading with the traditional data-first-action-later approach has left many businesses spinning their wheels, unable to get the endpoints to the place they need to be to reveal their true value within the organization. This is where communication and hierarchy come into play. Data-savvy business leaders are those who know the teams and departments that can get the most use out of every insight, and as such, send the information their way for immediate action.

How data managers can mine for real value

How then, can those responsible for manipulating and using the data make the most use out of it? The first step is to separate the value of the data itself from the technology used to generate it. For instance, though AI insights are changing the game and the technology is an exciting way forward, there are other steps that smaller companies can take before rolling out a massive AI investment. Streaming analytics and processing are valuable, smaller-scale implementations that can result in equally valuable sets when performed by data scientists working upstream. From here, they can build robust models that can later be injected back into the data stream, enabling real-time analytics - and action.

Before you can understand the value that a set of data holds, you must closely and thoroughly understand the data itself. When a company takes in a large amount of big data, that information quickly turns into what's known in the industry as "dark data," or oversized segments of unknown origin that no one knows how to act upon and so they don't. While most experts will emphasis looking at the source of data governance and at its origins, that understanding is limited in scope. Data managers must also seek to discover how a particular set of data fits into the organization's greater data scheme. How does it work with other data? Where does it fit in?

Understanding data in context

Ultimately, a dataset's value lies less in what it contains inside and more so in how it works in tandem with other, existing sets. By blindly taking it into a data lake and veritably dumping it out, most leaders fail to make those critical connections. Though in the future the data systems themselves will be aligning these relationships automatically, especially as AI continues to sophisticate. For now, however, data managers and enterprise leaders hold the key. 

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