The Top 10 Data & Analytics Articles of 2015
The online world of data and analytics is fast approaching epic portions.
It’s easy to get overwhelmed.
Because, not only has big data been big business in 2015 … but posts, articles, podcasts, webinars, and resources abound.
Some are worth your time. Some … are not.
To help you dig through the very best articles from this past year — as well as to highlight exactly what to take away from each — we’ve put together this list of the The Top 10 Data & Analytics Articles of 2015.
Author: Stuart Frankel
Publisher: Harvard Business Review
The first of two Harvard Business Review articles, Frankel aims directly at addressing the promise of “big data” and its failure to deliver results:
Executives are taking a hard look at their depleted budgets — drained by a mess of disparate tools they’ve acquired and elusive “big insights” they’ve been promised — and are wondering: “Where is the return on this enormous investment?”
After walking through the maze of complexity, redundancy, and drain that big data often brings upon two key industry’s — medical and financial — Frankel offers concrete advice regarding the necessity of setting concrete goals to guide your entire analytical process.
To do this work, the system starts with the goals of the report (e.g., did this portfolio outperform the benchmark?).
The use cases for these systems are countless, but they all start with the question: What do I want to communicate that currently requires a significant amount of time and energy to analyze, interpret, and share?
Authors: Oscar Lingqvist, Candace Lun Plotkin, and Jennifer Stanley
Publisher: McKinsey & Company
In this article, the authors explain the main differences that exists between direct consumers and business purchasers.
Consumer-buying behavior is influenced heavily by family, friends, direct-to-consumer advertisements, as well as factors such as brand reputation, coupons, and special deals.
Business purchasers, however, go through several distinct stages. The formal procurement process and the potentially high switching costs associated with B2B make the research and need to consider functionality essential.
The post goes on to explain that the dynamics in business buying have become more social, more real-time, and more modular over the last few years. To succeed, therefore, you must adapt a system of integration where the sales and marketing teams work together closely, map out your ideal customer’s buying journey, and — of course — let real data guide your decisions.
The ground is shifting in B2B buying behavior as customer-directed journeys replace the traditional funnel. This is the new and promising territory for organizations that embrace data, reallocate budgets, and do the hard work of bringing more collaboration to sales and marketing.
Authors: Tanguy Catlin, Jay Scanlan, and Paul Willmott
Publisher: McKinsey & Company
How do you create a digitally intelligent organization?
This article begins by cautioning against following specific personalities (or, thought leaders) and instead makes the case for gradually building the ideal digital culture of your organization organically.
The digital world is changing at such a high rate that rarely will you stop to think that what you’re experiencing is only its infancy. So what do you do? Raise your “digital quotient” which, as the phrase suggests, means increasing your digital know-how.
However, this may not be as easy to do on an organizational level as it is on a personal level. The post highlights a survey carried out by McKinsey & Company to show how big the digitization challenge is to companies around the world as well as offer a suggestions on how organizations can go through that process with less hassle.
Companies get their digital strategy right by answering three important questions. First, where will the most interesting digital opportunities and threats open up? Second, how quickly and on what scale is the digital disruption likely to occur? Third, what are the best responses to embrace these opportunities proactively and to reallocate resources away from the biggest threats?
Author: Kellog Insight (based on Florian Zettelmeyer)
Publisher: Kellog Insight
As a leader in your organization, your job should not be about assigning duties (i.e., delegation) but more specifically creating results.
This means aligning yourself with the data.
Unfortunately, data analytics is a complicated process. This has led to many leaders to offload their analytics to data scientists.
In opposition to this tendency, Zettelmeyer challenges leaders and managers to go the extra step of ensuring they can themselves distinguish between good and bad data and know where applying analytics can be of benefit to their teams and overall company.
To know the distinction that exists between good and bad data, Zettemeyer says leaders have to understand the process of data-generation first. This is a nice read to remind you that while your data scientists may be the expert in analytics, you yourselves must be the expert in data application.
So how can leaders learn to distinguish between good and bad analytics? “It all starts with understanding the data-generation process,” Zettelmeyer says. “You cannot judge the quality of the analytics if you don’t have a very clear idea of where the data came from.”
Authors: Timothy Morey, Theodore “Theo” Forbath and Allison Schoop
Publisher: Harvard Business Review
In our second HBR post, Timothy Morey, Theodore Forbath and Allison Schoop shine the spotlight on the future of gathering customer data, the need for transparency, and the consequences associated with ignoring it.
They explain that now, more than ever before, consumers are more conscious about their privacy. The need for transparency is therefore growing. Customers now demands to be “kept in the loop” about not only the type of data that is collected about them but also the purpose it serves.
Companies that are transparent about the information they gather, give customers control of their personal data, and offer fair value in return for it will be trusted and will earn ongoing and even expanded access.
Author: Jon Cifuentes
This post by Jon Cifuentes is the executive summary VentureBeat’s just release December 2015 study “The State of Marketing Analytics.” While you can certainly download the entire report for free by subscribing, the summary does an excellent job of highlighting its key finding.
Jon’s driving point is that more data has brought more problems … especially to marketers. In other words, the vast amount of data generated day-in and day-out has made it increasingly difficult for marketers to not only make sense of it, but also to take action.
However, it’s not all bad news as the report itself helps you to answer some of most pressing questions regarding “the types of advanced analysis your marketing organization needs to be investing in now to compete for customer relevance.”
Fragmented data sets are driving rapid innovation in data management, storage, and access.
Organizations struggle with finding/delivering the right information, at the right time, in the right format, to the right audience. The data is there. The content and services are catching up. Marketer skills still lag significantly in this area, but there’s reason for optimism.
Author: Kimberly A. Whitler
In an interview with the CEO of LatentView – Gopi Koteeswaran – Kimberly A. Whitler seeks answers to some of the most burning questions about data analytics.
If you have been using or researching about data analytics you will definitely relate to most (if not all) of the questions.
From the definition of analytics itself and its distinction from marketing research and big data to what makes an A-class data analytics firm to the challenges facing firms when it comes to analytics and advise on how to excel in your own analytics, this post covers a huge chunk of data analytics knowledge.
The analytics maturity of an organization depends on a number of factors, the most important of which is the culture of the organization. Those that are highly mature in terms of their analytics capabilities prefer to rely on data rather than gut feel during decision making.
However, one of the questions they [firms] need to ask is how analytics minded are the decision makers. And how can the company enable the firm’s leaders to be more data driven? Even traditionally creative functions are becoming highly analytical.
Author: Jeff Sauer
In this Moz article, Jeff Sauer looks at content analysis in depth and majors on “groupings” as one of the most effective ways to carry out content analysis on Google Analytics. In this comprehensive post, he explains step by step to exactly how to make content groupings work for you as you try to make sense of the data.
Jeff gives an example from his own site saying that this process helps him to better understand “which topics resonate most with readers” as well as know the topics which drive organic traffic to the site.
Paying attention to which of your content efforts are working well is the cornerstone to data-driven marketing. The companies that make these investments can produce tremendous results.
Author: Ritika Puri
Publisher: The Next Web
If you’re like most people, while you may care about big data, you don’t really bother to explore what happens to the information Google, Twitter, Facebook, and other channels collect about you.
Yet in this post, Ritika Puri says you should.
Why? We all know that the services offered by these sites are free for a reason: they give you their platforms in exchange for you giving them information (and the chance to advertise). To ensure a fair deal, you should follow up to know what happens to the data they collect, who can access it, exactly what do they want to know and why it’s so important to them.
The post explains that your privacy should be paramount and you should use sites that guarantee you that.
At a minimum, consumers should pay attention to the following when using online services:
– The company’s monetization model.
– Value placed on user experience.
– Potential tradeoffs for using other options.
Author: Kristi Hines
Kristi Hines massive list-post aims to help you “get the most out of Google Analytics.”
It’s a comprehensive list of resources you can use on Google Analytics to ensure effectiveness.
To make things easier for you, she has organized the list into four categories: Official Google Analytics Channels; Google Analytics Integrations; Tools for Google Analytics; and Articles about Google Analytics.
There are so many Google Analytics resources out there. Some you may not have heard of before. Rather than attempting to use/visit all of them in a single sitting (which may not be possible unless you have the ability to sit for really long), it helps to bookmark or even print out the resource and go after one at a time.
Entering 2016 with the Best of 2015
Well, there’s our list.
The best data and analytics articles of 2015 … to help you move into 2016 with confidence and purpose.
Of course, we couldn’t have possibly covered them all. So we’d love to hear about your favorites in the comments.