'Big data' and 'analytics' - Two of the top buzzwords everyone secretly hates
Buzzwords. What would a meeting be without at least one thrown out per minute? Everyone uses them (although some more often and/or correctly than others), yet many of us secretly cringe every time we hear them.
But why do we hate buzzwords? Afterall they're often useful and (are intended) to describe a relevant phenomenon…at least initially. Buzzwords are akin to the technology adoption curve, except that the early adopters are typically describing a specific niche concept with one particular shared meaning.
Like most words, they are initially descriptive and useful. Unfortunately, once it reaches the mainstream, it usually goes downhill from there.
Buzzwords are frequently abused as an attempted credibility builder. A way of showing others that you're in the know. They get even worse when you're meeting with other departments, and you're not privy to the 'inside joke' because you're unacquainted with the particular buzzword du jour.
What is even more frustrated is being surrounded by people consistently misusing buzzwords. This misuse is the inevitable downside of the mass adoption of a fashionable term. It's like the game of telephone.
Someone hears a word at a conference and starts using it partially correctly based on their interpretation, best they can remember. That person then takes that term with them and uses it to the best of their abilities to establish their brilliance, corrupting it a little in the process. This starts a vicious cycle where the word quickly means different things to different people and, therefore, nothing at all.
Communication across and even within departments is difficult. Differing priorities, asymmetric information, and numerous other factors make things tough enough. At a minimum, we should all agree on what words mean, or we're pretty well doomed to get on different pages.
To combat this, I made a list of more than a dozen commonly used buzzwords, and then narrowed my attention to the two that are most closely associated with data management – big data and analytics. Here are my thoughts on each.
Big data is the quintessential buzzword. People typically aren't that far off from what they mean when they use the term. Still, its meaning has been continually watered down to a point where just saying "big data" will require further discussion to decipher what is meant.
The term big data became popular several years ago as executives and marketers in every walk of life jumped on the data train. Awareness that we were generating data at an ever-increasing rate reached critical mass, and that the analysis of massive data sets was becoming possible because of innovations in computing power, data warehousing, and the sheer amount of data generated daily.
When the masses accepted the concept of data as an integral business component, big data seemingly became the catch-all term people used to express their understanding of the broader phenomenon. I heard people say variations of "We need to start using big data" and "Big data is pivotal to our strategy" enough times to probably qualify as "big data" under this arbitrary definition.
To help set the record straight, let's clearly define what big data truly means. In 2001, Gartner provided the definition most still use today:
Big data is high-volume, high-velocity, and/or high-variety information assets that demand cost-effective, innovative forms of information processing that enable enhanced insight, decision making, and process automation.
Volume, velocity, and variety are known as the three Vs of big data architecture.
Volume refers to the quantity of data being produced through means such as eCommerce, social media, IoT devices, and the countless other sources that are feeding multitudes of data.
Velocity is the speed at which data can be received and (potentially) acted on. The timely access and use of data have significant implications for real-time, point-of-interaction exchanges and require architectural considerations. A given system must ensure it can process the information effectively and efficiently to make it useful when it is needed.
Variety refers to the numerous data sources contributing to data sets that previously didn't exist. As Gartner suggests, "No greater barrier to effective data management will exist than the variety of incompatible data formats, non-aligned data structures, and inconsistent data semantics." The majority of these sources of data are relatively new and mostly unstructured (e.g., text, images, audio, and video), and unlike traditional data sources, don't enter databases cleanly. This large variety of data demands significant effort to integrate the data effectively as well as to know what to do with it.
In simple terms, big data encompasses massive data sets, both in terms of volume, as well as the complexity of the data introduced from the ever-increasing number of new sources. Because of the size, speed, and variety of data, traditional software isn't capable of managing and analyzing these data sets, requiring more sophisticated platforms and architecture able to handle and process the data effectively.
Over the past few years, two new Vs have entered into the big data equation: value and veracity. All data has intrinsic value, but it is often tough to discover this value. It's also critical to understand the reliability and accuracy of the data because if you can't trust the data, then you certainly can't trust the analysis using it.
Much of the challenge is weeding through the noise to find what is useful to whatever business problem you're trying to tackle, and then finding a way to integrate it effectively. But with this challenge comes the unparalleled ability to answer business questions and devise intelligent solutions that have never been possible before.
Analytics is arguably the most frustrating term on this list, although I’ll admit some personal bias here. Analytics has become a catch-all for anything dealing with the analysis of data. Depending on the one using it, analytics can mean reporting basic summary statistics, advanced statistical analysis, predictive modeling, the analysis of big data, and much more.
Analytics is essentially the combination of analysis and metrics slammed together into a word that lacks a clear definition. The term spun out of statistics and implied the use of computational power to analyze data.
There are many definitions out there that are pretty good, but they vary extensively. As a result, the field of “analytics” covers numerous areas, ultimately leading to the term lacking a clear standalone meaning without additional clarity.
On a personal level, the term analytics is frustrating because the easiest way for me to quickly describe what my business (Ins & Outs) does is that we do analytics. It serves as a general starting point to a discussion by eliminating the many things we don’t do (e.g., architecture or dentistry), but requiring far more explanation to describe the services we provide.
One of the most common things we’ve heard clients say over the past 10 years or so is, “We need analytics.”
This statement can be a good starting point if the client is open to further defining what this means. However, clients are often frustrated because they don’t understand that they’re not describing anything beyond a broad categorical term and believe the request is explicit. In truth, this is as unspecified as saying, “We need marketing” or “We need technology.” It's a starting point, but not much more.
When we hear, “We need analytics,” we start asking questions to hone in on their specific situation and needs to begin clarifying the statement. Do you want to measure something? Are you trying to predict a particular behavior? Is your goal to influence something? In other words, what are you trying to do? Do you want to grow revenue by optimizing your product/service bundles and directly targeting the consumers most likely to purchase? Increase profit by optimizing your supply chain and internal efficiencies?
Often what people actually mean is, “I need to make sense of my data to do x…” Even then, it's not uncommon for further discussion to reveal that there’s no data to analyze. So, in that case, you need data harvesting, not “analytics.”
These are just two examples of buzzwords that make many people cringe. This reaction is for good reason because words without clear meaning prevent effective communication. If a term has different meanings to different people in the same conversation, then there will inevitably be a lack of clear understanding.
But it isn’t the word’s fault. The problem lies with us. We all want to fit in and sound smart. However, buzzwords from the mouths of many expose them as not knowing what they’re talking about.
Everyone is guilty of this from time to time in certain situations, but the frequency you hear these five words and many others in every single meeting is baffling. The continued use without a clear definition makes the words more and more ambiguous in the minds of most, and therefore aren’t helpful in clear communication.
After all, clear communication should be the goal. If I ask you to bring me "something sweet" from the store, I can’t get mad if you show up with a candy bar when, in actuality, I wanted ice cream. That's on me for poorly communicating what I had in mind. I should’ve asked you to get me ice cream and then clarified my flavor choice if I'm going to be picky.
Clear communication increases the collective effectiveness of the group, allowing you to get what you want, decrease stress on all involved, and not make you the subject of another eye roll or grunt.
In other words, it will increase your influence.