The Data Lake Has Landed

I'm in hi-tech marketing. I live in a sea of buzzwords, business jargon, and acronyms (most of which are actually abbreviations, but I've learned to let that one slide). They spread faster than a virus in a daycare center. I hear people on conference calls saying things like "Dave, let's double click on that thought and explore it further." Seriously? Do what? Do I sound like that? Or, I'll read marketing materials that say things such as "...Our full stack enterprise-grade cloud solution for business acceleration speeds time-to-value, increase margins, enhances performance, and reduces risk." Yeah...thanks for clarifying that - at first, I didn't know what you meant.

The biggest, and possibly the most hated buzzwords to happen to IT in the last 10 years have been... Cloud, Internet of Things, and drumroll... Big Data (featuring its ugly stepchild the Data Lake). And over the past 10+ years, I've had the job of explaining these "things" to people (many of whom are not technical).

Nobody likes new buzzwords - at least not the kind of people I like. But if they become understood, and validated, people start to understand why they're legitimately needed. And this is the year, 2015, when Data Lake earns its keep.

Cloud was a tough one. The average joe watching the 2007 Superbowl didn't understand why IBM was telling them that the IBM cloud is here. Is it gonna rain? Why did consumers need to understand the IBM cloud? Consumers used browsers on the internet. Nobody cared to tell anyone if cloud was a place, a thing, or a process. Or, why cloud matters. The initial common-man definition of cloud was "it's basically the internet." Although this was not true, it was good enough. Now the general population has experienced the use cases and gets it. They understand why "basically the internet" was inadequate.

Briefly, those of us who focused on these types of IT thought leadership topics, spent some time on "The Consumerization of IT." This quickly turned out to be a waste of time for IT marketers. It was about how enterprise IT customer (such as employees), were increasingly using personal devices and consumer-grade online services to bypass the slow and frustrating process of dealing with their own IT departments. IT was losing controls of its users that were using their own smart phones, tablets, and services such as DropBox and Gmail to do their work. Unfortunately for us marketers (at least at the enterprise IT software firm I was working), the reality was that there wasn't much money to be made by promoting our understanding of this trend. Why? It was an unstoppable trend that was a problem for IT. We had no solutions to stop the trend. Our only advice to our customers was to "embrace the consumerization of IT" - whatever the hell that meant. As if that was helpful. Stupid.

Then came Internet of Things. It's a really bad name although I am becoming desensitized to it. Back in the 2000, LG launched the world's first Internet refrigerator for a cool $20,000. To some extent, devices/appliances like this gave rise to the now-common practice of putting the word "smart" before anything that can connect to the internet. However, the term smart was being used in related ways such as "smart house" which was home automation for hvac, lighting, and home entertainment. Over the past few years, Internet of Things is starting matter and people have an association. As Bill Shmarzo examines in his blog about profiting from the Internet of Things, we now have wearables, plus our ubiquitous devices, smart appliances, TVs, cars, meters, thermostats, etc., and those are collecting massive amounts of real-time sensor data, and providing access, insight, and control through touch screens and integrated apps.

And Big Data. What's that? A large quantity of data? Is it just about storage? Is it about being able to find a needle in a haystack (more in this in one of Bill Schmarzo's latest blogs)? Is it about analyzing data to find out what happened and why? No, but that's how a lot of people explained it at first. And initially that was good enough for most people. But now regular people are beginning to understand that big data is about analyzing internal and external data, from structured and unstructured sources, with real time analytics, to move from understanding what happened (descriptive), to understanding what will happen (predictive), to understanding what we should do (prescriptive). It's created the ability to influence business events while their still unfolding ("customers who bought this item, also bought this"... add to cart).

And now, within the blossoming world of Big Data, we have the data lake, which has been struggling for acceptance and validation. 2015 is the year where this happens. As big data moves past the hype, the data lake is what will deliver on its promise. A few years ago, most of the big data use cases were hypothetical. But now, data lake architectures, such as the Federation Business Data Lake enable a business to transition to a single, efficient, and effective storage repository model and analytics platform for all the data they may want analyze.

The data lake enables easy and cheap ingestion and storage of data from multiple places, including unstructured and real time data, by using with technologies such as Hadoop. And by integrating storage and analytics, with CloudFoundry PaaS, it becomes possible to move from data, to data insight, to new business value by feeding the analytic insight into data driven apps that help businesses reach new markets, improve customer experiences, develop new products, or enhance internal operations.

The key to acceptance of all of these terms is a general understanding of the use cases for them. And marketers are terrible and understanding this. Instead they will do a bad job of telling you what a data lake is, or just that you need one. We need to get better at including not only what it is, but also what it does, as well as what it means to the business. Identifying use cases is often the best way to start with big data and data lakes, as seen in this short video. At EMC, we've found that the best place for customers to start is with a Big Data Vision Workshop, which helps them understand and prioritize use cases for big data based on the data they have access to, and the top-level strategic objectives of the business. From there, we model an analytics solution for their priority use case that can also serve as a platform for future use cases. Here is an infographic that explains the process.

As new big data use cases become understood, and as the success stories continue to appear in more headlines, even the awkward Data Lake will soon become an accepted term that seems as normal as "email," or "Internet."

• Data Lake and Data Refinery – Gartner controversy!
• Strata + Hadoop – Security in the Data Lake
• Shots Across the Data Lake

• Finding Your Best Big Data Use Cases