Just When You Thought It Was Safe To Understand Big Data

Data is just data, right? Okay we know that’s not true anymore; there is structured data (like figures and some spreadsheets), semi-structured data (like some documents) and unstructured data (like emails, video and images) – plus there’s big data which may well be the sum of all those parts.

So if we accept all of the above, then things are simple then? Ah well not so much… unfortunately now we also have:

• People Data: this could be email or calendar items but also mobile messaging, enterprise IM and social media.
• Line of Business (LoB) Data: this could be Enterprise Resource Planning (ERP), Customer Relationship Management (CRM) data or similar (often Saas-based) LoB-level dedicated business tools.
• Internet Mechanics Data: this could be log events from websites as well as logs from critical applications, servers and devices.
• Internet of Things Data: recorded information from sensors, wearables and all the rest of the IoT party.

Cheeky nomenclature & taxonomy
Alright some of this is color, flavor and spin i.e. Internet of Things data is potentially quite structured, people data is almost always unstructured and Line of Business and Internet Mechanics data is a mix of all types. What this new somewhat cheeky nomenclature and taxonomy is intended to do is show that use cases for data are becoming more granular in their very definition.

As a human example of enterprise analytics, we don’t just want to know if a car is going from A to B and what speed it is going. We want to know journey, speed, fuel economy, size of engine, number of passengers and, crucially, WHY the journey was undertaken in the first place. If we really want to lay down some big data analytics insurance we will also ask what color the car was and what genre music was playing on the radio – after all, this big data is useless to us now, but it might become useful to us in the future.

The problem we have in the enterprise space is being able to cross-correlate and integrate all these data types and sources to make sense of them. ZL Technologies is trying to convince the IT market that it has an answer in this vein. The firm’s new ZL Enterprise Analytics (ZL EA) product works with its own Unified Archive (ZL UA) big data governance platform to build structured and unstructured analytics capabilities directly within singular data management architecture.

Intrinsically interwoven data
The firm argues that current analytics products function largely as stand-alone data “silos” depending on data sampling and retrieval. Without a consistent data capture and long-term management, data sampled for analysis can easily be skewed, outdated, or incomplete and duplication and unmanaged data sprawl result pretty fast.

ZL argues that analytics initiatives must be “intrinsically interwoven” with data management efforts rather than independent of them.

“ZL EA is the first system in the market able to mine and correlate people, business, and machine-generated data all in one seamless analytics environment. This will enable organizations to derive insights that otherwise wouldn’t be clear using traditional, disparate analytics platforms and produce enterprise intelligence. The ZL ecosystem enables analytics that are reflective of the real-time, living enterprise rather than outdated snapshots of data,” said the firm, in a press statement.

Vast integration & cross-analysis
ZL Enterprise Analytics boasts of what the company calls a “vastly integrative” approach to data types, discarding outdated divisions between unstructured and structured content. This allows merging and cross-analysis of our three major but previously-segregated data categories i.e. people, business and machine data.

“Leveraging these three data types in concert provides synergistic insight that exceeds the sum of its parts, allowing the enterprise to pinpoint patterns and relationships that previously would have gone undetected. The holistic ZL EA approach contextualizes formerly isolated data sources, allowing different data streams to give powerful interpretive context to each other. The result is an analytics platform that tears down walls to data access, pooling all available content for spontaneous analysis as business questions naturally arise,” said the firm.

This commentary (and product news) is aimed at combatting the so-called ‘data lake’ of extreme enterprise information management. In this lake, governance is tough, integration is tough and analysis is also tough. CEO of ZL Technologies Kon Leong claims he has an answer to the big-picture big data analytics perspective that is unavailable in what he calls out as “most of today’s myopic analytics tools” no less.

Internet of Food data?
Is ZL Technologies bigger and wider in scope than some? Yes it is. Is the firm’s technology proposition big enough and wide enough to swim through all big data types both now and in the future? Only a foolish man or woman would bet on such a statement surely?
What about Internet of Food data? Okay, we’re kidding, but you get the point – tomorrow never knows.

Credit: Forbes