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Making Data Work’ the Theme of This Year’s Strata Conference

October 12, 2016

‘Making Data Work’ the Theme of This Year’s Strata Conference

Organizations are looking for their investments in big data and data analytics to pay off this year, with time to value being a key concern. At the Strata and Hadoop World conference in New York, Information Management spoke with Eric Tran-Le, global chief marketing officer at Logtrust, about what this means for his company.


Information Management: What are the most common themes that you heard among conference participants?

Eric Tran-Le: While the conversations at the Strata+Hadoop 2016 conference mainly focused on how businesses can gain more and better value from data by utilizing analytics and data science, the conference itself highlighted many of the current tools and technologies on the market for putting Big Data to work to gain insight.

At Logtrust, the themes we heard being echoed from the industry experts and the Big Data professionals revolved around the overall idea of “making data work” to achieve their organizations’ unique goals, such as: Innovating data and serendipity; using machine learning to save lives; and how data and analytics can benefit all Americans.

Through our experience deploying real-time Big Data platforms with industry-leading companies in Europe, we’ve found that achieving true ‘insight into data” requires two architecture capabilities:

Real-time analytics of data as-they-come: Contextualizing new data with past data is a challenge as each raw event stream and its eventual related events occurr in a very short time-window. If you don’t capture all the events and their possible relationship to individual events on-the-fly, you lose the ability to derive meaning from the data you are collecting.

Real-time analytics on data at-rest: Because some events arrive at a later date than when they actually occurred, this can affect the ability to act on insight in a timely manner. For example, a hacker who might have compromised a system two years ago, may not trigger an attack alert until the present moment.

Unless you’re able to compare the past very quickly with the most recent data, you may not be alerted to critical events-of-interest that will potentially result in an advanced persistent threat morphing into an attack.


IM: What are the most common data challenges that attendees are facing?

Traan-Le: At Logtrust, we focus on real-time clouds of events, which are realized only when you have “virtual data models” instantaneously built on-the-fly to provide meaning to unstructured streams of data. In this case, the data lineage is less important than the event object lineage.

In other words, in order for you to achieve notifications on true “events of interest,” the data processing must capture raw events, filter out, and aggregate a higher level of events, then prioritize them by order of “event-of-interest.” This is important to be able to retain and stay in compliance with raw events for a long period of time while you aggregate raw events into higher level events to extract meaning, all in a timely manner so that your response time does not impact “vital systems” or your business.


IM: What are the most surprising things that you heard from attendees?

Tran-Le: The challenge we heard most frequently about MDM and data governance initiatives are around the data analytics platforms being FAST before they are BIG. This means that event streams of data are not only mostly unstructured or pseudo structured, but their arrival rate may be also chaotic (the same set of time-stamped data may have chunks arriving out of order).

We have heard from organizations involved in IoT, or with multiple vendors, that data models can be erroneous. If MDM assumes a well-defined data model, such as an industrial data model for collaboration, this is not possible in the world of FAST DATA streams arriving continuously and unpredictably.

You need a real-time, Big Data-in-motion platform that allows you to dynamically create virtual data models on top of raw data and can scale, compute and store data in the cloud elastically. The MDM must be dynamic, otherwise it will have the same poor outcome as a static configuration management database (CMDB) incapable of coping with changes in the various sources of events.


IM: What does your company view as the top data issues or challenges in 2016?

Tran-Le: Of the many issues affecting MDM, Logtrust recognizes that the need for a dynamic MDM platform built for the real-time connected enterprise is the most critical.


IM: How do these themes and challenges relate to your company’s market strategy this year?

Tran-Le: Logtrust has successfully deployed a real-time, Big Data-in-motion analytics cloud platform in major european banks, telecommunication providers, and cyber security service providers that enables virtual teams and enterprises to collaborate over virtual dynamic data models. Our main strategic goal is to enable extreme agility with real-time data insights and fast decision-making, so the theme of this year’s Strata+Hadoop Conference of “making data work” is very much in line with our core strategy.

Being at the forefront of cyber security services as a real-time data pipeline, Logtrust has been able to carry out complex event processing for SIEMs such as ArcSight or IBM QRadar, as well as provide them with a real-time unified monitoring platform for end-to-end quality of experience(QoE) for broadcast TV providers, telcos and fraud prevention on the back-end analytics platform.

Our firm has gained tremendous knowledge and experience, placing us at the forefront of delivering real-time insights into streams of data as-they-come and at-rest, without the need for multiple vendors.



More Data. More Clarity. More Confidence.