A Talksum Perspective – Understanding Data Early in the Process

Barry Strauss, Talksum Head of MarketingBarry Strauss, Head of Marketing, Talksum

With the amount of data increasing at exponential rates, you would think that businesses today would have it made, that they would be overwhelmed with information, and that they could apply information to problems on the spot, react instantaneously, and even be more proactive than ever. Unfortunately, this is most often not the case.

The inordinately massive amount of available information has ironically put a strain on the enterprise, more specifically on the network infrastructure. Big Data that gets bottlenecked and can’t be served properly becomes responsible for down time, operational inefficiencies, potential disasters, policy violations, lost opportunities, higher costs, security violations, and other problems.

Ultimately, the ineffective infrastructure triggers financial losses that occur because of the inability to make real-time decisions with the data.

The traditional approach to fixing this problem is to first store data, and then to make something out of it. The focus of innovation is to make storage bigger and faster (improve traditional databases and build new storage solutions such as Hadoop, in-memory databases, and others) and build analytics on top of it. This becomes complex and expensive to implement and also raises concerns in scalability and stability.

Talksum has tackled this problem with an efficient approach – understanding data and then acting upon it in real time before storing. This allows enterprises to apply business logic early in the process before data is stored, and optimize what needs to be acted upon in real time and what needs to be routed to respective downstream sources.

Again, the key point of the Talksum approach is to understand the data first before it is stored, as opposed to today’s approach of storing first, and then trying to figure out what is in the data.

At the end of the day, it all comes down to saving $$$$ and eliminating financial losses. The underlying, innovative approach built into the Talksum product allows a single Talksum Data Stream Router (TDSR) to replace racks of servers in the data center. This drastically reduces the infrastructure footprint, staffing and support needs, energy consumption, the number of required software licenses, and other costs. At the same time, the TDSR reduces the number of steps and the amount of time (from days or weeks to seconds and minutes) needed to make timely decisions, create reports, build charts, and take appropriate actions.

The Talksum solution also helps data centers solve one of their biggest challenges – dealing with multiple logging formats and data schemas – by providing a universal logging profiler, as well as other systems interoperability problems.

The TDSR performs all of this while enforcing security and policy compliance.

To sum it up, the TDSR reduces many solutions that apply to challenges and problems to a single solution that covers all. And it does this in real time at network speeds.


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Data Science as a Solution

Dale Russell, Talksum CTODale Russell, CTO, Talksum

As our understanding of data science problems evolves, we find that effective solutions apply a systematic approach to testing, measuring, and building knowledge of the whole data system. In order to effectively and efficiently create this holistic view of data, first consider the entirety of the data landscape from Infrastructure to Layer 7. A comprehensive data science solution should not have biased access to data from any one layer more than another. When architecting a solution, keep in mind that business requirements will change, message types and objects will change, and the volume of data from various OSI layers will change, especially as the Internet of Things (IoT) becomes more of a reality.

To best deal with an ever-changing data landscape, follow this important principle: Never leave work for a downstream process. Datasets will continue to grow in volume and diversity, and solutions will be expected to take less time to process data or make it actionable. Store-and-sort is a costly strategy regardless of who owns the infrastructure. We found the best approach is to sort first, then store.

Over the last 15 years, exceptional and innovative storage solutions have been developed utilizing distributed software and socket libraries and advanced cloud services. These come with substantial performance increases, benefiting data center environments where concerns about latency, growing storage, or increased demand for analytics on datasets arise. As innovations in this sector brings more data into your landscape, you can enable great data science by taking a broader approach.

While some solutions focus on a subset of problems, a great data science solution deals with the entirety of information across the data landscape. In working with our customers and partners, we found that any acceptable solution must not only accommodate changing data requirements, it must do so in a manner that maintains the highest level of data fidelity. If new analytical processes are created, the solution should easily direct the correct data streams to new processes without a lot of work for your team.

A proper data science solution empowers the organization to focus on asking forward-looking questions of their data, not requiring them to constantly invest time searching for new data solutions every time the data landscape changes (as it will continue to do).