The data race is in full swing. Nearly every customer I work with is attempting to collect and store as much data as possible. Twitter/Facebook feeds, web activity logs, 3rd party demographic data, GIS data, internal server logs, IoT data and more than before are being stored in ever larger data stores. This race to capture and retain everything that is valuable today, or might be valuable tomorrow, has contributed to the amazing rise of “Big Data”. The big data ecosystem has made ingestion and organization of massive data sets possible.

The problem for many customers today isn’t necessarily determining what data should be captured, or even how to organize and store such massive amounts of data, but instead, what to do with it. This lack of clarity around how to derive insights from big data has led to many big data projects to be viewed as failures in the ROI department.

That being said, getting insights from data has nothing to do with justifying Big Data projects. It has everything to do with corporate survival. The days of intuition and “experience” driving corporate decision-making are long gone (and if you’re reading this and fall into that legacy decision-making style, that’s your cue to modernize). Companies in the future will succeed based on their ability to make data-driven decisions just slightly better and faster than their competitors. An example of this is manufacturing firms using IoT & predictive analytics to proactively service assembly line machines BEFORE a failure, allowing companies to increase throughput and provide a slight edge in an industry where profitability is dominated by operational efficiency.

Data-driven decision making is the new king, and it can only happen with Data Science, the new tech-fueled incarnation of the old statistics or actuarial departments. Data Science is one of the most in-demand professions today (and for the foreseeable future). This analytic function, however, is much less mature than the data engineering function in most organizations with which I interact. That means that most organizations have far more data than they have the capacity or capability to derive insights from that data. Enter: Data Science …as a Service.

As a leader in Big Data consulting for over half a decade (essentially the lifetime of true big data applicability in the enterprise), Intersys has also been a big proponent in advance analytics, for what’s worse than a consulting project that is deemed a failure because it’s not delivering value to the business. As a result, in many of our Big Data projects (or traditional data for that matter), we strive to include a data scientist on the Intersys team to help our customers achieve value and insights from day 1 of their shiny new data store. Although data science is often viewed as a function that should be made up of FTE’s, the insights an outsider can bring to an organization simply due to the fact that their data modeling experience is broad and cross-industry is huge! Intersys has a consulting/project-based model for advanced analytics, and a subscription service model (i.e. Data Science…as a Service). So, whether you have data and need help making it useful, or you have a specific outcome or deliverable in mind, or you just need on-demand access to some of the best and brightest data scientists around, Intersys has you covered.

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