When many people think about Intel, they think about silicon and only silicon. While there is good reason for this view—Intel is renowned globally for its relentless innovation in microprocessors—Intel is actually about far more than silicon. Intel is, in fact, also very much a software company with a sharp focus on driving advances in data analytics with a huge commitment to open source software.
Prior to joining the company in 2015, I pretty much shared the prevailing view of Intel throughout my career. I entered the tech industry as a software engineer and then moved into the entrepreneurial realm, founding and running tech companies. Throughout my career, I thought of Intel as a world-class company that made the infrastructure at the bottom of the IT stack.
And then came my conversations with Intel, which led me to my current role leading big data solutions business development. Through this process, I became intimately acquainted with the work Intel is doing to accelerate the development of innovative solutions for data analytics that span the silicon and software stack.
Intel has a long and successful track record for investments in open source software initiatives and new technologies, including, in recent years, big data. Let’s consider some examples:
- Intel invested early on in driving advances in the open source Apache Hadoop platform for storing and processing big data in its many forms—from social media to transactional systems. To that end, Intel has made a significant investment in Cloudera, a leader in Hadoop solutions.
- Intel technologies for data analytics are at the heart of many solutions for precision medicine—such as solutions that analyze data from an individual’s genome to develop narrowly targeted treatments for cancers.
- At the Oregon Health & Science University, cardiologists leverage the Trusted Analytics Platform to analyze big data from wearables in a clinical study that merges 24x7 lifestyle data with wearable devices, home monitoring devices, and clinical records.
- Intel innovations are all over the Internet of Things (IoT), from drones that capture and transmit data pertaining to the land below to the snowboards of world-class athletes. At the X Games Aspen 2016, the Intel Curie compute module was embedded directly on athletes' snowboards to capture everything from speed and jump height to air time and numbers of rotations—all in real time in an effort to enhance the viewing experience for fans.
These are just a handful of examples to illustrate the bigger point: Intel is not just inside servers, desktop computers, and mobile devices; analytic investments and innovation from Intel are present in all layers of the IT stack—from infrastructure to applications.
Without analytics, big data isn’t worth the media it’s written on. You can’t do anything with it. But with today’s advanced analytics tools, you can put data to work to save lives, improve athletic performance, drive better business performance, and make countless other things possible.
I’ve spent the past six years excited about and motivated by the potential of big data, and I’m equally excited about the work we are doing here at Intel to help organizations and individuals realize the benefits that emerge at the intersection of massive amounts of data, advanced analytics tools, and high-performance processors.
You can expect to hear much more from me on these topics in the weeks and months ahead. In the meantime, if you’re heading to the Strata + Hadoop World conference in San Jose this week, you can get a close-up look at some of the exciting work that Intel and its partners are doing to help organizations accelerate the value to be derived from big data via analytics.
And if you’re ready for a deeper dive right now, you can get a look at some innovative use cases for big data—from smart clouds to more precise farming, from heart-health programs to cancer research labs—at the Intel New Center of Possibility site. And to dive down into the capabilities of the Trusted Analytics Platform, visit trustedanalytics.org—and learn how you can close the gap between big data and the applications that need to consume the data to deliver value.