Data and analytics are quickly becoming central to the modern enterprise. As companies transition beyond hype to adoption, many are realizing how much data science and analytics relate to things they are already doing. Beyond simply storing the data or experimenting with data in the lab, we’re beginning to see more analytics projects break free of labs and powerpoints.
Analysis itself is a long standing tradition in parts of the enterprise, like Operations, where algorithms have guided decision-making for over fifty years. Data architects were diving into new and unknown data long before universities began to offer degrees in data science. Computer Science and software engineers are hardly strangers to data.
What’s different today is the recognition that there is value in data outside of this handful of professions. Stories of analytics success have increased the pressure on companies to use existing and available data to become more competitive. Now we have professions, departments, and entire companies created to help organizations pull some kind of value from all that data.
The emerging enterprise analysis structure actually looks pretty straightforward. Successful new projects across the organization become a part of that organization’s regular operations.
The data science team, like your typical special projects or center of excellence, look for opportunities to turn data into solutions. As each analytics project proves its value it becomes operationalized, essentially put into production. Thanks to recent advances in machine learning it has become easier than ever to turn these data science projects into robust and sustainable solutions.
The process is the same in the emerging arena of IoT. Analytics and data science are key to planning and assessing IoT projects, but when sensor data on the warehouse floor or inside of a cell tower are part of a standardized data workflow they become fundamental to regular operations. While it’s true that IoT will create enormous volumes of data, any IoT project that proves worthwhile will become a part of a recurring data workflow.
Standing in the way of this positive cycle of data discovery and automation is our reliance on analytics that aren’t built to connect easily with other systems and deliver actionable results.