Technology is an enabler of new ways to improve the business and getting to the end result. If the next Uber drives down the cost, makes the ride better, improves the publishing process so that more writers are willing to dip their toe into the pool, that is ultimately a good thing. It forces us as business owners, service providers, and up-and-comers to stay sharp and responsive to the needs of our consumer base. The specific job may change or even go away — but the requirement for the service does not. It is just accomplished in a more effective and efficient manner and we need to adapt and grow with the opportunity to change as a business and service provider.

If we believe we’re technically savy because we have a daily report or dashboard that tells us what happened yesterday, we may be a bit off-the-money. By definition, a report is historical information.  It lays out activities that already happened.  Nothing in a report can be changed –the data is what it is. It can be parsed, and integrated, and shaded in ways that are often valid only in the eyes of the interpreter.  This is important however, because although the data itself doesn’t change, the interpretation is dependent on its relative position to the rest of the world around us.

Finding and placing value around data is accomplished by understanding what was occuring with one item while another event was taking place.  Those rows and columns in simple spreadsheets, data marts and more complex data bases gather and place relational context around a number of similar data points that allow a basic understanding of the events around them and provide a table of context.  Deeper value and insight begins to take shape when tables are compared to other tables and databases are compared to other databases.  A world of business tools exist to simplify, ease, and automate how we manipulate, visualize, and more clearly understand data relationships that are important to helping us drive our business and gain business intelligence from those relationships in order to make better business decisions.

This is all well and good for relational data that can be easily broken and compiled into columns and rows.  The more difficult questions facing most organizations today involve describing how to leverage the huge amount of other data, the databit streams coming off small devices and pieces of equipment that does not lend itself to obvious relationships.  The Internet of Things, the Internet of People, the Cloud, Big Data, significant technology advances are all changing the way businesses think about how they leverage data to improve the business.  The real task, then, is to place all that “big data” stuff into some kind of context so it can make sense and can be used to provide additional insight by sorting through the volume and variety of data streaming (and screaming) across the network at a velocity that makes business value difficult, but necessary, if we want to use it in a way that will help us make better, valid decisions (and get a leg up on our competition).

Data scientists bring multiple computing skills to discover data relationships among all the apparent noise, using advanced analytic algorithms to learn how to put Tab A into Slot B, find what uncommon events might precede a result, and identify relationships that are otherwise not apparent.  In truth, the companies working to bring those advanced analytics skills to every business user will be winners in the next round of data success.  Tomorrow’s technology leaders will be those thought-leaders pushing the leading-edge even further beyond what we now consider “advanced” analysis and data discovery, who will in the not-to-distant-future incorporate autonomous, self-learning tools and solutions built to make better business and mission decisions than today’s best data scientist, program manager, or chief executive officer.