What will you do when your job and the future of your company hinges on your ability to analyze almost every piece of data your company ever created against everything known about your markets, competitors and customers – and the impact of your decision will determine success or failure? That future is closer than you think. Data on an entirely different level is coming, and much faster than anyone realizes. Are you prepared for this new paradigm?
- Technologists have been talking about “big-data” as a trend for more than a decade and that it is coming “” “Soon” is now in your rear-view mirror.
- Companies have been capturing and storing operational and business process data for more than 20 years (sometimes longer), providing a deep vault of historical data, assuming you can access it.
- IoT is leading to the creation of a massive stream of new operational data at an unprecedented rate. If you think volumes are high now, you’ve seen nothing yet.
- The free flow of user-generated (un-curated) information across social media has enabled greater contextual insights than ever before, but concurrently the signal-to-noise ratio is off the charts.
What does all this mean? It means big data is already driving everything we do. The analytics capabilities of IT systems are becoming more sophisticated and easier for business leaders to use to analyze and tune their businesses. For them to be successful and make good decisions, however, the data on which they rely must be trustworthy, complete, accurate and inclusive of all available data sets.
Delivering the underlying quality data that leaders need is no small feat for the IT department. The problem has transformed from“ not enough data” to “too much of a good thing.” The challenge facing most organization is filtering through the noise in the data and amplifying the signal of information that is relevant and actionable for decision-making.
Inside your organization, historical data may be available in data warehouses and archives, but is likely fragmented and incomplete, depending on the source systems in use and the business processes being executed when the data was created. As IT systems have matured and more business and manufacturing processes are automated, the amount of operational (transactional) data created has increased. As part of stitching together business processes and managing changes across IT systems, data is often copied (and sometimes translated) multiple times, leading to large-scale data redundancy. IoT and OT are enabling instrumentation and the collection of an unprecedented volume and diversity of new operational data (often available in real-time) for operational monitoring and control, but the data from these collectors may have a limited useful life.
Outside your organization lies a wealth of contextual data about your customers, competitors and markets. During the past, experts and journalists published information for accuracy and objectivity – providing a baseline expectation of data quality. During the age of social media, a large volume of subjective user-generated content has replaced this curated information. Although this new content is lacking the objective rigor of its curated predecessors, the value associated with quality has been replaced with an exponential increase in data quantity and diversity available for consumption. The challenge for business leaders is filtering through the noise of opinion and conjecture to identify the real trends and insights that exist within publicly available data sets.
For you to make the big decisions on which the future of your company depend, you must be able to gather all of the available data – past, present, internal and external – and refine it into a trustworthy data set that can yield actionable insights, while simultaneously being continuously updated.