Business is moving at breakneck speed and as it does the requirements of ever improving customer engagement continue to get more demanding. Big Data techniques applied against data lakes and unstructured distributed data stores have transformed how we are able to understand and predict customer behaviour and preferences, but what these static data stores have not been able to do is provide insight on customer behaviour based on events as they happen in real-time.
Data in motion is the key to this next step in the evolution in a data-driven business world. Analysing data in motion allows for real time alert driven decision making, based on notifications on activity as it happens.
As an example, for large retailers with multiple outlets, analysing real-time data on a store-by-store basis such as stock levels, customer traffic, sales data, local competitive pricing and even external factors such as traffic and weather allows for decisions to be made by the second that can impact sales and productivity. On-the-spot decisions can be made to move stock and even staff between stores during a working day.
This kind of analysis is going to become increasingly commonplace, especially with the rise of advanced technologies such as artificial intelligence and machine learning, which can leverage real-time data to truly harness the value of enterprise data and turn it into powerful insights.
So let's consider what happens to your business if you don’t keep pace and leverage your own data in motion.
If your competitors are more able to respond to sudden localised spikes in demand in a way that you cannot, you may get left with excess stock that you can't shift as the competition has consumed the demand spike before you were able to react.
When you launch special offers or promotional days, your competition will be able to react, respond and resource accordingly on the same day, minimising the effectiveness of your own promotions.
Whilst your competitors are developing an agile workforce to respond to customer traffic in near real-time, you will need full resources on a store-by-store basis in order to cope with the maximum "guestimated" customer traffic.
Data in motion improves your ability to service customers and does so whilst driving efficiencies and lean structures. In effect, you can deliver better service, higher yields with more lean agile and cost-effective resources.
To be clear, data in motion and real-time data streams don't replace historical data stored in big data repositories, the two go together. Big data systems such as Cloudera Data Platform (CDP) analyse real-time data streams alongside historical data to ensure that experience gained from data collected over many years is used to strengthen the decisions being made on the real-time data stream.
The reality of our data driven world is that business decisions even in brick and mortar businesses like retail really will be made on a second by second basis. Competitive advantage will be based on the decisions you make on a daily basis, not on a seasonal strategy. If your business does not embrace this reality, the writing really could be on the wall.