Data and analytics can be powerful tools to improve your business, but you won’t solve anything without a clear objective.
An article on the Information Age website discusses the potential of “fast data” technologies — such as QlikView — within the spectrum of Big Data. Higher velocity data can create value because it gets information into the hands of decision-makers while it’s still relevant.
“The vertical applications of what we call ‘fast data’ are many and varied,” the article explains. “In manufacturing, it can improve quality, reduce waste, [and] make production and replenishment more effective by analyzing shop floor data in real time.”
It doesn’t matter how “big” or “fast” the data is. To be successful, the data must be aligned with business objectives and strategies. What decisions are you going to make with the data? That’s the heavy lifting.
The technology to get the data in front of you is almost becoming a commodity. What manufacturers struggle with is deciding what to measure and what objectives to focus on. That requires a different dimension of thinking that doesn’t come out of the IT department.
The reality is the information that executives think they need today is sometimes outdated by tomorrow. That’s where business intelligence (BI) platforms like QlikView can help with associative searching. The in-memory platform allows manufacturers to examine things from different perspectives.
For example, perhaps you need to know how your business is performing by product line, by Internet store versus brick-and-mortar store, by age group and by days of the week. Those are multiple dimensions that you’ll need to slice.
The insights you could gather from that data are tremendous. Imagine being able to understand what age group purchases from your business, what they buy and when they buy. If it’s all done through the Internet, you could reach out to them via automated workflows when they’re most likely to buy. That’s the power of data and associative search in-memory systems such as QlikView.
Manufacturers should leverage technology to improve processes and operations, but it’s a mistake to think it can do all the work for you. The key is to decide what to analyze and the best way to go about evaluating it.
Source: Information Age, August 2013