Companies need to start thinking about how analysts or business managers will be tasked with "actionizing" real-time data in ways that affect real-time decisions.
Srikanth Velamakanni, CEO and founder of Fractal Analytics, recently cited several key trends that he sees in companies' big data initiatives. Among them are:
- The need to consolidate big data into a single repository;
- The rise of machine intelligence and data;
- An increase in firms using locational data;
- More collaboration between humans and machines; and
- Use of big data for new analytics experimentation.
Most of these initiatives are organized around immediately "actionizing" big data so it can act on situations "in the moment," whether these actions are automated or human responses. For the same reason, we are seeing more organizations looking at storage investments and big data initiatives that use real-time data that can enable real-time decision making.
Real-time big data in retail
One of the most active industry sectors in "actionizing" data is retail, which has gone from watching web traffic trends, shopping cart abandonment rates, and customer buying preferences to new experimentation into customer behavior that now includes direct responses to customer browsing patterns that will hopefully produce more sales.
For instance, a consumer on an ecommerce site might show great interest in purchasing an item as demonstrated by the number of product pages on the website the consumer has reviewed, yet not go through with the purchase; or the consumer might go so far as to add the item to her shopping cart without completing the purchase. In these cases, some ecommerce retailers are now taking real-time steps to come back to consumers, perhaps with a downwardly revised price to see if that will incent the sale. The goal is real-time engagement with consumers in the decide-and-buy process to capture more sales.
Real-time big data in manufacturing and utility companies
In the manufacturing and utility sectors, companies are busy harnessing a plethora of big data being kicked out by sensors and other machine-based communications. They want to improve their process automation capabilities and to correspondingly reduce the labor intensiveness of operations.
Global companies believe that improvement in process automation driven by big data delivered over the internet will enable them to integrate their manufacturing workflows so a process in the US can trigger a subsequent flow of work processes in a factory in another part of the world, such as the Philippines. "For some time, we have been thinking that machines will entirely replace humans in these processes," said Velamakanni, "but what companies are discovering is that machines can capably accomplish roughly 90 percent of the tasks — but they still require human management and judgment to address those parts of processes that present unique problems that machine automation can't easily solve."
The need for highly skilled employees to "actionize" data
In both of these use cases, the call will be for more highly skilled workers and managers who can collaborate with the automation in real time to make the decisions needed on the factory floor, or for modifying online marketing strategies to capture revenue. This is an area of big data "actionizing" that has not yet garnered much attention in company employee and management skills training and recruitment nor in college and university IT and business curricula.
The requirements for highly skilled employees who can "actionize" real-time big data consequently fall between the cracks of how many companies currently have their big data/analytics teams organized. Business analysts are assigned to these teams, and it is their primary job to define the correct queries of big data to make a business difference for the company. Their biggest job is on the front end of data harvesting, where they lay out approaches that help determine which data to capture and how to best exploit it.
In the future, these analysts, or perhaps more likely line of business managers, will be asked to "actionize" the results from this data that continuously pours out in real time so they affect real-time decisions that impact manufacturing, sales, energy management, and many other real-time concerns. This is the "next frontier" of business line management skills that companies need to be thinking about now.
Can companies do this?
Mark Sucrese, marketing technology director at Dell, discussed the company's multi-year journey into a transformation of customer touch points that included strategy revisions to customer relationship management (CRM), technical support, sales channels, and email. The goal, according to Sucrese, was to "understand all vehicles" with which customers communicate with Dell and to move to real-time decisions on this data that could enhance customer relationships and generate more revenue. Sucrese reports a 30% increase in customer click rates on emails, a 40% increase in revenue per click, and an overall 19% improvement in sales. He cites the move to real-time decision making based on real-time data analytics as a major contributing factor.
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