Adding Humans Back Into Your Big Data Insights
Automation, after all, appears to be the wave of the future, where everything from manufacturing to customer service is handled with big data algorithms and platforms. While wanting to become a data-driven business is reasonable, all too often companies overlook the human element, which could be to their detriment. If you find yourself emphasizing the data aspect of your processes over the human aspect, it might be time to take a step back and re-evaluate, because human minds are crucial to uncovering new big data insights.
Take the example of improving the customer experience.
At the moment, many organizations collect data on customer interactions, whether through video analytics, social listening tools, or other methods. From these interactions, an automated platform is set up to respond to customers with concerns, questions, or complaints. It’s a fast responding technique meant to instantly tackle customer issues and resolve problems. While the method may be effective in certain circumstances, as a whole you’ll find more success if actual humans are involved. Automation too often acts as a shortcut or money saving strategy rather than something organizations use because it’s actually better.
Big data can inform in process such as these, but humans need to have a say.
Think about the last time you dealt with customer service. If you talked with an actual flesh and blood representative, what was it like? Now compare that with the last time you talked to an automated message. Sure, the automated message may respond to your complains based on its algorithm, but even if it solves your concern, there’s something about the interaction that just feels off. Customers want to talk to an actual person, not some machine. Employees who have been trained in customer service will be able to notice certain nuances that big data can’t quite pick up on yet.
This same idea applies to customer interactions through social media. Many organizations have set up automated platforms that directly respond to customers on Facebook and Twitter, but the results have been mixed. For example, how does a big data algorithm respond to sarcasm on social media? While big data may advance at one point that it will be able to detect subtle hints and different tones, for now, humans are much better since it’s something we deal with any day. You’re more likely to get a satisfactory result if actual customer representatives respond to customers on social media than an automated tool.
Big data should simply be looked at as an ingredient in a bigger recipe. It can inform, enhance, and even improve upon already established guidelines, but even then it requires human input. People are simply better at deciphering underlying messages and meaning, and as with any business decision, intuition and interpretation are incredibly important.
If you’re able to find that perfect intersection point where your human expertise connects with the technology you use, then you’ve found the sweet spot for discovering more hidden insights from big data. Once those insights are in your hands, you can then apply it to even broader principles across your entire organization. All of this leads up to a strategy that’s flexible and responsive, combining the best of both human minds and big data technology.
Improvements through big data are all about asking questions. That’s where you need to use human expertise. Big data algorithms only show information; it’s the people who have to determine where the improvements need to be made and why those changes have to happen. Using tools like Qubole hosted Hadoop can help, but ultimately it comes down to people to run through their options and identify where to focus. You can collect more data than you’ll ever need and it will still go to waste if you don’t have humans working on it.
Automation is great and it can certainly simplify a number of processes, but better results come when people work on the problem.
Some opinions expressed in this article may be those of a guest author and not necessarily Analytikus. Staff authors are listed http://www.dataversity.net/adding-humans-back-big-data-insights