Why data analytics increasingly matter in daily business operations
By Max BurkhalterMay 1, 2018
Industry 4.0 has ushered in periods of unprecedented productivity for many businesses, regardless of vertical. For the first time, companies have access to more data than is humanly decipherable. Fortunately, hardware processing power has advanced to accommodate this vast amount of information. However, it is not enough to simply have these tools.
While many organizations are racing to take full advantage of the benefits of Industry 4.0, many appear to be lost in terms of specific direction. This is evident as many businesses upgrade themselves periodically, embracing a new software solution in one department, additional interface options in another. While each of these acquisitions will likely still help productivity, they are not being properly used.
Data analytics has been a field that has long existed but, like many other aspects of workflow, its potential has been dramatically improved through Industry 4.0 technologies. It represents the vital, often overlooked tool, which will allow the enterprise space to become more fluid, have greater oversight and more rapidly adapt to change and adverse conditions.
"Not all obtained data is being used in a meaningful way."
Why data analytics need to be properly implemented
There is a ton of data to collect, but companies aren't capturing all of it, according to McKinsey. More problematic still, not all the information being obtained is being used in a meaningful way. This highlights a common problem in the age of data analytics.
A Deloitte survey found 83 percent of organizations that had invested in big data and data analytics solutions had reported some level of improvement in competitive positioning. However, an indicator of perhaps missed potential is the degree to which these businesses are now better performing. For instance, nearly 30 percent of the above respondents reported only a minor change in competitive advantage.
Data analytics is a transformative new tool so having organizations report such minor change is a troubling sign. With comprehensive internal analytics, companies should be able to readily identify weak points across a wide spectrum of departments. Hardware and employee performance, potential work traffic jams and other factors should all be viewable and measurable.
To properly work, however, data analytics must fight against the traditional segmentation that occurs throughout the enterprise space. In this previous system, corporations saw siloing departments as an achievement. This allowed employee teams to be better managed. While this is still true, data analytics, with the right information, can have more insight into productivity than any manager.
But if work data is siloed then it will be difficult, if not impossible, for data analytics to provide executives with readout that accurately reflect the state of the company, never mind identify problem areas that can be improved.
For instance, a manufacturing division can exceed the deadline with a product, quickly releasing several support patches. Without direct communication from customer service and sales, however, executives might not understand why the product is failing to sell to its expected audience. For decades, executives have been trusting gut feelings and, if they're lucky, cherry picking a couple data points to validate their logic. With data analytics, the company can have a complete overview into every stage of the product life cycle, with data present at every phase. This will enable organizations to easily spot the deficiency and better allocate resources to fix the error.
A new standard in customer service
Customer service, in particular, is one aspect of business operations that stands to benefit greatly from data analytics. Anyone who has worked this aspect of enterprise understands the plethora of variables that can influence performance. All vendors, whether B2B or B2C, need to understand that underperforming products are not necessarily inferior. That said, figuring out the exact nature of the problem can be a challenging experience.
For one thing, product engineers and creators don't often have easy access to call center data, which - if recorded properly - can often spell out the exact nature of the majority of problems. That is, if the call center employees are well-trained and empathetic. Negative customer service can also sour consumers on a product.
With data analytics, this information can be recorded and transmuted into easy-to-understand graphs and charts.
A vital tool for cybersecurity
Lastly, data analytics is essential for a strong, adaptable cybersecurity policy. Research sponsored by Anomali highlighted that 70 percent of CISOs and other IT staff are inundated with jumbled, confusing data reports. This cripples response times, which can be devastating when dealing with a data breach. Without a comprehensive analytics model, it is near impossible to quickly sort the useless data from the needed.
Regardless of specific need, the vast majority of companies can benefit from data analytics. However, before this technology can be properly deployed, organizations must first equip themselves with hardware that will create a strong, stable network infrastructure. Contact Perle today to learn how we can help upgrade your hardware and online capabilities to better understand big data and data analytics.
