Trends for Data & Analytics

One of my favorite things to do is to talk to Cisco customers, partners and industry analysts about common trends they are seeing in their business and the overall market. We look at trends similarly to why we conduct analytics, not only to see what is happening now but to also help predict what will happen next. This ability to “see around the corner” helps us be more agile as we adjust data and analytics strategies, and therefore our business.

As we head into 2016, today I’d like to share a few the top trends I consistently see and hear about data and analytics.

The increase of hyper-distributed data environments – Simply put, the term hyper-distributed data environments describes the large volume of data within an organization’s environment and the wide array of locations in which that data exists (i.e. distribution). In almost every industry, data is being created in places it never has before, which is producing these “hyper-distributed data environments.” But data itself is no longer the number one problem; connected data is the problem. It is becoming increasingly difficult to reach that data, secure that data, much less draw insight and enable a person or process to take action on the data. For example, data created by a retailer’s in-store video camera can be highly beneficial to learn about customer behavior and buying preferences in real-time. Yet, a store employee can only act to help influence a purchase if he/she is empowered with insight while customers are in the store. To overcome this challenge, organizations need to add edge analytics to their existing strategy, analyzing data close to its source instead of sending it to a central place for analysis.

Integrating different data sets to get a single, accurate view is still a major challenge – Data is more complex, messy and in many different formats than ever, largely due to hyper-distributed data environments. This continues to be a costly issue for almost any organization that does data integration manually. Some even embark on multi-year journeys looking for ways to cut costs and have a better understanding of customers. Luckily, software like Cisco Data Virtualization can have a major impact on the time and cost of data integration projects. In using this method, a global pharmaceutical organization was able to improve data quality and reduce the time required to develop projects by 50%; a global nonprofit organization was able to achieve a 25% faster time to market for new programs and initiatives, and lastly, a global entertainment company was able to increase revenue by more than $21 million through better business decisions made from data insights.

Analytics is helping accelerate the Internet of Things (IoT) – Many examples of hyper distributed data environments can be found in the IoT. From terabytes of data created by sensors in offshore oil wells to extremely time sensitive data created by robots in manufacturing facilities. IoT devices and sensors can highlight failing machines or dangerous conditions before they become serious issues. The next step is to analyze the data. Looking for patterns in it could illuminate ways for employees to improve operations, such as doing more preventive maintenance or designing more efficient processes. When data is combined with analytics, real opportunities arise. A great example of this is Cisco’s recent partnership with Mazak on its SmartBox. The SmartBox is a mini electrical cabinet mounted on the side of a manufacturing machine enclosure. Inside of the SmartBox is Cisco Streaming Analytics embedded on a Cisco Industrial Ethernet (IE) 4000 switch. This enables us to measure things like vibrations and temperature on the manufacturing floor in real-time. By analyzing this data, manufacturing personnel are able to identify and easily fix downtime-related inefficiencies to improve overall equipment utilization.

The race to become digital – Lastly, one of the biggest themes I am seeing and hearing is the need to adopt and deploy digital technologies and business models. According to Gartner, Digitalization is the use of digital technologies to change a business model and provide new revenue and value-producing opportunities; it is the process of moving to a digital business. What does this mean for analytics? The more a business is digitized, the more sources can be pulled for better and more accurate analytics. This point is crucial and a must to maintain competitive advantage. One study claims that 4 of the top 10 incumbents in each industry will be displaced by digital disruption in the next 5 years. Digital disruption is not just an issue for firms in high-technology sectors. Take for example a car manufacturer using Wi-Fi analytics to pinpoint foot traffic patterns or repeat customers. As a dealership manager, if I can understand the ratio of customers waiting for sales versus service, I can make adjustments to staffing and resources in order to create a more positive customer experience.

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