Chaos Turns to Action – When Big Data Meets Artificial Intelligence
Author: Gilad Brand
It is no secret that big data technologies have the potential to turn infinite streams of data into actionable business intelligence. Take the murderous Boston bombing as an example: CNN reported that FBI agents were using the enormous amount of photos and videos that were generated by Marathon spectators’ mobile phones. Fed by Twitter, YouTube, Facebook, Instagram and Vine among other social media outlets, FBI agents used sophisticated analysis capabilities that can crunch big data to quickly identify suspects.
Crowd-sourced investigation would not be possible without big data, however, it is easy to realize that the value is tied to the analysis tools that can automate the identifying process, suggest candidates, and eliminate candidates and so on. In this instance, the analysis had to be completed in a timely fashion to protect people’s life. In an enterprise scenario, faster analysis is directly tied to the bottom line of the company.
The Creators of Big Data
During the last years, enterprises have transformed their workflows to leverage mobile devices, employees, vehicles and other sensors, all of which generate an enormous amount of actionable business intelligence. Collaboration and Enterprise Social Networks enable us to easily engage with customers and other employees, while they share files and create content. Cloud computing is another technology available today that enables us to cost-effectively connect numerous feeds, sensors and mobile devices to a scalable and secure big data warehouse that can store the unstructured, high volume data and turn it into actionable information.
These producers and enablers of big data present an opportunity for companies with a mobile workforce to leverage big data to improve workers’ efficiency, increase customer satisfaction, and reduce overall cost.
Making Big Data Actionable for Mobile Service Workers
The big data hype creates the wrong perception within enterprises that collecting data is the target, while taking future insight for granted. Unfortunately, the reality is way more complex. While data collection is a mandatory initial step, the secret sauce is the suite of algorithms that can provide valuable suggestions or execute corrective actions based on historic data and real-time context. It would be wrong to assume that every business intelligence solution can make an accurate calculation based solely on regular data without adding the element of rich context to the event.
Take service organizations as an example: By adding human decisions along the service delivery process, recorded together with the outcomes of these decisions, artificial intelligence can take into account all context dimensions to find the most suitable suggestion. For instance, if the business objective is to reduce travel costs plus overtime costs, then the dispatcher/manager will need to consider the location of engineer, the travel time to customer site and the actual task duration in order to choose which engineer should be assigned to a new task. A good solution will go even one step further in the analysis and show the engineer’s success record and even former engagements with that customer. This will makes the impact of the decision in line with the company’s business objectives and will have an even greater influence on customer satisfaction.
There are three connected positive impacts on the mobile worker:
1. Predictive everything
The first and obvious is that everything becomes predictive – travel time, task duration, customer satisfaction and even lunch time and duration. Thus, managers can significantly improve their ability to estimate service delivery cost, customer satisfaction and to plan ahead.
2. Proactive analytics
The second positive impact lies within the fact that artificial intelligence can provide this information ahead of time and even suggest what should be done in order to increase the odds to reach the desired target. This way, human decisions of mobile workers as well as managers are improved and result with higher success rate. An example can be to provide critical information for a successful upsell opportunity when visiting a customer.
3. New user experience and engagement
The third positive impact is the creation of a new user experience by pushing the required information based on context to the user just when needed. This way, mobile workers can save critical time instead of swiping, scrolling, pinching and clicking their way through the device in order to find what is needed.
Contextual Computing and Big Data
Successful context-aware artificial intelligence solution requires maximizing the context dimensions that are collected into the big data warehouse. For instance, GPS that becomes abundant in mobile devices enables us to use the mobile worker’s location. More and more built-in sensors are being introduced and becoming a commodity: Accelerometers, gyroscopes, digital compasses, barometers and others, all generate information that can be and should be collected.
Additional context dimensions that should be considered are the service that needs to be delivered, the customer and/or the asset to which the service should be delivered and the mobile worker himself. All together, these total hundreds of pieces of information that should be carefully collected and associated together.
Even more, external sources stream data that becomes part of the context. The weather is a good example of this. Other sources may generate text that requires additional processing using technologies such as machine learning to analyze customer or market sentiment of the company’s products and services.
Gaining a Competitive Edge
Enterprises that will adapt solutions that collect big data and use artificial intelligence to feed mobile and non-mobile users with insightful suggestions will gain a competitive advantage in the market. Solutions such as ClickButler leverage Artificial Intelligence techniques and serve as context-aware intelligent personal assistants that anticipate the user’s needs, send notifications and proactively act with greatest efficiency. Companies that will implement these mobile assistants early on in their business will empower more productive employees and happier customers and essentially gain a competitive edge in today’s service market.