The latest aspects of mobile application development cannot stand separate from data and analytics. In a changing world, consumers demand an immediate insight to their “mobile moments,” the exact points where real-time data fuels the decision-making process and prompts buyers to initiate a purchase, while allowing businesses to present their brand consistency across different devices.
Big Data and Business Analytics
To make this happen effectively, enterprises need to have access to real-time data from a wide range of sources to facilitate these processes, where business analytic and big data play vital. To assess its significance in the changing world, here we will focus on global supply chain management (SCM) to demonstrate the relationship between Big Data and Business Analytics to ensure an effective business process. To make the excerpt as brief as possible, we will limit the discussion to the exact few points.
Firstly, the size of data when it comes to handling big data analysis is overwhelming. Going past the era of petabytes and zettabytes, now we are moving to the yottabyte, which is 10 to the power of 24 bytes.
Next, what makes this huge amount of data more complicated is that it is mostly unstructured and cannot be stored in traditional databases. There may be a mix of text, videos, word documents, PDF files, presentations, telecommunications, and in the last few years, the volume of these types of unstructured data surpassed the amount of the entire data ever created before it.
The latest advancement in Internet of Things (IoT) contributes more to big data than any other sources. Having watchful sensors across the globe ranging from wristbands, home electronics, cars, street corners, and GPS to everything else, IoT is exponentially growing.
All these have significant benefits. In the book “The Second Machine Age,” Andrew McAfee and Erik Brynjolfsson (MIT professors) point out the capabilities of apps to recognize third-party inputs from users and process them to share meaningful data points back to the users.
They mention Waze, the mobile app which turns all user smartphones into sensors which constantly upload data to the servers. With information shared by thousands of such sensors, users share information like where an accident took place or where a police aid center is. All the data then gets processed in the form of intelligence which allows thousands of users to know what is going around and how to ideally respond to the situations. This was almost impossible before the introduction of IoT and analytics.
Business Intelligence and Big Data in Global SCM
When it comes to supply chain management, most of the administrators are not much empowered, and the others largely leverage business intelligence that is derived out of big data.
To find the actual wisdom that lies therein, business administrators are looking for tools like predictive analytics to get answers for questions like:
What will happen by giving everything we know?
What should we do to attain success?
If someone runs an SCM, e-commerce, debt consolidation company, or logistics business, when it comes to what one should do to attain success through actionable insights, it goes mostly to what Gartner calls “Prescriptive Analytics,” which is out of the scope of this article and to be discussed later.
Prediction of future developments can be achieved through the predictive analytics platforms. A handful of industry giants include the likes of Oracle, Tibco, IBM, and Esri, and there is a unique genre of companies specializing in data visualization and geospatial analysis. The business analysts apply well-thought algorithms to different data sets to get back real-time predictions at the granular level.
A Sample Scenario
We will take a sample scenario to explore the power of predictive analytics.
Assume that Company A manufactures parts of airplanes and transports only by trucks, train, or cargo ships by keeping the cost effectiveness factor in mind. In order to shorten the delivery time and the inventory carry, Company A sends its shipments to Europe via train, and uses the company-owned trucks to transport goods all across the United States, and do cargo shipping to far-flung places like Germany. From the port, it is a German truck delivering it further to Company A’s clients in the country.
For Company A, each and every shipment presents a lot of challenges. Railways may break down or get delayed due to bad weather. There may be strikes on the ports to which and from the shipment is made, which has nothing to do with Company A operations, but may impact badly. These also may result in idle truck time, high storage costs, and excessive inventory. The distant clients may also make a second thought about their supplier choice before the cargo even touches the land.
Mobile Apps Fueled by Analytics and Big Data
In the above example, Company A surely needs to rely highly on analytics to find actionable solutions. But what's next?
In this scenario, the chaos may raise many questions, such as
How to convey the information across the supply chain?
What sort of a business intelligence should the company practice?
How can the company bring in transparency so that all stakeholders know what’s happening?
How do the company associates on the other end make decisions? For example, the cargo may arrive in 18 hours, and the truck fleet should be available by then.
There are costs involved in all the above business decisions, and to minimize it, analytics-powered mobile apps can be effectively used to transmit these data-driven insights worldwide in real time. The mobile triggers are now built-in to the business intelligence tools, which, in the case of shipments, can send across information about dock conditions, delays, the exact position of the cargo, weather conditions affecting shipment, and a wide variety of similar inputs. Similarly, any other businesses may benefit in the same way as getting online debt reviews or product feedback for an e-com store, for example.
All these are subjective points which can be uploaded through a mobile device, tablet, or other mode of digital input so that Company’s A’s analytics can instantly recalibrate the predictions, and thereby prepare outgoing messages, alerts, predictions, and instructions to its teams for effective functioning. This is a really big deal, which makes big data and analytics the future.