Software What is Big Data? Analytics, definition, meaning & examples
There's no way around Big Data anymore. Nevertheless, many companies are still hesitant to address this topic. Read here what Big Data means, which concrete application scenarios exist, and which trends experts predict for Big Data technologies – including practical examples.
The origin of the term Big Data: Big Data is used to describe large amounts of data that consumers, users, and companies generate on a daily basis. At the consumer level this includes data on online search behavior, transaction data or data on purchasing behaviour and, at the company level, production or transport data. Based on these large amounts of data, intelligent software solutions such as the Blockchain can be used to make predictions and identify important facts.
Chip.de provides a slightly different approach to defining Big Data: "A large amount of data is called Big Data if it is too large or too complex to be processed manually. This applies in particular to data that is constantly changing."
Rarely has there been such great consensus about a statement between politics, business and society in general: Data is the raw material of the future. Digitalisation has turned almost every company into a collector and user of Big Data. This applies in particular to logistics, which has always dealt to a large extent with the gathering of information and the resulting conclusions.
When it comes to big data and logistics, the first thing often thought of is trade and transport, where e-commerce giants like Amazon & Co. have long been using their own software to evaluate their customers' data as comprehensively and precisely as possible. But it applies equally to intralogistics. In this area, too, huge amounts of data are generated, which – if used correctly – lead to a significant increase in efficiency and enable completely new business models.
Reservations against Big Data
Big Data offers promising prospects. However, the corporate world often plays by different rules than the digital society. According to the Building Trust in Analytics study, conducted on behalf of KPMG by Forrester Consulting which surveyed decision-makers in more than 2,000 companies in ten countries worldwide, 52 % of companies in Germany worry that data analysis and the use of Big Data could damage their own reputation. Worldwide, the figure is as high as 70 %.
Commenting on the study, Dr. Thomas Erwin, study director and expert for Data and Analytics at KPMG, explains: "German companies use data and analytics to a much lesser extent than their global competitors. The reason for this is the lack of confidence in data analysis. As a result, companies tend to shun the use Data and Analytics or only use it to a very limited extent. As a result, vast data and analytics potentials remain untapped. Another alarming fact is that seven out of ten decision-makers worldwide think that data analysis represents a reputational risk. Our study also shows that decision-makers in Germany doubt that their company is ready to lift the data treasure and that they have the right skills for the utilisation of Big Data at their disposal."
Enhancing the connotation of Big Data
So, how are companies supposed to address their concerns about this new business area, which, according to unanimous expert opinion, will be an important part of business in the future? And how can this be achieved in logistics in particular, where, as shown in DHL's Logistics Trend Radar 2016, it will be particularly important? A first step could be to assign a more appropriate name to the topic, one that more accurately describes the core than the relatively undifferentiated term "Big Data".
Dr. Roland Fischer, Managing Director of the Fraunhofer-Arbeitsgruppe für Supply Chain Services, is also dissatisfied with the term: “I would no longer speak of Big Data, but rather of analytics, because that's what it's all about." This terminology takes us directly to the point: not only the collection of large amounts of data, but also their evaluation, and the conclusions we can draw from them. "As digitalisation progresses, data will accrue that has the potential of creating added value. It starts with the generation of data on machines, objects, and workers. This information will be dealt with in a descriptive manner. This can happen in the Cloud, for instance," Fischer continues.
Added value of Big Data in logistics
A brief glance at possible application scenarios shows why intralogistics in particular should concern itself with the value of data. "An analysis based on historical and real-time data can help to illustrate the complex, highly variable logistical interrelationships in a simple and transparent way and serve as a basis for decision-making. In this way, decision making processes for warehouse technology are also accelerated and made more reliable," outlines Prof. Michael Schenk, Director of the Fraunhofer Institute for Factory Operation and Automation. "The automated capturing of the operational status of the warehouse is also a permanent source of data that allows novel, modern analysis methods to be used. In dynamic day-to-day business, they can ultimately be used to make even more reliable decisions and increase warehouse availability while simultaneously reducing inventory levels and resource consumption." One of the results of this development could be that, for example, innovative software will allow inventory management to be fully automated in the future. However, Fischer points out that this will not happen overnight.
Examples for Big Data applications in logistics:
- Utilising traffic forecasts: CEP service providers and other transport companies have to deliver ever faster in order to prevail against the competition and meet customer requirements. Real-time traffic analysis is a blessing for them - and Big Data makes it possible: The storage of movement data from countless road users allows precise forecasts to be made, e.g. where congestion can occur and how long alternative routes will take.
- Revolutionising risk and inventory management: Production loss due to a shortage of raw materials is a horror scenario for manufacturing companies. Thanks to Big Data, inventories can now be managed much more efficiently with the help of appropriate software. The analysis of consumption data allows to generate warning systems that notify the company of any bottlenecks in advance.
According to experts, Prescriptive Analytics is still far too rarely discussed. It is no longer just a matter of giving recommendations for action, but of determining which is the best option available. This is based on mathematical methods that can suggest different solutions based on Big Data and also show how much these options deviate from the optimum. "We shouldn't be too fixated on the idea that at some point machines decide everything. However, the proposals for decisions can be made by the machine and the person can make the final decision. Once we have built up enough trust, there may well be automated solutions," says Dr. Fischer. "It will be precisely this data that will be used to create new business models in intralogistics. The prognosis is like a weather forecast. But through Prescriptive Analytics, we will be able to influence and change the weather."
This insights will provide a further incentive for companies to become involved sooner rather than later with software for capturing, processing, and evaluating Big Data: Other - possibly entirely new - players will do so. Online giants like Amazon and Google are already exploring markets for new ways to leverage their expertise and skills in new industries, and start-ups are springing up in a variety of industries - some with developments that established companies haven't even thought about. Suppliers of steel and iron products, and of services that are primarily tailored to specific industries, are still relatively unaffected by this development. But, just as intelligent IT solutions are becoming increasingly important to customers, it is only a matter of time before this protective wall collapses.
Where does the data come from?
There are sufficient data sources for Big Data in intralogistics and there is already a large amount of data available. You only have to think about how much data is generated by forklift trucks - movement sequences, routes, goods codes, and so on. A large part of this data can already be collected via control systems, but they are rarely used because the necessary tools are not known and trust in Big Data is still low.
However, if one looks at the notion of the Internet of Things (or cyberphysical systems), it becomes clear that innovative software is urgently needed to handle Big Data. "We are still at the beginning of this development," says Schenk. "On the one hand, the required technologies are available. Thanks to the possibility to network mobile objects with new radio standards, we are even advancing to a new level of technology. In addition, the prices for the technology are dropping while at the same time their performance is improving. On the other hand, there is a lack of interface standards from source to sink. Logistics urgently requires an international and standardised availability of telecommunications infrastructures. This is a priority that must be addressed in the coming years."
But once these steps have been taken, companies face a flood of data that can no longer be traded conventionally. Only machine systems can recognise the patterns that can then be used to generate added value. In this environment, this will also happen decentrally, with objects communicating directly with each other, finding the best local solutions for current problems, and then integrating them into an overall context. The accumulated data can be used in the sense of Big Data to gain new insights.
In this short video Great Learning presents five exciting trends on the subject of Big Data.
Big Data trends in the coming years
All of these new possibilities will certainly result in new requirements which also demand new skills from the management and employees of a company. According to Fischer, the areas they will have to deal with in the coming years are based on four pillars:
- How is the data generated and stored?
- How is the data analysed and the forecasts made?
- How is the data used?
- How is the data utilised and how can it be used to generate business?
This implies the question of how one's own company can be transformed in such a way that it remains competitive even under the new conditions presented by Big Data. According to the experts, there is no alternative to facing Big Data. In other words: go for the technologies, as Fischer explains: "Companies must consider where they can integrate this technology into their products and services and what new services they can create.” The aim should be that working with you company will increase customer loyalty by creating substantial added value for them.
These developments will undoubtedly create a new working reality, partly because they further advance the development towards extensive automation. "In intralogistics in particular, automation can and will continue. Driverless transport systems and robots will transform transport, storage, and handling. However, since buildings and material flow structures can only be changed in the long term, this will probably only be taken into account for new buildings," explains Schenk. "Innovative approaches are likely to arise relatively quickly in the production of new vehicle concepts, especially by the removal of rigid belt structures by original equipment manufacturers. The role of people is therefore not always up for discussion.
This article was first published on MM International