IT Trends 2022: The Year Of Digital Evolution
Companies’ IT systems will continue to develop in 2022. Corona has provided unprecedented dynamism in one area: digitization. The momentum in this development will also be evident in the coming year using the example of several IT trends.
Five Technology Drivers That Will Have A Noticeable Impact On Corporate IT Applications In 2022
Distributed Cloud And Edge Computing
The need for flexible adaptable software that is available everywhere via the cloud will also increase in 2022. After the development from large monolithic systems to small-scale microservices is already clearly recognizable, its infrastructure is now following with the distributed cloud. There is no longer a central data center with this architecture approach, but the computer load is distributed over small regional clouds.
This networked and distributed servers infrastructure also provides the ideal basis for a concept derived directly from it: edge computing. The aim is to bring servers and applications closer to where the data is generated and thus noticeably shorten their processing time.
The decisive advantage of the distributed cloud and edge computing and lower latency and better performance is more excellent reliability, as the individual regional clouds can work independently of one another. This means that if a cloud server fails, this does not forget the entire system. In 2022, the demand for distributed cloud and edge computing will primarily be driven by data-intensive applications, for example, in the area of machine learning.
Also Read: Why Intelligent Chatbots Should Be Part Of Your Marketing Strategy
Process Mining And Data Mining
In 2022, many companies will also have to be prepared for irregularities in their processes. Companies will be forced to interrupt supply chains and ongoing shortages of components such as microchips. Against this background, process mining will gain importance. What is meant is the systematic analysis and evaluation of business processes.
Process mining has its origins in data mining, i.e., analyzing large amounts of data to recognize new cross-connections, patterns, and trends. For example, companies can use data mining to personalize their offers or carry out shopping cart analyses. In process mining, this procedure is transferred to a complete process. The events occurring therein, so-called events, are logically linked in chronological order. On this basis, a process can be visualized and analyzed in real-time.
However, fully digitized processes are a prerequisite for this. This is the only way to create the database required for process mining. The integration of AI also makes it possible to receive even more well-founded and, particularly, more intelligence because of prescriptive analysis. Keyword predictive analytics. For example, it can show when the demand for a particular product will increase in the future and how much.
The Internet of Things (IoT) combines software applications, machines, systems, and tools to form an integrated and increasingly autonomous overall system. This produces a massive amount of data every day. This creates an essential basis for analyzing company processes across the entire value chain, improving them, and making them more efficient based on these data evaluations. Taken in isolation, however, the recorded data does not yet offer much value-added value.
They only develop their usefulness in a larger context.
The ERP system plays a decisive role in this, in which all operational information comes together and is then filtered, classified, and forwarded to the downstream applications. Against the background of such a networked system, the data flow can also go beyond the boundaries of a company and include suppliers, for example. The ERP system thus becomes a central software platform and an integration hub for the Internet of Things. At the same time, a new generation of ERP systems will move into companies and businesses.
However, the decisive requirement for ERP 2.0 is that all components of an IoT architecture can be integrated with the ERP system via standardized interfaces. They are starting with the database technology, through all analysis systems, to the corresponding methods for external business partners who are also part of the network.
Ethically Responsible AI
The potential of AI is known by now, but there are also very mature systems in which the technology proves itself to be practical daily – such as in advanced data analysis. Here, AI helps to make strategic decisions based on data. AI is thus becoming a powerful tool for companies to position themselves in the best possible way against competitors in the market. The findings derived from AI systems also repeatedly lead to critical queries. They revolve around data protection and compliance topics and come from the ranks of the stakeholders of those who use this technology. Against this background, it will no longer be enough in the future to blindly use AI in the interests of one’s own business goals.
In the future, AI will no longer only be viewed from a purely functional point of view, for example, with a view to process improvement or automation. Instead, this technology is increasingly being placed in a direct relationship with the stakeholders it is intended to serve. In this context, fairness in competition and transparency vis-à-vis control authorities are also at stake.
Data Hygiene Through Data Quality Management
The value of data as the basis for reliable company decisions is increasingly recognized. With the exponentially growing amounts of data available to companies, the question of data quality will also get louder. At its core, it is about avoiding dirty data. Against this background, a challenge for many companies is the sheer number of operational data sources and thus the fragmentation of data, which are often available from outdated systems in different formats, metadata, forms, and obsolete database formats. As a result, this leads to poor data quality.
Therefore, data quality management will increasingly move into the focus of entrepreneurial activity. The aim here is to ensure the data quality from the outset and to prevent dirty data from arising in the first place. A prerequisite for this forward-looking form of data hygiene is data governance. More and more companies will, therefore, in all likelihood adopt a set of rules for handling data in the company.
Conclusion: 2022 Will Be The Year Of Digital Evolution
Next year, it will be essential for companies to follow the logical and necessary next steps in digitization to position themselves optimally for 2022 and beyond after the cloud comes to the distributed cloud, after big data comes targeted data, and process mining, as well as a stronger focus on data hygiene. ERP needs an update to ERP 2.0, and instead of simply using the AI potential, the focus is on ethical responsibility with artificial intelligence. Therefore, in the coming year, we will have a year of digital evolution.