## 7 RegTech and Industrial Internet of Things
### The Core of RegTech  
The data-relevant treatment is the focal objective of the entire regulation compliance process, which involves data from both regulators and regulated entities. Due to the existing legacy systems, the integration of varying user systems is the key to large-scale RegTech adoption. Such integration must be low-cost and involve minimal disruptions to operations and existing practices. RegTech is expected to gain unprecedented popularity if its implementation can achieve this goal with the lowest cost in terms of time and monetary investment and, at the same time, help companies operate and run more efficiently under the premise of protecting enterprise data. It is suggested that the general activities of RegTech are data collection, data processing, data analysis, and data visualization [5].  
* _Data collection_ is a continuous, repeating process that involves quality management internally and regulatory compliance externally. Considering the privacy of data and the protection of organizations' interests, a screener is necessary to be designed in the data collection process to make sure that only the compliance-related required data are retrieved. In the traditional regulation audit process, regulation-related data are prepared in advance by the organizations on paper or digital devices. Collecting authentic data is a significant challenge for supervisors, as these documents can be easily forged or tampered with.
* _Data processing_ is the step where the authentication of data should be verified and the fake, invalid, and irrelevant data should be cleared.
* _Data analysis_ is the process where the generated data are matched with the standard of regulatory rules, and a positive or negative decision is made along with it.
* _Data visualization_ is the proper presentation of data, showing the result of data analysis in a readable and easy-to-comprehend format. Data storage, or data archiving, is the definitive treatment of data. In this step, all the data should have clear identification, and they deserve various destinations as per their categories. In terms of complexity, the most time-consuming procedure is data processing.  
There may be some additional steps, such as _data storage_ or _archiving_. The development of regulatory technologies benefits some key drivers that are relevant to data processing. It is mentioned that the following data issues in the financial sector, to some extent, drive regulation technology forward [5]:* _Unstructured Data_: Data is collected from various sources within organizations without a unified format, so it is time-consuming for regulators to aggregate information and identify risks.
* _The complexity of Received Data_: The increasing regulations, which are increasingly complex, challenge the expertise of professionals to make timely and correct judgments or recommendations.
* _New Risks Caused by the Dynamic Development of Entities_: Migrating financial companies to virtual companies may bring new potential risks to the industry in terms of regulation and management.  
We can conclude that the financial industry and other industries are facing these problems, too. Although other industries started later than the financial sector, the issues and needs in these regulatory activities are similar. Other industries even face more problems. A major difference between the financial sector and the other physical industries is that there needs to be more filed data in the former, but field data in the latter is one of the main data sources.