Data Architecture & Data Management
With the Industrial IoT revolution well underway data management is key to successfully guiding your company into Industrial 4.0 standards. With many aspects of Data Management. Today we look at its cores aspects including Data Architecture.
What is Data Architecture?
Data Architecture refers to the models, policies, rules and standards which dictate which data is collected, and how it’s stored, arranged, and put to use in a database system. There are several layers of data architecture; Metadata Management, Master/Reference Data Management, Data Quality, Data Governance, Data Integration, Analytics & Data Privacy. Data Architecture shows how each subject area fits into the data management framework.
How can I effectively manage Data?
There are a number of simple steps to manage your data, first, you need to consolidate your data. This requires a holistic view; people are what drive this as it is people who make the company work. They need to be able to efficiently and effectively use the data, to assist this you need to create an inventory of data assets such as a data lake.
Once the data is gathered you need to structure it to create an element of “self-service” to allow the appropriate OT level experts access to the data without having to call upon the IT department sourcing the data. This creates views for cross-department or cross-data source reports and analytics.
The third step is process mapping and automation. You need to understand the process in order to automate it. You need to get involved with all levels of the company accessing the data and examine how they run processes. With these elements created you can map it out in the form of a diagram. There are many solutions available that facilitate this, many of which will be on display at Industrial IoT Europe. With the physical data and digital data at hand, you can adjust your process effectively to maximise potential output.
Variables can change, whether it be the process and parameters, the decisions, rules or algorithms or even the data itself. You need to have a system in place to be able to govern the data and establish its source and validity and maintain structure. A data lake is useless if you look at a specific field in a table and can’t work out where an element of data has come from, or what it means.
Processes require stories to deliver explanations as a way to suggest, implement and review changes. They require occasional reviews to maintain maximum efficiency and ensure they are not stale or out of touch. Parameters, decisions and any other core algorithms need similar reviews and change control processes. Success depends on having an efficient way to adapt ever-evolving systems.
The use of data as a resource is a very wide and complex structure facing many industries, tackling the nature of analysing data and implementing change can be tricky. But with events like Industrial Internet of Things Europe, you can gain an in-depth understanding of the processes from IoT solution providers and other industry leaders who are already experiencing the benefits of innovation.