By 2020, every person will generate 1.7 megabytes in just a second, and globally there will be around 40 trillion gigabytes of data (40 zettabytes). At the same time, the types of data, which the businesses collect, store and interpret, are evolving with a fast pace: Big Data, Small Data, Dark Data, Smart Data, Real-Time Data, and in the near future it is expected new data types to appear, together with new data-storage options, and new analytics tools.
With such enormous amounts of generated data and so many possibilities for managing it, it will be essential, yet challenging, to generate only high-quality data and store it securely. As a result, businesses are stimulated to embrace and adopt a Data Governance policy before it's too late. And when it comes to the business entities operating in Industry 4.0, this is a must.
The reason for this is the fact that enormous amounts of industrial data are generated through a number of different devices and systems throughout the supply chain: machines, assembly lines, mobile devices, utility meters, smart sensors, automated appliances, routers, robots, and others.
For those organizations, time, quality, security, and transparency are essential factors, because of the vast amounts of data which they generate on a daily basis. And in order to efficiently and adequately deal with their collected data-sets, and get the best out of them, it is crucial to adopt Data Governance policy. For those organizations, this is the key to optimize their working processes, enhance their decision-making and strengthen their risk assessment, in order to ensure continuous business growth, boost the quality of their performance and maintain the competitive edge.
And without high-quality Data Governance, the generated data stays either unusable or does not bring significant valuable insights to the company. Now, let's see in detail how great DG contributes to successful industrial operations and why it is a key tool in Industry 4.0.
Gives clarity and transparency
Data Governance allows companies to efficiently manage complex data sets, as well as to improve their navigation experience through the collected data. This way, the organization has more clarity on what kind of data is available, where it is stored, what is the quality of these data, are there any outliers or unnecessary sets.
Having high-quality data is crucial for smooth operations, especially when AI and IoT are used in industrial processes. Those two, together with Machine Learning, significantly rely on excellent, high-grade data in order to operate properly and derive superb business insights.
However, without proper Data Governance strategy, the industrial organizations cannot ensure the collection of such high-class data; therefore, they cannot guarantee the quality of their analytics, based on IoT networks, machines and robotics. What DG does, in this case, is ensuring strict guidelines in the processes of collecting, storing and interpreting data sets, in order to provide the employees with trustful analytics, presented in a systematic structure.
Such data management gathers all sources of metadata, including primary sources, data lakes, data warehouses, error systems, and others, into a responsive, structured framework. And by integrating data catalog into DG, it becomes even easier for the employees to find specific data, and view lineage and relationship between sets. What is more, by using filters and integrating industrial and business terms into the framework, it becomes convenient and fast to use the search features and narrow down adequate results. As a result, organizations can quickly track data based on specific production processes, dates, batches, and other referent points.
As the data is accurate across all industrial systems, operational transparency is achieved. This ensures all the employees and management have full trust in the data quality, architecture, and reliability. As a result, DG provides a great opportunity to always use relevant data, and make operational decisions entirely based on trusted, high-quality data-analytics.
Mitigate risks
Excellent Data Governance ensures that every industrial business entity is auditable and complies with the laws and regulations in the sector and country of operation. That means that it can properly report information regarding its data integration activities, fulfilling certain criteria and standards, established by the authorities. This is very important, because, without regulatory compliance, all data collection, storage, and analysis are outside the law, which can cause financial and operational penalties.
On the other hand, proper DG strategy guarantees high data security and prevents unauthorized use of the data sets. In that sense, Data governance is essential when it comes to Blockchain. However, there are still a lot of debates if Blockchain can replace or complement DG, or the contrary: Blockchain cannot exist without high-quality data governance. In any case, secure and transparent data transactions are key for smooth supply chain and manufacturing activities flow. But why?
Structured and transparent databases guarantee instant identification of outliers in the data sets, as well as no missing or lost data (throughout collection, processing or transaction processes). This opportunity for real-time error identification and quick access to all kinds of data, helps the industrial organizations to immediately intervene and solve any occurred issue, ensuring high-quality data-driven insights generation, as well as the smooth flow of supply chain operations.
Breaks down silos and hidden data
When we have all of our collected data structured, and we have created a transparent framework with trusted data sets, the chance of having hidden data or data silos is extremely low. Proper DG policy ensures that all the collected data is properly managed, processed, and distributed across all the departments in the industrial enterprise. This gives organizations the opportunity to optimize the data value through business analytics and AI applications, which leads to better business intelligence.
Lowers costs
Lowering costs through adopting high-quality data governance may sound rare, however, this practice has an extremely positive impact on the profits in the long-run period of operation. But how?
Having access to trustworthy, high-quality data results in two major advantages:
- Better knowledge of customers behavior. The business enterprises, operating in Industry 4.0, are put under a lot of pressure due to the increasing demands and quickly evolving desires and needs of the customers. DG can help the businesses to know their clients better by providing them with systematic and structured databases, which can be used for high-grade business insights. And when we know our customers, we serve them better, which leads to higher profits.
- Fewer errors. Lost data, mistakes, and outliers need time to be put in order. Often, the mistakes are related to duplicated data, missing sets, or discrepancies in data relationship and tendencies. Data Governance prevents both individual and systematic errors throughout the whole industrial organization. This saves time for the employees in terms of identifying, locating, tackling, and solving the problem. As a result, they can focus on their primary tasks, instead of spending time on extra assignments, which would lead to extra costs, due to functional and operational delays.
Conclusion
By 2020 Data Governance will no longer be an option, but will become a necessity for obtaining high-quality data sets and deriving excellent business insights. And for industry 4.0, which benefits from IoT, AI, and ML, it is essential to ensure constant data quality, security, accessibility, and transparency. All that for smooth industrial operations and continuous profit and organizational growth.
