Data has been always described as a 'set of facts'. No matter whether it is meaningful or not and ordered or not. There’s always data, even in the stone age and currently, when a man(object) sees(collects) the Sun rising(data), he gets up and starts his day(decision) is a classic example of that 'decisions are made on the basis of data'. With the growth in the tech industry for the last 100 years, data generation exponentially expanded and now there is no stop to data generation as trillions of GBs of new data is being produced every second.
Machine Learning(ML) and Deep Learning(DL) are among the fastest growing enterprise applications consuming all this digital data as they’re data-hungry. But right data must be provided to ML algorithms in order to correctly utilize ML. So we must know ● What data do we have? ● Where did this data come from? ● How is this data used? ● Who is responsible for this data? ● Can we trust this data? ● What kind of data would work right for this problem? ● What's data integrity? Data Governance and Data Management address most of these questions however, there are some differences between the two.
Data Governance covers the definition of organizational structures, data owners, policies, rules, processes, business terms, and metrics for the end-to-end lifecycle of data. While Data Management is the technical implementation of Data Governance. Data Governance involves the process of managing the availability, usability, integrity and security of the data in enterprise systems, based on internal data standards and policies that also control data usage.
ML is highly dependent on the data. So one cannot simply risk their solution by choosing data without analyzing it. 1. Identify roles and responsibilities for the people governing data. For example Who creates it? Who approves it? Who uses it? 2. Define your data domains, set and standardize data parts and their properties like data-type, values, hierarchy etc. 3. Establish data workflows, it refers mainly to define data supply-chain system. 4. Establishing appropriate controls and processes to optimize your data’s quality and integrity. 5. Identify authoritative data sources and use them. 6. Establish policies and standards and forward it to stakeholders and relevant resources.
Data governance helps in controlling and policing of data usage. This reduces the possibility of misuse of data or affect data integrity which is important as an enterprise can have multiple ML applications, with many of them are using the same shared data.
Data providers are also very concerned about the utilization of their data so that one cannot create any loophole by breaking cybersecurity and policy with their data means.
Poor data governance can also hamper regulatory compliance initiatives, which could cause problems for companies that need to comply with new data privacy and protection laws. Effective data governance ensures that data is consistent and trustworthy and doesn't get misused.
We are willing to use such data for our ML business model as there exists the complete analysis of data and even policies about its usage. By this, we get the sense of how many choices of correct data and its integrity means in the ML domain.
These days users' information is the main source of enhanced business models as there are thousands of recommendation systems and most of them are based on daily basis users' data, providing loopholes to be exploited by hackers to get such data and misuse it. That's one of the applications where Data Governance plays a vital role and keeps data safe and also lets data be used safely. So not only does it assist the ML domain, but it also opens the door for new opportunities for researchers and is highly coupled with performance factors of ML applications.
In today's world, the importance of Data Governance cannot be ignored specially for ML industry and there is still room for further improvements in this field and the integrity of data is being improved every other day.