Artificial intelligence also termed as machine intelligence, is the intelligence demonstrated by machines in comparison to the natural intelligence which we receive from human beings. It is the study of ‘intelligent’ likely to be a device that studies the environment and understands and finally reciprocates by the actions with a motive of achieving the objectives successfully. It is also used to describe the cognitive functions of machinery such as learning and problem-solving which are associated with a human mind. The two important techniques for conducting appreciable AI are machine learning and statistical analysis. It helps in the identification of hidden patterns and correlations in the huge data survey which is quite impossible for a human mind.
The advanced infrastructure setup, low computer hardware cost has to lead to be a significant asset of Artificial Intelligence. For adequate data governance, it is highly recommended to avail maximum usage of artificial intelligence. It depends on the quality of the data. The worse quality data results in worse Artificial Intelligence whereas good or better quality data results in better Artificial Intelligence. It is quintessential to know about the quality, type and specifications of the data to be governed. Data governance provides immense support to the organizations in managing the data wisely particularly on grounds such as availability, security, integrity. Better and optimum utilization of technology also results in an increase in business values and Artificial Intelligence transformation.
Data governance training along with Artificial Intelligence talks majorly about:
- Security of data: Artificial Intelligence measures the reliability of the dataset and evaluates how reliable one is. It points to a single data field and gradually relates to the number of outliners caused. Reasons for causes of outliners can vary from database failures to wrong collection strategies, etc. it ensures the timely security of data generations and its acquisition for utilization. A delayed data bank is obsolete and hence loses its ability to reflect the physical phenomena. It helps in calculating of the consistency of data when the data is stored at different locations. It ensures the availability of a complete percent of data and leaves no clues behind the missing values.
- Right measurement: it monitors the various dimensions which can vary from different from each other due to different business requirements. For example, in companies’ data sheets, the most important quality features to be considered would be timeliness and accuracy. For better governance, consistency and integrity would also be an appropriate metric for a quality dimension check. After the monitoring of the data and its dimensions, defining a baseline with values and ranges is highly recommended. Inaccuracy in data governance leads to huge mistakes while the formulation of business policies which can result in a decrease in sales opportunities, increased customer dissatisfaction, etc.
- Maintenance of high-quality data: For the maintenance of data, its quality is first determined and the weighting factor of data is evaluated. For appropriate determining and evaluation factor of metadata management solutions is required and further the data is evaluated. For appropriate determining and evaluation, baseline rules are formulated which makes assessing the high-quality data easily. This entire process becomes part of the preparation phase. After the preparation, an acquisition is the collection of data is conducted. Next comes to the assessment of the quality of data and several resolution processes are implemented for correct monitoring. This entire process helps in producing a data quality report whenever required. The set scores in the data system also help in tracking the quality of data.
- Adequate solving and monitoring issues: it is an important feature wherein the flagged issues by the software or humans are adequately monitored and several steps are followed for their timely solutions. It widely depends on the quality of data, higher the information in data, the higher are the chances for perfect resolution of the data quality problems are. It becomes quite easy if each such issue has a unique identifier attached to it. The best example of a good identifier is the sequential numbering of issues. It gives a picture of the number of issues resolved to date. Not only solving the issues is the priority but it also enables the data used to make a comparison which further highlights the number of resolutions, accuracy, and integrity.
- Helps in maintaining of the asset as a strategic asset: intelligence can work only with the availability of high-quality data. Artificial Intelligence is considered to be a strategic asset these days. For proper data governance, it is very important to set a few defined data policies, standards, and processes. These are considered to be quick hacks leading to intelligent data governance. Various data policies help in the management of data. The set data standards maintain the data’s integrations and lineage whereas clear and defined processes make sure that the quality and results are maintained. Surely, data governance is not a day task but Artificial Intelligence helps it to be a long term plan aiming to the competency of data.
Artificial Intelligence helps the data governance task go on ease and is a boon for data scientists and Machine learning engineers. Data governance approach aims at preserving the data up to date to ponder continuous discoveries, efficient address of data problems, etc. It is highly recommended to start with data governance soon as sooner the management, sooner you get the benefits and later you do it, higher is the cost of fixing.
