Information governance makes a positive contribution to businesses. Businesses can leverage information governance to create new income by using big data mining techniques to acquire valuable corporate data.
The maximum value of big data is realized when the output of computing is efficient at creating new wealth. It is important to find meaningful data among a bulk of information. That way businesses can see the bottom line benefits of making the business case for big data governance.
Ultimately, information governance should be "the rules-based management of digital information that connects to the advancing, the wealth creation, and wealth preservation goals of an organization.” The key is to uncover hidden data through big data mining because this can help efficiently filter data to find what is actually worth taking a look at.
For big data analytics to be successful, information governance rules must be followed closely by every person in an organization. Think of big data analytics as a machine, where the engines are performing calculations on the numbers, but they can only do this with correct input -- the data has to qualify, it has to meet the rules that are useful for the engine to compare to find the patterns, and if the data does not qualify, it is useless.
1. Data must conform to rules
Information must be first validated. Then data needs to be compared with the rules in order to decide whether or not to pass it through to the analytics engines that are being operated.
2. Locate sources of new data
This includes warehouse data, graph data, text data and multimedia data. Event record logs, where every machine and application creates records of how they perform their function, are also an important part of information governance because they allow information governance officers to see what has happened to the data. Records can hold important information such as who used what device to type specific keystrokes, and what authentication sequences were carried out.
3. Establish rules for defining event logs
The information governance process should be seen as a front-end process rather than a back-end one. It is important for information governance experts to be involved in making the rules for governing information and handling data provenance. The systems being used today are designed and engineered without the realization of how to utilize their output -- their performance data and event logs, to create new channels of revenue.
4. Data provenance aligns with the rules you set
Data provenance is the records of the entities, people and processes involved in producing a piece of data. It is important because it allows information to be more easily identified as being what it purports to be. Understanding the data’s provenance makes the data much more valuable.
5. Provenance chains together to improve the value of data for analytics
Data provenance improves the value of information intelligence by eliminating excess validation. The validation exercise requires checking the metadata. It chains together so that the result is the net outbound data coming through systems that have been enhanced with information governance policies.
6. Effective information governance requires engineering the information life cycle
This helps with the business’s efficiency. Information governance should be a part of the engineering life cycle because this will allow for opportunities to discuss each data asset’s revenue opportunity.