Big Data Governance: Controlling and Handling Data
Big Data Governance: Controlling and Handling Data
Accountancy within organizations has been around practically since the existence of organizations itself. As long as we can recall, we have kept track of what happened within an organization, or in the old days while farming or herding. The earliest records of accounting data back 7.000 years ago, from the ruins of Babylon, where the growth of crops and herds were recorded. Since then, accountancy has evolved into an entire industry that helps organizations understand their resources and determine the correct value of their assets and liabilities.
With the new era of big data, a new era of accountancy is required; data accountancy or data governance. One that is able of handling high volumes of a variety of data that has to be checked and controlled whether it is correct or not, with extensive and smart algorithms that perform incredible analyses within a fraction of a second and that provide predictions and visualizations that are used to define the course of an organization that will affect many stakeholders.
With so much data within an organization it is extremely important for organizations that the data they generate, store and analyze is 100% correct. With information-centric organizations that base their decisions on algorithms, it is fundamental that the algorithms and their (predictive) analyses are accurate. But who is qualified of checking and controlling thousands of Petabytes of data or extensive and really complex algorithms that improve over time? How do we ensure that consumer data is kept secure, private and is not abused? How do we ensure that the predictions made are based on the right variables? How do we know that green is really green and not perhaps red?
The era of big data will oblige a new form of auditing and control, of checks-and-balances and perhaps as well of quality labels for organizations. An ISO for big data? It could result in a completely new industry being developed next to and part of the global big data industry that is being formed in the coming years. Especially when organizations start to place big data on the balance sheet after they have determined the ROI, auditing or regulating organizations will pay very close attention to how the data is stored, collected, analyzed and visualized as it could make or break an organization.
There are three pillars belonging to big data governance: the data itself, the algorithms and the auditors responsible for the checks and balances. In the next post, I will discuss these in more detail and give insight in how organizations have to deal with big data governance.
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Frequently asked questions
What is big data governance?
Big data governance is a new form of data accountancy needed in the era of big data. It involves handling high volumes of varied data that must be checked and controlled for accuracy, using extensive algorithms that perform analyses and provide predictions and visualizations used to guide organizational decisions affecting many stakeholders.
Link to this questionWhy is data accuracy so important for organizations?
Information-centric organizations base their decisions on algorithms and predictive analyses, so it is fundamental that the data and algorithms driving those decisions are accurate. With so much data being generated, stored and analyzed, organizations need it to be completely correct, since inaccurate data or flawed variables could seriously affect stakeholders and outcomes.
Link to this questionWhat are the three pillars of big data governance?
Big data governance rests on three pillars: the data itself, the algorithms that analyze it, and the auditors responsible for checks and balances. Together these elements ensure that data is properly collected, stored, analyzed and visualized, and that the algorithms producing predictions and insights remain accurate and trustworthy over time.
Link to this questionCould there be an ISO standard for big data?
The era of big data may require new auditing and control systems, along with quality labels for organizations, similar to an ISO standard. This could lead to an entirely new industry forming alongside the global big data industry, especially once organizations start placing big data value on their balance sheets after determining ROI.
Link to this question