Artificial intelligence has taken the business world by storm, bringing changes in decision-making supports. It is a new addition to the information component toolkit that all organizations must implement (or at least consider). The introduction of the General Data Protection Regulation (GDPR) in Europe in 2018 and its equivalents in other countries and continents have highlighted the challenge that proper information governance poses to organizations.. In this article, we tackle two elements of the new situation: artificial intelligence and information governance. After a refresher on what information governance entails, we will address two questions: how must information governance take into account the new artificial intelligence tools, which are both consumers and producers of information that needs to be governed? How can these same tools help organizations with data and information governance?
1. What is information governance?
Information governance refers to the establishment, in a company or organization, of an information management policy, with defined objectives and a planned implementation, including human, organizational, and technological resources. The terms “data governance” and “information governance” are used more or less interchangeably to refer to this kind of information asset management strategy. Using the term “data governance” emphasizes the digital aspect of the strategy, but may limit the scope to structured data. Information governance encapsulates all forms of data, regardless of their form: structured data in tables, semi-structured data in documents, or less structured data in messaging software or images. Ultimately, we can define information governance by its objective: maximizing the value of information while minimizing the associated costs and risks.
Information governance includes a set of key processes, such as managing data quality and security (availability, integrity, confidentiality, traceability). While these processes often use IT processes, they are often the responsibility of other business units, and rightfully so.
Information governance is relevant for all areas and sectors of activity. It is especially pertinent when the three complementary dimensions of value, cost, and risk are significant. For example, take the banking sector, where the cost of information management can represent over 10% of revenues and the associated risk is significant, as it can involve, for example, handling sensitive information like money in electronic form! In the health sector, the risk – meaning information disclosure or error – is the most important feature, though we cannot ignore the other dimensions.
Information governance is increasingly better organized in public and private companies, and more broadly, in all organizations. The latter are becoming more advanced, as indicated in other studies. That said, they must constantly face new challenges, including artificial intelligence.
2.The singular experience of governing artificial intelligence
Artificial intelligence tools store data and information to understand, decide, and learn. Data and information are increasingly voluminous and varied. As with all the company’s information, they must be under control, which is one of the key aspects of information governance. Be it data warehouses, data lakes, or another way to store information, nothing must be left off of the information map and all must fit into the dimensions of valuation, risk management, and cost control.
Data quality must live up to its name: it must facilitate the use of artificial intelligence by rendering the processes of collecting, locating, and cleaning data readily available.
Above and beyond these classic aspects of information governance, artificial intelligence raises new questions, like the presence of human biases in both algorithms and data and how this could lead to poor decisions. In addition to collecting personal data, and more generally, sensitive data, the use of information gathered by artificial intelligence must also include an explanation of these algorithms.
This is the challenge of explainable AI, which refers to the methods that equip the “black boxes” of AI with modules that at least partially explain the results they provide. In many cases, this becomes a regulatory challenge. It can also be a trust issue for users to rely on the precious aid AI can provide without reluctance. Finally, it is an ethical challenge for organizations, who are thus able to ensure that their processes are transparent, even when they use artificial intelligence tools.
Artificial intelligence at the service of information governance
Conversely, artificial intelligence tools could also reveal themselves to be a precious tool for information governance.
Therefore, mapping information must, among other things, identify personal data to ensure compliance with the GDPR. Artificial intelligence could help maintain this map, a tedious but important task, which requires analysis and decision-making skills. The volume and omnipresence of data makes this task impossible to accomplish manually.
Humans will remain the masters of the information governance ecosystem, but artificial intelligence and other emerging technologies, such as blockchain, will play a very important role in helping analyze information and ensure compliance. Automation could also help with archiving and destroying information, the ultimate phase of the information life cycle, thus addressing both regulatory and efficiency needs.
Software editors who specialize in information governance have reported integrating artificial intelligence into their component offering, though it remains difficult to evaluate the scope of this integration. Some use intelligence techniques to identify new data, uncover relationships in the data, and classify documents. Learning techniques make it possible to describe the type of content that the software can recognize after sufficient training. In natural language processing, a technique of artificial intelligence, the automatic recognition of characters is increasingly improving. Software robots can execute repetitive tasks, like locating bills in a set of administrative documents and providing the human with an analysis that he or she only needs to confirm, like how ATMs can process cheques automatically.
Explainable AI would also help the organization demonstrate its strong information management when dealing with regulators or auditors. Finally, organizations must face regulations that are constantly changing and that vary from one country to another, in a world with increasingly rich and varied data: in this context, information governance has much to gain from making use of artificial intelligence tools.