Data governance is important for more than just compliance standards and customer registries. Today, machine learning is one of the fastest growing fields in the world of computer science, and staggering breakthroughs appear in the news regularly. It’s easy to let routine and enthusiasm blind you to the truth of machine learning. No matter how advanced, an AI cannot provide original ideas, and any machine learning program is bound to the data that drives it. This is why data governance drives effective machine learning.
Proper Data Governance Transforms Your Data
To understand how important data governance is for improved machine learning, you must go back to data governance’s basic premise. Data controlled by strict governance looks nothing like data as it natu
rally enters your system. This comes from organization, classification, and a thorough labeling system that accounts for data’s flexibility. Data does not fit into a single category. An email, for example, needs a security classification and the contents must be linked to the appropriate department, project, and/or team. On top of that is the fact that an email is different from a document or spreadsheet, and must be filed as such. Emails create additional complications because a thorough governance system accounts for the sender, the recipient, and any secondary recipients who received copies.
All these categories may seem simple to a human, but they represent the framework for any machine learning program. More importantly, humans understand errors and complex classifications. A computer cannot. This means any error or nuanced categorization in your data governance protocols will set back machine learning significantly. It may take days, weeks, or even months to find the source of the problem. Fortunately, good data governance can actually enhance machine learning.
Less Work and More Results for Machine Learning
It’s important to remember that even though machine learning transforms how businesses operate, machines do not have the scope of reference and understanding humans do. Even the most advanced AI programs cannot keep pace with a seven-year-old. A small child’s synaptic connections grossly out perform any available AI. That means to improve machine learning, you must make the challenge easier.
The clearer you make your data governance guidelines, the faster machine learning advances. Create simple, logical categories and classifications. Found every connection on basic logic. If a piece of data doesn’t quite fit, don’t just slap a title on it that is ‘close enough.’ An AI builds on basic instructions, but that doesn’t mean it can move outside of clear, logical paths. It builds connections based on previous instructions. It cannot generate new ideas, so don’t expect a machine learning system to figure out what you meant. When in doubt, always choose the shortest, simplest solution. The easier your data governance protocols are to follow, the faster machine learning programs advance.
Machine learning is the end result of a long and complicated process, and that process begins with data governance. Improving machine learning depends far more on how you organize and store your data than anything else. Data governance creates your machine learning program’s basic intuition.