Data is one of the most valuable things for businesses today. Precisely, data is known as the new oil as it is used to derive insights, which can help drive customer retention, upselling, new revenue models, advertising, etc. If data is the new oil, insights are the new money. Due to advances in computing, the internet of things, machine-generated data, and more, data volumes are now exploding.
Basically, it is not enough to have data. One needs to have a data practice, which is a commonly understood and reliably executed set of principles for managing data. In order to create a good data practice and avoid data related fires, organizations should pay attention to these four principles:
Firstly, you need to understand where the data came from. Data abounds, but it is not of the same quality all the time. Some data is dirty, some are flat out wrong, and others may be fictional. This is particularly true if you mostly rely on the data gathered from the public domain. Some datasets contain bias, which can create major risks for businesses if used in an AI. A solid understanding of where the data comes from is essential to know whether the insights the data generates are valuable or even safe for the organization.
As AI is becoming prevalent, cities and countries are imposing new rules about how customer information can be used and what rights consumers own regarding the use of their data. As these laws thrive, users of human originated data need to pay particular attention to how this data is used and protected. You always need to remember how you can use the data.
Notably, data privacy is a form of data protection. It ensures that the data access is controlled to protect privacy. Furthermore, another aspect of data protection is to ensure that the data remains available to those who need it. The more important that data becomes to a company, the more its loss can impact the business.
As it is very well understood that raw data is not terribly useful. For data to be fully leveraged for insights, it is necessary to make it refined through the process of data cleaning, analyzing, and evaluating. This process is precisely known as data preparation. Notably, having a good strategy for data protection is a major key as it can make a prominent difference in the quality of insights generated from the data or the quality of Artificial Intelligence that are trained from the data.
As business models evolve, more businesses are likely to find that data is one of their biggest assets. These principles will definitely help protect and grow this asset and show useful and valuable insights.