Data insights refer to the understanding of a particular business phenomenon you are able to achieve by using machine learning and artificial intelligence (AI) technology to analyze a dataset. For example, a machine learning model that estimates the likelihood of a customer to churn will reveal what factors drive churn rates, allowing decision-makers to make changes to business strategies and processes.
One of the best ways to understand and communicate meaningful insights from data is to use tools that help visualize a model’s outcomes and give different ways to explore and understand your data. This translates to real business value in the form of increased ROI on advertising efforts, more accurate loan default predictions, and much more. The clarity of vision from data insights allows users to make better decisions based on increased model interpretability, allowing analysts and other users to explain model outcomes to key stakeholders.
Why are Data Insights Important?
Insights allow users of all skill levels to understand what the model is doing “behind the scenes,” which is especially important when it comes to highly regulated industries like banking and healthcare. If you don’t understand why your model is drawing the conclusions it does (i.e., you don’t have any insight into the inner workings), the model’s practical usefulness is limited.
Data visualization tools help users understand and explain insights from machine learning model outcomes. Whether it is through simple graphical representations like word clouds or more complicated and flexible data visualization tools like Tableau dashboards, these tools make it easier to understand and communicate the value uncovered by the model and drive better business decision-making.
Data Insights + DataRobot
DataRobot’s Insights functionality has a host of tools for model interpretation, all of which allow users to easily understand the models. For example, if a hospital’s dataset includes a feature with doctors’ notes in the form of free text, DataRobot automatically generates a word cloud to indicate which words in those notes are related to the hospital’s target variable.
The example below shows words from just such a dataset which relate to patient readmission rates. The red words are correlated with high-risk patient notes and the blue words are correlated with low-risk patient notes. The size of the word represents how frequently it occurs in the dataset.
DataRobot also includes tools that measure and rank the impact of individual features (see Feature Impact) and provide details of how the model works and the processes it runs on input data, as well as Prediction Explanations that give the top reasons for the model’s outcome for each individual record.
What Is Data Governance?
Data governance is defined as the organizational framework that applies to how data is obtained, managed, used, and secured by your organization. Having a strong data governance strategy in place empowers your organization to trust the integrity of their AI and machine learning models by ensuring that their data originates from reliable sources. These structures also ensure that your machine learning models are instructed to follow your organization’s key principles and values. With data originating from multiple sources that rarely, if ever, follow the same data science practices, a strong data governance framework is the key for your organization to follow best data science practices.
Why Is Data Governance Important?
First, data governance is one of the four key principles of AI ethics – which also include ethical purpose, fairness, and disclosure. Governance (the fourth principal) is necessary to study and understand the outcomes of AI failures and push your organization to apply high standards of risk management to your models. This is especially important in situations where life and death are stake, such as hospital treatments and healthcare.
Second, as businesses, organizations, and agencies often rely on data from numerous sources with different workflows and data-handling practices, governance is essential to reaching best data science practices. Establishing a reliable governance framework can provide your company with assurances that the data used by your models is reliable and consistent – and that the model reflects and follows your organization’s internal values. Understanding your own data governance definition enables your organization to more easily trust your machine learning models. It will also enable you to more easily explain relevant findings to others and how the model arrived at its conclusions.
Finally, following existing regulatory compliance obligations is another important motivation for firms to craft and adhere to a data governance model. Recent data breaches have prompted many organizations to make security an integral part of their own data governance frameworks. Having a clear data governance framework in place can help your organization remain compliant with existing laws like the European Union’s Global Data Protection Regulation (GDPR).
Traditional approaches to data governance are often based on corporate policies that are implemented across organizations in a top-down fashion. However, the reality is that these policies do not accurately reflect the way business teams or individuals interact with data. As data and analytics becomes increasingly democratized, it is important for data governance policies to reflect the day to day activities and interactions with data. This notion of bottoms-up data governance is often referred to as emergent data governance.