Data value management is a set of frameworks that allow an organization to proactively manage its data assets to help meet its business objectives, and the key to this is the ability to measure the impact of data initiatives based on activity and value. A data management program is essential for protecting sensitive data and complying with privacy regulations. The right approach can also improve the way people use, analyze and share data across the company, increasing accountability and improving results. A TDWI study revealed that 36% of data leaders consider data governance to be a key priority for improving the success of an organization with business intelligence (BI) and analytics.
In this framework, the term “company” represents the nature of data management that encompasses the entire organization. Diagnosing the problems that hinder the use of data in your organization could be the first step in improving the value of data. A common query layer that covers the different types of data storage allows data scientists, analysts and applications to access data without needing to know where it is stored and without the need to manually transform it into a usable format. Just as a car manufacturer cannot manufacture a new model if it lacks the necessary financial capital, it cannot make its cars autonomous if it lacks the data to feed the integrated algorithms.
In fact, it turns consumers into data stakeholders with a true legal recourse when organizations do not obtain informed consent when capturing data, exercise poor control over the use of the data or location, or do not meet the requirements for data deletion or portability of data. Data cataloging solutions, such as ColLibra and Alation, can help companies move towards data intelligence by linking business and technical metadata in one place and providing the necessary context for all data users. In the financial industry, banks and financial institutions automate the preparation of data entered by customers, allowing employees to spend valuable time on customer service tasks that require human experience.
Data management systems are based on datamanagement platforms and can include databases, data lakes and warehouses, big data management systems, data analysis, and more.
Increasingly popular cloud database platforms allow companies to scale up or down quickly and cost-effectively. A data science environment automates as much of the data transformation work as possible, streamlining the creation and evaluation of data models. The search terms you type to answer an important question, the amount of time you spend in an application, and even unstructured data, such as the content of your most recent LinkedIn post, are raw data that has value to someone, somewhere. A data management platform is the fundamental system for collecting and analyzing large volumes of data in an organization.
Data scientists combine a variety of skills including statistics, computer science and business knowledge to analyze data collected from the web, smartphones, clients, sensors, and other sources. A set of tools that eliminates the need for manual data transformation can streamline the formulation of hypotheses and the testing of new models. Data is a corporate asset of pure gold when it comes to making assertive decisions and designing strategic action plans.