4 ways to monetize data using IBM Cloud Private for Data
Virtually every company is trying to discover the potential of the data they are collecting, generating and analyzing.
It’s not the data itself that creates the commercial value. Value creation depends on data management when insights can be found in a timely manner. Not all organizations are equally adept at translating data into dollars, but their ability or inability to do so is impacting how well they compete.
Data monetization requires a platform and architecture that can meet technical demands, including data integration, governance, machine learning and all cloud deployment characteristics.
IBM Cloud Private for Data is a data and analytics platform that provides that cohesive ecosystem to accelerate data monetization to impact the bottom line without the data leaving the organization. This multicloud platform delivers a broad range of core data and analytic microservices with the option to add more from a growing services catalog.
Here are four ways to monetize data using IBM Cloud Private for Data:
- Build a vision and roadmap. You can measure your effectiveness for using data and analytics against this roadmap to optimize key business processes, uncover new business opportunities or deliver a differentiated customer experience.
- Get ahead of the 80/20 data science dilemma by solving the data problem quickly. Data resides in silos and is difficult to access. It can be unstructured and difficult to consume. Data can also lack lineage. This makes it harder to make data-driven decisions.
With Data Virtualization Service available on IBM Cloud Private for Data, clients can identify and integrate data sources across multiple types and locations and turn it into one, logical data view. Using data virtualization, users can query data across many systems without having to copy and replicate data, which can help to reduce cost. It also simplifies analytics and makes them more up to date and accurate, because you’re querying the latest data at its source rather than replicating the data.
- Plan, develop and operationalize machine learning using built-in analytics or add-on services. With IBM Cloud Private for Data, data scientists, data engineers and application developers can collaboratively and easily work with data to build and train models at scale. IBM Cloud Private for Data also allows for self-service discovery of data, models and more, activating them for artificial intelligence, machine learning and deep learning from the Enterprise Data Catalog.
IBM Cloud Private for Data provides data scientists and application developers a choice to create assets in Python, Scala and R, as well as use open source frameworks that are already installed. The IBM SPSS Modeler add on enables data scientists to discover and prepare data, develop and manage models, and visualize data with no coding required. Watson Machine Learning Model deployment feature models can be deployed into production models without writing any additional code, and they can be monetized in an operational context.
- Create a cohesive information architecture. With a cohesive information architecture, your data can be mapped to your standard set of business terms and categories and follow information governance policies and rules that you designate. IBM Cloud Private for Data helps enable you to structure your enterprise information in a logical way, discover relationships between assets and keep your data up to date. Your enterprise has a lot of data and many assets are related to one another, but not in an obvious way. This data changes every day.
Using IBM Cloud Private for Data, you can create a data dictionary with a common business vocabulary, which can help define all important aspects of your enterprise.
Monetizing data requires that right platform which can be deployed in your Enterprise to make data driven decisions easy. IBM Cloud Private for Data is that platform. Give it a try today and see how you can build an AI-powered app in our trial journey – IBM Cloud Private Experiences.