Where are you in your artificial intelligence journey? Whether your organization intends to use AI to support front, middle, or back-office activities, you must overcome fundamental data challenges.
Is your organization prepared for the era of artificial intelligence, or AI? It is an acronym that is on many minds and questions abound: What can we accomplish with AI? How can we integrate it into our operations? Where do we even begin?
Answers to these questions can vary depending on your organization’s specific objectives. While the journey may seem daunting, particularly for smaller enterprises or startups, it all starts with a single crucial step: data. Clean, accessible, and organized data lays the foundation for successful implementations of AI, machine learning, data science, and predictive analytics.
A quick Google search reveals that data scientists can spend a sizable portion of their time cleaning data. Estimates vary, with some sources suggesting up to 80%. Regardless of the exact figure, it is universally acknowledged that these valuable experts often dedicate too much time to mundane data cleaning tasks.
When data science teams have access to reliable, organized, and governed data it unlocks so much more potential for innovation. This is where Grandview Analytics can help.
Grandview Analytics consultants have solved some of the most challenging and complex data transformation initiatives in the investments industry. This experience can be leveraged to help you prepare for the deployment of AI tools and capabilities. Whether you seek to deploy AI to support front, middle, or back-office activities, you cannot be successful without solving some of the fundamental data challenges that we routinely help our clients conquer. Below, we highlight some foundational building blocks where our expertise will help with your AI preparedness.
- Unified Data Ecosystem: Our clients often initially struggle to harmonize various internal, external, and market data sources into a single cohesive data set. One data element might be in one database, the next is in another, and a third is duplicated across other locations. This is a frequent issue, and Grandview routinely brings order to this chaos for our clients. We have worked with asset managers, insurance companies, stock exchanges and others to develop roadmaps and build solutions that migrate and consolidate data to provide a consistent supply of high-quality information that can be used to train AI models more effectively.
Investment organizations must establish a solid data foundation to drive successful AI initiatives.
- Controls Framework: Imagine two business units: Business units A and B. Both put together reports for a client that contain some overlapping data points…but the values are different. Not a good look, and something that may be difficult to recover from. The client may always question whether you provide them with accurate information, or if your team is competent enough to be trusted. Grandview data management experts have thousands of hours of combined experience deploying data governance controls, business rules, data quality checks, and supporting workflows. We work with our clients to define the desired state of their data and execute initiatives to deliver it. We will help you build the necessary controls to ensure your data is transparent and auditable, and that rules and guidelines for handling sensitive data are followed, which helps build trust in AI models and the output they deliver.
- Data Cataloging: Data can be all over your organization. It can be structured, or unstructured. It can come from APIs, flat files, web/document scraping, etc. Without good organization, definitions, and tagging of meta data points, it is bound to get lost, or worse, inappropriately used. We have helped clients boil their complex datasets down to a lone source of truth for critical data points, reducing the time it takes to find what you are looking for. The rollout of AI will depend on your data being searchable and organized. Without it, your AI models may ingest incorrect data and yield unanticipated results.
- Data Platforms: AI operates on substantial amounts of data; all being effectively channeled to various models. Asset managers must invest in platforms to handle these demands. Those platforms should be seamless extensions of current data capabilities and integrated into regular workflows. A proper platform will empower innovators to iterate on their models and leverage computing power on demand. Grandview Analytics has deep experience helping our clients make informed decisions on the right technologies to deploy for the needs of tomorrow, not just the use cases of today.
Regardless of your definition of AI, or your specific use case, AI cannot get to the right answer without first completing fundamental data prerequisites. Grandview Analytics can help you navigate these foundational requirements, setting you up for success when the time comes to deploy AI in your organization.
ABOUT GRANDVIEW ANALYTICS
Founded in 2014, Grandview Analytics is proud to celebrate its 10-year anniversary as a technology consulting and investment data management software company serving financial institutions and investment managers. We offer data strategy, technology implementation, systems integration, and analytics consulting services as well as an outsourced investment data management and reporting service powered by our proprietary, cloud-based platform, Rivvit.
Our services drive improved business processes, integrated technologies, accurate and timely data, and enhanced decision-making capabilities. Our seasoned team of financial industry professionals brings deep business and technical domain expertise across asset classes and trade lifecycle. With hands-on financial industry experience, we execute on complex initiatives that help clients optimize ROI on data and technology investments.
