Future Proofing the Front Office: Data Analytics

    solving the data challenges
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    Data Analytics Powered by APIs – Solving the Data Challenge

    Part 2 of a 3-part series on Future-Proofing the Front Office. Want to download the full Whitepaper directly? Get it here!

    Initiatives for future-proofing the front office all depend on integrating innovative technology. In the second part of this series, the learning relates to artificial intelligence (AI) data analytics. In order for data to provide valuable insights, you must have an analysis engine supplemented with AI. Buy-side OMS (order management system) and portfolio management software deliver meaningful gains in terms of automation, associated time savings, and greater transparency. These gains are valuable, however, the most critical aspect of these systems is the potential to make better decisions with the underlying data that resides in these systems.

    Leveraging data analytics and artificial intelligence requires a robust tech infrastructure. Legacy architecture isn’t sustainable for these pursuits and can inhibit operating models. The future of artificial intelligence data analytics demands that you rethink your tech stack as well as systems used for trading and portfolio management.

    In this article, we’ll review the history of investment management data analytics, its importance in the industry, technologies advancing it, and what to consider when evaluating underlying systems.

    Background: Investment Management Data Analytics

    The world has been discussing how to handle growing amounts of information for nearly 80 years. As far back as 1944, Fremont Rider, the librarian of Wesleyan University, estimated that libraries were doubling in size every 16 years. His response was one of the first times anyone thought about how to handle this information. In the age of the internet, the average amount of data created, consumed, and stored in 2023 is 120 zettabytes.

    This continuous accumulation of information is massive, and the term “big data” has become part of the lexicon of science.

    In the decades since then, the use of data has moved beyond scientific circles into the realm of business. While many predictions from the last 20 to 30 years have come true, others, like the idea that Hadoop-based platforms would make SQL obsolete, have not. 

    Big data has a significant role in the investing world. The conversation on deciphering big data and making it useful is now at the forefront. How will we use data analytics in investment management? The key to its value is analysis, which should lead to data-driven predictions and decisions. Today’s focus primarily consists of data storage and the processing software required to analyze large amounts of data.

    After all, no matter what types of data any investment firm collects, it’s only as good as the plan to manage it and the ability to leverage intelligent analytics to gain insights. Processing massive amounts of data to attain these things requires more than humans can do. Taking things to the next level is now possible with AI.

    The Importance of Data Analytics in Investment Management

    The new horizon of data analytics is combining AI with underlying systems and approaches. Almost every industry has been leaning this way, and investment management is no different. The needs of investment managers are unique, but the ultimate goal is similar in terms of acquiring a competitive edge. Obtaining this in a complex landscape requires finding new opportunities and expanding existing business relationships.

    Data analysis has always been a way to identify these opportunities by gathering metrics that you may have overlooked in one way or another. However, as the size of the data has grown, it’s no longer possible to analyze it without leveraging emerging technology.

    Artificial intelligence data analytics is now the foundation of gaining value from data. If you want to future-proof the front office, you must embrace this path. You’ll need the right software tools in a big data world to remain competitive and distinct from the rest.

    Moving forward involves comparing and evaluating software for data analytics and artificial intelligence. There are several capabilities that you should prioritize in this search, including:

    • Support for  ways of finding new clients
    • Functionality that offers the ability to identify new market opportunities from existing clients
    • Delivery of improved insights to internal and external stakeholders
    • Enabling more accurate decisions and assistance with decision-making

    Decisioning is a constant in terms of features, which makes sense, as data should always deliver more context and informed conclusions. Improving decision-making and accelerating it comes to fruition with AI and automation.

    AI, Automation, and Data Analytics

    By 2025, Gartner predicts that “95% of decisions that currently use data will be at least partly automated.” There has been much discussion on how AI and automation will change how we work. While introducing these things into business processes eliminates a lot of manual work, it will never replace the intelligence of investment and wealth professionals. Rather, it augments the workflows and identifies patterns and trends, which leads to better interpretation.

    As a result of this new phase of digital transformation, investment in data analytics is soaring. Global expenditures on these systems will surpass $68 billion by 2025. Investment management data analytics follows this trend, representing one of the industry’s fastest-growing segments. A significant component of this is the use of application programming interfaces (APIs).

    Understanding APIs for Data Analytics

    While we know the critical role of data analytics in investment management, understanding how to use the actual data to power solutions is more complex. To simplify this work, APIs for data analytics deliver the most complete and effective solution.

    What Are APIs?

    APIs are not a new concept in the world of the buy-side front office. Defined simply, an API is a set of definitions, communication protocols, and tools for building software. An API might be for a web-based system, an operating system, a database system, computer hardware, or a software library.

    How Do APIs Serve Buy-Side Wealth Management Firms?

    With the emergence of the cloud and the internet as the way most software interactions occur, web-based APIs become the most important type of API. Their key purpose is to connect systems and share data.

    According to the Gartner Group, “Application programming interfaces (APIs) make digital society and digital business work. They connect people, businesses, and things. They enable new digital products and business models for services and create new business channels. APIs make digital business work.”

    Investment firms can analyze more complete data sets in real-time by using APIs to integrate the various systems and software. As a result, you’ll get a deeper comprehension that yields better decisions internally for the firm and externally on behalf of their clients.

    Wealth Management APIs Create a Competitive Advantage

    Buy-side firms are under ever-increasing pressure to modernize systems. Most are well aware of this challenge, which now also prioritizes artificial intelligence data analytics. There are many considerations when implementing advanced technology. There’s the ever-changing regulatory environment, increased investor scrutiny, and downward fee pressures. As a result, systems must do more to provide firms with intelligent analysis of the data within them.

    data silos

    This can be achieved with open APIs. These types of APIs must be publicly available and easy for developers to integrate with. When open APIs are part of your technology platform, you have the power to proceed with integrating data analytics.

    APIs have numerous uses that can effectively connect mission-critical systems in a plug-and-play fashion. Some examples include portfolio modeling/optimization, risk/analytics engines, compliance, OMS trading, and back-office systems.

    Without good wealth management APIs to connect systems, front-office software quickly leads to data silos. When data exists in silos, it’s not as transformative. When systems can’t communicate, there is a risk of missed opportunities and more costly challenges, including:

    • Time-consuming manual data manipulation and associated errors
    • Slower reaction time due to outdated data
    • The reduced ability to deliver total transparency to clients

    Even worse, many buy-side front-office vendors don’t fully support APIs or do so in name only. For example, established vendors may provide an API for custom application development but fail to produce an API that connects with external systems to gather real-time data.

    Sometimes, it’s a business decision for vendors who don’t believe providing a comprehensive solution for investment firms is a strategic priority.

    That’s not our philosophy. INDATA supports the idea that wealth management APIs represent the best opportunities for investment firms. We developed the INDATA iPM SaaS with integrated artificial intelligence data analytics to ensure that you can deploy a flexible and comprehensive solution. This creates numerous competitive advantages, reduces costs, and allows for faster deployment of mission-critical information across the organization.

    The Role of IMC in Investment Management Data Analytics

    imc quote

    Big data analytics powered by APIs has a leading current and future role to play in wealth management. They connect all crucial systems and drive front-office analytics, investment decision-making, compliance, and other essential functions for investment managers.

    In addition to understanding APIs, it’s critical to know that data analytics continues to shape the world of front-office software systems through the analytical tools themselves.

    Of the tools currently available, in-memory computing (IMC) is one of the most important. It stores data in RAM rather than in databases hosted on disks. This structure exponentially speeds up data access because RAM-stored data is available instantaneously. IMC can cache massive amounts of data, enabling swift response times and storing session data to achieve optimum performance.


    Artificial Intelligence Data Analytics Represents the Future of Wealth and Investment Management

    In a world of cloud-based software dominating the front office, data analytics driven by APIs will become even more essential. As the amount of data investment managers produce grows, systems must seamlessly share and analyze every byte of data. And, because the cloud will host most of the data, open APIs are essential for modern data analytics in investment management firms. Your data analytics technology must also have an AI component in order to be competitive.

    Don’t get left behind by not modernizing and future-proofing the front office. The time is now to implement this new technology. It has immeasurable value for you and your clients.

    As you assess OMS and portfolio management system providers, ask, “What can we do with our data with your system?” 

    The answer you’ll receive from INDATA is a wealth of possibilities. Buy-side firms trust our cloud-native, SaaS-based solutions for OMS and portfolio management that data analytics powered by AI. These solutions reside in INDATA’s iPM Private Cloud, which integrates robust data analytics and AI tools. Schedule a demo today to learn how we can propel your firm into the future.