As we take stock of the data-first models being implemented across financial services, we see that companies are placing big bets on data infrastructure and analytics platforms. Yet, adapting to an era of more data-driven decision making has not always proven to be a simple task. Many organizations are struggling to develop talent, business processes, and organizational muscle to capture real value from their data strategies. This is becoming a matter of urgency, since analytics prowess is increasingly becoming the basis of industry competition. The people leading the charge are staking out large advantages. Meanwhile, the technology itself is taking major leaps forward—and the next generation of technologies promises to be even more disruptive.


Financial services companies have long been at the forefront of deploying data and analytics programs and hiring highly specialized data experts.


Successful data and analytics programs require data scientists, data engineers, data architects, and data visualization experts. However, these highly specialized professionals are expensive to hire and often spend most of their time performing operational tasks that do not generate insights for the business, and have a hard time turning R&D insights into tools used by the business.


With ever-greater pressure to commercialize data science efforts, financial services firms are looking for ways to speed-up the deployment of their strategies across the enterprise to help them realize return on investment.


Beacon is a cloud-native platform that allows data scientists to build, test and deploy applications to business users quickly, and at scale, by leveraging a unified data infrastructure. Beacon’s approach allows firms to cope with both the demand of implementing data-driven strategies, and with the scarcity of talent because it brings Strategists and DevOps onto a single enterprise platform.


The Typical Data & Analytics Landscape

When a company launches a Data Strategy, it typically breaks it down into four phases:

1) Acquisition

2) Processing

3) Structuring

4) Commercialization


Acquisition and Processing do not require specialized talent and can be performed by business analysts or can be automated. But when it comes to Structuring and Commercialization, there is both a resource bottleneck and fewer tools available.


1) Data acquisition has become highly commoditized with a variety of vendors, data-brokers, data-consultants, and ISVs readily available to offer solutions. Such companies include Nasdaq, M-Science, CME, Bloomberg, Refinitiv, ICE, Neudata, and many others. Though companies can easily get data, many still struggle to get it into a form that is useful for their business.


2) Data Processing requires a lot of time and effort. Recognizing the need to simplify the onerous tasks of ETL, cleansing data, ensuring formats are correct, ensuring that there are no gaps in content, that time horizons are aligned, etc., many start-ups have emerged to offer solutions. Companies like Snowflake, Databricks, Teradata, Cloudera, and others are widely-used solutions that offer data transformation and integrity assistance.


3) Data Structuring is where companies start to use data to generate insights and capture their proprietary edge. Structuring includes functions like feature extraction and pattern recognition, which are non-trivial and highly technical. Often there is an over-allocation of resources to this part of the data journey as each data set needs to be assessed with an individual lens. However, with appropriate machine-learning techniques integrated into user-friendly applications, there is tremendous potential for business users with training in statistical analysis to extract hard-won insights and make data-driven decisions. For example, a portfolio manager could evaluate a data set based on its ability to deliver alpha.


4) Commercialization. Creating intellectual property cannot be automated (yet!). Nor can data science experts be commercially effective if they are isolated from the business, or even from each other. The reality of most data and analytics efforts is that they remain limited to research environments without the proper platform to deploy their findings. For example, in the asset management industry, even large companies with lots of proprietary data and analytics struggle to deliver insights to their clients “on-demand” through an interactive portal or app, and instead rely on emailing PDFs with a turnaround time of weeks.


There are a myriad of solutions to help with data acquisition and data processing however there remains little innovation for steps 3) and 4), Structuring and Commercialization. These steps are Beacon’s core value proposition for customers looking to productize their data assertions.


Finding Value in Data-Centric Ecosystems

Proliferating data sources, including public market data, alternative data, ESG data, are creating torrents of information. While access to data has become easier, sharing is not always seamless, and deriving value from data outputs is more difficult still.


Understanding the value in data that needs to be gathered, sifted, and analyzed is a tricky proposition, particularly since organizations cannot nail down the value of data until they are able to get to the application/production phase. In the four steps we described between raw data and actual application of data-derived insights, there are multiple players that are seizing market opportunities. The workflow involves an ecosystem of cloud storage, data distributors, data aggregators, analytics providers and development toolkits to help bring insights to production.


Beacon Platform helps firms with their entire data workflow by partnering with companies that specialize at every stage. Our platform was designed for commercialization, which is the final and most important part of the data journey because it is the one that generates revenue or reveals opportunities for cost savings.


Beacon delivers an enterprise platform that makes it easy for data scientists, strategists as well as developers to deliver results at an institutional scale. We combine integrated data interfaces with an application framework that allows for the build-out of low-level analytics all the way through to tools that business-people will use to see tangible results. This keeps data scientists commercial by giving them control of the stack and allowing them to work at the commercial edge of the business, with no other group between them and the user.


Data strategies are most effective when combined with deep industry and functional expertise. Beacon is uniquely positioned as a cloud-native platform that allows financial developers to build, test and deploy applications to business users quickly, and at scale.


Beacon’s software has been designed to provide a significant uplift in developer productivity and reduce time-to-value. We empower organizations where data science may not be a core competency to develop their talent as well as provide an infrastructure for highly specialized data science teams to capture real value from data.