Raybeam: Business-Centric Approach to Data Integration

Bob Briski, Principal
A number of popular technology blogs are becoming the go-to source for enterprises that want to expand their data technology stack or invest in new data integration solutions. These blogs, while insightful, often fail to consider factors that are unique to an organization. There are political, organizational, and personnel-related factors that determine if a specific data architecture will meet the needs of a company. But data integration and data warehousing experts such as Raybeam work hard to keep their clients from taking wrong turns in their digital transformation journey. “It’s one thing to understand how different technologies fit together, and a whole different ballgame to recognize where those solutions fit within a particular company. Our biggest value-add is the ability to think beyond just the technology involved in data integration and architecture; we gauge the entire ecosystem (of a client) before building solutions,” says Bob Briski, a Principal at Raybeam.

Due to its diverse history of solving data-related challenges across different industries, Raybeam understands the viewpoints of various departments of an organization. When big data began influencing how advertising and marketing teams collected data, Raybeam recognized that marketing and data architecture go hand-in-hand. The company predominantly served advertising and marketing organizations from 2008 through 2015. In recent years, however, Raybeam has helped clients in retail, healthcare, insurance, and finance, too. “We see that a data-driven and data-aware mentality is permeating every layer of the organization. For example, there’s now an expectation that product and customer service be able to justify their decisions, and data can help with that. As the need for data increases, we can step in and solve problems across industries,” stresses Briski, noting that Raybeam addresses issues “not from an engineering perspective but a business-wide perspective.”

Some of the data-related challenges that Briski is referring to are focused on governance and security around data. With EU’s GDPR and the soon-to-be-implemented CCPA putting stringent restrictions on how companies collect and use data, Raybeam’s clients rely on the data integration vendor to translate what the laws mean in their use cases and implement solutions in a way that follows the law. Starting with the belief that an ideal data architecture “is where it’s simple to do the right thing,” Raybeam ensures its clients are not only compliant with existing laws but also remain futureproof for the emerging regulations. “When we build out a data system, we focus on governance right from the beginning. This way, when additional laws come into effect, the new monitoring and security protocols can be integrated into the platform,” explains Briski.
Raybeam’s solution suite can be divided into three pillars: data warehousing & engineering, business intelligence (BI) & analytics, and software development & automation. Data warehousing solutions break down data silos within an organization and build a common view of the business across the entire spectrum of data sources (both internal and external). Raybeam often works within a client’s existing technology stack. Just as often Raybeam uses a client’s technology roadmap to suggest improvements. “If we feel the stack is not ideal, we’ll partner with our counterparts in engineering leadership and work together to improve the entire stack,” says Briski. After deploying a data warehousing solution, Raybeam often recommends a managed service provider to defray the continued costs of maintenance and support. He adds, “We want to use their available resources, but we are also sensitive about the post-deployment stages. The idea is to design success into the project, even after we leave.”

Time-to-insight is a key component of our analytical solutions

Raybeam’s BI and analytics projects are just as pivotal. By layering analytics and machine learning atop its data warehousing solutions, Raybeam strives to shorten the duration it takes clients to progress from “insight to experimentation, and finally implementation.” Briski informs, “Time-to-insight is a key metric in developing analytical solutions. This quick pace helps a client to experiment with the insights before socializing them across the company.” Quicker iterations on top of solid experiments help companies develop an engine that can drive innovation across the entire enterprise.

Looking ahead, Raybeam will continue to take a “business-first approach” to solving data integration issues for its clients, looking at the business first and the tooling second. With over 25 years of experience in data warehousing—beginning with involvement in the early search engines—Raybeam possesses the deep technological expertise to address both existing and forthcoming trends in the marketplace. Trends that Raybeam is closely monitoring include explosion of metadata coupled with modern ML/AI techniques, IoT data storage and processing, and the rise of data governance as a driver of strategy. Also, as mentioned before, Raybeam isn’t averse to SaaS services and managed service providers bolstering its solution offerings. “While some may feel managed services are taking business away from us, it’s actually the opposite. Now, we can do custom work atop the foundations laid out by these vendors, allowing us to get to a successful outcome more quickly,” concludes Briski. “Managed services make the time-to-insight of a Raybeam solution even faster.”


Austin, TX

Bob Briski, Principal

Raybeam specializes in breaking down the silos of data within the organization and building a common view of the business across the entire breadth of data sources within the company. Working with leaders within the organization and industry best practices, they can help to establish core KPIs that are actionable and allow the business to measure success efficiently and optimize continuously. They deliver turn-key data warehouse solutions that make use of technologies appropriate to the clients’ organization as well as the technical challenges of the data