Many analytics teams are naturally skeptical when they first hear about no-code tools that empower them to self-serve across the analytics value chain. Common reactions include: "Don't I need to be an engineer to do that?", and "Can a no-code tool enable me to build something that is custom enough for my organization's use cases?"
While many no-code tools are indeed limited in scope and functionality, an advanced no-code tool can empower analysts with powerful toolkits that combine reusable no-code elements with hyperconfigurability and light-touch custom coding functionality to fulfill their personalized needs.
At Redbird, we've seen a wide array of analytical ecosystems and complex organizational structures. While every organization is unique, we have observed some common themes, which we'll outline in this article.
Organizations have become significantly more data-driven, and the number of analytics tools has exploded. Business stakeholders also have increasingly complex and custom needs. This has led to near infinite demand for custom analytics applications that can help employees achieve their objectives in a timely manner. Compounding this is the fact that business stakeholders outnumber data engineers by on average 100:1 in a majority of organizations.
As a result, engineering roadmaps have extended, leading to intra-team dissatisfaction and ultimately poor business outcomes. Business teams resort to highly manual efforts when the automation that a high code approach could produce is not an option. No one is to blame for this dynamic, it's simple supply-demand math. Enter no-code. With the right no-code tool, teams can reduce time to develop custom applications by 90% and become heroes within their organizations.
On average, business team members lack the technical expertise to self-serve across the full analytics value chain. Most BI reporting and visualization / dashboarding tools (e.g. PowerBI, Tableau, Looker) today are built under the premise that data is already prepped and ready for visualization - that is, data has been ingested, wrangled, processed and transformed. The ugly reality is that analytical ecosystems are messy, only a portion of key data sources already live within the company's data warehouse, and even when data is in the warehouse further transformation and advanced analysis is required before visualization tools can be effective. Most importantly, employees are finding themselves in need of applications that go beyond basic dashboarding tools and are seeking more advanced apps that integrate with and automate their business actions.
Advanced no-code tools can be useful in flipping this problem on its head by democratizing data prep, wrangling and advanced analytics work to anyone within the organization.
In most organizations data engineers waste time on low-value, soul-crushing work to meet business stakeholder needs. They have to provision views in the data warehouse for stakeholders to access the right data, and write custom code for less technical stakeholders who have an aversion to writing SQL queries, Python code or learning how to use the latest modern data stack tool.
Engineers and business stakeholders often feel like they're not speaking the same language. Data teams require technical precision in their requirements, which is often difficult to get from business stakeholders. Business stakeholders often don't speak code, and so both sides inefficiently produce requirements and set expectations that ultimately fail to be met.
This is where semantic abstraction layers can add a lot of value, translating complex technical details into plain language and point-and-click interfaces.
Many no-code tools are designed with business users in mind, but more advanced no-code tools have emerged that empower both technical and nontechnical users to benefit. A common thought many engineers have is that a no-code tool by definition must be rigid and limiting. The reality is that the right no-code tool affords engineering teams a powerful toolkit to speed up the development of high-quality, complex applications. This frees up time to focus on coding more exciting and innovative projects, the type of work that most engineers prefer.
Some analytics teams are skeptical when it comes to leveraging no-code tools, which is understandable. However, not all no-code tools are created equal. As organizations become increasingly data-driven, innovative teams are beginning to adopt productivity tools to be successful in their jobs. Advanced no-code tools with adequate depth of functionality can drive many benefits: