How to Democratize Access to Data Insights for Businesses of All Sizes

Messy government data has been part of the reason we've been unable to understand the COVID-19 pandemic. If federal organizations can't decode big data, what hope do small businesses have?

We’ve never had this much data on our hands. But as the material we’re dealing with grows, we need to ensure the skeleton that supports it is evolving sturdily alongside it.

That’s no small feat, given the breakneck speed at which our data load increases every day. The more data we have at our fingertips, the more unbridled its potential is - but the harder it also becomes to harness it, especially for companies with less of a tech background or smaller R&D capabilities. A lack of accessible and affordable tooling would leave smaller players behind, making access to big data analytics an increasingly exclusive privilege.

Nevertheless, the past decade has shown us that data analytics and tools have the capacity to speedily advance to accommodate the growing wave of information.

And Development Analytics has a big role to play in that. As all companies are also becoming tech companies, engineering teams need to develop software that captures big data and uses machine learning so they can stay in the game. Development Analytics tools like Waydev are there to help engineering managers fully understand and improve the development process of these analytics projects. So it’s a bit of an analytics inception if you will.

In today’s landscape, three essential pillars have emerged in data analytics - visualization, open source, and AI. Here's how the future of data analytics and tools can make this sphere more democratic, and what this means for software development.

Startups are no stranger to big data. They can get huge swaths of it relatively straightforward, but that doesn't mean they hold the keys to understanding it.

One of the first data visualization tools, the Gantt chart, was invented in 1910 to visually track data across large teams. Big data is typically unstructured, making it difficult to draw conclusions from the figures. But if companies can't accurately interpret data, its value is wasted. Raw data doesn't always tell a story; to do that it has to be processed and curated in a more visually appealing form.

The next generation of big data tools will prioritize simple data visualization for all users. Graphics, maps, tables, and patterns are far more intuitive than text and can stitch seemingly abstract stats together - which is especially useful for small business leaders who don't have a tech background. In fact, we're already seeing the shift with tools like Visme and Tableau, which transform big data into engaging charts and infographics.

Visualization becomes all the more powerful when coupled with relevant data sources to provide business insights and help leaders gain a better understanding of their team’s work.

Gantt charts may have been an innovation for its time but in the Agile software development environment, they are of no use.

Development Analytics tools like Waydev and Pluralsight Flow use user-friendly dashboards, heatmaps, color-coding, charts, and diagrams to signal progress, issues, and steer the user’s attention to the important values.

The popularization of data tools has paved the way for open source software like Android (the operating system used by Google phones), which can be freely used or modified by anyone. Whereas data tools used to be exclusively reserved for paying customers (read: deep-pocketed corporations), open source tooling is free and made for the public, by the public.

Many tools that were once closed source have made the switch to open source in recent years. For example, Apache Hadoop, the framework that uses programming models for distributed processing of large data sets, quickly became supported by NoSQL databases and other open source infrastructure when it started to be widely adopted.

Today, the majority of infrastructure used to store and stream large volumes of data is open source, including databases like MongoDB and tools like TensorFlow. Even the notoriously secretive Apple has hopped on the bandwagon, releasing its programming language, Swift, as open source in 2014.

Data science wouldn't be as large in scope, nor as efficient, without open source tooling. As data grows, small businesses need more sophisticated data processing tools, which will benefit from open contributions. The more people that contribute to code, the higher the quality of the tool. And, as more people own a cell phone, are active online, and share information digitally, data sets will continue to grow exponentially and push demand for data-processing tools. In order to maximize the functionality and interoperability of these tools, businesses should start embracing open source from the get-go.

Despite finding its roots in the 1950s, AI didn't really get started until the 1990s. In 1997, the world chess champion was beaten by IBM's AI chess-playing program, Deep Blue. In the same year, Dragon System developed and implemented its speech-recognition software on Windows. The two creations were a big step for AI, and for many people, signaled its impending uptake across industries.

The reality though, is that AI is yet to be adopted on a mass scale. A 2020 survey revealed that only 8.9% of US businesses use AI, and the ones that do are mostly big companies that have the money to invest in the tech: not surprising when companies pay between $6,000 and $300,000 for AI software.

Although the uptake is relatively slow, early signs of more financially flexible AI exist, and they go a long way toward democratizing data tools for all companies, regardless of their size.

Data and analytics tools won't be fizzling out any time soon. Both have progressed considerably in recent years, and with expanding data sets at the heart of these tools, they'll not only get better, they’ll also get smarter.

In software development, in particular, we’ll be seeing widespread adoption of development analytics in the following years.

Engineering leaders will keep investing in Development Analytics tools so they can meet the growing demands of their organizations, and remain ahead of the innovation curve. This only means we’re getting one step closer to normalizing a data-driven culture in software engineering.