Business environments are growing more complex, and a majority of stakeholders are finding it harder and harder to plan for the future.
Decision-makers need to assimilate and understand more factors and trends, all of which change frequently. While business data analytics promises to help with decision-making, people can’t always maintain a grasp over all the information that they need to process.
Hence the rise of decision intelligence, which brings together data, insights, and patterns in accessible formats. The idea is to consolidate all the information in one location, focus on the points that are most relevant for the decision at hand, and present it all in a way that is easy to digest.
Decision intelligence fulfils the promise of business data analytics to guide smart data-driven decision-making – but it only works when it’s fully accessible for every decision-maker. Even the least tech-savvy line-of-business (LOB) user has to be able to understand and utilize the information independently. Data science professionals can’t keep jumping in to explain the insights, and all of it has to be useful for decision-makers in the context of their own working environments.
Pyramid Analytics, a generative business intelligence software company, is all about making this a reality in the enterprise. Here’s how its decision intelligence platform succeeds.
Natural-language queries
One of the most common barriers to decision intelligence is the need to translate LOB questions into database queries. Pyramid Analytics flattens that barrier by accepting naturally-worded text or voice prompts in any language, processing the query behind the scenes to accurately reflect user intent.
Users don’t need to understand data science or even know which kind of visualization to ask for, because Pyramid automatically produces the most appropriate format for their needs. Companies can roll out their decision intelligence platform to the entire workforce without worrying that a lack of data skills could hold employees back from using it effectively.
Even vague requests are met with clear and relevant responses. Decision-makers can then easily manipulate visualizations through follow-up queries, to focus on specific subsets or delve deeper into certain trends or data points.
Seamlessly integrated embedded analytics
Busy team members simply won’t use business intelligence tools if it means that they have to switch screens or upload data into a BI platform themselves. That’s why many companies are turning to embedded analytics solutions, allowing them to include BI modules within other host apps, such as CRM and ERP, where people actually do their work. However, many embedded analytics solutions are simple iframes that don’t truly integrate with the host app’s workflows.
Pyramid Analytics removes this friction with smooth integrations that allow users to access data analytics within their current context. There’s no need to copy data over from one location to another.
The Pyramid coding framework connects directly to any data source, injecting personalized data insights into the apps that LOB users are using anyway. Employees can view and interact with meaningful decision intelligence immediately, independently, and without having to move data or change contexts.
Automated data ingestion
Decision intelligence rests on up-to-date, dynamic data, otherwise insights quickly become irrelevant. But most platforms have complex data input processes, so only data science experts can collect, prepare, and input fresh data for analysis.
The process can take a long time, leaving LOB users waiting for data updates or struggling to connect their own data sources. Vital decisions may be postponed while decision-makers wait to compile all the information they need, or are taken in a hurry without a full understanding of the various factors.
But Pyramid Analytics offers an automated data intake and preparation pipeline that streamlines the entire process. Data teams don’t need to constantly perform manual ETL and data cleaning processes, while non-DS experts can bring in their own data with minimal friction. The no-code, self-service data prep module allows anyone to clean, prep, and blend datasets into a single view, and the heuristic engines suggest the best model designs for data analysis algorithms.
Flexible LLM connectivity
Enterprises are learning an increasingly important lesson about the differences between various large language models (LLMs). For companies in certain industries, a generalized LLM such as ChatGPT can’t deliver precision outcomes. It takes custom models trained on specialised datasets to understand legal jargon, for example, or predict healthcare outcomes.
These models need to be accessible to LOB users too. Pyramid Analytics’ multi-LLM connectivity enables every employee to quickly tap into specialized AI models that are optimized for their topics, shortening the path to accurate insights.
Instead of wasting time trying to obtain answers from a generic LLM – or worse, losing trust in decision intelligence altogether – decision-makers can turn to niche-specific models, or even those trained on the company’s own data.
Diverse data sources
Diverse data is one of the prerequisites for reliable decision intelligence. For many enterprises, this requires uniting data from hundreds of sources, ranging from locally-hosted databases to cloud data feeds. LOB users often struggle to bring all the relevant data into their business analytics engine, which creates a barrier to decision intelligence.
Pyramid Analytics offers a database-agnostic information management platform that can draw, purify, and blend data from almost any source. The underlying query engine can capture data from where it resides, whether it’s structured or unstructured, relational or non-relational, and deployed on any framework.
This overcomes the obstacles that stand between LOB users and the data sources they need to access decision intelligence.
Decision intelligence on demand
As LOB users grapple with making decisions that are increasingly complex and increasingly pressured, decision intelligence is becoming less of a luxury and more of a must-have. As enterprises invest in decision intelligence capabilities, they need to choose solutions that are fully accessible to every user, no matter how tech-savvy or data-literate they are.