With ever-increasing data volumes, getting actionable insights is comparable to finding a needle in a haystack. It takes going through multiple sources for relevant data and analyzing it to arrive at specific insights for your business. The life sciences industry faces this challenge more than ever with new data sources available, including EMR, patient health, and drug trials.
For the past 15 years, however, most life sciences companies have continued to use traditional business intelligence (BI) tools to produce the insights they need to find opportunities, identify accounts at risk, and track industry trends via reports and dashboards. However, these solutions fail to scale to growing data and business needs leading to data silos, low user adoption and expensive run and maintain.
Limitations of Dashboard Data Analysis
Traditional BI solutions are capable of analyzing only a limited amount of data to ensure timely refresh of dashboards and query response time. So, every time users need deeper insights based on more data or new sources, they need to create data silos and new reports. Frequently, it leads to dashboard deluge resulting in confusion and lack of a single version of truth.
Most large organizations have an army of developers to create dashboards and maintain such solutions. Despite this investment, IT still struggles to provide business with timely insights. It is common in the life sciences industry to pre-process data for dashboard solutions, which takes days and massive compute power and is often two weeks delayed. Plus, end users like sales and marketing are inundated with tens of dashboards to stitch together a picture of their business. We are seeing less than 40% adoption of such solutions, and, as per Gartner about $48B are wasted every year on failed BI projects.
Today every job is data-driven; hence all users are looking for easy, timely and contextual access to actionable insights. However, it doesn’t mean that everyone needs to be a data analyst. Unfortunately, traditional BI tools require end users to be analytics savvy to draw conclusions from dense charts and reports. No wonder about 70% of sales reps in pharma go unprepared to client meetings.
Finally, the established BI platforms are struggling to adopt artificial intelligence (AI) and machine learning (ML) to keep pace with new technologies. They have great solutions, but they’re geared towards the needs of decade-old organizations. Today, users expect instant response to their business questions in natural language across billions of records. Traditional solutions are trying to bolt on such interfaces on their archaic architecture, which doesn’t solve the fundamental problem of lack of scale, ease of use and automatic insights.
What BI Tools Are Supposed to Be
The unit of work for BI has changed from building dashboards to directly answering business questions. In many cases, intelligent applications deliver answers even before users ask the question. New-age solutions use artificial intelligence to make sense of data, understand business vernacular and create analytics on-demand.
Natural language processing is quickly evolving to understand the nuances of business terms and grammatically incorrect sentences — and handle short forms and abbreviations. Industry-focused BI solutions are pre-training their NLP engines to provide a robust interface out of the box to their clients.
The latest advent in data analytics technology has made it possible to analyze billions of records in sub-seconds. Current BI products are leveraging it to scale to new data volumes in business. Now it is possible to instantly answer a complex business question that requires processing entire data set at the most granular level of details. It provides unparalleled power to business users to compete in the market
The modern stack also includes machine learning algorithms that not only look at data for anomalies, patterns and insights but also learn user behavior and preferences with usage. Users don’t need to learn how to use software; rather, applications adapt to user preferences.
Cognitive insights platforms learn how users work and what types of information and insights are most important to them. For example, if a sales rep checks on account status first thing each morning, the platform’s AI model will learn that routine and automatically provide those insights – delivering the information users need before they even ask.
Similarly, users don’t need to be data analysts to infer insights from reports. The software proactively generates and describes such insights from the data in a timely manner.
The Future is here
Although dashboards fulfill certain needs, business users are craving faster, contextual, and easier access to insights to perform competitively. Leading organizations are already adopting newer platforms to bring this agility and democratize analytics to provide an edge to their teams. It is time to go for better.