Gain Market Share and Get Switches with AI-Powered Self-Service Analytics
During the past decade, the influence of big data on the life sciences sector has been staggering. And it’s growing larger. By 2022, the global healthcare market for big data is expected to reach $34.27 billion. In this environment, it’s obvious that pharmaceutical companies which effectively harness big data will inevitably grab more market share. What isn’t clear, however, is how they’ll achieve that goal. Traditional BI solutions like self-service analytics have made big promises for data transformation. In reality, however, they’ve largely failed to deliver. Thankfully, today there’s a new paradigm for uncovering hidden data insights. The next generation of self-service analytics solutions powered by artificial intelligence (AI) and machine learning (ML) has capabilities for true data transformation and a competitive edge. Here’s why AI-powered self-service analytics is essential for life sciences companies that want to boost sales reps efficacy and increase market share.
What is Self-Service Analytics?
Essentially, self-service analytics describes a form of business intelligence (BI) software that provides tools for querying data and generating reports to inform real-time decision-making. Self-service analytics is supposed to help users be more self-reliant when it comes to exploring data and answering business questions. These BI tools are intended to be simple enough to be used by non-technical business users—and less dependent on IT or data analysts
Today’s Self-Service Analytics Solutions Fall Short
Most self-service analytics solutions aren’t self-service at all. As a result, adoption rates typically hover between 25 to 30 percent. Most solutions underdeliver due to a range of issues, including:
- IT is involved. Although self-service analytics solutions are supposed to be accessible by everyone, IT usually ends up being a data gatekeeper. Most users have to ask IT to create new reports and dashboards, and then wait days or weeks for results.
- Training and technical skills are required. Accessing analytics data involves a steep learning curve. Users must be trained on how data is organized, how to use the right query terms and the right phrasing, which dashboards are relevant to them, and how to create new visualizations.
- Data is siloed. Most self-service analytics solutions are slowed down by terabytes of data. To speed up results, data is broken into siloes, like sales, marketing, R&D, supply chain, and manufacturing. This means there’s no single source of truth.
This situation can’t continue indefinitely. Today the volume of data is increasing at an incredible rate. If a company is already struggling to store and analyze its data now—and its self-service analytics solution isn’t effective—then it will be drowning in data in the next few years.
AI-Powered Self-Service Analytics is a Game-Changer
The next generation of self-service analytics solutions powered by AI and ML differ from traditional BI intelligence software because they feature natural language processing (NLP) coupled with intelligent automation of analytics processes like charting, reporting and calculations. With NLP, users can ask questions just like they might ask a colleague. On the backend, AI analyzes terabytes of data, providing an answer in a matter of seconds. Instant access to answers improves business users’ immediate, on-the-spot decision-making as well as informing them on a bigger, strategic scale. This enables executives and managers to revise long-held assumptions and old practices, as well as identify new business opportunities. Ultimately, this improves revenue growth and profit margins. Real-time data also empowers sales reps with key insights when interacting with HCPs, which makes every interaction count.
- Analytics capabilities can be embedded in apps across the enterprise. This means that users can ask questions about the data at the moment they occur. They simply speak into a mic or type their question into a search box. The system instantly responds with answers, even though no engineers anticipated them.
- Answers are formatted in a way that makes the most sense for users. Whether it’s a bar chart, a line graph, or some other visual, AI-powered self-service analytics is intelligent enough to generate the right visualizations on demand. Users simply pin each chart or visual onto a board to create a report.
- Analytics are scalable across organizational data. AI is intelligent enough to train itself on your products, geographies, competitors, and more, which gives it unlimited scalability. As a result, BI is no longer trapped in silos.
How Next-Gen Self-Service Provides an Edge in the Rare Disease
There is a myriad of ways that AI-powered self-service analytics can increase market share. One standout example is its ability to predict market opportunities for orphan drugs that treat rare diseases. Today’s pharma industry trends indicate there’s an increasing focus on orphan drugs. Orphan drug sales are expected to grow 11 percent annually between now and 2024—a pace much greater than the overall pharma market, which is growing at 6.4 percent. By 2024 orphan drug sales are forecast to total $262 billion. While orphan drugs are unlikely to generate the sales seen with bestsellers, they can command very high prices and are highly profitable thanks to much smaller sales and marketing spending. About 7,000 rare diseases are affecting 25−30 million Americans and 400 million worldwide. The question for pharma companies and sales reps is: how to identify HCPs with patients who would benefit from their orphan drugs? The answer is an AI-powered self-service analytics solution that can analyze secure, anonymized third-party clinical trial data and patient universals. AI can mine this data for a range of predictive, actionable insights, including which HCPs are seeing patients with rare disease symptoms. These insights enable sales reps to proactively reach out to HCPs with educational resources on their therapy.
WhizAI is Self-Service Analytics for Tomorrow’s Market Leaders
WhizAI has designed the only AI-powered self-service analytics solution for the life sciences sector. WhizAI’s self-service analytics revolutionizes how pharmaceutical companies store, sort, analyze, and extract meaningful insights from data, and how field sales reps interact with health care providers (HCPs). With WhizAI, life sciences companies can now take advantage of all their data and access predictive insights to increase market share and beat the competition. The data explosion means that many life sciences companies risk becoming data-rich but insight-poor. Get a demo to see how your company can beat the odds.
Subscribe to our blog
People also viewed
Customer Centricity and Digital Experiences which are Redefining Pharma
It is no secret that the pharmaceutical industry has historically flourished from blockbuster products with substantial brand equity, favorable pricing
Awash in Data, Yet Starving for Insights? Transform Life Sciences Commercial Teams with Augmented Analytics
Personalized communication is something the HCPs have come to expect. To do that, pharma sales reps need to know their customers well. In order to know the customer well, they need data. Enterprise pharma companies, however, have many data resources and lots of data at their disposal. In fact, there is a data explosion. So what do you do?
Readiness of Augmented Analytics for Life Sciences Commercial Teams
Demand for smart, agile, and near-instant commercial analytics in Pharma is rising. More contextual, continuous, and connected data and analytics are required to respond to more complex and uncertain business decisions.
Adding Up Data Analytics Total Cost of Ownership (TCO) and How to Lower It
High data analytics expenditures don’t have to be a cost of doing business for life sciences companies. WhizAI lowers total cost of ownership (TCO) while making data insights more accessible to business users. Traditionally, data analytics TCO has been a significant expense for life sciences organizations.
Transforming Life Sciences Commercial Function with AI and Augmented Analytics
In this whitepaper, sponsored by WhizAl, Everest Group explores the business case and profound impact Al and Augmented Analytics can have on the life sciences commercial function across various use cases in Sales, Marketing, and Market Access. They examine what top pharma enterprises are looking for in an augmented analytics tool, a roadmap for how to get started, and a blueprint for adoption.
Augmented Analytics is “On the Rise” on Gartner’s 2022 Hype Cycle for Life Science Commercial Operations
The Gartner® 2022 Hype Cycle™ for Life Science Commercial Operations introduces a new category, Augmented Analytics, this year. WhizAI is listed as a sample vendor in the Augmented Analytics category in this Hype Cycle. This summary report covers the following highlights.
Why Domain Expertise Is Essential for Life Science Analytics
Domain expertise matters. Consider this: Enterprises choose transportation and logistics companies that have created the specific types of services they need. Businesses seek professional services with experience in their industries and look for consultants with good track records in their markets. However, remember that domain expertise benefits extend to analytics platforms, particularly those deployed for life sciences.