What is Decision Analytics & How Does it Help Life Sciences BI?

Businesses need all the tools they can get to better understand the customer, current and future market forces, and to plan for that future. With the explosion of cloud computing, storing business data is easy. So what’s the challenge? Analyzing and turning that data into useful insights. With data from multiple departments and software systems often siloed, it’s tough to merge and clean it. What’s also hard is providing business intelligence in a timely and useful manner.

Decision analytics versus business intelligence

Decision analytics seeks out trends and insights from analyzing data sets and uses these insights to inform decision-making and to predict what should or might happen. Business intelligence applies these analytics to make decisions in the present. Both fields — decision analytics and business intelligence — use data to find patterns that would be difficult to uncover without the vast data sets, and they rely on the numbers and trends rather than assumptions and personal judgments. The data may be interesting on their own, but the value is in using the insights to inform the decision-making process. That may include a better understanding of the risks in various approaches or strategies.

4 types of decision analytics

Data analytics can be seen as an umbrella term encompassing several other types of analytics. These include:

• Descriptive analytics: These analytics provide insights into past performance, describing what happened. They may use metrics like return on investment (ROI) or key performance indicators (KPIs).
• Diagnostic analytics: This focuses on the question of “why” things happened, using descriptive analytics as a foundation and uncovering the causes.
• Predictive analytics: Not surprisingly, this looks to what may happen in the future, using historical data, statistics and AI to understand the likely future results.‍
• Prescriptive analytics: This helps answer the “what to do” question, assisting with decision-making when there is uncertainty. It analyzes past events and decisions and estimates the likelihood of different outcomes based on various factors.

Both business intelligence and data analytics use a systematic approach, with quantitative analysis plus visual demonstrations like graphs, charts and tables. The business intelligence and data analytics tools may also incorporate economics, psychology and other management techniques as well.

Analytics for decision-making in life sciences

Data analytics uses one version of the truth that everyone can rely on for their analysis and decisions. Companies relying on a data-driven approach to decision-making, who also weave in behavioral economics, increase their gross margins by 25% and perform better than 85% of their peers in sales growth, according to Gallup. Analytics for decision-making has been a huge driver of growth in life sciences. By moving data out of the siloes, data scientists and researchers can more quickly make connections between the data sets, whether in research and development, or business operations. This can speed up new therapeutic discoveries while helping the sales office as well.

Using data in pharmaceutical R&D

On the R&D side, marrying clinical, research and pharmaceutical data provides insights not typically available because of the siloes. Predictive analytics can incorporate past learnings to advance current research in finding drug targets or new therapeutics. Clinicians and researchers can better collaborate across sites and geographies. And data analytics can assist in developing personalized medicines. Research in all these areas becomes more efficient when using data analytics, accelerating the potential development of new products.

Using data in pharmaceutical biz dev

Analytics for decision-making can also help on the business development side. That means identifying new opportunities by making connections that would be difficult or impossible to make without AI. All four of the analytics subtypes help describe past actions, predict future results or give valuable information that can be used to increase market share and inform sales calls. Data analytics provides very usefully granular information to sales and home offices so they can micro-target their prospective customers.

Use cases for using data analytics in life sciences

Let’s explore some of the ways that data analytics can impact the performance of specific roles in life sciences. Field sales: The sales rep is about to meet with a provider. Data analytics can tell the rep about the provider’s prescribing pattern for their product and the competitor’s. It can show changes over time, whether and how the provider accessed educational information sent by the rep and predictions of future prescribing activity. This information makes it easier for the rep to have an informed and personalized conversation with the provider, and to go in armed with a helpful background. Sales managers: Sales managers can use data analytics to understand the big picture of sales results in their territory using metrics, or drill down to the individual provider level or individual sales rep level. The manager can access predictive information for the future to help with planning, and also see anomalies in prescribing patterns to understand past performance. Sales operations: The sales operations staff ensures that the sales reps are given the best information going into the field and that the territories are properly aligned. They can also do causal analytics for anomalies and identify opportunities to gain market share. Market access: Data analytics provides competitive intelligence about the competitor’s and the company’s market status. It provides market information based on past results and current events, that can help a business development team gain better market access to a formulary or patient enrolments. Patient services: Providers can use data analytics along the patient journey, from diagnosis to recovery. It can help providers anticipate and monitor medical needs, adherence and potential results. This allows for earlier intervention, keeping patients healthier and saving money as well.

WhizAI can help

Many companies provide business intelligence, but only WhizAI is 100% focused on life sciences. WhizAI provides easy-to-use algorithms using natural language processing, machine learning and advanced statistical techniques, developed specifically for this industry. Data requests are made in plain English [or in G5 languages], by typing them or asking with your voice. There is no dashboard needing interpretation or preset reports that aren’t helpful. Instead, the data is analyzed in seconds, with results showing on graphs, tables, or whatever format is the easiest to understand including a description of analytics in plain language [NLG]. While traditional business intelligence providers need extra time to crunch the data, WhizAI’s proprietary system makes it available instantly, and it’s customized for the user. That’s why our clients have a nearly 100%  adoption by business users. There is no analytics system to learn – just ask the question and get the answer. That means there’s no reason for a sales rep to go into a provider meeting without the latest information – and the recommended next best steps. There’s no reason for a sales manager to ask anyone for data – it’s available with a simple question or automatically surfaced as an insight. Let us demonstrate how WhizAI can increase your market share and make your team’s life easier. Contact us to schedule a product demo today.