When you use data in your organization’s decision-making your data will yield no useful insights if your teams, tools, and processes do not share a sound approach. Building a solid data analytics foundation necessitates a company-wide commitment. Here are four crucial stages to help you lay a solid foundation for your project.
Here are four steps to get started using predictive analytics to achieve a competitive advantage for pharmaceutical businesses and healthcare providers:
1. Gather the information.
The first step is to construct a centralized repository that can be accessed by multiple organizations inside the company. Data from internal sources (ranging from call centers to direct shipments), data from clients (such as patient information), syndicated data (for example, sales or managed care), and data from third-party sources can all be easily accommodated using Big Data architecture (such as email marketing vendor and Medscape).
2. Perform a data query.
The second step is to make querying the data as simple as feasible for as many people as possible. The days of sending technical questions to IT are long gone. Users must be able to query data using an interface that is as close to natural language as feasible for quick results, preferably one that interprets needs and provides appropriate data sets. The ability to conduct “business or domain friendly search,” which allows users to construct a query utilizing attributes derived from the data, such as categorizing healthcare providers by value or insurance plan, is also critical. “Identify healthcare practitioners with strong sales and big percentages of patients on favorable plans,” for example, should be an easy query.
3. Add to the information.
Because new data is continually arriving, scalability and extensibility are essential. Extending the corporate repository to accommodate and ingest new data sources, whether structured or unstructured, should be reasonably easy. That means having connectors for a wide range of data kinds and formats, including social media, and relying on metadata-driven transformation to reduce or eliminate the need for manual processing in a standard ETL (extract, transform, and load) process. To maintain high quality and reduce duplication, the ability to simply configure and execute quality checks on the data is also critical.
4. Make analytics accessible at a low cost and at a high speed.
Companies should recognize the value of repurposing and reusing analytical methods and investments to reduce costs and accelerate time to insights. This is made possible by the development of an analytics framework, which starts with a business rules engine and progresses to advanced analytics with plug-and-play algorithms. Finally, a user should be able to easily create applications for a given requirement from data processing components (e.g. text analytics) or a pre-packaged machine learning tool(e.g. collaborative filtering).
Pharmaceutical businesses may lay the groundwork for predictive analytics across a variety of departments, from sales and marketing to research and customer service, by following these four steps. Consider the difficulty faced by a pharmaceutical company with a dwindling market share in a specific consumer niche. The sales staff would presumably be curious as to what is causing the market share loss and how it is split among insurance products.
To address this question, the organization might employ predictive analytics on a big data platform and assemble a range of data sources, including consumer demographics, market share, and customer responsiveness to previous campaigns, provider practice composition, and sales representative feedback. This aids in the identification of prospective recommendations, similar to how Netflix and Amazon employ algorithms to recommend movies and books.
When compared to the competition, the analysis may reveal that the product has disadvantaged formulary access (lower ranking on the list of medications available) for the majority of customers in a specific market and that healthcare practitioners have a strong affinity for peer recommendations but have not received any material in the last few months that highlights peer support.
Predictive analytics can recommend a plan of action that promotes client engagement while also undermining competitors’ competitive advantage. It can also use customer feedback to feed into an “intelligent suggestion engine,” ensuring that the “next best actions” supplied to sales teams and contact centers improve over time.
Predictive and prescriptive analysis can also be used to:
- Segmentation of customers, retention, and cross-sell opportunities
- Estimation of new medication marketing budgets
- Forecasting revenue based on medicine efficacy and outcomes
- Using historical data, forecasting daily patient load by diagnosis and treatment.
- Predicting Adverse Drug Events Using Signal Analysis
- Anticipating and planning for disease outbreaks based on previous outbreaks
- Predicting sales based on marketing ROI statistics from the past
- Provide guidance on the optimum clinical procedure based on historical symptoms and outcomes.
It’s worth noting that delivering simplicity to consumers on the front end usually always requires a degree of complexity on the back end, requiring domain knowledge, third-party data sources, open-source technologies, statistical analysis, machine learning/AI approaches, and more. Because these skills are in high demand, it can be difficult to locate people who have them all. Creating a sustainable, rewarding predictive analytics engine, on the other hand, can provide businesses a competitive advantage—and, given the rate of change in the data and analytics arena, it may soon become standard practice.
You’ve defined your strategy, collected data, and assessed your results; now it’s time to create an action plan and figure out what you’ll do next.
Gather stakeholders to share your results and make recommendations based on your findings. This may prompt your company to make some minor adjustments or launch a fully new strategic effort. You’ll be stuck attempting to identify critical insights and recommendations if you don’t pay attention to the first three steps of the analytics process. To successfully use data to guide business choices, you must first build a solid analytics foundation.
Decisions in healthcare can sometimes mean the difference between life and death. To make the best decisions, you must have a thorough grasp of the situation. Our professionals at ResolveData extracts the most value from data to help you achieve better results. Speak with one of our experts today to learn more.
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