Enterprises’ Blueprint for Business Value from Augmented Analytics Tools
Data analytics offers life sciences commercial teams a range of benefits, from an optimized commercial strategy to a greater return on the company’s data investment. However, many analytics implementations fail to provide anticipated value. Gartner estimates that only 20% of analytic insights will deliver business outcomes. Augmented analytics tools, leveraging machine learning, deep learning, natural language processing (NLP), and other forms of artificial intelligence (AI), can help your company reach those goals by making insights easily accessible to users. But a successful implementation takes a well-planned strategy.
When preparing to deploy an augmented analytics tool for your organization, use these four steps to keep your business on track.
Step One: Goal Setting
Once you’ve decided to make data analytics insights easily accessible to your commercial teams with an augmented analytics platform, the first decision you need to make is independent of technology. You need to determine what you’re “hiring” the augmented analytics tool to do.
Goals for an augmented analytics implementation should be specific, measurable, and attainable, for example:
- Personalizing sales strategies to increase engagement with decision-makers
- Optimizing the marketing mix with data-based decisions about pricing, messaging, and promotions
- Reducing churn with visibility into trends so your team can intervene when needed
- Identifying and mitigating revenue leakages
Also, evaluate your current analytics processes and tools to understand how well they can help you meet those goals and objectives. Note issues and gaps, such as lengthy times from data acquisition or a request from the sales, market access, or patient services team until insights are available. Also, consider whether the level of dependence on your IT or data teams is contributing to that lag and preventing your tech resources from pursuing higher-level activities.
Base your decisions on implementing an augmented analytics tool on these well-defined goals and the capabilities your company needs.
Step Two: Management Buy-In
All stakeholders, from the C-suite to line of business leaders and the IT department, must be a part of the planning process. Transparency is key; all participants in the planning process must clearly understand the commercial teams’ challenges and pain points and how technology can solve them. Decision-makers also need budget and timeline constraints to create a realistic strategy.
In addition, the planning team should also champion the project, creating excitement among the commercial team about how the solution will help them perform their jobs better and save time. Full transparency about the project should extend to employees, and training and welcoming feedback should be included in your change management strategy.
Step Three: Build the Right Culture
Deploying an augmented analytics platform will alter how people work. The sales team will no longer have to request that the IT team build a dashboard to answer their questions about prescriptions, geographic trends, or brand performance. They’ll just have to ask the augmented analytics tool a question and get the answers they need within seconds. Commercial teams will need to build data-based decision-making into their workflows, which may take some time and training. Additionally, the data team may need to adapt to the change and refocus on other tasks.
Assure all employees impacted by the transition to an augmented analytics tool that this will be a change for the better for your organization and for them individually.
Step Four: Choose the Right Augmented Analytics Tools and Processes
Choosing the right augmented analytics tool and technology partners is a pivotal decision. The tool must meet the specifications you identified in the first step and support the culture you want to develop.
Ensure the augmented analytics tool provider offers a solution trained with life sciences data so that implementation is streamlined and your team can interact with it conversationally. Also, research whether the tool can easily integrate with your current tech stack and how much time your internal IT team will need to devote to maintenance.
Also, research the solution’s architecture. For example, investigate whether it is designed with micro services architecture which makes it possible to develop deployable services without impacting the entire ecosystem. Also, consider whether the solution can scale beyond commercial functions to benefit other areas of your company.
Set Up Your Commercial Teams for Success
This blueprint for an augmented analytics tool implementation will help you define objectives and goals, build support, and match technology to your needs. However, it will also help your organization move forward as a team to achieve the best outcomes with the solution. Build each step into your strategy so that you can meet your business goals for your life sciences company’s commercial function.
To learn more, download The Everest Group whitepaper, “Transforming the Life Sciences Commercial Function with AI and Augmented Analytics. “
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