In Part 1 and Part 2, we discussed the self-service analytics platform and its various components. In this part we will look into the important things to consider before implementing a self-service analytics platform. The historical way of representing clinical data includes spreadsheet driven models and custom SQL queries which not only increased development time and cost but also led to data quality issues. Self-service analytics has changed the way IT and business users collaborate to get insights from their information sources. Here are a few important considerations for implementing a self-service analytics platform.

Focusing on business impact

It is important for the leadership to identify how a self-service analytics platform can help them meet their business objectives. They should consider the impact on business functions and systems, involvement levels of various stakeholders, the capabilities required and the benefits that can be achieved. Self-service platforms represent the journey from retrospective reporting to the latest analytics capabilities (predictive analytics, AI). To be effective, a self-service analytics model should be flexible enough to accommodate and address the needs of various stakeholders and provide an easy path for them to achieve their business objectives.

Removing barriers to analytics adoption

Complex technology environment often limits the success of analytics efforts. In such environments, more than half of an analyst’s time is spent on overcoming analytics-related challenges such as collating scattered data across sources, improving data quality, and re-presenting insights to decision-makers. Moreover, dependency on IT staff for overcoming the above-mentioned challenges can delay the analytics process. A Self-service analytics platform can simplify the technology environment by automating data preparation tasks. This helps to shift the analysts’ focus on discovering and delivering valuable insights. Self-service analytics can also improve follow-through and responsiveness by automating and streamlining data provisioning and data distribution. Thus, it is important for Pharma companies to restructure processes and consider platforms that enable self-service analytics.

Implementing modern analytics architecture

Stakeholders of clinical trials need a flexible architecture to combine data from multiple sources and in multiple formats and then work with the combined dataset using their preferred analytic tools. New technology platforms can simplify tasks such as report generation and dashboard creation. Integrated platforms such as the MaxisIT’s Integrated Clinical Development Platform can streamline access to relevant data, automatically generate technical metadata, facilitate data preparation, and deliver consumable insights through front-end applications. The platform also offers advanced capabilities such as data discovery, data visualization, and artificial intelligence thus helping move analytics closer to end-users to impact business outcomes. Such an integrated platform positions the organization to take advantage of the convergence of other capabilities such as machine learning, digital assistants and the Internet of Medical Things.

Creating an Analytics governed and Data-driven strategy

Pharma companies can enhance their strategic decision making from the ground up by empowering stakeholders with on-demand insights about clinical trial operations. Doing so can help stakeholders make more timely decisions and course-corrections, often resulting in significant performance improvements. Self-service analytics users can benefit from capabilities such as automated data catalogs, common business rules, vetted algorithms, and business metadata that guide them to trusted analytics insights. When aligned with enterprise priorities and comprehensive data governance program, the organization can gain a repeatable, scalable framework that increases the agility and effectiveness of future analytics projects.

Keeping these considerations in mind can expedite the implementation of a self-service analytics program and help avoid many of the pitfalls that impede value realization.

About MaxisIT

At MaxisIT, we clearly understand strategic priorities within clinical R&D, and we can resonate that well with our similar experiences of implementing solutions for improving Clinical Development Portfolio via an integrated platform-based approach; which delivers timely access to study specific as well as standardized and aggregated clinical trial operations as well as patient data, allows efficient trial oversight via remote monitoring, statistically assessed controls, data quality management, clinical reviews, and statistical computing. Moreover, it provides capabilities for planned vs. actual trending, optimization, as well as for fraud detection and risk-based monitoring.

MaxisIT’s Integrated Technology Platform is a purpose-built solution, which helps Pharmaceutical & Life sciences industry by “Empowering Business Stakeholders with Integrated Computing, and Self-service Dashboards in the strategically externalized enterprise environment with major focus on the core clinical operations data as well as clinical information assets; which allows improved control over externalized, CROs and partners driven, clinical ecosystem; and enable in-time decision support, continuous monitoring over regulatory compliance, and greater operational efficiency at a measurable rate”.