Self Service Analytics Platforms in Clinical Trials – Part 3

By Suvarnala Mathangi | Date: September 30, 2019 | Blog | 0 Comment(s)

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”.

Self Service Analytics Platforms in Clinical Trials – Part 2

By Suvarnala Mathangi | Date: September 30, 2019 | Blog | 0 Comment(s)

In Part 1 we introduced self-service analytics and discussed what an ideal self-service analytics platform should accomplish. In this part, we will be discussing the various components of a modern technology platform that enables self-service analytics.

Data ingestion – In a clinical trial setting, both structured and unstructured data is available from an ever-expanding range of sources. These sources even include streaming data and data generated by connected devices. Such data has the potential to enhance insights. Self-service analytics is all about accessing, preparing, and analyzing disparate data from across the data sources. To succeed, pharma companies will require the technology that can ingest data from a variety of sources and be scalable enough to accommodate newer sources. The MaxisIT ecosystem has a platform designed to efficiently ingest and store large sets of structured and unstructured data from traditional sources of data as well as other applications. The platform makes this data available for real-time access.

Storage and preparation – This layer organizes and transforms data in a format suitable for further analysis. The rise of cloud-based technologies has made it possible to store large volumes of data in their native formats. Automated data preparation tools can be used to represent the native data in a format that allows scientists to derive analytical insights from them. These tools help analysts understand the data by exploring the range of values, the format of fields and the plausibility of the data captured. Data Visualization tools can further help in understanding the distribution of the data before deep diving into analysis. How does this help? The benefits are two-fold

• Such an exercise saves time, improves analytical results which allow the process stakeholders to employ more time on analysis as opposed to data preparation.
• Stakeholders can now employ machine learning and advanced techniques to accelerate the process of profiling, blending and organizing data for end-user analysis.

Through automated data preparation, stakeholders can generate comprehensive metadata that supports and supports and confirms with the data governance standards of the regulatory authorities.

Data consumption – Users of clinical data have different data needs at different points in time. No one business intelligence tool can comprehensively address those needs. This is where the modern self-service analytics platform steps in to fill the void. Through a common semantic layer that represents data in a non-technical manner, stakeholders can bypass the underlying complex data environment and find what they need using common business parlance.

Data Governance – The data governance process is needed to manage the integrity, usability, and security of clinical data. The importance and relevance of data governance are growing with the expansion of the clinical data ecosystem which now includes data from sensors and connected devices. Governance processes should include details on how data is ingested, transformed, prepared, stored, presented, archived, shared, and protected. Standards and procedures should be developed to manage data access by authorized personnel and ensure ongoing compliance with regulations. The platform should support Master Data Management (MDM) by allowing IT and stakeholders to access a centrally managed business glossary, data dictionary, metadata, and reference data. Last but not least, data governance workflows with clear accountabilities should support how stakeholders exchange information and manage data assets.

Once an organization deploys a self-service analytics program, it should have an integrated platform in place with underlying capabilities and infrastructure components to promote adoption and end-user satisfaction. The platform should

• Enable provisioning new data sources and management of the technical environment.
• Enable self-service analytics with various toolsets and methods for use at scale.
• Provide a reliable, safe environment where authorized access to, and use of, sensitive data resources complies with appropriate regulations and standards.

Self Service Analytics Platforms in Clinical Trials – Part 1

By Suvarnala Mathangi | Date: September 30, 2019 | Blog | 0 Comment(s)

The pharmaceuticals and lifesciences industry is undergoing transformation at an unprecedented scale mainly due to the regulatory, diminishing margins, growing amount of data and push for AI. One way to keep up with this pace of change is to create a robust analytics infrastructure that will help sponsors organizations to share information more efficiently and engage everyone involved to maximize value.

Most players in our industry are still laggards when it comes to leveraging the potential benefits of analytics due to lack of appropriate technology, processes, and required expertise. To make timely strategic decisions, decision-makers need easy access to actionable information. Success lies in overcoming the challenge of legacy systems and archaic processes.

A novel approach, which will enable users to access data faster without compromising on the security, is very much needed. To that end, modern self-service analytics can help users derive actionable insights by giving them an easier and timely access to data. Read on to know-how.

What is self-service analytics?

Self Service analytics (SSA) is vastly different from traditional business intelligence (BI). While tradition BI tools require background and expertise in statistical analysis and data mining, SSA helps clinical and business professionals to access data independently. It does so by automating data access, preparation, consumption, and analysis. In a self-service analytics environment, users can create and access specific datasets and reports on demand without the help of an IT resource.

An ideal self-service analytics platform should be able to

• Ingest data (structured & unstructured) across multiple sources in real-time.
• Store, prepare and provision large volumes of data to service analytical requirements.
• Serve data to the business in a consumable format through an easy-to-use interface.
• Manage the quality, integrity, and availability of the data through robust governance.

Gartner predicted that by 2020 self-service analytics will make up 80% of all enterprise reporting. While the prediction is accurate, it is disheartening to see that our pharmaceutical industry contributes to a large chunk of the 20% population who are still to adopt. Pharmaceutical companies need to restructure their analytics models to become more agile and successful. The ones who make the transition early are sure to reap the benefits.

By leveraging a modern analytics architecture and a platform for self-service analytics that can be scaled across the enterprise, these organizations would transcend from traditional reporting and business intelligence tools to automate data preparation and advanced analytics capabilities.