Self-Serve Analytics: Democratizing Data Access and Insights in Healthcare

Anugya Kanswal
Mon 18 Sep 2023
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Accelerating Decision-Making with Data-Driven Insights On Demand

Have you ever felt hindered by the complexities of data analysis? Have your data analysts ever complained about depending on data teams, IT, or 3rd party vendors for simple data analysis reports? If the answer is yes, you’re in the same situation as the majority of providers, their staff, and their in-house data analysts who struggle to quickly harness data-driven insights.

Imagine if you and your analysts and data scientists could effortlessly access, analyze, and extract insights from data without relying on technical IT experts or vendors. What if there was a way to rapidly transform your need to know into actionable knowledge with accurate data, all while speeding up decision-making?

Welcome to the realm of self-serve analytics—where the power of data-driven insights is at your fingertips.

Understanding Self-Serve Analytics

At its core, self-serve analytics is a departure from conventional methods of accessing and using healthcare data. It empowers people in healthcare organizations to independently access, manipulate, and extract insights from complex datasets; or to package, deploy, and use models and algorithms created for the entire organization's benefit.

The self-serve approach eliminates long-standing barriers to data exploration, democratizing access to crucial information and fostering faster, more informed decision-making. The significance of self-serve analytics goes beyond mere accessibility. It aligns with the unique demands of healthcare. By enabling users to directly interact with data, self-serve analytics ushers in an era of business agility, personalized insights, and efficient resource allocation.

The Multifaceted Benefits of Self-Serve Analytics

The emergence of self-serve analytics offers an array of benefits that can fundamentally reshape healthcare operations through:

  • Faster Decision-Making: Self-serve analytics empowers healthcare professionals to access and analyze real-time data, leading to faster business and clinical interventions and enhanced financial, operational, and experiential patient outcomes.
  • Reducing Cross-team Dependency: Self-serve analytics addresses traditional operational bottlenecks, giving users direct access to data and insights without having to rely on or burden intermediaries.
  • Personalized Insights: Tailoring data exploration to specific roles ensures insights are relevant and actionable, enhancing decision-making.
  • Exploratory Analysis: Self-serve analytics encourages creative interactions with data, revealing nuances in insights and trends that traditional approaches might, and often do, overlook.
  • Resource Efficiency: By allowing non-technical users to manage routine analyses, self-serve analytics optimizes resource allocation and efficiency, freeing IT teams to focus on other pressing issues.
  • Empowerment: Self-serve analytics fosters ownership and innovation, engaging users directly in data-driven decision-making, building their analytics skills, and fostering continuous professional improvement.
  • Timely Insights: Rapidly evolving situations demand insights be available in near real-time. Self-serve analytics makes this possible, ensuring users can tap data immediately and directly, yielding the most up-to-date and actionable insights in an instant.
  • Cost Savings: Reducing business leaders’ reliance on specialized support from IT or other teams can help reduce operating costs by allowing those teams to focus on higher-value activities.
  • User-Centric Approach: Self-serve analytics tools prioritize user-friendliness and accessibility, encouraging broader use of data-driven decision-making, which leads to better business and clinical outcomes.
  • Innovation: Independent data exploration can result in greater, deeper exploration of data; and lead to unexpected discoveries and novel insights that foster a culture of innovation.

It's crucial, however, to acknowledge that while self-serve analytics offers substantial benefits, it also presents new challenges. Ensuring data accuracy, safeguarding data security, providing effective user training, and preventing the formation of additional data silos are essential considerations to be addressed.

At Innovaccer, we systematically address these challenges, ensuring that end-users can unleash the power of accurate, secure, and compliant data analysis to tackle business decisions with trust and confidence.

Challenges Users Face When They Need Data Analysis

Role Top Challenges to Analyze Data and Derive Insights
Business Leaders (CXOs, VPs, Senior Directors)
  • Access to data: In many organizations, data is siloed in different departments or systems, making it hard for business leaders to get the data they need to make informed decisions. Self-service analytics can help break down these silos, giving business leaders the data they need when they need it.
  • Slow, cumbersome data analysis process: The traditional data analysis process can be slow and burdensome, forcing business leaders to wait days, weeks, or even months to get the insights they need—and then the insights can be out of date. Self-service analytics can expedite the process, providing quicker (even instant) access to vital insights.
  • Low data literacy: Many business leaders don’t have the technical skills, knowledge, or time to use traditional data analysis tools. Self-service analytics tools are designed for any knowledge worker to use, including those with limited data literacy.
  • Lack of ownership: When data analysts create reports and dashboards, the business leaders who need the insights often don’t feel a sense of ownership over them. Self-service analytics can empower business leaders to take ownership of the data insights they require by letting them create and manage their own reports and dashboards.
Analysts (Business Analysts, Clinical Analysts, Data Analysts)
  • Time crunch: Data analysts are frequently overwhelmed with work from across the organization, and often don’t have sufficient time to perform all the data analysis they’re being asked for on a timely basis. Self-service analytics can reduce this burden because people who would otherwise be requesters can now conduct their own analysis in a significant number of cases.
  • Skills gap: Data analysts possess varying levels of skills and knowledge. Self-service analytics tools can accommodate users with a range of skill levels, facilitating the sharing of expertise among data analysts. Even highly skilled analysts will take advantage of self-service analytics for appropriate requests or requirements, reducing reliance on SQL or other technical solutions to speed the delivery of insights.
  • Siloed data: Healthcare data is often compartmentalized in different departments or systems, making it difficult for data analysts to activate the data they need. Self-service analytics tools can dismantle these silos by giving data analysts access to unified data.
  • Data quality: Poor data quality can pose a significant challenge for data analysts. Self-service analytics tools can help identify and clean dirty data, streamlining the process of obtaining accurate results and enhancing confidence in insights obtained.
Data Scientists
  • Data exploration and feature engineering: Data scientists typically spend 60-70% of their time exploring, cleaning, and analyzing data to create meaningful variables (features) for building machine learning models. Additionally, without version control of these features, substantial time is devoted to validating their accuracy.
  • Time to Build Pipelines: Data scientists often need to construct and maintain complex data pipelines and machine learning models. This can increase technical debt, hindering model maintenance and updates. Self-service analytics tools can mitigate technical debt by offering pre-built models and data pipelines data scientists can tap into on demand.

How Can Innovaccer Help?

Embark on a transformative journey driven by data as with a range of new, powerful, and growing portfolio self-service analytics tools from Innovaccer. Each one is designed to align with your unique business needs.

Effortlessly customize quality measures and craft dynamic patient cohorts. Easily build and maintain personalized dashboards and visualizations. Standardize and streamline machine learning models. Get deep, data-driven insights without the need for a single line of SQL code. Innovaccer’s suite of self-serve analytical products redefines your relationship with healthcare data, putting the power of instant insights at your fingertips.

Here are five of our expanding library of self-service analytics tools. Each is designed to make your work day easier, your decisions faster and better informed, help scale data-driven decision making throughout your organization, and free your IT team to focus on higher-value work.

  1. Measure Builder: Craft Your Own Quality Measures

    Customize existing quality measures or create new ones with ease, all through an intuitive user interface that requires just a few clicks.

  2. Cohort Builder: Generate Dynamic Patient Cohorts

    Specifically tailored for data analysts, care coordinators, physicians, and leadership, Cohort Builder offers a straightforward yet potent way to create patient cohorts based on each specific use case. Store and access dynamic patient groups seamlessly across our care management and outreach applications to take tailored actions on cohorts.

  3. SQL Workbench and Dashboard Builder: Create Personalized Dashboards and Visualizations

    Recognizing the uniqueness of every healthcare organization, we give you the flexibility to create your own dashboards and visualizations, in addition to how many standard out-of-the-box options. This lets you shape your data narrative as you envision it, ensuring your story is told your way.

  4. ML Platform: Streamline and Standardize Machine Learning Models

    Accelerate model development with a dedicated healthcare feature store, and integrated data quality checks. Manage multiple model versions with ease, ensuring continuous monitoring of ML model performance for accuracy, drifts, and biases.

  5. Sara, Your AI Assistant: Get Data Insights Easily and Instantly

    Ask questions in plain English and watch Sara translate them into insightful tables or graphs. Dig deeper, drill down, and explore the data. Then easily export it for further analysis in your preferred tools or integrate it with your BI tools. Continuous refinement driven by user feedback ensures ever-sharper data interactions and results.

Learn more about how Innovaccer’s self-service analytics solutions can enhance your performance. Schedule a demo with our experts today.


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Tags: Self-Serve Analytics
Anugya Kanswal
Manager Data Analytics at Innovaccer

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