Analytics Industrialization

    Realise the full potential of data to perform advanced analytics

    Develop analytics assets that can be reused for faster decision-making.

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    Analytics industrialization

    When it comes to analytics, Life Sciences companies rely heavily on data scientists to model their analytics needs. With the siloed point solutions currently available, the work that they do is not easily shareable throughout the enterprise and difficult to reuse. This leads data scientists to re-invent the wheel for every brand, business unit or geography when developing their analytic models.

    Given the dynamic nature of their business, influenced by the variety and volume of data, Life Sciences companies are realizing that analytic insights need to be available more frequently at the point of decision making. Being able to democratize analytics, beyond just data scientists, to a broader business audience in your organization is the key to moving analytic insights closer to the point of decision.

    Axtria’s analytics industrialization offer

    Axtria’s Analytics Industrialization offering introduces standardized Life Sciences-specific solutions that democratizes analytics, putting the power of data scientists into the hands of the business decision-makers. The offer allows for the collaborative development of analytic assets that can be stored in a library for reuse and sharing, moving insights to the point of decision-making across diverse personas within the business.

    Pre-built analytics assets

    Reusable business assets that save time, effort, and resources, when building analytic solutions.

    • Commercial Life Sciences analytics assets for Marketing, Sales, specialty
    • Multiple utilities to perform common analyses like deciling, and clustering each tuned to the specifics of Life Sciences uses
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    Analytic model development

    Define, visualize and manage the development of an extensive set of analytic models. Make specific business decisions or answer business questions driven by quantitative data.

    • Define mathematical models that are used to capture multiple elements of the Life Sciences commercial and clinical ecosystems
    • Get a holistic visualization of the model to better align the solution to your business needs and ensure that it is operating on the data it processes correctly
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    Workflow management

    Overall efficiency, rapid re-use and modification, of past work to meet current requirements.

    Easily manage model

    • Creation
    • Storage
    • Dissemination
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    Data discovery

    Enabling consolidation of all business information into a single view and more easily find what you are looking for

    • Scanning your environment and determine where data (both structured and unstructured) resides
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    Data visualization

    Easily identify trends, patterns, and outliers within large data sets by making the data more natural to comprehend

    • Get a clear idea of what the information means by giving it visual context through maps or graphs
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    Data wrangling

    Make the data more appropriate and valuable for a variety of upstream purposes such as analytics

    • Transform and map data from one “raw” data form into another format
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    Personas

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    Direct users

    • Data Scientist
    • Data Engineer
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    Capability Consumers

    • Business Analyst
    • Analytics Leader

    Customer success stories

    Case Study

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    Case Study

    Data Analytics-driven Roster And Alignment Optimization For A Major Medtech Company

    Case Study

    Marketing Mix Analysis For A Specialty GI Drug Contributed Significant Top-Line Growth For A Top-10 Pharma Company

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