Alteryx: Helping Pharma Companies run Efficient Business Solutions by Maintaining Comprehensive Data Simplification, Compliance and LineageAlteryx: Helping Pharma Companies run Efficient Business Solutions by Maintaining Comprehensive Data Simplification, Compliance and Lineage https://datazymes.com/wp-content/uploads/2022/10/Alteryx-1024x683.jpg 1024 683 deepak https://secure.gravatar.com/avatar/acfa5c400ac94d9a541d4f108658dcbb?s=96&d=mm&r=g
Big Data Management in Pharma: Background
Post-Pandemic, the pharmaceutical industry saw significant changes. These include a flood of remote data and a combination of other factors, that accelerated the pace of change in the way this data is mined and managed. Pharma companies implemented several initiatives to increase access to care, value-based payment models and greater consumer involvement in drug accessibility and digitization of valuable information.
With faster and more updated data management analytics being the need of the hour, the focus shifted overwhelmingly for better data analytical tools in the business. According to a survey by EMR, the market for big data analytics in life sciences in 2020 was valued at USD 22.5 billion. The industry witnessed a further growth in the forecast period of 2022-2027 growing at a whopping CAGR of 15%. This is projected to reach USD 52 billion by 2026.
Traditionally, pharma data management analytical tools faced a challenge in broad enterprise-wide acceptance. Legacy data analytics software like SAS was complex, difficult-to-use, and costly. Data teams found it prohibitive to put sophisticated analytics directly in the hands of analysts in departments such as marketing and operations outside of IT teams.
The other challenge was the absence of enterprise readiness of newer solutions that could ensure data governance and safety efficiently. With a steady rise in demand, decision makers needed a data management tool that comprehensively managed pharma data and enabled data teams to have a single comprehensive view of myriad and complex user and drug data.
These processual challenges perpetuated the need to provide data analysts with a simple but efficient data processing tool like Alteryx. Alteryx also addressed the top concerns of data scalability and usability by providing customers an integrated and commercialized solution. It helps pharma companies improve their decision-making speed by providing data analysts and line-of-business users the ability to prepare, blend, and analyze data themselves without depending on IT teams and technical staff.
Simplifying Data Architecture Management and Better Data Governance: Addressing the Pharma Business Problem with Alteryx
Most pharma companies generate humungous amounts of data that need a more simplified data architecture management. Alteryx addresses this concern by creating an easy-to-understand data pipeline from source to insights. It also provides a well-planned data architecture that is at the heart of all digital transformation efforts in the industry. As an automated end-to-end analytics platform simplifies the data eco-system to gain clear insight into data structures, Alteryx ensures a consistent and reliable data architecture that reduces the chances of data loss or data theft. It is also a tool that is less expensive and helps companies implement a solid data strategy enabling changes without expensive restructuring. Alteryx also helps in simplifying design decisions and the implementation process by allowing workers to focus on analytics transformation.
Pharma companies also critically lack a definite design and architecture for data processing. They are at a loss to find connections between data sources and repositories. At some point, most realized that data is critical for survival and there were no design or standards governing data structures. There was an urgent need to create and accumulate new data stores and data warehouses that offered specific guidance about collection, management, security, and the lifecycle of the data.
By typically leveraging various data sources for business needs like transactions, finance, and HR, companies carry out analysis of data directly against source systems by creating contention with the ingestion of new data and operational reporting. Moreover, data analysis requires joining of data from disparate systems. Adopting a data warehouse to store, aggregate, organize and analyze data without impacting internal sources is an urgent need for businesses. To shorten time to value, a cloud-based data warehousing as a Service or DWaaS was born. DWaaS offers numerous benefits over on-premises data warehouses such as scalability, elasticity, redundancy, unlimited storage, and time to value. Two of the most popular DSWaaS providers are Amazon Redshift and Snowflake. Alteryx works with these two tools and pulls data from the data warehouse to process in a workflow on the Alteryx designer and Alteryx server machine using a standard input tool.
Since pharma data stores grow in isolation from one another, it becomes more difficult to synergize these together. Smooth integration and interaction among systems warrants a seamless data architecture management with superior design. With Alteryx, companies can integrate internal and external data sources to transform millions of rows of data into insight. Business managers want to identify relevant sources both external and internal and use workflows and analytics that unites new and historical data from sources that are diverse. Alteryx automates the work of processing, enriching, connecting, transforming, and outputting the data so that data analysts can query the resulting, combined data sets to create simple visualizations. Alteryx also optimizes data pipelines and management with full visibility in the data lifecycle.
With the Alteryx Analytic Process Automation Platform, data teams can automate repetitive tasks and spend more time deriving results and insights from a flood of data. This AI powered platform helps in assessing and analyzing data with speed and precision. Traditionally, multiple teams would process data separately and in an ad hoc fashion that would be time consuming resulting in data protection risks. Automation like Alteryx improves visibility and cuts indirect costs by heavy lifting of data thereby affecting outputs and outcomes. Alteryx is a self-service data analytics platform that helps data teams do high level creative work with data.
Also importantly, data lineage or the data path from origins to insight, is crucial for the establishment of trust and understanding data assets. Pharma data has complex origins, and it is difficult to establish relationships to other data assets. Alteryx offers technical data lineage by loading metadata from source and target systems and interpreting Alteryx workflows. The data platform can track where an organization’s data comes from and the journey it takes through the system to keep it compliant and accurate. Good data management offers a stepwise record of how data has arrived at its current form, including both transformations made to the data and its journey through different business systems. Since pharma data is amassed constantly from a variety of sources such as inventory data, point-of-sale, and Internet of Things (IoT) devices, Alteryx cleanses this data by organizing, storing, and maintaining it. Alteryx helps companies understand data lineage overall by eliminating differences in technical data and other business data such as those in operations. Business data lineage is also critical for pharma organizations and Alteryx enables companies to understand whether the data has come from a trusted source and transformed in accordance with best practices and stored safely.
How Alteryx Creates Efficient Business Solutions?
Pharma companies need to identify errors in their data and see how these problems originated. Locating issues allows them to make process changes that specifically target problems with a clear understanding of where it occurred and what impact it has on the downstream in the drug supply chain. Alteryx helps consolidate data privacy processes by clearly identifying issues and ensures there is clarity about the people involved in the chain of responsibility. The data automation platform ensures trustworthiness of data and addresses change control effectively. It also locates the origin of the problems in the data cycle. This allows data teams across departments like marketing, supply chain management, manufacturing, operations, sales, and customer support to make strategic business decisions and glean insights from field research and operational data. All of this in turn impacts business growth including product and service development and creates efficient business solutions.
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