Guide to setting up a Cloud Data Management PlatformGuide to setting up a Cloud Data Management Platform https://datazymes.com/wp-content/uploads/2020/12/Cloud-Data-Platform-Step-by-Step-Guide.jpg 874 600 Vivek g Vivek g https://secure.gravatar.com/avatar/927c001dd3a13b6e015a946097c9aa5c?s=96&d=mm&r=g
Our guide for a sucessful Cloud Data Managament platform implementation, through our deep expertise
With the expansion of digitalization, Big data, and customer first attitude, we have noticed a shift in pharmaceutical companies’ technical driven innovation, drug development and commercial phases. Across globe, pharma companies of all sizes are trying to enable modern cloud platform for faster business decisions and attain maximum commercial success of brand using power of data.
Pharma companies today are hassling with various structured and unstructured data to derive meaningful patterns and insights. And with the continuous expansion of social network, compliance, and pharmacovigilance, pharma and life science companies are pacing up to bring modern technology driven cloud platform to get pulse of Patients and healthcare ecosystem.
While working through multiple engagements across a range of Pharma customers from top US pharma customer to small-medium scale Pharma customers, we crafted strategic and key guidelines to enable a strong and scalable foundation of Cloud data management platform.
Our learnings, strong pharma domain expertise, and technology partnerships across leading technology organizations helped us refining and defining steps for a successful and cloud-first data management platform.
- Step 1: Evaluate and Finalize data assets as per business use cases
- Step 2: Carefully select optimal cloud technology stack
- Step 3: Onboard, Ingest and Integrate Data assets
- Step 4: Enable Business & data quality rules
- Step 5: Bring Insights and Analytics
Data Assets Per Business Use Cases
Evaluate and finalize data assets as per business use access
The data assets used wisely and accurately become the most crucial and strategic business advantage in Pharma commercial market landscape. As Pharma drug advances in commercial lifecycle journey, primary and secondary market research data becomes key to take right business decisions.
Thus, it becomes imperative to precisely identify, evaluate and finalize the data assets relevant for business insights and informative decision-making processes.
Identifying the relevant datasets as per business use cases is the first strategic milestone to enable a cloud data management platform.
Below guidelines can be considered during assessment phase of data assets:
- Data offerings by data provider(s) as per business use cases and as per drug lifecycle
- Data across subject areas such as Sales, Claims, Customer, Product, Market basket, Alignment, Commercial Operations, CRM activity etc. to enable integrated data foundation
- Market and geographies coverage of data
- Frequency at which data collected from ground to gauge latest business situation
- Quality of data and timeliness
Optimal Technology Stack
Optimial technology stack with ease of connectivity across is needed
The exponential growth in cloud technology has opened unlimited options and path for pharma organization to achieve their goal. Selection of optimal cloud technology stack to start small and scale with business are vital in cloud data management enablement.
While assessing a Cloud technology stack one needs to evaluate few points and see if the technology features or support services provided are suitable to enable data as key business asset.
- Technology and Services fitment with design architecture
- Interdependency of technologies and ease in connectivity among technology solutions
- Ease of maintenance and upgrades
- Operational support and ease of change management for business adaption
- Cost plays an important role in deciding which service provider you choose. It is important to consider the cost of services and cost involved during operations
- Business continuity, data security, disaster recovery and back-up planning
Onboard, ingest, and Process
Rapidly onboard data files, streamline data vendors, ingest and process data assets in data management platform
With advances in big data capabilities and cloud technologies, Pharma companies now can churn-out business insights and analytics information using both structure and unstructured data sets.
Collating offline, online, structured, and unstructured data in a unified and centralized data management solution gives you the 360-degree view of commercial journey of drug. In ‘data to insights’ journey, data assets need to go through various phases of data processing as follows:
- Onboarding: This process ensures that data from disparate homogenous and heterogenous sources are available in raw format and ready to onboard within cloud data management platform. These data sources could range from external data partners, Veeva CRM, Alignment, Salesforce, MDM systems for customer and product, and internal source etc. Data onboarding involves primarily subject area wise storage of file at cloud data storage such as AWS S3, input file format finalization, and metadata driven file layout maintenance.
- Ingestion: During this stage raw data files from S3 are processed into a centralized database of cloud data platform along with data quality checks. Data ingestion strategy depends on business requirements and constraints such as timeliness, processing prioritizations, business rules, type of data as in structured or unstructured, batch processing, real time processing etc. For instance, if business needs high speed ingestion process to enable the analytics or self-serve layer for data scientists, then configurable parallel processing could be an option.
- Integration: This process enables to integrate data sets across subject areas such as master dimensions -Customer, Product, Employee, Payer, Plan, and business measures such as Retail Sales, Non-Retail Sales, Call Activity, IC, Patient Level data etc. In addition to integration across data sets, multiple business processes such as Customer Universe, decile, Segmentation, created during this level of processing.
Centalized and configurable rule engine to empower business users and to reduce IT dependency for rule management
Data quality and business rules are always needed and important for right data process. Configuration based rule setup empowers business community to easily change, validate, and maintain business and data quality rules with minimal IT intervention.
Centralized rule engine enables all analytics data platforms and business applications to refer one common repository for business and data quality rules. This process not only streamlines business and data quality rules at enterprise level, but also strengthens data governance.
- Finalize functional use cases needed to support business operations
- Finalize business rules based on business use cases
- Finalize data quality rules to for data governance and trust on data assets
- Define business rules not only to validate technical ingredients but also to validate business trends, key metric deviations, and un-usual business scenarios
- Enable metadata driven and configurable data quality and business rules in the cloud data management platform
- Enable configured DQ and business rules within data processing flows
Insight and Analytics Readiness
Analytics empowers you by finding significant, actionable, and purposeful insight in your data
To enable business insights, cloud data management platform needs to be ready and integrated with Reporting Solution and Advanced Analytics. Insights and Analytics ready cloud data management platform also needs to support subject area and business use case driven reporting marts and analytics ready datasets (ARDs). Additionally, quick integration and connectivity with multiple business reporting and analytics platforms are important for faster business insights.
Reporting marts and analytics ready datasets based on Pharma commercial business use cases to be considered as few examples
- Pharma commercial subject area such as Sales force, Payer, Veeva Call activity, Managed market, Medical affairs, Patient, Marketing etc. driven scenarios
- Product/drug lifecycle such as pre-launch, launch tracking, and post launch commercial success driven use cases
- Patient ecosystem driven use cases such as integrated view for Patient HUB activity, Specialty Pharma and APLD
- Integrated business views across retail, non-retail sales, claims, Veeva CRM activity, customer, product, market, competition subject areas
- Marketing oriented views to enable Customer 360, Next best action, launch campaign etc.
Transform your commercial data platform into business outcome driven cloud data management platform.
DataZymes DZHarmony is a modern, ready to deploy Data Platform that reimagines how Pharma Commercial and Technology teams interact with data by removing the barriers between back-end data management and front-end analysis. DZHarmony enables users with varying technical ability and deep subject matter expertise to work meaningfully with data. With DZHarmony, business can source, connect, and transform data – structured and unstructured- into data platform to take-action. The platform enables cloud data warehouse and data lake capabilities with scalable and configurable architecture, data catalog and lineage features. DZHarmony promises to be the enterprise grade data management platform for the organization by being the central data foundation to drive collaboration, discovery, and serendipity across functions.