Single View
of Data
Struggling with siloed data? You’re not alone. Pulling and consolidating data spread across cloud, legacy systems, and home-built apps is no easy feat. Luckily, achieving a single view of your data is possible with the right tools and processes.
What is a
Single View?
A single view of data is not just about consolidating data from multiple sources a creating a singular representation for each consumer. It’s about establishing a single source of truth and then dynamically creating views for consumers and serving data to them based on their needs and privileges.
Establish a single data foundation for context-specific views
Having the exact same single view for everyone is not safe or practical. Instead, different teams and departments need a single view tailored for their needs.
Single source of truth
Marketing view
Risk view
Securely provide tailored views to users and systems from a single source of truth
A single view of what?
Customers
Understand behavior, track purchases, provide better customer support, and market more efficiently.
Products
Curate product attributes, manage inventory, and have accurate information available to customers.
Assets
Accumulate information about equipment based inputs from technicians and sensor-generated data.
Vendors
Better understand the value your vendors bring, plan orders, and manage risks.
Locations
Analyze data based on city, state, country, district, etc.
Reference data
Ensure proper functioning of critical business processes, such as reporting and analytics, and avoid manual reconciliation.
A single view by industry
Achieve benefits from the single of view of data
no matter what industry you are from.
Financial
services
Customer 360 for risk management, predictive modeling, cross-sell/up-sell, campaign targeting, and better customer support.
Life
Sciences
• A single view of HCOs and HCPs for spend management and reporting (Sunshine Act).
• A single view of substances for better categorization and management of clinical studies (by connecting this data with data about research institutions).
Healthcare
Patient 360 solution for centralized patient information management (diagnoses, treatment plans, contact details, billing information, and clinical studies) and consent management.
Retail
Consolidate data about customer activity from different channels for personalized offers and improved customer satisfaction.
• Create a single of view of suppliers by centralizing authoring and onboarding external data.
• Author & onboard product data in a centralized repository for improved control over the inventory, price management, and time-to-market.
Telecom
• A single view of customers for reduced churn, reliable consent management, effective marketing, and efficient customer support.
• A single view of equipment such BTS’s to predict maintenance, prevent breakage, and provide consistent, complete, and up-to-date data to technicians, architects, and asset managers wherever they access it.
Transportation
& Logistics
• A single view of customers for more personalized offers, better customer experience, and improved analytics.
• A single view of equipment data to predict breakage and plan utilization.
• A single view of locations with complete data, enrich from external source for optimized route planning.
Government
• A single view of citizens decreases service times, improves efficiency, and facilitates data sharing between department for tax compliance and law enforcement.
• A single view of assets simplifies audit, enables efficient infrastructure management and modernization initiatives
How to achieve a single view of your data
Here is a simplified checklist to get you started with a single view project.
Define scope
Pick a data domain you want to start with, e.g., customer, and the systems from which you want to consolidate data. You don’t have connect all relevant systems and create the most complete data model from the very beginning. It’s best to start small & reasonable and then grow you solution.
Identify data consumers
Who are the consumers of data from your single view solution? People, downstream applications, ESB, a message queue? Answering these questions will help you define interfaces, modes (batch, online, streaming), and possibly applications for providing you data.
Identify data producers
Pick the source systems that will contribute data for the single view. As mentioned above, you can start with a subset and then add more after you test and go live with a smaller solution.
Choose implementation style
Based on the needs of consumers, pick the implementation style: - Analytical. Consolidate data and provide data to users and downstream systems. - Operational. Consolidate and/or author data and provide it to source systems. - Mixed. Combine the two styles.
Configuration & Execution
Map attributes that contain the same type of data from different source systems to a single attribute in the canonical model. For example, cust_name in system A, xds_11 in system B will map to cust_first_name in your data model for the entity Customer. Repeat for all attribute in your data model.
Standardize data to enable accurate identification of duplicates
Get rid of discrepancies in naming conventions used in different source systems. For example, when matching addresses decide whether you want to use Street or St., and transform all incoming data to that format. Having standardized and cleansed data helps with accurate record matching.
Group duplicate records
Configure matching rules to catch duplicate records and group them. This is your way of saying that they are they same. For example, in the case of customers, your rule might be: when name, address, and birth date are the same, this is the same customer.
Merge duplicate records (if required)
Merge data from the groups formed in the previous step and set the rules for picking the best (representative) value for each attribute. For example, “Always pick address elements from the CRM” (because that’s where this data is most up to date). In some cases (and domains), merging is not required.
Enrich records with external data
Pull data in real time from external registries or internal systems that provide frequently changing data that you don’t want to manage in your data model.
Review & iterate
Review steps 1,2, and 3 and expand your solution with additional producers and consumers. Tweak the data model when necessary. Review SLAs and adjust performance settings.
How Ataccama helps achieve a single view of any data
Flexible data model
Manage any single- and multidomain models. No limits on the number of entities. No limitations on attributes.
Automated matching rules
We use machine learning to auto-generate matching rules. But you can fine-tune them and configure experts rule, too.
Expansive connectivity
Connect and provide data to a variety of sources (files, DBs, cloud platforms, and message queues).
User-friendly UI
A web app tailored for data stewards to fix errors, override values, create new data, all subject to a configurable workflows.
AI suggestions
Our machine learning models improve themselves and suggest new potential matches or splits.
Fast data processing
A scalable engine capable of processing any data volumes from various sources.
Built-in data quality
Ensure accurate matching & merging with data standardization. Prevent poor data entry with real-time validations.
Flexible implementation
Deliver analytical and operational use cases in one solution to provide data in combined batch + online workloads
Get a single view of any data with Ataccama.
Schedule a demo
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Discover the Ataccama
ONE Platform
Ataccama ONE is a full stack data management platform.
See what else you can do.
