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.

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What is a single view of data?

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

Personal information
Contacts
External address enrichment
Related parties
Consents

External risk registry information

Repayment history

Purchase history

Web traffic information

Channel affinity score

Banking app usage

Branch visit information

Marketing view

Personal information
Contacts
Communication consents by channel

Customer score

Active products

Past products

Channel affinity score

Customer type

Next best offer

Risk view

Personal information
Current risk score

Total loan amount

Fraud score

Loans elsewhere

Behavioral scoring

A single view of what?

Customer data

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.

10 steps to achieve a single view of your data

Here is a simplified checklist to get you started with a single view project.

Preparation

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

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.

Configuration & execution

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.