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.

Systems and data modernization
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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?

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Customer data

Understand behavior, track purchases, provide better customer support, and market more efficiently.

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Products

Curate product attributes, manage inventory, and have accurate information available to customers.

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Assets

Accumulate information about equipment based inputs from technicians and sensor-generated data.

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Vendors

Better understand the value your vendors bring, plan orders, and manage risks.

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Locations

Analyze data based on city, state, country, district, etc.

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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
  1. 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.

  2. 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.

  3. 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.

  4. 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
  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. 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
  1. 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.

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