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Why is Data Quality Important ?. The DQ Business Case & Interdependence. “It is not necessary to change. Survival is not mandatory.” W. Edwards Deming.

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Why is Data Quality Important ?

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Why is Data Quality Important?

The DQ Business Case & Interdependence

“It is not necessary to change. Survival is not mandatory.”

W. Edwards Deming

“A business case captures the justification for initiating a project or task by estimating the effects a particular decision will have on profitability.”

The Business Case

Why is Data Quality Important?Impacts

  • Operational Impact

    • Internal business systems and processes

    • Compliance with regulations

      • Sarbanes-Oxley, Basel I, Basel II, HIPAA, EU 1169/2011

  • External Impact

    • Outward facing processes that influence opinions and buying behavior – remember “perception is reality”

Shared handling of data between entities with different business rules and data definitions creates inconsistency and leads to poor data quality across the supply chain.

Poor data quality negatively impacts the following key management areas:

Why is Data Quality Important?Operational Impacts


  • Enterprise Intangibles

    • Ease of doing business with Users

    • Decision making - inaccurate information cannot support well informed decisions

    • Organizational trust

    • Confidence in enterprise

  • Risk

    • Regulatory

    • System investment & development (cannot be fully utilized)

    • Integration (new systems, acquisitions)

    • Fraud – exploitation of failures or loopholes within the system

    • Costs

      • Error prevention – (proactive)

      • Error detection and correction – (reactive)

      • Overpayments (claims/settlement costs)

      • Rework /Increased workload/Increased process times

      • Increase cost per volume (throughput, avg cost transaction, volume pricing)

  • Revenues

    • Impaired forecasting

    • Erroneous bill-backs/Invoicing

    • Delayed or lost collections

    • Low level of confidence in analysis and reporting

  • Why is Data Quality Important?External Impact

    • Customer Satisfaction

      • Agility in responding to consumer demand & market changes

        • Early identification of challenges

        • Opportunities for innovative solutions

      • Available when, where, and how the customer wants it

      • Trading partner demands

    • Brand Image

      • One common global representation

    • Reputation

      • Trusted source of data

      • Sustaining a competitive advantage

    Improving data quality increases the strength and overall viability of the organization

    Why is Data Quality Important?For Manufacturers

    Effectively managing product information throughout the supply chain helps manufacturersincrease revenue and decrease costs by:

    • Accelerate new product introductions

    • Increase market share for early arrival of new items

    • Reduce item maintenance efforts

    • Reduce costs through consistent packaging and fewer retailer-specific processes

    • Decrease error rates

    • Increase productivity

    • Reduce rework administration

    • Reduce out-of-stocks

    • Minimize invoice deductions

    Effectively managing product information throughout the supply chain helps Retailers increase revenue and decrease costs by

    Why is Data Quality Important?For Retailers & Distributors


    • Category Management & Promotion

      • Less need for local agents or intermediation

      • Ability to expand supplier base

      • Improved visibility for stock-level planning

      • Simplified/enhanced category reporting

      • Quicker and easier new item introductions

      • Shorter lead time on product promotions

      • Price changes or corrections easier to manage, less need for costly human intervention

    • Administrative Data Handling

      • Less in-store labour required: cost savings

      • Less administrative personnel needed: cost savings

      • Less time spent maintaining catalogues

      • Less need for duplicate catalogues

      • No need for cross-reference tables

      • Fewer invoice disputes

      • Fewer order defects

      • Better fill rates

    Why is Data Quality Important?For Retailers & Distributors (cont’d)


    • Smoother logistics

      • Savings from more accurate weights & measures

      • Error-free shipment receiving

      • Fewer return shipments

      • Fewer backorders

      • Less excess or "safety" stock

      • Optimized location despatch

      • Reduction in shrink

    • Better Bottom Line

      • Increased sales

  • More Satisfied Customers

    • Better on-shelf availability

    • Quicker checkout times

    • More promotions

  • Initiatives

    • Traceability

    • Supply Chain Efficiency

  • Why is Data Quality Important?In a Nutshell…

    • Why improve DQ?

      • Reduces costs

      • Increases profitability

      • Increases operational efficiency,

      • Preserve your reputation

      • Generate better business information to enable more informed decision making.

      • Achieve and sustain a competitive advantage

    Data Quality is Interdependent

    Why is Data Quality Important?Interdependence

    Coming together is a beginning. Keeping together is progress. Working together is success.

    ~Henry Ford

    "In God we trust, all others bring data“ Unknown

    Information Supply Chain

    Information Supply Chain – B2B2C



    Business Users

    End Users



    Data Recipients

    Data Providers


    Information Supply Chain – B2B2CData Sources

    • Supply Chain

      • Supply chain is not linear

      • Multiple operations, systems, and applications

      • Different data a different plant – multiple versions

      • Strategic Sourcing – increased complexity

      • Transactional and Logistical analysis/information is dependent upon Product data

    • Crowd-sourced and third party data - not linked to data owner


    Data Providers


    Information Supply Chain – B2B2CData Sources

    • Marketing

      • Product databases which are incomplete, include non-standard data, or are missing attributes

      • Managed on spreadsheets.

      • Multiple versions of the same product data

      • Combination of manual and automated processes to re-key or update data

      • Multiple sources of customer or prospect data (avg 3)

      • “Work arounds” are the standard solution rather than address the root cause

        Requires high level of visibility and coordination


    Data Providers


    Information Supply Chain – B2B2CData Synch

    • 30% SKUs updated each year (avg)

      • Not including seasonality, promotions, packaging changes

    • Manual processes to get data into the system

      • Labor intensive

      • Error prone

      • Slow - likely out of date by the time it’s entered – needs monitoring

    • Trading Partner constraints – different levels of sophistication

    • Options

      • File Transfer (not synchronised)

      • One-to-one trading partner interface

      • One network interface


    Data Providers


    Information Supply Chain – B2B2CAggregators

    Compile information from multiple sources

    • iTrade – Food industry

    • Nutrifacts – Nutrition information

    • GHX – Healthcare

    • Edgenet – Hardlines

    • GS1 MO – multi-sector


    • Receive upstream from multiple platforms

      • Multiple locations for same info

    • Manage catalogue updates and maintenance

    • Deliver downstream in multiple formats


    Data Recipients

    Data Providers


    Information Supply Chain – B2B2CData Recipients

    Business Users / Retailers

    • Unit of Measure – confusion or misuse

    • Weights (net, gross, tare)

      • One SKU with multiple weight depending on case pack - “rounding” impact s freight charges

      • Truck Cube – orders don’t fill or are too large for truck,

    • Packaging Dimensions – fit on shelf, Plan-O-Gram

    • Promotion & Rebates – Time sensitive, unit specific

    • Manual data management efforts

    • Store operations - Availability and Out-of-stock


    Data Recipients

    Information Supply Chain – B2B2CData Recipients

    End Users / customers use data to make an informed purchasing decision. Data must be:

    • Available

      • When – Considering or Making a purchasing decision. If data is missing, the product may be invisible and the sale is lost.

      • Where & How – Received in the customer’s preferred medium. Web, mobile, kiosk, smart shelves, tablet, and next new technology…

    • Fresh

      • Data is perishable, it must be current and up-to-date

    • Accurate & Reliable

      • Is it from a trustworthy source?


    Data Recipients

    Information Supply Chain – B2B2C



    Business Users

    End Users



    Data Recipients

    Data Providers


    Why is Data Quality Important?Information Supply Chain

    Without reliable data in the Information Supply Chain, trading partners are forced to set up additional means to control data quality, resulting in a longer, more complicated ‘road’ for the information.

    Why is Data Quality Important?Summary - Elevator Pitch

    Improving Data Quality Enables:

    • Common understanding of business policies and processes across enterprise and with business partners/channels

    • Singular definition and location of master data and related policies to enable transparency and auditabiltyessential to regulatory compliance

    • Cross-organisation implementation of shared application solutions

    • Uniform communications with data owners and recipients through multiple channels based on the veracity and accuracy of key master data

    • Continuous data quality improvement as data quality processes are embedded throughout the Information Supply Chain

      Failing to address the quality of data will cost organisations money and may damage your reputation

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