For decades, before "Big Data" became a part of the mainstream lexicon, CPG supply chain partners operated on the assumption that shared product data was correct and complete. If inaccurate data was found, it was only visible to supply chain partners, who often considered it simply a cost of doing business. This is no longer the case today. The smartphone is now a primary source of information and the Millennial generation - an entire population segment with increasing buying power that has never known a world without the Internet - considers complete, accurate and timely product information paramount to their shopping experience.
To keep pace with this cultural shift, CPG manufacturers are leveraging data quality as an asset to their business growth strategies and embarking on data quality improvement programs recognizing the need to work on foundational supply chain data and the processes that govern it. Initiating a data quality program can eliminate inefficiencies, but it can also be a complex process. These five tips can help a company achieve success and sustainability in 2016:
Start small. It is recommended that an organization develop a project plan that is small in scope—do not try to change the entire enterprise all at once. Instead, select a business process or a single product category, establish what success looks like, demonstrate a tangible ROI, and then widen the scope one process or category at a time.
Be systematic. Industry stakeholders have outlined the three pillars of the GS1 US National Data Quality Program: data governance process, education and training protocol, and attribute audit. Organizations that want to implement the framework can start with any pillar, with the ultimate goal of attaining GS1 US certification that they have the proper processes and procedures in place to sustain quality data over time.
Strengthen the foundation. Recent GS1 US company audits found that data accuracy is often an issue linked to foundational product attributes, such as weights and dimensions. When weight and dimensional attributes are incorrectly communicated through the supply chain, it becomes a challenge for carriers to calculate the efficient transport of product and for retailers to fit products in the allocated space.
Perform a self-assessment. Perform an attribute audit to develop a baseline of accuracy. Often, a physical audit will provide directional feedback as to the data governance and education levels within the organization. This feedback often uncovers simple fixes, such as transposed measurements, or deriving the weight of a case without physically weighing the product, for example.
Maintain a standards-based environment. Ensure the appropriate resources are trained and remain current on the benefits of GS1 Standards, as they set up a common foundation for uniquely identifying products, capturing information about them, and sharing them with other companies.
In 2015, data quality reached a “tipping point” where it became a key performance indicator for supply-side and demand-side trading partners. The benefits of improving data quality permeate through all internal and external data-sharing business processes, impacting trading partner relationships as well as consumer satisfaction. In the year ahead, CPG companies will continue to focus on accurate data, as it is needed to collaborate effectively, influence purchasing decisions and sustain brand loyalty.
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