Digital transformation starts with smarter data

Digital transformation is a hot topic for many companies, a popular hashtag on social media. But it’s not just talk — according to Gartner, public sector CIOs predict a rise from 42% to 77% of companies embarking on a digital transformation project in the next 5 years.

Given its popularity, it begs the question — what exactly is digital transformation? What does it entail or promise to deliver? CapGemini defines it as “the use of technology to radically improve performance or reach of an enterprise.” They cite transforming internal operational processes — automating them to make them more efficient and scalable — as critical to its success.

How companies collect, manage, and share their data is a great place to start.

Data is the DNA of retail, critical to driving business decisions across the value chain. Digital has ushered in omni-channel retail, and with it created a proliferation of data (it’s estimated that 90% of the data in use today is less than 3 years old).

Why is data growing so fast?

• There are more products than ever. The typical supermarket carries approx. 40,000 SKUs … 3X what it carried in 1980. Due to promotions, seasonal operations and local assortments, the number of products has not only grown, but turnover has accelerated: one of our large hypermarket customers lists 400K new products every year, equating to 2K new products everyday.
• Digital technology has transformed how brands go to market. Today, manufacturers sell their products across more channels and store formats, both digital (mobile, e-commerce) and physical (discounters). In response, brands sell more variations of the same product, in different packages and at different prices, to differentiate across channels and deliver a compelling shopper experience across any touchpoint shoppers choose. That differentiation comes at a cost, with each new product adding complexity in the supply chain.
• Shopper expectations have risen. Shoppers now demand better product information on nutrition, health and environmental impact, multiplying the volume of information that brands must provide for the products they sell. Before, manufacturers could list products with just 8 attributes: name, package size (length, width, height), weight, and GTIN/EAN. Today, they need pictures, descriptions, nutritional facts to effectively sell online, with more than 500 attributes possible due to growing shopper needs, more stringent industry regulation, and internal demands to fuel advanced/predictive analytics.

But it’s not just the quantity of data that’s an issue … the quality of product data has failed to keep pace. In developed markets, product information is found to be inconsistent 80% of the time.

Poor product data impacts all facets of retail strategy:

• Marketing that fails to communicate through the clutter
• Promotions that don’t motivate nor break even
• Products missing on the shelf …
• … Or offered at the wrong price point

Online, the story remains the same. Poor product content = lost sales.

“In the online environment, missing key information used for product identification is the equivalent of being out-of-stock in a physical store.”

- David Smith, Solutions Manager, GS1 UK

How are teams at retailers and manufacturers addressing this challenge? Many teams resort to manual processes and outdated tools to manage timelines and data requests from trading partners, and quickly find themselves buried under a mountain of data.

Instead, what they need is cost-effective, efficient means of sharing information to drive business decisions and engage shoppers at the point-of-sale. At Alkemics, we believe that better retail happens when retailers and manufacturers work together around data that’s precise and synchronized so it’s never obsolete. Or, as we refer to it, smart data that’s:

• Complete: all required fields + added fields that shoppers may desire
• Accurate: no errors
• Instant: always on, synchronized so it’s never obsolete
• Fluid: able to evolve as new requirements come
• Shared: available to all those in the ecosystem that need it
• Organized: structured to reflect product hierarchies and purchase dynamics

In the past, retailers and manufacturers have defined data savviness in terms of how much data they collected and stored. Today, we need to think about data more holistically, challenging ourselves to extract more value and drive business action.

We believe that there are three phases to driving stronger, more cohesive collaboration:

Collect: provide a 360-degree view of products, sourcing data whereever it resides

Correct: automating data management (structure, quality checks, enrichment) using algorithms and machine learning

Connect: sharing it seamlessly with retail partners in the format needed to drive decisions

Imagine what you could accomplish with smarter data and stronger collaboration with trading partners:

• Better online merchandising that engages shoppers at all points of interaction
• Better in-store execution that blends the depth of digital content with the proximity of the physical store
• Better logistics, reducing out-of-stocks by connecting similar products to reduce SKU volatility

(editor’s note: check out the original article in Spanish at infoRETAIL)