Retail is at the Transformation Forefront but Has Room to Improve on Integrations
Retail has become one of the hottest industries poised for digital disruption. As per PwC reports, it will emerge as the hub with the greatest opportunity for artificial intelligence (AI) impact by 2030.
Looking back at the great strides retailers have made, from consumerizing virtual reality (VR) to leveraging the internet of things (IoT) for inventory monitoring, it’s evident that retail businesses have actualized digital transformation. However, the integration of all data sources to create insights is a challenge for the industry. Couple this with new data exhibiting poor technical skills (36%) and budget constraints (30%), and you would know why many retail transformation initiatives fail.
Now, as retail ecosystems are reopening their doors of opportunities, it’s essential to address these gaps if they wish to improve customer experiences and loyalty. However, the conventional approach of innovating isn’t going to be enough anymore.
Integration is One of the Key Drivers of Retail Innovation
As per Gartner, “conventional, monolithic and supposedly fail-safe methods of assessing technology are ill-suited to support the quickening pace of digital business.”
Retail ecosystems can augment value and brand differentiation by adopting transformative technologies such as AI and advanced analytics. However, without strategic data integration kickstarting time-to-value and streamlining change is almost impossible.
Integration is critical for creating a successful omnichannel retail experience, something that’s become foundational to business survival in the era of the remote consumer. Ecosystems that neglect their data integration strategy risk falling behind competitors and losing the trust and loyalty of their customers.
Here are some challenges retailers face in regards to data integration, and how they can address them.
Adapt to Change
Similar to all data-driven industries, retail ecosystems need agility to respond quickly to address ever-changing consumer demands and shifting priorities. Plus, they need to handle different types of customer data residing in different formats to comprehend their needs and requirements. For this, they need a modern data integration platform that serves as a solution for onboarding, ingesting, and integrating large amounts of data to generate actionable insights, and use that information to make decisions.
Eliminate Data Silos
Data silos in a retail ecosystem can pose a huge threat. They prevent relevant data from being shared, give rise to inconstancies, discourage collaboration, and more. Retail ecosystems aiming to deliver maximum value need to handle these data silos, and data integration platforms can support this initiative. By integrating data, they can eliminate data silos across the ecosystem – enabling smoother operational efficiencies (and quickly identifying the root causes of application slowdowns and data discrepancies), so that users can make better decisions faster without difficulty.
Apply Accurate Analytics
If data is king, then analytics is the kingdom. Data analytics tracks shifts in trends, performance, inventory fluctuations, and other insights that pull in data from various sources. Solid data integration helps retailers consolidate data and create a 360-degree view of customer journeys, inventory tracking, billing, payroll and more.
All of the myriad systems that need to be connected to deliver competitive and seamless omnichannel retail experiences require a smart and flexible data integration strategy. With that, the role of AI ( particularly in data mapping) deems critical as it supports them accurately integrate data faster and therefore embrace latest changes in customer needs. In the next few years, AI will play a central role in creating and restoring bridges across operational data silos and democratizing the flow of information across retail ecosystems.
So, retail ecosystems must rely on data integration solutions to streamline their digital transformation journey and deliver maximum customer value.