Retail Data Integration to Drive Business Success

Retail Data challenges in distributed environments

When data is scattered across multiple systems, stored in disparate formats, or contains inconsistencies, it impedes decision-making processes, hampers operational efficiency, and undermines organizational performance. Fragmented and inconsistent data present significant challenges for efficient management and to drive growth. Such challenges include the following:

• Limited Data Accessibility
  Data scattered across various systems and silos makes it difficult for teams to access the information they need when they need it.
• Data Inconsistency
  Without a unified solution, inconsistencies in data formats, definitions, and quality are common.
• Lack of Data Visibility
  The absence of a solution makes it difficult to have a holistic view of organizational data, leading to blind spots and missed opportunities.
• Inefficient Data Management
  Managing data scattered across multiple systems requires redundant efforts resulting in inefficiencies and increased operational costs.

In a scenario where data is scattered and lacks coherence, decision-makers often struggle to access accurate and up-to-date information. This lack of reliable data leads to delays in decision-making as managers spend valuable time reconciling conflicting data sources or trying to verify the accuracy of information.
Furthermore, inconsistencies in data can lead to errors in operational processes. For example, inventory management systems may report conflicting stock levels, resulting in overstocking or stockouts. Similarly, sales forecasts based on consistent data may lead to accurate production planning, causing inefficiencies in the supply chain.
In an era where clients always raise the bar of their expectations, bad decision-making, and inefficient operations can lead to customer dissatisfaction.

Data Fabric Basics and Benefits

In a recent analyst report, Gartner states that the enterprise data fabric “is the future of data management”.
Data Fabric serves as an architecture layer and toolset designed to link data across various systems and inside organizational data silos, thereby establishing a unified view.
With a Data Fabric, existing data sources can remain intact as the tool connects them seamlessly. Data Fabric acts as a virtualized data layer that sits over current systems to connect data.
Users can access the data as if it were local, in multi-cloud environments, and deliver it to teams anytime, anywhere, enabling easy reusability. With real-time data, stakeholders can make better and faster decisions. Developers are no longer required to wait weeks or months for migrations before creating new applications.

Ultimately, through the utilization of a robust data fabric solution, companies can establish their own Digital Integration Hub (DIH). This DIH operates on an event-driven architecture, gathering data from all existing systems and organizing them according to the company’s specific business requirements. This creates a singular point of truth, ensuring consistent and real-time data flow 24/7, thus enhancing operational efficiency and responsiveness.

Organizations aiming to enhance software developers’ and business teams’ speed and agility can derive significant value from adopting a data fabric approach. These benefits can be categorized into four main areas:

Data Integration
Combine and consolidate data from various sources and formats into a unified and coherent structure.

Real-Time Data
Access and use of data as it is generated or updated, near real-time, to make informed decisions.

Data Availability
Ensure accessibility and readiness of data for use and guarantee that data is stored securely, reliably, and in a format that can be easily retrieved and utilized when needed.

Data Governance
Manage availability, usability, integrity, and security of data, establishing policies, procedures, and controls to ensure compliance, quality standards.

Unify siloed retail data to achieve an Omnichannel experience

Connecting and unifying enterprise data are fundamental to making informed decisions and harnessing data across the enterprise. In this way, companies can revolutionize their operational efficiency and elevate the overall shopping experience.

Having unified data leads to offering a cohesive customer journey across multiple channels, ensuring a unified and seamless shopping journey for a rich Omnichannel Experience.

By virtually linking all the data, boosting Customer Retention becomes much easier: cultivating trust and nurturing long-lasting personalized connections are pivotal for maintaining competitiveness and achieving consistent growth.

In the end, to elevate customer experience, digital transformation aims to merge digital and physical, creating a Phygital Integration.

Practical Retail and GDO Use Cases

Below, you will find a rich series of practical examples of how a Data Fabric can solve some issues in the GDO and Retails fields. The examples provided below are not mutually exclusive; one solution can fulfill all these purposes and beyond.

Product Stock & Warehouse Movements
Provide real-time inventory status with aggregated and easily readable data

Orders
Provide real-time orders from the e-commerce platform updating the actual availability of the products to be sold

Sales Transactions
Provide real-time sales transactions from all the company’s points of sales

Customer Data
Provide real-time updates on customer data and enable seamless integration among the CRM and the other Systems of Record.

Logistics Data
Provide real-time updates on logistic data and shipped product data from/to the transportation management system.

Merchandising Information
Provide real-time updates on product sheet data, shop and warehouse information, price promotions, and more.

Supply Chain Data
Provide real-time updates on customer data and enable seamless integration among CRM and other Systems of records to enforce the visibility of all the actors involved in the supply chain.

Product Catalog
Ready-to-use pluggable connectors allow you to easily retrieve data from your systems and expose it to the company touchpoints.

Analytics
Near real-time data availability enables you to have an updated overview of the relevant business results.