Partner Tier: Premium
Partner Type: RSI
We are a technology consultancy specialising in the field of Data and Metadata Management. We have the ambition to guide our clients to help them exploit the full potential of Data. We want to improve decision-making processes by making them data-driven to enable effective decisions based on data, supported by automated and intelligent systems. We are committed to ensuring the high quality of the data we manage for our clients: being able to rely on accurate, reliable, authentic, non-repudiable data derived from structured and verified processes is fundamental to being able to create data-driven processes and to spread the culture of data within the organisation. We democratise access to data by allowing easy, timely and secure access to quality information.
Legacy systems struggle to integrate with an increasing number of external applications due to scalability issues. Data is extracted from legacy systems mainly with batch integrations, thus causing constant misalignment between systems. The development and maintenance costs of point-to-point integrations between the legacy and target systems are high. Multipoint access to legacy systems exposes them to the risk of misuse of data, especially sensitive ones. We help our customer to modernize their systems with a modern and non-invasive approach.
There are many disadvantages when inventory is not managed proactively: overselling if inventory is managed with infinite stock; underselling if inventory is managed with guarantee buffers; difficulty in monitoring and optimizing logistics; inability to reactively manage replenishment policies; inability to analyze sales in real time to offer targeted and contextual promotions. With offer professional services to help our customer to properly manage the inventory and the related processes.
The main problems related to the absence of a Customer Data Platform are: Silos between business functions and costs of integration in omnichannel logic; Lack of a single source of truth on which customer marketing models can be based; Increased costs of managing a constantly growing and redundant volume of data for different needs; Lack of or poor exploitation of first-party data, also in view of the end of third-party cookie support by Google; Difficulties in monitoring and verifying the use of customers’ personal data in a compliant manner. We provide professional services to help our customer to design and implement CDPs.
The main problems resulting from the absence of a centralized hub are: Lack of governance and increased complexity of managing, organizing and monitoring large-scale IoT devices throughout their lifecycle; Inability to perform real-time predictive analysis of data collected from devices; High operational costs to configure and manage complex, distributed architectures often composed of multiple technologies developed by different vendors. We provide professional services to help our customer to design and implement solutions for their IoT Hub.
In various business sectors, the speed of decision-making processes is increasingly crucial for the organization’s profit. By leveraging continuous monitoring of core business performance based on real-time data analysis, management can make timely decisions to maximize revenues from various channels and mitigate losses due to incorrect initiatives or external factors. For digital-native companies, real-time analysis of phenomena such as customer behavior in interactions with touchpoints allows for rapid improvement in the effectiveness of automated user suggestions, increasing engagement, and enhancing the overall service experience. Conversely, the lack of timeliness in adapting or correcting a digital initiative toward the end customer can lead to a loss of attractiveness and trust in the service, resulting in potential revenue losses or subscription cancellations. The ability to have real-time visibility into business performance can also be a competitive advantage for companies in more traditional sectors, such as retail, enabling them to launch marketing initiatives or promotional campaigns much more quickly than in the past, especially during annual sales events or special occasions. Furthermore, the capacity to process domain events in real-time is often a necessary requirement to support the operational aspects of the business itself, as in the case of fraud prevention functionalities for banking institutions in online banking activities or real-time inventory calculations for multichannel retail companies. In this use case we show our approach to design architectures for data integration and processing in support of real-time analysis.
The Digital Integration Hub architecture is a valid solution to harness the benefits of a data-centric approach. The following diagram illustrates the design of a Digital Integration Hub architecture. It involves a real-time data offloading component from sources driven by events, which centralizes data in the shared integration platform, importing it with the lowest possible latency. To distribute domain events to multiple consumers, it is necessary to introduce an event broker and streaming platform component, allowing for fan-out of the same data to different subscribers and potentially some real-time data transformations.
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