“At VirtusLab, we collaborate closely with our clients to understand their needs and develop tailored solutions that address their key operational use cases. We provide ongoing support to ensure the reliability and effectiveness of our solutions, enabling our clients to focus on their core business operations.”
Businesses become competitive, once they avoid guesswork, create an environment for data-driven decisions, and use data from analytics platforms in their operational use cases. VirtusLab helps to achieve this goal by offering expertise in both data engineering and data science engineering. Our tailored solutions enable clients to utilize their analytical data, regardless of their platform’s context and specificity.
Machine Learning-driven retail and analytical data platforms
Machine learning-driven solutions (such as analytical data platforms) are revolutionizing the way and time frame we handle data, providing valuable insights into customer behavior, optimizing operations, and enhancing personalization. Businesses leverage these solutions to increase revenue, improve customer satisfaction, and gain a competitive edge in the market.
For instance, retailers can use machine learning to optimize their operations by analyzing their data. By identifying patterns through insights, businesses make informed decisions about product stocking, pricing, financial forecasts, and marketing strategies. Historical sales data is analyzed using machine learning algorithms to predict future product demand, which helps retailers optimize inventory levels and avoid stockouts or overstocking.
To achieve these benefits, retailers can partner with companies that provide data engineering and data science engineering expertise, leveraging technologies such as Python, Spark, Hadoop, Flink, Kubernetes, Kafka, and AzureML. By working closely with such service providers, retailers gain tailored solutions that address their unique needs and enable them to become more data-driven and competitive.
Real-time personalization
In today’s digital age, businesses understand the importance of real-time personalization in engaging with their customers. By analyzing data such as website user journeys, preferences, and user behavior in real-time, businesses can offer personalized experiences tailored to their customer’s unique interests and needs.
To achieve real-time personalization, data engineers implement scalable streaming and online ML training models. It allows them to process and analyze data in real-time, enabling businesses to make informed decisions based on up-to-date information. Operational processes like real-time data ETL (extract, transform, load) or online training can enrich analytical data and deliver personalized experiences. ETL processes extract data from multiple sources, transform it into a unified format, and load it into a target system such as a data warehouse or analytical platform. This helps businesses deliver accurate and relevant personalization to their customers.
Technologies such as Python, Spark, Hadoop, Flink, Kubernetes, Kafka, and AzureML are used to provide scalable and reliable solutions that can handle large volumes of data and deliver real-time personalization at scale.
Data Science workflow
Effective data science workflows are critical for data-driven decision-making, and they involve a series of processes and steps in the creation of data-driven solutions. Collaboration with data owners and establishing clear communication channels is essential for ensuring timely and reliable results. Businesses work with expert data engineering and data science engineering service providers to establish effective workflows that meet their specific data needs.
Once data is acquired, data cleaning and preparation are necessary to ensure data accuracy and reliability. This involves removing errors, missing values, and inconsistencies from the data. The next step is feature engineering, which involves selecting and transforming relevant features to improve model performance. Proper model development is crucial and involves selecting appropriate algorithms and building and testing models using the available data.
The next step is model deployment. It involves integrating models into the business operations and ensuring their ongoing performance and reliability. Tools such as model monitoring and performance tracking can be used to maintain model performance and reliability. Additionally, establishing a feedback loop to collect feedback from stakeholders is necessary to ensure continuous improvement of the models and the data science workflow.
Businesses leverage the expertise of data engineering and data science engineering service providers to establish effective data science workflows. These providers use the latest technologies, such as Python, Spark, Hadoop, Flink, Kubernetes, Kafka, and AzureML, to provide tailored solutions that enable businesses to become more data-driven.
At VirtusLab, we understand the importance of effective data science workflows and have helped our clients establish reliable and timely workflows. By working closely with data owners and leveraging the latest technologies, businesses can ensure that their data science workflows deliver value and enable data-driven decision-making.
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Moving to Cloud Solutions
Transitioning to cloud solutions is a crucial component of modern data-driven operations. Cloud solutions provide scalability, flexibility, and cost-effectiveness that on-premise solutions lack. Many businesses move to cloud solutions to serve use cases such as model-as-a-service and improve their overall data-driven operations.
Let’s say you’d like to migrate your business to cloud solutions. VirtusLab first assesses your specific needs and use cases. This involves understanding your data sources, workflows, computing power, storage, and security requirements. Based on this assessment, we develop a customized migration plan that minimizes disruption and maximizes benefits for your business.
One of the significant advantages of cloud solutions is their scalability. Businesses easily scale their computing power and storage by leveraging cloud resources such as virtual machines and containers as their needs change. This is especially crucial for use cases such as model-as-a-service, where businesses must handle large volumes of data and require significant computing power.
This immediately translates into flexibility. Cloud solutions offer a range of tools and services, such as machine learning tools, data warehousing, and streaming analytics that can be easily integrated into existing workflows. It enables businesses to develop and deploy data-driven solutions quickly and efficiently. Additionally, it allows for experimentation and exploratory features or data analysis.
Cost-effectiveness is another critical benefit of cloud solutions. Businesses eliminate the need to invest in expensive on-premise infrastructure and hardware, resulting in significant cost savings.
Yet, sometimes it can be arduous to migrate. Therefore, software engineering service providers use tools and technologies, such as Kubernetes, Docker, and Terraform, to help businesses migrate to cloud solutions. VirtusLab, for example, provides ongoing support to ensure the reliability and security of your cloud solutions. We have helped clients achieve the mentioned benefits and more through cloud solutions. By leveraging our expertise in cloud, data, and data science engineering, and using the latest technologies, we provide tailored solutions enabling businesses to become more data-driven.
Improving store-level operations through analytical data platform
Let’s see how a centralized and accurate data source can help retailers to make data-driven decisions, save costs, and improve customer satisfaction:
Fulfillment process optimization
Optimizing the fulfillment process is a use case where analytical data can significantly impact store-level operations. Businesses predict future product demand by analyzing historical data about sales and inventory levels and optimizing the fulfillment process accordingly.
Product availability and misplacements
Another use case where analytical data can improve store-level operations is by creating daily reports for store managers. These reports provide insights into product availability and misplacements, enabling store managers to make informed decisions that optimize store-level operations and improve customer satisfaction. Frequent reports for analytical teams can also help identify fluctuations in product availability throughout the day, leading to better inventory management.
Leverage expertise of data engineering and data science engineering service providers
To achieve these benefits, businesses leverage the expertise of data engineering and data science engineering service providers and use analytical tools and techniques such as data profiling, ETL, and feature engineering.
Addressing key operational use cases
In modern data-driven operations, businesses must address key operational use cases to improve efficiency, reduce costs, and deliver better products and services. Businesses can achieve these benefits by analyzing operational data and developing tailored solutions. Let’s take a brief look at real-life examples:
One of VirtusLab’s clients has benefitted from reports for store managers. Our client uses these reports daily to make informed decisions about optimizing store-level operations, improving customer satisfaction, and reducing waste. This has resulted in significant cost savings and improved customer loyalty.
In another use case, VirtusLab has helped our client generate hourly reports for analytical teams. These reports enable them to identify fluctuations in product availability throughout the day, leading to better inventory management and cost savings. Additionally, analytical teams use these reports to gain insights into customer behavior and preferences, enabling them to develop better products and services.
We used various analytical tools and techniques to achieve these benefits, such as data profiling, ETL, and feature engineering.
A Data Engineering Solution
Modern data-driven operations rely heavily on effective data engineering solutions. These solutions involve a series of processes and steps crucial in building, maintaining, and optimizing data infrastructure. Let’s take a look at how a smooth data engineering solution has helped VirtusLab’s clients establish reliable and accurate solutions:
Reporting system on an analytical platform
We helped our client in developing a reporting system on an analytical platform. By creating a centralized and accurate data source and implementing an export mechanism, we have made it easier for store managers to access reports through commonly used tools such as Excel files and email reports. This has resulted in more informed decisions and better store-level operations.
Having limitations given by the already existing platform in mind, we addressed issues related to small files that arose when dealing with fluctuating volumes of data and resulted in inefficiencies and increased costs. To ensure the accuracy and reliability of data, we implemented various strategies such as merging and deduplication, and separation of raw data, capturing from transformations and reporting, monitoring and alerting, and automatic gap filling. This leads to more efficient and cost-effective operations.
Analytical data platform: Loss mitigation and monitoring
In modern retail operations, loss mitigation and monitoring are essential. Businesses need to integrate data from various sources, create reports and aggregations, and gain insights to benefit their technical and analytical teams. This helps to implement effective solutions to prevent fraud, incidents, and losses.
Overcoming data challenges
During one use case, VirtusLab was analyzing the behavior of employees and customers of the store. We examined how they used the store properties, identified potential issues, and took preventive measures. This leads to cost savings, improved security, and better customer experiences.
We helped our client by expanding the reach of their data-driven solutions. By pushing data seamlessly to external sources, businesses can extend the benefits of their solutions to a broader audience. This resulted in increased revenue, improved customer satisfaction, and better business outcomes. To achieve company goals, we used various analytical tools and techniques such as data profiling, ETL, and feature engineering.
Maximizing profit through analytical solutions
By analyzing operational data and developing customized solutions, businesses can identify opportunities to increase revenue and reduce costs. You can benefit from consolidating data from various sources into a single analytical platform. This holistic view improves efficiencies, reduces waste, and increases revenue. A service partner, like VirtusLab, helps clients make informed decisions by analyzing sales trends, customer behavior, and market conditions. We provide recommendations that enable stakeholders to make data-driven decisions.
VirtusLab collaborates closely with clients to understand their specific needs and develop tailored solutions to address their key operational use cases. They provide ongoing support to ensure the reliability and effectiveness of their solutions.
Maximizing profit through analytical solutions is critical to modern data-driven operations. VirtusLab helps clients increase revenue, reduce costs, and deliver better products and services through customized solutions, leveraging their expertise in data engineering and data science engineering and utilizing the latest technologies.
The technology behind all of it
In modern data-driven operations, businesses must leverage the latest technologies to analyze operational data and develop tailored solutions. At VirtusLab, we understand the importance of technology and use a range of technologies to deliver effective solutions for our clients. We often use the right combination of open-source and paid-source technologies.
Some of the technologies we use include:
- Python: A widely used programming language in data analysis and machine learning, Python offers a range of libraries and tools for data manipulation, visualization, and analysis.
- Spark: An open-source distributed computing system used for large-scale data processing, Spark offers high-speed data processing capabilities and supports several programming languages, including Python, Scala, and Java.
- Hadoop: An open-source distributed computing system used for large-scale data processing, Hadoop offers high-speed data processing capabilities and supports several programming languages, including Python, Scala, and Java.
- Flink: An open-source streaming data processing system used for real-time data processing, Flink offers high-speed data processing capabilities and supports several programming languages, including Python, Scala, and Java.
- Kubernetes: An open-source container orchestration system used for managing containerized applications, Kubernetes offers automated deployment, scaling, and management of containerized applications.
- Kafka: An open-source distributed streaming platform used for real-time data processing, Kafka offers high-speed data processing capabilities and supports several programming languages, including Python, Scala, and Java.
- AzureML: A cloud-based machine learning platform used for building, deploying, and managing machine learning models, AzureML offers a range of tools and services for data processing, model training, and model deployment.
- Scala: Scala is a programming language that is used for building scalable and high-performance applications. It offers a functional programming paradigm and supports object-oriented programming.
- HDFS: HDFS is a distributed file system that is used for storing and processing large volumes of data. It offers high availability, fault tolerance, and scalability.
- Yarn: Yarn is a resource management system that is used for managing resources in a Hadoop cluster. It offers efficient resource utilization and supports multiple processing frameworks, including Spark and Hadoop.
- Hive: Hive is a data warehousing system that is used for querying and analyzing large datasets stored in Hadoop. It offers a SQL-like interface for data querying and supports data summarization, filtering, and aggregation.
- Sttp: Sttp is a high-performance HTTP client library for Scala that is used for making HTTP requests. It offers a functional programming API and supports features such as streaming, compression, and authentication.
- Monix: Monix is a reactive programming library for Scala that is used for building asynchronous and event-driven applications. It offers a range of features, including backpressure handling, error handling, and cancellation.
- Oozie: Oozie is a workflow scheduling system that is used for managing Hadoop jobs. It offers a range of features, including job scheduling, dependency management, and job monitoring.
Conclusion
At VirtusLab, we are committed to helping businesses become more data-driven by leveraging their analytical data for operational use cases. We collaborate closely with our client’s data owners, analysts, external APIs, and business/product owners to deliver tailored solutions that enable them to utilize their analytical data fully. By providing actionable steps and real-life examples, we hope this blog post has helped show you how to leverage your analytical data platform for operational use cases.
Curated by
Sebastian Synowiec
