

AI-Driven Affiliate Offer Optimization
Discover how we transformed affiliate marketing with AI-driven offer optimization, boosting publisher engagement, increasing advertiser profits, and driving network growth.
We specialize in creating data engineering solutions that break down silos and streamline data integration.
By leveraging serverless components on leading cloud platforms, we reduce costs and eliminate manual data handling.
We understand that every business has unique data challenges and opportunities. That’s why our data engineering solutions are tailored to meet your specific needs. Whether you’re looking to optimize your existing data processes, migrate to a new database system, or start from scratch, our experts are here to guide you every step of the way.
Our team is experienced in setting up Linux servers and managing both relational and non-relational databases, specializing in MariaDB and MongoDB. We work with both Platform as a Service (PaaS) components and serverless databases, helping you maximize efficiency while minimizing operational overhead.
We are well-versed with the leading cloud service providers – Azure, Google Cloud Platform (GCP), Amazon Web Services (AWS), and Snowflake. Whether you are already using these platforms or considering a migration, we can leverage our familiarity to design and implement the most effective data engineering solutions for your business.
We leverage our deep knowledge of Python to implement multiprocessing, allowing us to perform multiple data processing tasks simultaneously. This not only speeds up operations but also optimizes resource usage, providing you with faster, more accurate insights.
We understand the importance of data integrity and compliance. Our team is skilled in data governance, ensuring your data lineage is clear, your data is discoverable, and you remain compliant with relevant regulations.
Our data engineering services include seamless integration of data across multiple sources, ensuring consistency and reliability. Whether it’s integrating with your existing ERP, CRM, or other business systems, our solutions are designed to provide a unified view of your data.
Our data engineering services start by identifying and aligning with your business goals. We understand that each organization has unique data challenges and opportunities, and our approach ensures that our solutions are tailored to meet these specific needs.
With a focus on scalability and efficiency, we build robust data infrastructures that support your business growth. Our expertise spans across various platforms, ensuring seamless integration and high performance.
We start from your strategic objectives and work our way back to the right mix of solutions and technologies, not the other way round
Our data engineering process begins with a thorough understanding of your business needs and business intelligence (BI) reporting requirements. This step involves identifying the key metrics, refresh frequency, and the specific reports needed to drive decision-making within your organization.
We then identify the right data sources and obtain access to the underlying systems. This involves collaboration with various departments to ensure all relevant data is considered, whether it's from CRM, ERP, web analytics, or other sources.
In this step, we extract the data from the identified sources, transform it to fit the required format and structure, and load it into appropriate storage solutions such as databases, data lakes, and data warehouses. Our ETL process is designed to handle both structured and unstructured data, ensuring comprehensive data integration.
We test for retroactive data updates to ensure data integrity. This involves identifying any overlap periods and performing upserts or replacements as necessary to maintain accurate and up-to-date datasets.
We focus on creating reusable data assets and products. These are designed to be utilized multiple times across various departments, maximizing their value and utility. This includes creating standardized data sets, dashboards, and other BI tools.
To encourage data consumption, we develop APIs and web interfaces. These interfaces allow for easy access to the data products, whether through reports, API endpoints, files, emails, or other means. This ensures that the data is readily available to all stakeholders, enhancing operational efficiency.
Finally, we provide ongoing monitoring and maintenance of the data infrastructure. This includes regular updates, performance optimization, and ensuring compliance with data governance standards. Our goal is to maintain a high-quality and high availability data environment that supports continuous business growth and innovation.
When you require efficient data integration, robust infrastructure, and scalable solutions to manage and utilize significant volumes of data.
This enables seamless data access, supports advanced analytics, and drives informed decision-making across the organization.
At Witanalytica, we prioritize your strategic objectives to provide analytics solutions and technologies that yield tangible results and measurable business outcomes.
Our experts ensure that every decision is backed by robust data analytics, driving your business forward with confidence and precision.
Experience the speed of innovation with Witanalytica. Our agile data teams expedite the transformation of third-party data into well-structured data marts, producing your first custom dashboard in just two weeks.
Benefit from our flexible pricing and technology-agnostic approach, ensuring cost-effective and cutting-edge solutions that streamline your decision-making process.
Secure your market position with Witanalytica’s unparalleled analytics services.
Our governance processes and commitment to data quality deliver insights that are not just accurate but actionable. This allows your business to operate with heightened efficiency, gaining a competitive advantage that sets you apart.
With Witanalytica, clarity is paramount.
Our meticulous data preparation methods include comprehensive logs and alerts, ensuring that you understand the status and integrity of your data at every stage.
Trust in our clean, transparent reporting to make faster, more reliable decisions.
Experience the power of visualization with Witanalytica’s custom solutions.
Whether through industry-leading Business Intelligence platforms or bespoke web applications, we bring complex data to life with visuals that are as insightful as they are stunning.
Ensure that every stakeholder captures the full story behind the data with clear, impactful visuals.
Tired of guessing games in a data-driven world? Witanalytica equips you with the analytical tools necessary to make informed decisions.
With over 15 years of Business Intelligence experience, we deliver the analyses, dashboards, and machine learning algorithms essential for understanding complex data and driving your strategic goals forward.
Unlock the potential of your data with Witanalytica.
Our strategic analytics framework is designed to generate rapid, actionable insights that drive decision-making and business growth.
Dive deep into the data with analytics that uncover opportunities and optimize performance.
Suited for projects where the scope may vary, this pay-as-you-go option offers the flexibility to adjust requirements as your project evolves. We’ll work with you to estimate the effort involved and ensure transparency and fairness in billing.
If you have ongoing analytics needs, our retainer service ensures dedicated support for a set number of hours each month. It’s a great way to secure our team’s availability without the commitment of a full-time hire.
For businesses that anticipate a consistent, high-volume workload, we offer the option of dedicated resources. This model provides you with a team or individual fully focused on your data analytics needs for a sustained period, offering stability and deep integration with your operations.
AWS offers a broad range of data engineering tools designed to cater to various business needs. Key services include AWS Lambda for serverless computing and AWS Glue for efficient ETL processes. AWS Lake Formation helps in creating data lakes, while Amazon Redshift provides powerful data warehousing solutions. These services are known for their scalability, reliability, and integration capabilities, making AWS a preferred choice for diverse industries.
Azure excels in providing enterprise-grade data engineering solutions. It offers Azure Functions for serverless operations and Azure Data Factory for comprehensive ETL processes. Azure Synapse Analytics and Databricks are renowned for handling complex analytics and large-scale data processing. Azure's strength lies in its seamless integration with Microsoft's ecosystem, making it a popular choice for businesses already using Microsoft products.
GCP stands out for its powerful analytics and machine learning capabilities. It offers Google Cloud Functions for serverless computing and Google Cloud Dataflow for streamlined ETL processes. BigQuery, a fully managed data warehouse, is highly valued for its speed and ability to handle large datasets. GCP is particularly popular among digital marketing and advertising companies due to its seamless integration with Google Analytics 4, Looker Studio, and other Google services.
Snowflake is renowned for its innovative data warehousing capabilities, offering seamless scalability and high performance. One of its standout features is the ability to write and execute Python code within its platform using Snowflake Worksheets. This enables data engineers to perform complex data transformations and analytics directly within Snowflake, streamlining workflows and enhancing productivity.
Open Source solutions provide flexible and customizable data engineering tools suitable for various business needs. KNIME and the closed source Alteryx are popular for their user-friendly ETL capabilities, allowing businesses to build complex workflows without extensive coding. DBT (Data Build Tool) is favored for data transformation and modeling, enabling teams to manage and deploy analytics workflows efficiently. These tools offer robust solutions without the need for extensive infrastructure investments, making them ideal for businesses seeking flexibility and scalability.
Discover how we transformed affiliate marketing with AI-driven offer optimization, boosting publisher engagement, increasing advertiser profits, and driving network growth.
Explore our success in deploying Business Intelligence finance reporting, streamlining complex, multinational financial consolidation for a dynamic US affiliate marketing network.
Discover how we enabled a leading Romanian car rental startup marketplace, MasiniLaCheie.ro, to transform their data analytics capabilities with MongoDB, Python, MariaDB and PowerBI.
Explore the transformative journey of a healthcare clinic as they leverage a custom-built, data warehouse on GCP BigQuery to enhance their customer segmentation and optimize their digital strategy.
Explore how Power BI transforms Amazon ad performance analytics for a leading snack manufacturer, enabling strategic marketing decisions.
Explore how Tableau enables US retailer to analyze the profitability of their multi channel sales strategy
Read how we enabled a group of email marketing publishers to efficiently manage vast amounts of email-related data.
Learn how we supported the transition from HitPath to Everflow by integrating the data into a BI system and developing reporting tools to monitor the migration.
See how our affiliate marketing dashboards unify performance data, streamline reporting, and provide real-time insights to optimize ROAS, track trends, and boost profitability effortlessly.
Data Engineering as a Service (DEaaS) is a managed service model where data engineering tasks are outsourced to a specialized provider. This approach allows businesses to leverage the expertise of data engineering professionals without the need to build and maintain an in-house team.
The role of data engineering in a company involves designing, building, and maintaining data infrastructure to ensure data is accessible, reliable, and ready for analysis. Data engineers streamline data integration, optimize data processing, manage data storage, ensure data quality, and enable efficient data workflows.
This infrastructure supports data-driven decision-making with datasets and data products that can be used and reused across the organization, ensuring the company has curated and reliable sources of truth that facilitate alignment.
Data engineering enables decision-making by transforming raw data into structured, reliable datasets. It integrates diverse data sources, ensures data quality, and provides real-time processing. This foundation allows for accurate, timely insights, empowering businesses to make informed, data-driven decisions that drive strategic growth and operational efficiency.
A data engineering services company designs, builds, and maintains the data infrastructure needed for collecting, storing, and analyzing data. They integrate data from various sources, ensure data quality and consistency, and implement ETL (Extract, Transform, Load) processes. Additionally, they create scalable data pipelines, optimize data storage solutions, and support advanced analytics and machine learning initiatives, enabling businesses to leverage their data for informed decision-making and strategic growth.
Common challenges businesses face when adopting data engineering services include:
A company should consider contracting data engineering services when it:
Deciding between hiring an in-house data engineering team and outsourcing to an agency depends on several factors that align with your company’s strategic direction, budget, and long-term objectives. Here are some considerations based on the information provided:
Advantages of Outsourcing to an Agency:
Diverse Expertise: Agencies typically offer a broader range of skills and expertise, which can be beneficial if your analytics needs are varied or if you require specialized knowledge.
Cross-Industry Experience: Partnering with an agency gives you access to best practices and valuable insights gained from a wide array of industries, which can enrich your data engineering approach.
Flexible Engagement Models: Agencies offer different collaboration models, from pay-as-you-go to dedicated resources, giving you flexibility in how you manage and budget for analytics services.
Scalability: With an agency, you can quickly scale your data engineering and analytics capabilities up or down based on your current needs without the constraints of headcount and recruitment.
High Availability: Agencies prioritize client support and often plan their resources to ensure uninterrupted availability, which can be crucial for ongoing and time-sensitive analytics needs.
Disadvantages of Outsourcing:
Initial Onboarding: Consultants may require time to become familiar with your company and industry. However, this is often mitigated by the agency’s experience and ability to learn quickly.
Internal Resistance: Employees might be hesitant to work with external consultants. It’s essential to have management buy-in and foster a collaborative environment to ensure successful integration of external expertise.
Cost Considerations: While agencies might have a higher hourly cost compared to in-house salaries, they can provide value through flexibility, lack of long-term commitments, and by offering a variety of pricing models to suit different needs.
In conclusion, if you are looking for a broad range of analytics skills, need flexible and scalable support, and want to benefit from cross-industrial experience without the commitment of hiring full-time staff, outsourcing to an agency might be the right choice for you. However, if you prefer to have analytics expertise embedded within your company and are prepared to invest in hiring and training, building an in-house team could be beneficial. It’s recommended to weigh both options carefully and consider reaching out to agencies for quotes to better understand the potential costs and benefits.
Our services mainly cater to mid sized companies that have reached an inflection point where they can no longer effectively manage their operations, sales, marketing using Excel and Google Sheets. We cover the full spectrum of services such as data analytics consulting, data engineering, database administration, business intelligence and data science.
We can take care of the entire process of setting up an effective data analytics architecture from scratch or alternatively, however we can also help with targeted modular services.
For example, some of our customers have reached out to us when they needed to speed up the development and delivery of dashboards. As such, we have helped them with Business Intelligence development while they chose to keep data engineering capabilities in house.
We have also had customers contract us to build all the necessary pipelines and infrastructure to build a data warehouse from scratch.
Our data engineering services stand out due to our commitment to cost-efficiency and long-term value creation. We prioritize the use of open-source and serverless components, ensuring that we provide the most economical solutions without compromising on quality. By leveraging these technologies, we help our clients significantly reduce their operational costs.
Additionally, we focus on setting up robust data assets and products designed for future reuse. Working across various departments, we frequently encounter opportunities to productize and standardize data dictionaries, such as customer, affiliate, and product dictionaries. This approach not only enhances data consistency across the organization but also ensures that different departments are aligned, using the same accurate and up-to-date data.
Our methodology promotes sustainability and scalability, allowing businesses to maximize their data investments and drive more informed decision-making. By choosing our services, you are opting for a partner dedicated to delivering cost-effective, reusable, and high-quality data solutions.
We have worked with Affiliate Marketing, FMCG, Healthcare, Manufacturing, Transportation and Airlines, Logistics, SaaS and IT, Media and Advertising, Telecomm, Retail and Dealership companies.
Absolutely, please navigate to our Case Studies page and browse through the examples we have showcased there.
Our go to BI tools are PowerBI, Tableau and Domo but we also have experience with Looker Studio, QlikSense, MicroStrategy, Sisense, and Tibco Spotfire.
Choosing the right cloud provider depends largely on your industry and the tools you are already using. Here’s a tailored recommendation based on common industry practices:
Advertising and Digital Marketing: Google Cloud Platform (GCP): Many advertising and digital marketing companies prefer GCP because it integrates seamlessly with Google Analytics 4 (GA4), Looker Studio, and BigQuery. If your business heavily relies on these tools, GCP provides a familiar and powerful ecosystem for your data needs.
E-commerce: Amazon Web Services (AWS): AWS offers a wide array of services like Amazon Redshift for data warehousing and AWS Lambda for serverless computing. If your e-commerce platform is already utilizing tools like Amazon SageMaker for machine learning or Amazon RDS for databases, AWS would be a natural fit.
Finance and Banking: Microsoft Azure: Azure excels in providing enterprise-grade security and compliance, making it ideal for finance and banking sectors. If you are using Microsoft products like PowerBI for business intelligence or Azure SQL Database, migrating to Azure ensures seamless integration and robust data governance.
Healthcare: Microsoft Azure or AWS: Both Azure and AWS offer strong compliance with healthcare regulations (HIPAA). Azure is particularly strong if you are using Microsoft products for patient management systems, while AWS offers comprehensive healthcare-specific services and scalability.
Manufacturing and Logistics: AWS or Azure: AWS provides extensive IoT and machine learning capabilities, which are crucial for manufacturing and logistics optimization. Azure is a strong contender if your operations are deeply integrated with Microsoft products and you require advanced analytics through Azure Synapse Analytics.
Technology and SaaS: AWS or GCP: Both AWS and GCP are excellent for technology companies. AWS provides a broad range of services for building and scaling applications, while GCP is ideal if your products leverage Google’s AI and machine learning tools.
Your choice of cloud provider should align with your current toolset and industry-specific needs. Each cloud provider offers unique advantages, and the best choice will depend on your existing technology stack and the specific requirements of your business operations. If you need further personalized guidance, we’re here to help you assess your current setup and make the optimal decision for your cloud migration journey.
Our approach to data engineering development is grounded in a thorough understanding of your business’s key processes and data requirements. Here’s a detailed look at how we ensure robust and reliable data engineering solutions:
By prioritizing low-cost, serverless, and open-source solutions whenever possible, we ensure that our data engineering services are not only effective but also cost-efficient. Our focus on creating reusable data assets and products across different departments fosters consistency and operational efficiency, enabling your organization to make data-driven decisions with confidence.
Yes. This is known as writeback capability. There are very few BI tools that are able to do that, especially PowerBI (with PowerApps) and our partners at www.scaidata.com.
However, you might be looking for a web application instead. Here is our guide that highlights the differences.
Let’s talk to see what is the best solution for your needs.
We understand that data privacy and security are paramount. Here’s how we safeguard your data:
Apart from implementing widely know best cybersecurity practices, currently we do not have specialized cyber security personnel which is why we recommend and welcome 3rd party or your inhouse resources to regularly test our infrastructures.
Additionally, we understand the importance of accountability and are willing to explore obtaining insurance coverage that addresses potential damages directly attributable to our services. While we are dedicated to the highest standards of excellence and vigilance, we also recognize the necessity of defining liability.
In the unlikely event of damages and unless specifically insured for, our liability is capped at the total amount billed for our services in the preceding six months of our collaboration. This provision is part of our commitment to transparency and mutual trust in our business relationships.
Your data’s security is our top priority, and we commit to maintaining the highest standards of cybersecurity practices.
If you’re considering our data engineering services, know that we prioritize a partnership approach to pricing. We want to ensure that our services align with your goals and provide clear value. Here’s an outline of how our pricing models can work for you:
Time and Material: Suited for projects where the scope may vary, this pay-as-you-go option offers the flexibility to adjust requirements as your project evolves. We’ll work with you to estimate the effort involved and ensure transparency and fairness in billing.
Retainer Fee: If you have ongoing analytics needs, our retainer service ensures dedicated support for a set number of hours each month. It’s a great way to secure our team’s availability without the commitment of a full-time hire.
Dedicated Resource: For businesses that anticipate a consistent, high-volume workload, we offer the option of dedicated resources. This model provides you with a team or individual fully focused on your business intelligence needs for a sustained period, offering stability and deep integration with your operations.
We understand that each business’s needs are unique, and we’re committed to providing a pricing structure that reflects that.
For a detailed quote that’s tailored to your business’s specific data visualization requirements, please don’t hesitate to reach out to us. Our team is ready to discuss your objectives and how we can align our services for the best outcome.
The duration of a data engineering project varies greatly depending on its scope and complexity.
For specific tasks like one table development it can take 1-2 hours.
More comprehensive projects, such as building a data warehouse for multiple departments within the company, from executive to operational levels, may span several months to 2 years
We’re committed to flexibility and try to scale our resources to meet project deadlines as needed. While we’ve shifted focus towards longer-term collaborations, our goal remains to provide tailored, impactful data engineering services that foster enduring partnerships.
Engaging with our data engineering services is a simple and straight forward process, ensuring we align with your business needs every step of the way.
Here’s how it works:
Before Contract Engagement
Initial Consultation: It all starts with booking a meeting where we discuss your business objectives and how our data engineering consulting services can align with your goals. We conduct a detailed discussion to understand your business strategy and objectives. This helps us explain and show how data can be transformed into actionable insights for your organization.
Tailored Proposal: Based on our discovery meeting, we draft a custom proposal outlining the approach, services offered, and the estimated impact on your business.
Contract Finalization: After reviewing the proposal with you and incorporating any feedback, we finalize the terms and sign the contract, setting the stage for our collaboration.
After Contract Engagement
Onboarding & Data Integration: Our team sets up the necessary infrastructure for data ingestion, processing, and reporting, ensuring a smooth start to our partnership.
Solution Implementation: We implement the best bi solution for your needs that can also include setting up infrastructure, integrations, and the development any agreed-upon dashboards.
Training & Capacity Building: To ensure you get the most out of our services, we provide comprehensive training for your staff on the new systems and tools.
Ongoing Support & Optimization: We offer continuous support, including performance monitoring and optimization, to ensure the solutions evolve with your business.
For more detailed information and to get started with transforming your data into strategic assets, contact us for a personalized consultation.
Absolutely, we encourage you to read the Testimonials section on our homepage.
At Witanalytica, staying at the forefront of data engineering is central to our approach. We actively keep ourselves informed through various channels, including industry news, to ensure we’re aware of the latest developments.
Our team personally tests emerging technologies, often being among the first to access new tools and features through early sign-ups and beta programs. We also delve into product releases and updates, ensuring that we understand the capabilities and applications of new technologies. Beyond external research, we dedicate time for internal projects, experimentation, and training.
This hands-on approach allows us to not only stay updated but also to critically evaluate and integrate new technologies and methods into our solutions, ensuring our clients always benefit from cutting-edge analytics.
At Witanalytica, ensuring the accuracy and reliability of our data analysis is foundational to our approach. We adhere to stringent data quality standards, focusing on accuracy, completeness, consistency, and reliability.
Our process involves rigorous data validation techniques to minimize errors. We tackle common data quality challenges, such as incomplete data, duplicates, data integration issues, and outdated information, by implementing best practices like setting mandatory fields, using data cleaning tools, standardizing data across systems, and establishing regular data refresh schedules.
To maintain the highest data quality, we also ensure data security, assign clear accountability and ownership of data sets, and conduct regular data audits. This comprehensive approach not only mitigates the risk of inaccurate insights but also supports informed decision-making and maintains our clients’ trust.
For a deeper dive into our data quality assurance practices and the importance of high-quality data in analytics, feel free to read more in our detailed article: Data Quality: How to Ensure Your Data Analytics Deliver Accurate Insights
After a project’s completion, our support at Witanalytica doesn’t just end. We actively monitor the dashboards for alerts and triggers to ensure continuous, smooth operation.
Maintenance of existing data pipelines takes precedence, ensuring they perform optimally before we embark on developing new ones. We offer flexible support options tailored to your needs — from a fixed maintenance fee to time-and-material billing for any necessary adjustments or maintenance. This approach allows us to swiftly adapt to changes, such as updates in data sources to ensure that your reports remain accurate.
Moreover, we’re committed to the success and adoption of our solutions. We regularly measure and monitor their usage because we believe in delivering not just solutions, but value that is actively utilized and drives results. Our goal is to ensure that the reports we develop are not only technically sound but also widely adopted and impactful.
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