Data analytics consulting services

Whether you’re in healthcare, financial services, manufacturing, or logistics business, data analytics consulting is the right option if you:

• drown in data of various formats and volumes
• need a structured approach to overcome the data chaos
• lack meaningful insights from existing data analytics solutions
• struggle with accessing and analyzing data siloed across departments
• make business decisions based on guesswork and executives’ experiences

Your daily pain points play a much more decisive role in whether you should consider data analytics consulting services for your business.

To define data quality and accuracy before implementing data analytics solutions, a data analytics consultant first audits the reliability of data pipelines and external and internal data sources. It’s critical to assess the data flow from the entry point to the end product and define whether there are any issues in data relevancy, accuracy, completeness, and trustworthiness.

After initial data pipeline auditing, our data analytics consultants prioritize critical issues and create a roadmap for solving them quickly and without disruptions. For instance, if a business struggles to get accurate data from different departments. A possible solution could be creating a unified format for gathering data from different systems and setting criteria that data should meet before ingestion, such as completeness and timeliness. For example, in the logistics industry, if data elements lack shipment weights or have late order status updates, such data would be classified as inaccurate and prevented from proceeding in the data pipeline.

To build efficient data analytics frameworks that match your data and business requirements, we use:

• Cloud data platforms such as Amazon Redshift, Snowflake, and Google BigQuery for data warehousing and analytics at scale
• Data processing frameworks like Apache Spark, Apache Flink, and Airflow to handle both batch and real-time data pipelines
• Business intelligence and visualization tools, including Power BI, Tableau, Amazon QuickSight, and Apache Superset
• AI and ML toolkits to enable predictive and prescriptive analytics
• Integration with cloud services like AWS, Azure, and Google Cloud Platform for scalability, flexibility, and compliance

At Yalantis, we specialize in the seamless integration of analytics solutions with your current infrastructure—including ERP, CRM, EHR, or custom-built platforms. As a data analytics consulting company, we evaluate your data architecture, identify integration points, and ensure smooth data flow across systems without disrupting ongoing operations.

We deliver solutions with compliance and security embedded into every layer, ensuring that your business meets the strictest industry standards, laws, and regulations.

For clients operating in regulated environments such as healthcare, finance, and logistics, we implement a robust framework that includes:

• End-to-end data encryption (both in transit and at rest) to prevent unauthorized access
• Granular access controls based on user roles and responsibilities
• Audit trails and activity logging for full traceability and accountability
• Consent management systems that ensure proper user permissions and data handling in line with GDPR requirements
• Data masking and anonymization techniques to minimize exposure of personally identifiable information (PII)

Predictive analytics uses historical data and machine learning to forecast future outcomes. For example, in retail, it can anticipate inventory shortages during seasonal peaks. Global logistics leaders such as FedEx and DHL use predictive algorithms to anticipate package volume surges and adjust staffing. In healthcare, predictive analytics might forecast a patient’s risk of readmission based on their medical history.

Prescriptive analytics goes further by recommending specific actions based on those predictions. It helps you answer, “What should we do next?”—like optimizing staffing schedules in a hospital or adjusting marketing spending in financial services to target high-value users likely to convert.

Together, these approaches turn insight into action—helping businesses to see what’s coming and make confident, data-backed decisions to shape better outcomes.

The timeline depends on the project scope, data complexity, and integration needs. Typically:

• Data analysis consulting services and MVP development: 4–8 weeks
• End-to-end enterprise analytics platforms: 3–6 months

We start with a discovery phase to define goals and build a realistic roadmap, ensuring transparency and momentum from day one.

We provide post-deployment support, maintenance, and continuous optimization services. This includes:

• Performance tuning and issue resolution
• Updating models and dashboards as business needs evolve
• User training and documentation
• Scaling your analytics stack as your data grows

We can also help you ensure stakeholders’ buy-in by showing what value data analytics can offer them such as increased productivity, enhanced business performance, and data-driven strategic decisions.