Business Intelligence Consulting - AI / ML

With AI taking the market by surprise in recent years, to gain a deeper understanding of data that goes beyond traditional business intelligence, companies need to employ sophisticated data analytics techniques, including AI/ML-based Business Intelligence, that incorporate innovative technologies like machine learning, artificial intelligence (generative AI), and statistical models. These methods aim to predict future trends and events by examining large data sets, identifying patterns and deeper insights that can help businesses gain a competitive edge.

AI/ML-based business intelligence has numerous advantages over traditional methods, including speed, scalability, automation, and the capacity to extract deeper insights from data. Organizations are given the ability to make data-driven decisions more quickly, acquire a competitive advantage, and identify profitable business prospects.

Our services

We at RalanTech provide end-to-end solutions using varied approaches for advanced analytics including data mining, regression analysis, sentiment analysis, cluster analysis, and machine learning depending on the depth of requirement.
We empower our clients to predict future outcomes and prescribe action by leveraging structured and unstructured data using a top-notch data-science-driven approach through our BI solutions.
Our offerings include:

Cohort analysis

Categorizing data according to similar user actions during a specific period allows for analysing their behaviour and making comparisons between different groups.

Cluster analysis

Grouping data by similarities is a common initial approach to applying various techniques.

Retention analysis

Analysis of data from current events, alterations in business conditions, and surrounding circumstances is conducted to reveal potential opportunities and threats.

Complex event analysis

Analysing the data from current events or due to changes in business conditions and surrounding circumstances in real time to uncover opportunities and threats.

Regression analysis

By analysing a set of data, regression analysis can reveal the relationships between dependent and independent variables, as well as predict their future connection and impact on each other.

Sentiment analysis

Text can be analysed for sentiment using techniques that categorize language and interpret the emotions embedded within, ultimately determining whether the overall attitude is positive, negative, or neutral.

Data preprocessing and integration

Assisting in the preparation of data for AI/ML analysis, assuring data quality, and assisting in the integration of data from diverse sources. Data cleansing, data transformation, and data enrichment are all involved.

AI/ML model development and implementation

Building and deploying AI/ML models that are especially suited for business intelligence. This involves creating models for natural language processing, recommendation systems, anomaly detection, predictive analytics, and other AI-powered tools.

Predictive analysis and forecasting

Using historical data, statistical algorithms, and machine learning techniques to make predictions about future events or outcomes.

Data visualizations and reporting

Creating interactive dashboards, data visualisations, and reports that highlight insights obtained from AI/ML analysis is known as data visualisation and reporting. Users may benefit from this by being able to visualise and study data and successfully communicate discoveries.

Personalization and recommendation systems

Development of AI-driven recommendation engines that give personalised product recommendations, content suggestions, or targeted marketing campaigns based on user behaviour and interests. Personalization and recommendation systems.

Anomaly detection and fraud detection

Building AI/ML models that can spot abnormalities, outliers, and potentially fraudulent behaviour in datasets or transactional data. Anomaly detection and fraud detection.

Data governance and security

Helping businesses use data governance procedures, ensure regulatory compliance, and protect data security and privacy when utilising AI/ML-driven BI systems.

Cloud based BI solutions

Designing and putting into practise cloud-based BI platforms that make use of AI/ML technology. This makes it possible for businesses to benefit from the scalability, flexibility, and cost-efficiency that cloud service providers offer.

Consulting and consulting services

Offering advice on BI strategies, AI/ML adoption, and choosing the most appropriate AI/ML tools and algorithms for particular business requirements. This entails carrying out feasibility studies, determining how AI/ML will affect current BI systems, and developing implementation roadmaps.

Our Business Intelligence Consulting process

Discovery and Requirement Gathering

Discovering and Understanding the client’s company goals, pain issues, and particular needs. Identify key performance indicators (KPIs) and specify the parameters of the BI project in collaboration with stakeholders.

Data preparation and assessment

Evaluate the client’s data sources, availability, accuracy, and comprehensiveness. Determine any shortcomings or problems that should be fixed. To build a uniform and trustworthy dataset for analysis, clean, transform, and combine the data.

AI/ML model selection and Development

Choose and develop an AI/ML model by analysing the client’s needs and selecting the best AI/ML models and methods for the BI solution. Depending on the use case, develop and train the models using the prepared data, taking into account elements like regression, classification, clustering, or natural language processing.

Integration and Model Deployment

Either create a new infrastructure or integrate the trained AI/ML models into the client’s current BI infrastructure. To ensure seamless data flow and real-time or batch processing, depending on the needs, integrate the models with the data pipeline.

Reporting and Visualisation

Produce interactive dashboards and visualisations that clearly convey the insights gleaned through AI/ML analysis. Create reports that give stakeholders useful information they may use to make educated decisions. To improve usability, take into account user experience and interaction.

Testing and Validation

To assure accuracy, dependability, and performance, thoroughly test the complete BI solution. Validate the AI/ML models using real-world examples, and then improve them as needed. Address any problems or inconsistencies found during the testing stage.

Implementation and User Training

To acquaint users with the new AI/ML capabilities, implement the BI solution and hold training sessions. Give instructions on how to operate the system, analyse the findings, and make use of the sophisticated features. Provide continual help and support during the early stages of adoption.

Monitoring and improvement

Keep a close eye on the efficiency and performance of the AI/ML models and the overall BI solution. Track important indicators, examine user input, and pinpoint areas that need improvement. To increase precision and relevance, refine the models, the data flow, or the visualisation as necessary.

Continuous Improvement

Continuously improve and develop the BI solution in response to changing business requirements, technological developments, and user input. Examine the possibility of broadening the solution’s application and incorporating further data or algorithms.