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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.
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:
Categorizing data according to similar user actions during a specific period allows for analysing their behaviour and making comparisons between different groups.
Grouping data by similarities is a common initial approach to applying various techniques.
Analysis of data from current events, alterations in business conditions, and surrounding circumstances is conducted to reveal potential opportunities and threats.
Analysing the data from current events or due to changes in business conditions and surrounding circumstances in real time to uncover opportunities and threats.
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.
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.
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.
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.
Using historical data, statistical algorithms, and machine learning techniques to make predictions about future events or outcomes.
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.
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.
Building AI/ML models that can spot abnormalities, outliers, and potentially fraudulent behaviour in datasets or transactional data. Anomaly detection and fraud detection.
Helping businesses use data governance procedures, ensure regulatory compliance, and protect data security and privacy when utilising AI/ML-driven BI systems.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Create interactive dashboards, reports, and visualisations using Tableau, a potent data visualisation and analytics application. It supports a variety of data sources and has an intuitive user interface, making data exploration and analysis simple.
Power BI, created by Microsoft, is a cloud-based business analytics platform that offers self-service BI capabilities, interactive dashboards, and data visualisation. It offers numerous data communication possibilities and works nicely with other Microsoft applications.
Users can explore and analyse data using interactive visualisations and dashboards provided by the data discovery and visualisation platforms QlikView and Qlik Sense. They provide strong associative data indexing, which makes it simple to traverse and find insights.
A full BI platform that offers reporting, analytics, and data visualisation features is called MicroStrategy. It offers natural language querying, predictive analytics, mobile BI, and advanced analytics.
A collection of business intelligence (BI) tools including reporting, ad hoc query, and data visualisation features. It provides enterprise-level BI solutions and has a strong connection with SAP systems.
The data lake and analytics services offered by Google Cloud Platform rely heavily on Google Cloud Storage and BigQuery. We are skilled at designing and deploying data lakes using BigQuery for scalable data processing, ad-hoc querying, and analytics and Google Cloud Storage as the underlying storage layer.
Looker is a BI and data analytics platform that runs in the cloud and focuses on offering data exploration, modelling, and sharing features. Users can design their own dashboards and reports, and it connects effectively with a variety of data sources.
Domo is a cloud-based BI platform that includes tools for collaboration, data integration, and visualisation. It delivers real-time data insights and a variety of connections to access and analyse data from many sources.
Google Data Studio is a free data reporting and visualisation application that interfaces with a number of Google products and external data sources. It features a drag-and-drop interface that enables users to create interactive reports and dashboards.
Knowledge of NLP is essential for creating AI-powered business intelligence (BI) solutions that can comprehend and interpret human language. This comprises activities like topic modelling, language production, named entity identification, sentiment analysis, and text categorization. It is crucial to be knowledgeable about NLP frameworks and libraries like NLTK, spaCy, and BERT.
Implementing predictive analytics and modelling in business intelligence (BI) requires a solid understanding of various ML techniques. It is advantageous to have knowledge of techniques like deep learning (neural networks), support vector machines (SVM), decision trees, random forests, and regression (linear and logistic).
For processing massive datasets, proficiency with big data technologies such as Apache Hadoop, Spark, or distributed databases is helpful. Scalable and affordable AI/ML-based business intelligence solutions can be made possible by familiarity with cloud platforms like AWS, Azure, or Google Cloud.
Designing effective data pipelines, data preprocessing, and data integration require expertise in data engineering. This comprises mastery of data warehousing principles, ETL (Extract, Transform, Load) processes, data governance, and SQL skills for data manipulation.
It's crucial to comprehend the particular domain or industry for which the BI solution is being created. Designing appropriate AI/ML-driven BI systems can benefit from industry expertise in the business processes, data characteristics, and difficulties of sectors like banking, healthcare, e-commerce, or manufacturing.
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