Data, Analytics & AI Practice

The Bexton Data, Analytics and AI practice can help you unlock the value of data in your enterprise. We can help you collect it, migrate it, ensure it's quality and organise it to provide effective reporting and analytics.  Effective use of data provides organisations with better business decisions to create competitive advantage as well as adherence to regulatory requirements.  Our services include:

• Artificial Intelligence / Machine learning / predictive analytics

• Business Intelligence, Analytics & Reporting

• Data Sourcing / ETL

• Data Quality and Validation

• Data Migration and Conversion

• Data Governance, Master Data Management

Machine Learning / Predictive Analytics

Machine learning is a critical component of a data practice, revolutionising the way organisations extract valuable insights and patterns from vast amounts of data. Machine learning involves the development of algorithms and models that enable computers to learn from data without being explicitly programmed. By leveraging historical data, Bexton can implement machine learning algorithms to identify patterns, make predictions, and provide actionable insights, enhancing decision-making processes and finding opportunities for business growth.

In the Bexton data, analytics and AI practice, machine learning is used for a wide range of applications, including predictive analytics, natural language processing, image recognition, and anomaly detection. Our consultants use various machine learning techniques such as supervised learning, unsupervised learning, and reinforcement learning to train models and optimise them for specific tasks. As data volumes continue to grow exponentially, machine learning will play an increasingly vital role in uncovering hidden patterns and trends within data, enabling organisations to gain competitive edge and make data-driven decisions.

Business Intelligence, Analytics and Reporting

Business intelligence (BI) refers to the processes, technologies, and tools that transform raw data into meaningful and actionable insights for business decision-making. BI involves data gathering, integration, analysis, and visualisation to help organisations understand their performance, identify trends, and make informed strategic choices. It empowers executives and decision-makers with real-time or near-real-time access to key performance indicators (KPIs) and critical business metrics, enabling them to track progress, spot opportunities, and address challenges effectively.

Analytics goes beyond simple data reporting and involves the use of advanced statistical and quantitative techniques to uncover deeper insights and patterns in data. It involves data exploration, hypothesis testing, and predictive modeling to gain a comprehensive understanding of business operations and customer behavior. Analytics empowers businesses to make data-driven decisions by leveraging historical data to predict future outcomes, optimise processes, and identify potential risks and opportunities.

Reporting is an essential aspect of both business intelligence and analytics. It involves the creation and dissemination of structured and summarised data in the form of reports and dashboards. Reporting provides a snapshot of key metrics and performance indicators, making it easier for stakeholders to understand trends, monitor progress, and compare results over time. By combining business intelligence, analytics, and reporting, organisations can harness the full potential of their data, gaining valuable insights to improve operational efficiency, enhance customer experiences, and drive overall business success.

Data sourcing / ETL

Data sourcing and ETL (Extract, Transform, Load) are critical steps in the data management process that involve collecting data from various sources, transforming it into a standardised format, and loading it into a central data repository for further analysis. In recent years, several advanced technology platforms have emerged to streamline and enhance these processes.

One popular example Bexton consultants use is Apache Kafka, an open-source distributed event streaming platform. Kafka acts as a data pipeline, enabling real-time data ingestion from diverse sources including databases, applications, and IoT devices. Its high-throughput and low-latency capabilities make it ideal for handling massive volumes of data streams. Bexton consultants use Kafka's connectors and APIs to extract data from disparate sources, and with its built-in data transformation features, they can preprocess and format the data before loading it into data lakes, data warehouses, or other storage solutions.

Data Quality and Validation

Data quality and validation are more important than ever before because organisations today are inundated with vast amounts of data from various sources, including IoT devices, social media, and online transactions. As the reliance on data-driven decision-making increases, the accuracy and reliability of data become critical for ensuring the success of business operations, analytics, and strategic planning. Poor data quality can lead to erroneous insights, flawed decision-making, and potential reputational damage. Additionally, with the advent of advanced technologies such as artificial intelligence and machine learning, the quality of data used to train these algorithms directly impacts their effectiveness and fairness. Therefore, robust data quality and validation practices are essential to ensure data-driven insights are accurate, trustworthy, and contribute to informed decision-making in today's data-centric world.

Data Migration and Conversion

Data migration and conversions are complex processes involved in transferring data from one system or format to another. Bexton takes a well-planned and executed approach as it is essential to ensure a smooth and accurate transition of data. The first step is to conduct a thorough assessment of the source data, including its structure, quality, and relationships. Understanding the data's intricacies helps in identifying potential challenges and risks that might arise during migration. The next step is to define a migration strategy, which may involve choosing between a big bang approach, where all data is migrated at once, or a phased approach, where data is migrated in stages. During the migration process, data validation and testing are critical to ensure the accuracy and integrity of the migrated data. Regular backups and rollback plans are implemented to mitigate any unforeseen issues that might arise during migration. Finally, post-migration validation and user acceptance testing are conducted to ensure that the migrated data meets the required standards and is compatible with the target system(s).

Bexton are dedicated to implementing a streamlined data migration process that is characterised by automation, reusability, and modularity. This approach guarantees that the migration procedure is systematically validated and executed in discrete modules, well in advance of the go-live date, thereby eliminating any unforeseen challenges or uncertainties.

Data Governance, Master Data Management

Data Governance is a strategic framework and set of processes that ensure data is managed, utilised, and protected effectively throughout an organisation. It involves establishing policies, guidelines, and procedures to define roles, responsibilities, and accountability for data management. Data Governance aims to maintain data quality, consistency, and security while aligning data initiatives with the organisation's business objectives. It involves collaboration between different departments and stakeholders to establish a unified understanding of data definitions, standards, and data-related policies. A well-implemented Data Governance program helps organisations make informed decisions, improve data quality, reduce risks, and comply with data regulations.

Master Data Management (MDM) is a discipline focused on creating and maintaining a single, accurate, and consistent version of master data across an organisation. Master data refers to critical business data entities such as customers, products, suppliers, and employees, which are shared and used across various business processes and applications. MDM involves data profiling, data cleansing, data integration, and data enrichment to create a reliable master data repository. This centralised and authoritative source of master data ensures data consistency, reduces data redundancies, and improves data quality across the organisation. By implementing MDM, organisations can achieve a unified view of their master data, enhance operational efficiency, and enable better decision-making processes based on trusted data.