Data of high quality is essential for the accumulation of substantial amounts of data, referred to as 'big data'.
In today's digital age, businesses are increasingly recognising the value of big data - a vast and diverse set of information generated by staff, systems, and websites. Stuart Evans, CTO of Invu, emphasises the importance of clean data capture and process stages in this data-driven era.
Big data is being used extensively to analyse interactions and improve customer or supply chain engagement, as well as operational performance. By automating data capture, businesses can significantly enhance the efficiency and accuracy of their operations. For instance, the invoice approval process or purchase order process can be reduced to typically two to five days.
However, data captured from various sources, such as emails and paper-based documents, can contain errors when data is manually entered. To generate accurate and beneficial data, businesses should ensure their data capture and process stages are clean and efficient. Collaborative tools can be utilised to track and complete specific data, such as customer information and payment status. Fewer errors occur when data capture and process stages are automated, freeing up staff for data analytics.
To use data effectively, businesses must first collect the right data in the right way. This can be achieved by implementing integrated data pipelines and using specialized big data technologies such as Hadoop, Spark, and NoSQL databases. These tools enable automated extraction, transformation, and loading (ETL/ELT) of data from diverse inputs into unified data stores for accurate analysis and decision-making.
Key steps and strategies include data collection, data integration and pipelines, ETL/ELT processes, data consolidation techniques, big data technologies, visualization and advanced analytics, and performance optimization. By following these best practices, businesses can harness big data effectively to improve accuracy in analytics and decision-making.
| Step | Description | Tools/Technologies | |----------------------------|----------------------------------------------------------|----------------------------------------| | Data Collection | Gather data via surveys, social media, sensors, etc. | Questionnaires, APIs, IoT devices | | Data Pipelines | Automate data transfer and integration | Custom pipelines, Apache NiFi, Airflow | | ETL/ELT | Extract, transform, and load data | Talend, Informatica, cloud services | | Data Consolidation Methods | Append or join datasets based on common fields | Data integration platforms | | Big Data Storage & Processing | Store and process large volumes of data efficiently | Hadoop HDFS, Apache Spark, MongoDB | | Visualization & Analytics | Transform data into insights, forecasting | Tableau, Power BI, TensorFlow | | Optimization Techniques | Handle data volume & velocity | In-memory computing, stream processing |
Access to high-quality, plentiful data can enhance or transform businesses, but collecting such data can be challenging due to disparate systems. Cleaning data capture and process stages is crucial for generating reliable data for business analysis. As global cloud traffic approaches 5.3 zettabytes by 2017, businesses should prioritise data quality to make the most of their big data investments. Instead of diving into big data analytics without preparation, companies should first organise their data to ensure its quality and coherence. By 2013, 64% of companies were either investing or planning to invest in big data, according to Gartner. The future of business lies in effective data management and analysis, and businesses that master this skill will be well-positioned for success.
In this data-driven era, businesses can utilize big data technologies such as Hadoop, Spark, and NoSQL databases to effectively collect and analyze data, improving operational performance, customer engagement, and decision-making (Step 5 - Visualization and Advanced Analytics). To ensure the accuracy and reliability of captured data, businesses should prioritize clean and efficient data capture and process stages, employing collaborative tools to track and complete specific data (such as customer information and payment status) and automating data capture to minimize human errors (Step 2 - Data Pipelines).
