FEATURE : BUSINESS INSIGHTS
Opportunities on the horizon include the integration of AI and machine learning with Big Data , offering businesses valuable insights , automated decisionmaking processes , and predictive modelling capabilities . Industries such as healthcare , finance , and e-commerce stand to benefit from personalised recommendations , fraud detection , and more .
Moreover , the proliferation of Internet of Things , IoT devices will generate vast amounts of data at the edge , which can be harnessed for real-time monitoring , predictive maintenance , and improving operational efficiency across various sectors .
Challenges and inhibitors
These challenges include handling the sheer volume of data efficiently , as it can strain infrastructure and resources . Big Data encompasses various types of data , including structured , semi-structured , and unstructured data , making it a challenge to integrate and make sense of diverse data formats . Furthermore , the ever-growing data volumes poses a significant risk of data breaches and cyberattacks , necessitating measures for protecting sensitive data from unauthorised access .
The emergence of stricter data privacy regulations such as Protection of Personal Information Act , POPIA and General Data Protection Regulation , GDPR complicates data handling and sharing practices , necessitating the need for comprehensive data governance solutions .
Role of BI
Business Intelligence , BI is poised to continue playing a pivotal role in the future of business . It empowers organisations to make data-driven decisions , gain insights , and maintain a competitive advantage . The future of BI will likely involve advanced analytics , real-time data , self-service BI , data integration , data governance , and security measures .
To integrate BI effectively into their operations with the assistance of an IT partner , companies should assess their business needs , choose the appropriate BI tools , design dashboards and reports , and provide user training and support for successful adoption .
Multiple touchpoints
Effectively collecting and utilising data from various touchpoints , including edge data and new business areas , is crucial for businesses seeking to maintain a competitive edge and make informed decisions . An IT partner can significantly facilitate this process .
It begins with clearly defining business objectives and Key Performance Indicators , KPIs that data collection and analysis will support . Data initiatives should align with these objectives and develop a data strategy and roadmap for collecting , integrating , storing , and utilising data , including plans for handling edge data and data from new business areas .
Data extraction
The process of extracting insights from Big Data necessitates a combination of suitable technologies and tools tailored to an organisation ’ s specific needs . These solutions encompass data warehousing and databases , Big Data processing frameworks like Hadoop and Apache Spark , data integration and ETL tools such as Apache Nifi and Talend , as well as business intelligence and analytics tools like Tableau , Power BI , and Quick View .
Machine learning and AI libraries like Python and R , IBM Watson , and others also play a crucial role . It is important to lean on an expert in the field that can aid in the assessment of business needs , technology evaluation , vendor selection , and architecture and integration , enabling businesses to make informed decisions about the technology stack that best suits their Big Data analytics needs .
Amritesh Anand , Associate Vice President , In2IT Technologies
BIG DATA ENCOMPASSES VARIOUS TYPES OF DATA , INCLUDING STRUCTURED , SEMI-STRUCTURED , AND UNSTRUCTURED DATA ,
MAKING IT A CHALLENGE TO INTEGRATE AND
MAKE SENSE OF DIVERSE DATA FORMATS .
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