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Q12-3. What are the key components of an analytics IS?
The key components of an analytics information system include the five classic elements: hardware, software, data, procedures, and people. These systems rely heavily on data warehouses for storage, advanced software tools for data mining and visualization, and skilled analysts who can interpret data and guide decision-making. Critical attributes of analytics IS include scalability, ease of use, and dependency on both robust data infrastructures and user collaboration for successful outcomes.
Q12-5. How can analytics information systems support business processes?
Analytics IS support business processes by enhancing both existing and new workflows. For existing processes, analytics tools improve decision-making, efficiency, and responsiveness by providing timely and relevant data insights. For new processes, analytics can enable innovations like predictive maintenance or customer behavior forecasting, adding strategic capabilities that were previously unavailable. These systems help align operations with organizational goals by tracking key performance indicators (KPIs) across departments.
Q12-6. What is a Big Data analytics IS, and how is it used?
A Big Data analytics IS is designed to process and analyze extremely large, fast-moving, and diverse datasets. It uses tools like Hadoop, MapReduce, NoSQL databases, and in-memory systems like SAP HANA to manage the three Vs of Big Data: volume, velocity, and variety. These systems support business needs such as fraud detection, personalized marketing, and operational optimization by uncovering patterns and insights that traditional systems cannot detect.
Q12-7. How do businesses manage the risks of an analytics IS?
Businesses manage the risks of analytics IS by addressing both data and people-related challenges. Data issues include poor quality, inconsistency, and lack of governance, which are mitigated through cleansing, cataloging, and standardization practices. People risks such as resistance to change, misinterpretation of data, and over-reliance on models are managed through training, involving end-users in development, and promoting data literacy. Setting clear scopes and fostering critical thinking also help avoid flawed decision-making.
Q12-8. How does SAP do analytics?
SAP supports analytics through integrated platforms like SAP HANA, which uses in-memory computing to deliver high-speed data processing. SAP systems facilitate all three stages of analytics: acquiring, analyzing, and publishing. The platform provides tools for reporting, dashboards, and predictive analytics, allowing businesses to visualize real-time data and derive insights across functions such as finance, logistics, and sales. SAP’s robust infrastructure enables scalable and efficient enterprise-level analytics.
Q12-9. What new IS will affect the analytics process in 2031?
By 2031, new IS technologies like augmented analytics, digital twins, and systems that prioritize consumer privacy will significantly impact analytics. Augmented analytics will automate data preparation and insight generation using AI, making advanced analysis more accessible. Digital twins will simulate real-world processes for predictive modeling, while enhanced privacy systems will ensure data ethics and compliance. These advancements will redefine how businesses derive and act on analytical insights.
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