How does the DMBOK refer to an organization that values data as an asset and manages data through all phases of its lifecycle?
Answer(s): A
The DMBOK (Data Management Body of Knowledge) refers to an organization that values data as an asset and manages data through all phases of its lifecycle as a Data-centric organization. In such organizations, data is considered a key resource, and data management practices are embedded throughout the business processes to maximize its value and utility.
Multiple data warehouses, lakes, and swamps are an indicator that the organization values:
Answer(s): E
Multiple data warehouses, lakes, and swamps often indicate that the organization values technology acquisition over a single point of truth. This scenario suggests that different technologies or data storage solutions have been adopted without a cohesive strategy or centralized approach, leading to fragmented and potentially inconsistent data sources rather than a unified, trusted source of truth.
All the systems in the enterprise, apart from a website, are showing updated pricing information. This may be due to:
Answer(s): C
If all systems except the website are showing updated pricing information, it is likely that the website software is not integrating with the reference data repository. This could mean that the website is not pulling the latest pricing data from the central repository, leading to discrepancies between the website and other systems that are correctly updated.
The seven Vs of Big Data are:
Answer(s): B
The seven Vs of Big Data refer to key characteristics that define big data challenges:Volume: The amount of data generated.Velocity: The speed at which data is created, processed, and analyzed.Variety: The different types and formats of data.Variability: The inconsistency of data, often caused by changes in the data over time.Viscosity: The complexity and difficulty of handling and integrating big data.Volatility: The rate at which data changes over time.Veracity: The trustworthiness and quality of the data.These seven Vs help to describe the challenges organizations face when working with large, fast, and complex data sets.
Data masking is a practice where only correct submitted data:
Data masking is the practice of obfuscating sensitive information in a way that it remains usable for processes like testing or analysis, while protecting the actual sensitive data. Only the correct submitted data will be used in further analysis, ensuring that masked or anonymized data is handled securely and appropriately.
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