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Making the most of enterprise data is a top concern for IT leaders today. With organizations seeking to become more data-driven with business decisions, IT leaders must devise datastrategies gear toward creating value from data no matter where — or in what form — it resides.
Data engineers also need communication skills to work across departments and to understand what business leaders want to gain from the company’s large datasets. Data engineers must also know how to optimize data retrieval and how to develop dashboards, reports, and other visualizations for stakeholders.
Data engineers are often responsible for building algorithms for accessing raw data, but to do this, they need to understand a company’s or client’s objectives, as aligning datastrategies with business goals is important, especially when large and complex datasets and databases are involved.
Established and emerging data technologies: Data architects need to understand established data management and reporting technologies, and have some knowledge of columnar and NoSQL databases, predictive analytics, data visualization, and unstructureddata.
Data science is an area of expertise that combines many disciplines such as mathematics, computer science, software engineering and statistics. It focuses on data collection and management of large-scale structured and unstructureddata for various academic and business applications.
In this article, we’ll dig into what data modeling is, provide some best practices for setting up your data model, and walk through a handy way of thinking about data modeling that you can use when building your own. Building the right data model is an important part of your datastrategy. Discover why.
Master data management. Data governance. Structured, semi-structured, and unstructureddata. Data pipelines. Data science skills. Technology – i.e. datamining, predictive analytics, and statistics. Best practices for exploring collected data. Scoring – i.e. profitability or risk.
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