Organizations can use big data and predictive analytics to deliver the right information, product, service, or action at the right time. Big Data – large pools of data that can be brought together and analyzed to discern patterns and make better decisions – will become the basis of competition and growth for individual firms.
The insights gained from an effective use of big data can enhance productivity and create significant value for the world economy. In addition to reducing waste, smart data analysis can also help companies increase the quality of their products and services.
A master data management program generating high-quality, well-governed data will facilitate more reliable analysis, getting the entire enterprise on the same page. Data mining technology reveals patterns in huge data sets, which can be analyzed to answer complex business questions by improving the noise-to-signal ratio, pinpointing what’s relevant, assessing likely outcomes, and then accelerating informed decision-making. Predictive analytics technology can be used to identify the likelihood of future outcomes based on historical data, so organizations can feel more confident that they’re making the best possible business decision. These approaches are most effective with supporting infrastructure that can store large amounts of data and use a distributed computing model to process big data faster. Technology that supports real-time insights and rapid response will help organizations stay agile and make better business decisions, as well as running iterative and interactive analytics scenarios. Creative, inquisitive corporate data experts need to come up with new methods of applying data analytics in innovative ways, solving problems that companies don’t even know they have and identifying inefficiencies in production, marketing, or delivery.
In today’s high-paced, hypercompetitive business environment, companies must adopt a strategy that allows them to find and analyze relevant data quickly. Hardware-based solutions include increased memory and powerful parallel processing, or in-memory data combined with a grid computing approach. Companies need to find employees capable of working with new technologies and interpreting the data to find meaningful business insights. In addition, cross-functional teams must be created to fully leverage big data, combining IT, engineering, finance, and procurement. Security concerns about data protection are a major obstacle preventing companies from taking full advantage of their data. Data quality remains a concern; data must be accurate and timely in order to support decision-making processes, and this challenge only becomes more pronounced as data volumes increase. Data points need to be connected, aggregated, and managed in order to integrate operational technologies with information technologies. Visualization helps organizations perform analyses and make decisions much more rapidly, but requires proper context.
Big data technologies have achieved significant cost advantages and identified more efficient ways of doing business. Big data analytics provide the ability to gauge customer needs and satisfaction, leading to new products and services that give your customers exactly what they want. Faster, better decision-making processes based on sophisticated, innovative analytics allow businesses to analyze information in real time and act immediately, while minimizing risks and unearthing valuable insights.
What is the fastest and most effective way to go through the sheer volumes of data and access the level of detail needed? What types of domain expertise do data analysts need to have? Who should be on the team to maximize collaboration across functions and businesses? What is the best way to visualize big data? How can it be grouped and contextualized to display meaningful results? Is there a data governance or information management process in place to ensure data quality? How can risk management be used to find a balance between data security and operational effectiveness?
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