Data and Analytics Technology is changing at a rapid pace. These data and analytics technologies have significant disruptive potential within the next three to five years, possibly more. Data and analytics experts must analyze their organization impacts and adjust their operational, business model and strategic plans accordingly. These changes will alter how organizations collect and utilize data. It will also likely require new software systems, databases, dashboards and reporting applications. Organizations must stay abreast of emerging trends and must develop solutions to data-related challenges quickly.
Data is a key component to the success or failure of a company. Data science plays an important role in the data analysis process. Data science helps organizations define, collect and interpret their data sources in order to make informed decisions. In order to capitalize on big data analytics, companies must exploit available, analytic resources such as mobile devices, social networks, emails, video and internet usage, real-time data sources from devices used in the customer relationship management process, and other analytic opportunities available through digital transformation and optimization.
In order to understand and predict future trends, organizations must stay ahead of the curve and incorporate intelligence and analytical tools. Data scientists are the foundation for extracting big data from various sources and providing it to business managers and executives in order to forecast and facilitate organizational change. Trends can reveal internal weaknesses and potentials of a business or indicate new opportunities. Understanding these trends enables businesses to take informed decisions that lead to organizational growth and profitability.
Trends provide organizations with in-depth insight into the current business models and their weaknesses. Trends are capable of revealing tactical and strategic insights that help executives and business managers develop new business models or adapt to changing conditions. The key is to identify trends early and take advantage of them. The speed with which a business can respond to a trending question is dependent on its ability to analyze and interpret large quantities of data quickly and make reliable inferences.
The four types of data analysis are as follows
Data science projects based on predictive analytics provide business managers and executives with an objective view of future trends. Predictive analytics provides organizations with information about patterns in the past, present, and future. It gives organizations the ability to make informed decisions based on patterns that emerge. These patterns and trends are making clear using a mathematical framework called a machine learning algorithm. Machine learning algorithms take raw data sets and produce predicted result from those sets. A large data set is typically needed for machine learning algorithms to work; however smaller or simpler data sets are acceptable for the purpose.
Data science projects often depend heavily on domain specific technologies. Domain specific technologies must be developed and optimized for big data analytics solutions. Examples include language technologies, web server technologies, database server technologies, and web analytics solutions. These technologies must be scalable, reliable, and fast. They must also support real-time operation and be flexible enough to accommodate any transformations that may occur as a business grows and develops over time.
In order for companies to capitalize on the power of big data analytics, they must also develop and maintain their own analytic technologies. In order to do so, these companies should consult with and engage the services of several different technologies. For example, they may need to collaborate with Yahoo! Answers and BrightKide to build and optimize their blog analytics capabilities.
Data science experts at Yahoo! Answers and BrightKide help organizations better understand their customer base. These technologies help the organization to construct relevant and actionable intelligence. This intelligence can then be leveraged for situational awareness, customer management, and product optimization. The trends in the data marketplaces are ever-changing, and businesses must stay on top of them to have a competitive advantage.