How Is Big Data Analytics Shaping Data Management?
Big data analytics is the process of examining large data sets containing a wide range of data types — i.e., big data — in order to uncover hidden patterns, unknown correlations, market trends, customer preferences, and other useful business information. The findings of the analysis can result in more effective marketing, new revenue opportunities, better customer service, increased operational efficiency, competitive advantages over rival organisations, and other business benefits.
Big data analytics appeals to businesses by offering savings on three critical levels of any business: time, money, and people — saving money and using fewer resources to present data for better decisions translates to saving money and using fewer resources to present data for better decisions. For example, a traditional relational database costs $37,000, a database appliance costs $5,000, and a Hadoop cluster costs only $2,000 (Paul Barth at NewVantage Partners supplied these cost figures).
What is the future trend for Big Data Analytics?
Analytics 3.0 is the new wave of big data analytics, compared to Analytics 1.0, which is BI, and Analytics 2.0, which is used by online companies only (Google, Yahoo, Facebook, etc.). Analytics 3.0 is a new resolve to apply powerful data-gathering and analysis methods not just to a company’s operations but also to its offerings — to embed data smartness into the products and services customers buy.
Some of the attributes defining Analytics 3.0:
- The most important trait is that not only online firms, but virtually any type of firm in any industry, can participate in the data-driven economy.
- Multiple data types: Organizations are combining large and small volumes of data, internal and external sources, and structured and unstructured formats to yield new insights in predictive and prescriptive models.
- A new set of integration options: database appliances, Hadoop clusters, SQL-to-Hadoop environments.
- Technologies and methods are much faster: Big data technologies include a variety of hardware/software architectures, including clustered parallel servers using Hadoop/MapReduce, in-memory analytics, in-database processing, and so forth. All of these technologies are considerably faster than previous generations.
- Integrated and embedded: built into consumer-oriented products and features.
- Data science/analytics/IT teams will work together.
- Chief analytics officers are new leadership positions.
- Prescriptive analytics: There have always been three types of analytics: descriptive, that report on the past; predictive, which use models based on past data to predict the future; and prescriptive, which use models to specify optimal behaviours and actions. Analytics 3.0 includes all types, but there is an increased emphasis on prescriptive analytics.
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