Data analytics (DA) is the science of examining raw data with the purpose of drawing conclusions about that information. Data analytics is used to make better business decisions. Data analytics is used to describe everything from online analytical processing (OLAP) to CRM analytics in call centers. Banks and credit cards companies, for instance, analyze withdrawal and spending patterns to prevent fraud or identity theft. Ecommerce companies examine Web site traffic or navigation patterns to determine which customers are more or less likely to buy a product or service based upon prior purchases or viewing trends. Modern data analytics often use information dashboards supported by real-time data streams. So-called real-time analytics involves dynamic analysis and reporting, based on data entered into a system less than one minute before the actual time of use.
Business intelligence (BI) can be described as “a set of techniques and tools for the acquisition and transformation of raw data into meaningful and useful information for business analysis purposes”. The term “data surfacing” is also more often associated with BI functionality. BI technologies are capable of handling large amounts of unstructured data to help identify, develop and otherwise create new strategic business opportunities. The goal of BI is to allow for the easy interpretation of these large volumes of data. Identifying new opportunities and implementing an effective strategy based on insights can provide businesses with a competitive market advantage and long-term stability.
BI technologies provide historical, current and predictive views of business operations. Common functions of business intelligence technologies are reporting, online analytical processing, analytics, data mining, process mining, complex event processing, business performance management, benchmarking, text mining, predictive analytics and prescriptive analytics.
Supremus lets business users easily drag-and-drop data to combine large data sets into a single repository, and build dashboards with beautiful visualizations–all with no scripting, no expensive hardware, and little to no help from IT. Use built-in connectors to combine multiple data sources and formats, such as: Excel files, Google Adwords and Analytics, CRM information, and integration with Cloud applications like Zendesk and Salesforce. Everyone in your organization can then analyze the same numbers in real-time, and rely on a single source of truth. Behind every dashboard is Supremus technology that handles terabytes of data and supports thousands of users–all on a single commodity server.
In fast changing, global economy, businesses have begun to more heavily bank on insights from their internal processes, customers, and business operations in order to uncover new opportunities for growth. In the process of discovering and determining these insights, large complex sets of data are generated that then must be managed, analyzed and manipulated by skilled professionals. The compilation of this large collection of data is collectively known as big data.
In other words, Big data is a collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications. Big data is data that exceeds the processing capacity of conventional database systems. The data is too big, moves too fast, or doesn’t fit the strictures of your database architectures. To gain value from this data, you must choose an alternative way to process it. The value of big data to an organization falls into two categories: analytical use, and enabling new products. Big data analytics can reveal insights hidden previously by data too costly to process, such as peer influence among customers, revealed by analyzing shoppers‟ transactions, social and geographical data. Being able to process every item of data in reasonable time removes the troublesome need for sampling and promotes an investigative approach to data, in contrast to the somewhat static nature of running predetermined reports.
So, how big is big data?
Most professionals in the industry consider multiple terabytes or petabytes to be the current big data benchmark. Others, however, are hesitant to commit to a specific quantity, as the rapid pace of technological development may render today’s concept of big as tomorrow’s normal. Still others will define big data relative to its context. In other words, big data is a subjective label attached to situations in which human and technical infrastructures are unable to keep pace with a company’s data needs.
Understanding the Big Picture of Big Data
To gain a better perspective on how much data is being generated and managed by big data systems, consider the following noteworthy facts:
Big Data is Only Getting Bigger
- Growth in the Big Data Market — In 2010 -$3.2 billion ; In 2015 – $16.9 Billion
- 2.2 million terabytes of new data is created every day
- Compound Annual Growth Rate – 40% Big Data Market ; 5.7% Overall Technology Market (Information & Communications)
- According to IBM, users create 2.5 quintillion bytes of data every day. In practical terms, this means that 90% of the data in the world today has been created in the last two years alone
- Walmart controls more than 1 million customer transactions every hour, which are then transferred into a database working with over 2.5 petabytes of information
- According to FICO, the credit card fraud system helps protect over two billion accounts all over the globe
- Facebook holds more than 45 billion photos in its user database, a number that is growing daily
- The human genome can be decoded in less than one week, a feat which originally took ten years to complete
The Analytics and Information Management services team at Supremus balance the constant need for managing information as a true asset along with the immediate need for reporting and decision support, which is critical to ensure that data-related services are relevant, reliable, secure, timely, and adaptable to changing business conditions. Supremus recognizes that for organizations to reap benefits from their data strategies, it’s also important to consider the organization’s larger Enterprise strategy.