Exploration and analysis. One type of analytics is exploration and analysis. This approach involves navigating historical data in a top-down, deductive manner. To start, an analyst needs to have an idea of what is causing a high-level trend or alert. In other words, the analyst must start with a hypothesis and deduce the cause by exploring the data with ad hoc query tools, OLAP tools, Excel, or SQL. Here, the burden is on the business analyst to sort through large volumes of data and find the needle in the haystack. This type of analytics has been around for a long time and constitutes the bulk of activity done by business analysts.
Prediction and optimization. Another type of analytics is prediction and optimization. Although the algorithms used to power these types of analyses have existed for decades, they have been implemented only by a small number of commercial organizations. Business users model historical data in a bottom-up, inductive manner. They apply data mining tools to create statistical models to identify patterns and trends in the data that can be used to predict the future and optimize business processes. Here, the process is inductive. Rather than starting with a hypothesis, you let the tools discover the trends, patterns, and outliers on your behalf. (However, in reality, it takes some knowledge of the business process and data to apply these tools with reliable accuracy.)
Big Data Analytics
There are two major trends causing organizations to rethink the way they approach doing analytics.
Big data. First, data volumes are exploding. More than a decade ago, Wayne Eckerson, director of TDWI Research, participated in the formation of the Data Warehouse Terabyte Club, which highlighted the few leading-edge organizations whose data warehouses had reached or exceeded a terabyte in size. Today, the notion of a terabyte club seems quaint, as many organizations have blasted through that threshold. In fact, he contends, it is now time to start a petabyte club, since a handful of companies, including Internet businesses, banks, and telecommunications companies, have publicly announced that their data warehouses will soon exceed a petabyte of data.
Deep analytics. Second, organizations want to perform “deep analytics” on these massive data warehouses. Deep analytics ranges from statistics — such as moving averages, correlations, and regressions — to complex functions such as graph analysis, market basket analysis, and tokenization. In addition, many organizations are embracing predictive analytics by using advanced machine learning algorithms, such as neural networks and decision trees, to anticipate behavior and events. Whereas in the past, organizations may have applied these types of analytics to a subset of data, today they want to analyze every transaction. The reason: profits.
For Internet companies, the goal is to gain insight into how people use their websites so they can enhance visitor experiences and provide advertisers with more granular targeted advertising. Telecommunications companies want to mine millions of call detail records to better predict customer churn and profitability. Retailers want to analyze detailed transactions to better understand customer shopping patterns, forecast demand, optimize merchandising, and increase the lift of their promotions.
In all cases, there is an opportunity to cut costs, increase revenues, and gain a competitive advantage. Few industries today are immune to the siren song of analyzing big data.
This slideshow features a basic set of guidelines from TDWI and Aster Data for implementing big data analytics.
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