Big Data Analytics - Slide 7
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Know your users. First, understand who’s performing the analysis, the type, and how much data they require. If a power user wants to explore departmental data, then all they might need is an ad hoc query or OLAP tool and a data mart. If it’s an IT person creating a complex standard report with sophisticated metrics and functions, then it’s likely they can use a scalable BI tool running against an enterprise data warehouse. If a business analyst wants to run ad hoc queries or apply complex analytical functions against large volumes of detailed data without DBA assistance, then you probably need a specialized analytical database that supports analytical functions.
Performance and scalability. Second, understand your performance and scalability requirements. What query response times are required to make various types of analyses worth doing? If you have to wait days for a result set, then you either need to upgrade your existing data warehousing environment, offload these queries to a specialized analytical platform, or reduce the amount of data by aggregating data or reducing the time span of the analysis.
In-database analytics. Third, evaluate your need for in-database analytics. If complex models or analytics drive a critical portion of your business, then it’s likely you can benefit from creating and scoring these models in the DW rather than a secondary system.
Other. Finally, investigate whether the analytic database integrates with existing tools in your environment, such as ETL, scheduling, and BI tools. If you plan to use it as an enterprise data warehouse replacement, find out how well it supports mixed workloads, including tactical queries, strategic queries, and inserts, updates, and deletes.
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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