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The power within
In-database analytics initiatives spark savings while giving data an energy boost.
by Rob Berman
Even before the recent economic downturn, company leaders searched for ways to better perform vital, analysis-intensive tasks such as managing risk, predicting customer behavior, forecasting inventories, determining corporate strategy and combating fraud and money-laundering schemes.
As the economy tightens, it becomes even more important to have quick, easy access to actionable analytics to help with these kinds of tasks. Companies understand that fully leveraging their data—one of their most important assets—has gone from a goal to a must. It can mean the difference between a company’s sustainable success and growth, or its ultimate failure.
But all analytic methods and techniques are not created equal, nor do all modeling and analysis tools work equally well together. Companies looking to integrate and operationalize analytic capabilities in-house for the first time or to bolster their existing capabilities need a reliable roadmap, careful planning and access to industry best practices. Without these resources, some companies may encounter unforeseen expenses and extensive rework requirements as they struggle to integrate and use disparate tools and platforms.
A new class of analytics
In-database analytics is part of a new, richer class of analytic capabilities that can help companies spur growth and increase their competitive advantage. Companies can create and score models and perform detailed analysis on data without removing it from the data warehouse—saving time, reducing risk and vastly improving analytic results.
These capabilities are a significant leap forward from existing analytic techniques that can require massive data movement between the analytic environment and the database, often causing data replication and data latency issues. Not only do these other techniques fail to integrate IT and business analyst processes, but they also often require each group to create disparate, isolated processes. This disconnection adversely affects the company’s ability to easily leverage analytics across subject areas and lines of business.
The most robust in-database offerings include business intelligence (BI), data integration and analytic functions that work seamlessly with the data warehouse.
Cross-industry benefits
Organizations that are turning to an in-database analytic model span all business sectors, including financial, retail, telco, media, healthcare, government, manufacturing and transportation. While different industries or sub-segments may focus on varied business improvement opportunities, each can realize benefits by optimizing in-database analytic capabilities:
- Accelerated analytics. By virtually eliminating the manual data gathering, exploration and preparation steps of data analysis, company analysts and leaders can spend more time focusing on higher-value business opportunities.
- Increased efficiency. With reduced data movement and redundancy, many companies find that processing time can go from weeks to days, days to hours or even hours to seconds.
- Reduced costs. Better leveraging investments in existing platforms, software and processes means that more consistent, reliable and timely information is delivered to knowledge workers. The result is increased operational efficiency that yields faster time to value, greater return on investment (ROI) and reduced total cost of ownership for analytic solutions.
Many success stories support in-database analytics. A large specialty retailer traded a siloed information processing system that had latency, inconsistency and data redundancy issues for an integrated data warehouse and in-database analytics solution. As a result, the company’s statisticians spend about 25% of their time gathering data, rather than the 80% they used to spend. Now they can more productively manage the organization’s models to ensure accuracy, performance and usefulness for greater business insight. The company also increased its analytic output threefold, reducing the need to outsource and enabling it to expand its analytic practices to support other lines of business.
Likewise, when a credit card company realized its customers’ cash usage was dropping, it quickly analyzed cross-functional customer, transaction, merchant, authorization and other data to get immediate answers to questions, such as:
- Are we providing our new customers with the right information so they know how to get cash back?
- Which of our merchants are most successful at encouraging cash usage among customers?
In-database analysis enabled enhanced analytic results leading to better information for the company’s marketing campaigns and strategic plans.
Reduce your risk
Whether your company is new to indatabase analytics or is expanding its departmental capabilities to the enterprise, it’s important to find a partner that can help you ask the right questions, make the right choices and avoid costly errors.
A good partner should:
- Support a scalable and robust analytic platform that integrates best-in-class database and software offerings to meet current and long-term needs
- Provide tools, services and methodologies that accelerate analytic data model development and deployment so analysts can leverage accurate results, faster
- Deliver subject matter expert consulting and technical services support
- Offer integrated packages with flexible products and services that not only help you meet specific business goals and provide support for fully analytic cycle development and management but also match your company’s analytic maturity level
In a difficult economy, it’s especially important to correctly make real-time business decisions the first time. Addressing challenges by, for example, effectively halting money-laundering schemes, evaluating credit risk, or accurately forecasting demand for products or services is critical to your company’s success. Optimizing analytic platforms for in-database processing will arm you with data intelligence to take your enterprise decision-making capabilities to the next level.
Rob Berman, vice president of the global SAS program at Teradata, has been with the company since 1982. He and his team develop product and service solutions that integrate Teradata platforms with SAS software.
Photo illustration by Randall Nelson