Next Generation Approach Powers Better, Faster, More Cost-Effective Analytics
You're probably familiar with the old saying that "it's not what you know, it's who you know." That may have been true back in the days when conversations about competitive advantage concerned memberships at prestigious golf clubs and lavish expense accounts; in the days when data was regarded as mere records of transactions, production and inventory levels.
Today's conversations about competitive advantage may still include talk of personal relationships. More frequently, though, these conversations reflect the relatively recent appreciation of the intrinsic value of enterprise data - a value seen not just by senior executives, but also by employees in virtually every department. There is broad consensus in most organizations that enterprise data, and perhaps more importantly, the ability to analyze large volumes or smaller subsets of data at will, in real time, are crucial business differentiators.
Analytics - A Critical Competency
To be fair and accurate, capturing, studying and teasing actionable information and insights from enterprise data are not by any means new areas of business activity. Senior managers and dedicated data analysts have been doing this for years. The appearance of spreadsheet programs propelled a great leap forward in enabling their efforts. Subsequent technological advances, including data warehousing, data mining and the development of user-friendly tools allowing individuals to query data stores and generate their own reports without the need for IT mediators further enhanced organizations' analytic capabilities.
Challenges remain, however, in the effort to fully exploit enterprise data by democratizing access to enterprise data, and providing ever-growing and more diverse user groups across organizations with the ability to analyze the Niagaras of corporate information in a myriad of ways.
If there is a single must-have competency on which business success - indeed survival - depends today, it is this enterprise-wide ability to access and thoroughly analyze the enormous volumes of data businesses have gathered and continue to amass on a daily basis.
Unearthing Buried Treasure
It is now pretty much an article of faith for companies that effectively use analytics are able to outperform their peers and improve processes such as customer targeting and retention, product development, pricing, risk assessment and management, marketing and sales. The more advanced your analytical capabilities are, the better equipped you'll be to craft and fine-tune effective business strategies and make operational decisions on the fly with confidence that those decisions will increase productivity, efficiency, revenue and profitability, and enhance brand value.
The good news is that in most organizations there is no shortage of data to analyze. Organizations often have more data than they're even aware of. And more data floods in every day. The challenge is to find the most effective ways to process and collect that data, ensure its accuracy, store it in an affordable manner and increase the speed and flexibility with which end-users can interrogate it from multiple perspectives to uncover the treasure trove of insights it contains.
The Road to Predictive Analytics
Data warehousing applications have clearly been a boon to organizations as they strive to compete in today's ultra-competitive business environment. The introduction of the column-based analytics server, designed from the ground up for the unique rigors of analytics and optimized for analytic business intelligence, data warehousing and reporting, has been particularly empowering and beneficial.
Still, as enterprise data volumes have exploded from megabytes to gigabytes, terabytes and petabytes, and the pace of business has accelerated to the point at which in some industries, a performance gain of nanoseconds can make huge differences in business results, challenges remain.
Until now, enterprise information explorers have been stymied as they've tried to optimize the value of the data available to them. This has become particularly frustrating as the emphasis on predictive analytics has emerged and intensified. Predictive analysis requires the ability to process very large volumes of data very quickly and thoroughly. Data volume, accuracy and processing time represent the critical success factors underpinning predictive analysis.
The conventional wisdom, however, based on the limitations of traditional analytics approaches, has been that while this is true, you can only pick two of the three; achieving all three has simply not been an option.
That barrier to truly effective and optimal predictive analysis has now been broken.
Enter In-Database Analytics
As companies have used their favorite business intelligence tools to query enterprise data stores over the years, they've had no reason to think about what was happening behind the scenes in order to return their answers or to generate their reports.
The process has been transparent. Users have been shielded from the fact that traditional approaches to data analysis required the movement of data out of the database for analytical processing. These information explorers have not had to contend with the fact that the conventional approach to analytics has imposed performance constraints as data was moved out of the database and into a separate analytics environment. Nor have they had to consider the security issues inherent in moving data from one environment to another.
As the pace of business has accelerated, however, and the volumes of data to be analyzed have soared, users have begun to experience the challenge of simultaneously achieving the three critical success factors for predictive analysis. This has at times forced them to settle for one or two of these factors. For voracious information consumers, this sort of compromising has not been a welcome experience.
The emergence of in-database analytics eliminates the need to settle.
Greater Speed, Accuracy and Cost-Effectiveness
An in-database analytics approach is much faster, more efficient, and more secure than traditional analytics approaches. In-database analytics delivers immediate performance, scalability and security improvements because data never leaves the database until results are filtered and processed.
Eliminating the movement of data is accomplished by embedding analytical functionality directly into the database. For instance, in one example of an in-database analytics offering, an extensive library of numerical and analytical functions, ANSI SQL OLAP extensions, and new libraries of pluggable analytical algorithms have been embedded into a columnar analytics database.
Using this in-database analytics capability, organizations can now make business decisions that were not previously possible. By running predictive analytics business logic directly ina database users can perform critical drill-downs and deep analyses that were previously impossible or impractical. Businesses can also do so faster, more accurately and more cost-effectively than ever before in even the most data-intensive environments.
The Bottom Line?
With the ability to run real-time analytics on years of organizational data directly in the database, users across a broad range of industries can make better, more timely business predictions and decisions. Compromising on the three critical success factors for advanced analytics (data volume, processing time and accuracy) is no longer an issue.
This means that telecom organizations can conduct more thorough and meticulous churn analysis and create optimal pricing programs. Insurance companies can run more effective exposure assessments, improve fraud detection and develop targeted customer retention programs. Banks can enhance their ongoing portfolio analysis activities, better mitigate risk and increase individual customer profitability. Marketers in virtually every industry can conduct real-time campaign analysis, enabling them to tweak or abandon campaigns in order to maximize their marketing spend.
From C-level executives to line of business managers and frontline employees in sales, service, finance, marketing and other disciplines, leveraging in-database analytics will improve performance, productivity, innovation and overall competitive fitness.
No, it's not the technology equivalent of an all-powerful magical potion or some mythical crystal ball. But it sure does offer powerful and compelling analytics capabilities that enable organizations to out-think and out-perform the competition. And these days when it's what you know that powers success, you can ignore this capability at your own peril.
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