DRIVING COMPETITIVE ADVANTAGE BY
PREDICTING THE FUTURE
Competitive advantage is derived by an organization when it develops strategies, techniques, or
resources that allow it to outperform its competition. One such resource is the innovative use of
analytics to improve business decisions or operational processes. Decision makers can use analytics to
cultivate the data collected from day-to-day operations into key insights unavailable to the competition.
The field of predictive analytics is the next evolution in business intelligence — moving beyond the
practice of creating reports on past events, and towards the use of sophisticated statistical methods to
predict future outcomes. An organization equipped with the capability of scientifically predicting future
outcomes, while its rivals are merely looking at reports of past events and doing damage control, will
have significant informational and decision-making advantage over its competition.
This paper will introduce predictive analysis, contrast it with more traditional forms of business
intelligence, and then examine how embedding predictive analytics in existing business systems
provides operational efficiencies, cost reduction, and above all, exceptional competitive advantage.
INTRODUCING PREDICTIVE ANALYTICS
The difference between traditional business intelligence and
predictive analytics
From its earliest days of providing tools to generate static, tabular reports, business intelligence (BI) continues to evolve and provide businesses and organizations with the ability to convert growing stores of data into information. Organizations that invested in these products gained a wide range of capabilities, including data visualization; reports; the ability to “slice and dice” their data for information mining; analytic dashboards; and scorecards. These tools, when used effectively, can provide organizations insights that can improve business outcomes.
As advanced as they may be, all of these traditional BI techniques have one fundamental common theme: they aggregate data to report on past events. Armed with this information, business managers will use the other BI tools at their disposal to investigate what happened, and eventually, why it happened. It’s a bit like looking in the rear-view mirror of a car while attempting to drive the car forward.
Imagine if instead of reporting on past events, data could be used to anticipate business outcomes. This is the premise of predictive analytics.
Using predictive analytics, organizations have a new way to obtain real-time, data-driven insights about what the future may hold. They can leverage sophisticated statistical analysis techniques to mine their data, find what factors can impact their business, and build models that will simulate what will happen when certain conditions arise. These models can be used to conduct “what if” analyses and provide new insight so organizations can proactively manage their business objectives. Another important difference between predictive analytics and traditional BI is the specificity of the actions that the analysis can recommend. BI aggregates data together and reports on the resulting summary. On the other hand, pre-dictive analytics can focus on many variables, so that rather than guessing how the entire customer base might respond to a particular action, a company using predictive analytics can actually forecast an individual customer’s response to that action. For instance, businesses can optimize their revenue by studying the attributes of its customers in order to provide them customized information on products and services, enhancing future buying experiences based on their past behavior and current selections. This type of technology mines click-streams and then provides suggested items for online shoppers. Amazon and other major internet retailers use this technology to show items you may also be interested on various internet pages and sites.
Organizations using predictive analytics are no longer looking in their rear-view mirror to guide their operations; instead, they are gazing out the windshield, looking into the future, and taking proactive actions that will enhance their business outcomes.
Using predictive analytics
While the notion of building these sophisticated statistical models is exciting, it is not an entirely new concept. Predictive analytics, which uses relatively mature science, is experiencing a modern boon with the surge of e-commerce and the growing abundance of data. For example, leading insurance companies use these methods to enhance underwriting techniques, reduce risk from fraudulent claims, and increase marketing efficiency.
Similarly, many successful wireless telecommunication carriers also utilize predictive analytics. When purchasing a mobile phone, applicants are instantly screened to verify that they are a good credit risk.
New subscribers’ activities are monitored to see if they are exhibiting fraudulent inclinations, such as racking up a huge bill with no intention to pay. Customers particularly profitable to the company enjoy superior customer service and are offered specials designed to prevent them from transferring to the competition.
Leading businesses that rely on credit scoring, fraud detection, and churn analysis utilize several statistical modeling techniques. These companies learn which factors are indicative of future activity and apply those models to the actions of customers. Competitors that do not adopt these techniques can soon find themselves at a significant disadvantage, by not being able to identify less profitable customers, potential fraud, or other factors that negatively affect their business. More and more organizations within other industries are now looking to leverage their data predictively in order to intelligently manage their business. These organizations are finding that applicability of predictive analytics spans well beyond credit, fraud, and churn analysis, and can be used throughout many industries in a wide variety of business processes.
For example:
• Leveraging historical data, companies can predict how customers will react to changes in product offerings, such as whether customers will favorably or unfavorably receive certain product features or price changes. • A company using predictive analytics in its marketing systems can learn how to improve the response rates
and ROI for marketing campaigns by increasing effectiveness and decreasing cost.
• A supply chain that incorporates predictive analytics can forecast when retail demand for a particular product will increase or decrease based on various factors and can then adjust inventory accordingly — avoiding either overstock or back order situations.
Business managers can use predictive analytics to go beyond merely improving the efficiency of their current processes; they can create new opportunities or products based on the insight they gather from their data. Organizations that leverage and mine their data predictively have a significant competitive advantage over their rivals, as they can gain important insights and react quickly to expand their business in a way that was not possible without predictive analytics. Since just about any process that accumulates data is ripe for using that data to predict future outcomes, the key to successful implementation and use of predictive analytics is to identify processes that can benefit from this kind of analysis and provide an ROI.
The statistics of predictive analytics
Understanding the descriptive statistics that underlies simple reports is within the grasp of most business managers. Totals, averages, and percentages are not overly complicated concepts. But what about the inferential statistics required for predictive analytics?
In its simplest form, the statistics behind predictive analytics are
straightforward. For example, imagine creating a scatter plot of two variables, one on the x-axis and one on the y-axis. A line can be drawn that best fits this correlation and that line can be used to predict outcomes that aren’t specifically on this graph. This is the idea behind linear regression.
However, things can quickly get more complicated. Analyzing two variables isn’t nearly as interesting or complicated as looking at the relationships among
hundreds, or even thousands of variables. And finding the factors that are predictive is typically more difficult than our simple example. After all, in this complex world, the relationships in the data are often hard to find due to issues such as:
• Effects of seasonality, outliers, and other factors that create variability in the data • Small or sparse datasets that provide too little information
• Massive datasets that require significant computing resources for analysis
None of these issues are insurmountable, but the appropriate statistical method must be determined based on the data available and the analysis desired. A statistician can determine what methods — such as regression and time series analysis, or machine learning techniques such as neural networks or Naïve Bayes — are appropriate.
GETTING STARTED WITH PREDICTIVE ANALYTICS
Vendors that are on the leading edge of data analysis have started to provide their users with some ability to use predictive statistical techniques. Many BI firms now offer the capability to do this kind of statistical analysis on their customers’ data from within the context of their BI platforms. These tools provide an excellent avenue for organizations to go beyond reporting on past events and toward understanding how to construct predictive models.In many cases, organizations will make their first foray into predictive analytics by acquiring the services of a business analyst or consultant that is an expert in the use of statistical methods. The experts will then create these predictive models and analytic applications using the facilities of a preferred BI suite. Once the information is available, business managers can log into their BI tools to gain access to these capabilities.
What these suites do not provide is the appropriate user context to enable the most effective use of these insights. Once the predictive results are generated, that still leaves the question of how to tie this back to business objectives such as optimizing resources or minimizing cost, or both. The actionable results are still needed.
Both frontline workers and business managers typically use business applications other than BI software in their day-to-day activities. Depending on their work, they use business applications such as call center, inventory management, retail store management, or accounting software. So if the predictive analytics are only available from within the BI platforms, these workers are forced to leave their usual business systems and log into their BI applications to gain access to the insights revealed by the predictive models.
EMBEDDED PREDICTIVE ANALYTICS
Extending business systems by embedding predictive analytics
Imagine if the insights gained through predictive analytics could be delivered in the context of the business process — from within the very business systems that employees and managers use every day. In this context, the information would be far more actionable by providing frontline workers the opportunity to act immediately on these insights. Having predictive analytic capabilities embedded directly in the business systems, rather than in a separate BI tool, provides many benefits:
User efficiency
Users do not need to switch to another system to receive the insight from the analysis since it is provided directly from the systems they use every day. For example:
• From within the call center application, customer service representatives have information about what offers a customer will gravitate towards — and therefore can immediately suggest these options.
• From within store operations software, retail managers can be alerted to upcoming spikes or drops in demand, which allows adequate time to adjust store inventories.
Decision alert capabilities
The system can be extended to automatically alert then take action based on real-time analysis of the current business conditions. For example:
• Retailers with numerous outlets and a finely-tuned supply chain can not only predict upcoming spikes or drops in demand, but the decisions and inventory adjustments can be made directly within the ERP or inventory management system.
Operate on real-time data
BI suites often operate on data stores that are separate from the data contained directly in the business system. IT staff can move this data from the database that underlies the business system into a data warehouse, where the BI tools can later operate on them. If the analysis is embedded directly in the business system itself, the analytics can operate on the data as it is recorded — in realtime.
Reduction in total cost of ownership
There is no need to purchase and deploy separate BI tools for all users and the IT staff is not required to maintain the separate BI platform.
The greatest benefit of extending business systems with predictive analytics is the competitive advantage derived from providing a fact based understanding instead of speculative guess on key influences to the business. Organizations with embedded predictive analytics can take actions as conditions change, in effect proactively managing their business instead of doing damage control.
The technical underpinnings of embedded predictive analytics
In order to extend business systems with the benefit of predictive analytics, the developers performing these enhancements must have access to the requisite mathematical and statistical algorithms. While some may be tempted to create these functions themselves, these algorithms are complex, highly specialized, and difficult to develop and maintain. The development and maintenance can be fairly costly, which could impact the ROI of deploying predictive analytics.
Significant research and development time must be expended to codify the mathematical algorithm in software. As part of the development, a number of items must be taken into account:
• Performance must be optimized so that results are quickly returned • Computations need to be scalable across large datasets
• Option to be deployed in multiple environments, including clients, servers, databases, or embedded in applications deployed across the internet
• Significant testing must be undertaken to ensure proper execution and correct results
• Resources must be allotted to the maintenance and inevitably, the porting of the algorithm when the application is moved to a new hardware or software platform
In order to decrease the cost and risk of development and deployment of mathematical and statistical algorithms, organizations can choose to leverage previously tested and deployed libraries. There are commercially available libraries on the market, but ease of integration and deployment of these business systems are also factors when choosing the right software. Some important questions that decision makers and developers need to ask when selecting libraries include:
• Can the software do the analysis in the same location as the data, such as inside the database?
• Does the software require additional infrastructure to support the analytics, such as a separate server, bandwidth for additional network traffic if data synchronization is needed, or security for access and encryptions?
• How thoroughly are the algorithms tested? • Is the algorithm scalable?
• What type of support is offered? For open source, what support is available and what impact it could have if various contributors make changes to the algorithms?
• How does the performance of the algorithm compare to others? • Is the algorithm customizable?
All of this effort and inquiry is required for the development of just one algorithm. Typically, several statistical methods, as well as the mathematical algorithms supporting those methods, are required in order to achieve the desired results. As the data changes, other methods may better fit the problem or give a different perspective of the data, which would allow additional insight for businesses to leverage.
CONCLUSION
Embedded predictive analytics provides greater immediacy to the insights revealed by these predictive models. By embedding this capability into existing business systems, organizations can put these capabilities directly in the front lines of the business, rather than relegating them to a back office or headquarters function. By using embedded predictive analytics, organizations can realize a reduction in total cost of ownership, reduce development time and costs, and improve quality and maintainability, while putting more focus on their key competencies.
Three things are required to implement predictive analytics:
• Access to the right historical or real-time data that can be used to uncover predictive factors
• A solid understanding of the statistical techniques necessary to investigate the data and develop predictive models • Access to the necessary mathematic and statistical algorithms and infrastructure
Rogue Wave Software is ready to assist in extending existing business systems with embedded predictive analytics. Our products, such as IMSL Numerical Libraries, can provide the third item on the list above — the mathematical and statistical algorithms required to perform the analysis. Our professional service group can supply the second item on this list — the knowledge and know-how surrounding the use of the required statistical techniques. Rogue Wave’s team is staffed with statisticians and skilled technologists that can help in the understanding of what statistical methods are applicable to business processes, as well as help implement them in existing systems.
ABOUT IMSL NUMERICAL LIBRARIES
Rogue Wave Software specializes in the development of tools and embedded components that provide the mathematical and statistical infrastructure described in this paper. Rogue Wave’s IMSL Numerical Libraries are a robust and portable collection of embeddable mathematical and statistical functions available in native C, C++, C#, Fortran, and Java™, that provide sophisticated analytics for high performance, mission-critical applications. With IMSL, businesses and organizations reduce development time, realize a lower total cost of ownership, and improve the quality and maintainability of applications. Often used to create competitive differentiation in a wide variety of innovative solutions, IMSL has been the best-kept secret of industry leaders for over four decades.
IMSL Numerical Libraries are developed by highly-trained teams of statisticians and computer scientists and are designed to be:
• Embeddable – IMSL allows developers to embed algorithms directly into applications, whether for analysis inside the database or running pre- and post-processing of the data.
• Robust and reliable – IMSL provides superior error handling so users can easily identify errors when they occur. The decades of use by various industry verticals ensures that the libraries are extremely solid and that their reliability is unsurpassed.
• Scalable – IMSL is only limited by the size of the system on which it is installed.
• High-performing – IMSL leverages multiprocessor architectures and is capable of analyzing massive datasets.
• Portable – Written in light-weight native C, C#, Java, Fortran, and Python, IMSL guarantees the same results across platforms and environments, operating on systems from desktop computers to supercomputers.
• Expert support – Rogue Wave offers customers access to technical support specialists for assistance with installation, product functionality, and effective programming techniques.
Using Rogue Wave’s IMSL Numerical Libraries as the mathematical underpinnings of embedded predictive analytics will accelerate development time, reduce project costs, and deliver the flexible, high-quality, production-ready tools required to achieve the very best results. For more information about the IMSL Numerical Libraries, or to request assistance from our professional services group, please contact us.
