Making Predictive Analytics More Practical With Alteryx. White Paper

Transcription

1 Making Predictive Analytics More Practical With Alteryx White Paper

2 Data from industry research firm, The Data Warehousing Institute (TDWI), reinforces the competitive imperative for predictive analytics. In a recent report, Prediction has the highest business value among the four major categories of BI technologies Reporting, Analysis, Modeling, and Prediction. Unfortunately, Prediction was also noted to have the highest associated complexity. Introduction Businesses today are in a conundrum. While the current economic climate has made organizations hesitant to take a risk on a strategic investment that could be a bad bet, the pace of business and the competitive marketplace dictate that organizations move quickly to take advantage of opportunities that could create a huge revenue windfall and create a significant competitive advantage. Unfortunately, although the vast majority of organizations have a deep visibility into the past, thanks to traditional Business Intelligence (BI) tools that analyze the historical performance of the business, many still depend on intuition or simple optimism to better understand the present and future, forcing them to fly blind when mapping their company s future strategy. Why? There are two primary reasons: First, traditional BI tools and platforms do not provide forward-looking insight, rendering them useless in anticipating future performance. While they are able to deliver a wide range of detailed reports, sophisticated dashboards, and complex visualizations, all are based on historical information, leaving organizations stuck using guesswork, gut feel, or simple spreadsheets to anticipate the future, thereby ignoring the tremendous potential of their information assets that could give them predictive insight for competitive advantage. Second, most of the predictive analytical tools on the market today are complex, time-consuming, and expensive to use. Requiring multiple different technology systems and highly trained, specialized personnel to get from business question to predictive answer, companies that use the predictive analytical tools available today often find that the business opportunity is already in their rear-view mirror by several weeks or months before they have an answer. Clearly, this is unacceptable in today s competitive reality. What today s cutting-edge businesses need is a powerful yet easy to use predictive analytics platform that enables them to gain significant business value from predicting future business performance quickly and inexpensively using forward-looking insight rather than historical data. This paper will examine today s business reality of information overload, the hurdles organizations must overcome using today s complex, expensive, and time-consuming predictive analytical tools, and the new approach to agile predictive analytics offered by Alteryx. Panning for Gold in a Flowing Torrent of Information While organizations typically glean valuable information from Customer Relationship Management (CRM), Point of Sale (POS), and Enterprise Resource Planning (ERP) systems to make critical business decisions, new data sources are surpassing these conventional sources not just in volume but also in complexity. Today s device revolution and increasingly connected society including electronic sensors, Radio Frequency ID (RFID) tags, wireless smart devices, set-top boxes, and social media tools, to name just a few are generating extremely valuable new types of data at a rate never before seen. Recent estimates by IBM state that 2.5 quintillion bytes of data are generated each day. What s more, 90 percent of the data in the world today is less than two years old. 1 And not all this data is stored in a well-structured format and integrated into one central, relational database-based data warehouse. Rather, these new types of data, often called Big Data, are unstructured and must be stored in new data stores, such as Hadoop and Cassandra. 1 IBM Corporation Research, 2

3 Those that can do advanced analytics on top of Big Data will grow 20 percent more than their peers. The explosion of data volume, as well as its variety and velocity, will enable new, high-value advanced analytic use cases that drive growth and productivity. Rita Sallam, Research VP, Gartner, April 2012 Organizations that can harness this new torrent of Big Data have the opportunity to gain insight, drive competitive advantage, and achieve better business results by more thoroughly understanding their customers, their supply chain, and their competition. Unifying access to this data using sophisticated modeling and predictive analytics is critical to delivering on its business value. The Current Predictive Analytics Process Has Hit the Wall Unfortunately, it s not so easy for organizations to harness the value of Big Data for a competitive advantage. Why? Because the process required by today s predictive analytical tools to uncover the most reliable business insight is too complex, too time-consuming, and too expensive, requiring multiple people and handoffs, to provide organizations a solid ROI and actionable answers in a meaningful timeframe. Let s examine the typical approach used by organizations seeking to move from business question to predictive insight. Consider a business user who wants to identify the right location for a new store and predict first-year results for that store, as well as understand cannibalization from other nearby stores. First, the user must identify the data available to support the analysis. This could include data from POS systems at existing stores, loyalty cards, market share, and market sizing data, and demographic information about the nearby community. The traditional Big Data and predictive workflow is slow and inflexible Once the business user identifies the source data, he or she must work with an expert from the IT team who can determine how to access and integrate the data from those different data sources. Because the data will likely have gaps or errors (for example, loyalty card data with empty or invalid customer ZIP codes), the IT employee must also figure out how to cleanse the incoming data and model the data to provide adequate response times for users. But the IT person is not the only expert that must be involved. Advanced analytic techniques have been the near-exclusive domain of highly educated data scientists and statisticians who determine the right techniques and algorithms that must be used in order to validate both the incoming data and the outputs and to adjust analytical models to ensure he highest level of confidence in the model s prediction. Once both the IT employee and the data scientist have done their work, which can take several months to complete, the original business user finally has a usable data infrastructure and a reliable predictive model. He or she can now dig deeper into historical performance in other regions and at other stores, and adjust some of the inputs to the model to see how results would change if the local population grows more quickly than anticipated, or if marketing efforts yield higher-than-average returns. And because that business user is usually in an advisory role and unable to make the final decision without input and approval from others, he or she must determine how to present and distribute the information so that others can understand the situation and make a final decision. 3

4 From the heavy lifting of accessing and integrating disparate data sources to the rocket science of using advanced techniques to predict future business outcomes, today s predictive analytics process relies on multiple different types of expertise, from identifying and properly assembling the right historical information to properly applying advanced statistical and analytical techniques. Furthermore, it often requires multiple discrete software applications, each with its own hardware and software requirements, licensing costs, and user learning curve. At this point, let s suppose the business user realizes that he or she might improve the quality of the analysis by incorporating other data. For example, suppose he or she decides that looking at Foursquare Check-ins at the company s established stores and those of its competitors might provide additional insight relative to predicting first-year results for the new store. Here is where the process becomes even more time-consuming and complex: the analyst must go back to the data infrastructure expert in IT in order to integrate the new data and then approach the data scientist to make the associated adjustments to the predictive model. And that just describes the people-side complexity. On the technology front, the analytical process can require multiple tools, including Extract, Transform, and Load (ETL) tools to prepare and cleanse the data, data modeling tools to create the target database schema and model, and expensive, high-end analytical packages from companies such as SAS Institute or IBM to conduct the actual analysis. What s more, additional data analysis, presentation, and distribution might require yet another suite of BI and reporting tools. Of course, each of those tools brings its own licensing and maintenance fees, dedicated hardware, integration points, and independent upgrade cycles. These discrete environments not only complicate the process, but also they drive up costs significantly. Until now, organizations have wrestled with the technical complexity, succumbed to the constant complicated handoffs, and absorbed the high costs of predictive analytical tools because of the tremendous business value and risk reduction associated with high-quality predictive analytics. But today s economic reality means that organizations need a new approach that eliminates the complexity, reduces the time-consuming handoffs, and brings down the cost of predictive analytics and enables organizations to move from business question to predictive insight more quickly and efficiently. A New Approach to Predictive Analytics: Alteryx Alteryx has developed a new approach to predictive analytics that avoids the technical cost and complexity of the traditional approach, while obviating the need for highly specialized user skills and multiple handoffs throughout the predictive analytics process. This new, agile approach to predictive analytics enables organizations to save time and money while making faster business decisions in today s highly competitive market. The Rise of the Data Analyst Amidst the changes in the current business and technical environment, data analysts are dramatically changing the analytics landscape using a hybrid combination of traditionally discrete skills, including: Understanding and accessing data Creating powerful analytic workflows and applying sophisticated analytic techniques, and Presenting and delivering this information in a way that s immediately understandable to other business users Alteryx represents the workbench of the data analyst, providing an accessible, powerful, and integrated environment that delivers the capabilities to help move from data to insight to action. 4

5 Predictive analytics is the next major trend in business intelligence, according to industry research firm, Gartner. 2 Empowered with Alteryx, today s data analysts can rapidly create powerful, self-service applications in a single unified environment that supports the entire predictive analytic process, from initial data access to visualization and distribution. Using Alteryx, data analysts can access and integrate data from multiple sources, including databases and data warehouses, spreadsheets, cloud-based applications, social media streams, and more. What s more, they can cleanse the data, create analytic workflows, and apply powerful predictive analytic techniques for deep, forward-looking business insight. Finally, with Alteryx, data analysts can visualize and share the information as well as the entire application with other stakeholders, all using a single integrated environment, without requiring input or assistance from IT or data science experts. Alteryx enables organizations to integrate virtually all of the critical data sources required to make effective, strategic, forward-looking business decisions today, including: Social media data High-velocity, high-volume, high-variability data Third-party demographic data Mine Social Media Data for True Business Insight Social media channels, such as Twitter, Facebook, Yelp, Foursquare, and LinkedIn, provide a wealth of valuable information for forward-looking organizations, not only to better understand the past but also to predict the future. For example, social media analytics company Brandwatch recently showed how Twitter activity could be used to predict vote totals in Presidential Primary elections 3 and to do so much faster than traditional pre-election polling. The study shows the potential for organizations to use tools like Twitter to get a near real-time view of consumer sentiments and use it to enrich analytical models and predict business outcomes. Alteryx delivers Big Data and predictive analytics in a single, flexible workflow Integrating social media information also opens the door for organizations to understand the economic impact and value of social media activities, such as a Facebook Like. But doing so requires a holistic rather than a siloed approach to social media analytics. In isolation, social media can give an organization a feel for consumer sentiments and let it track the size of its social media footprint, However, it s not possible to measure how that social media activity impacts sales and customer loyalty or purchasing patterns unless that data is integrated into and compared with product usage, retail outlet, loyalty card and other information. 2 IT Business Edge, Gartner Taps Predictive Analytics as the Next Big Business Intelligence Trend, April 17, 2012, 3 Brandwatch, Using Twitter To Predict Politics, January 12, 2012, using-twitter-to-predict-politics/ 5

6 Analysts spend a significant amount of time, over two-thirds (69%), in data-related tasks in the analytics process, compared to the analytic ones where their time should be spent. Mark Smith, CEO and Chief Research Officer, Ventana Research By making it easy to mine social media data for business insight, Alteryx enables organizations to understand what is really happening in the business and make better, more informed business decisions based on that information, evolving social media from a business distraction to an accessible, understandable, and strategic source of insight. With Alteryx, organizations can use social media data to out-maneuver competitors and claim prime locations, optimize local merchandising strategies, and anticipate competitive moves. Integrate Three-V Data for Real-Time Analysis Social media is only one example of the rise of non-traditional data sources categorized as Big Data. High-volume, high-velocity, and high-variability data generated by electronic sensors, RFID systems, web servers, and cloudbased applications are also becoming critical sources of intelligence for organizations. However, this data is often unstructured or semi-structured, and cannot be easily modeled or stored in traditional relational databases or data warehouses. To handle these new types of semi-structured and unstructured data, new data management platforms, including the Hadoop distributed processing framework as well as other NoSQL platforms, such as MongoDB and Cassandra, are rapidly gaining traction because of their innovative architectures and low acquisition costs made possible by open source development models. These new platforms also provide horizontal scalability, enabling organizations to add inexpensive commodity servers to provide additional capacity as analytic workloads or data volumes grow. In order to harness Big Data for deeper insight, Alteryx enables organizations to incorporate unstructured and semi-structured data into their analytics, move beyond conventional sampling techniques, and build more robust predictive models that more reliably predict business outcomes related to customer adoption, future sales, and market growth. By allowing data analysts to easily access and integrate semi-structured data sources, Alteryx dramatically lowers the hurdle to driving business insight from unconventional data sources. Plus, direct integration with popular Hadoop-based and NoSQL platforms enables Alteryx to easily exploit new data platforms while reducing IT costs. Include Third-Party Data For a Complete Picture In addition to Big Data, organizations are also increasingly pressured to take advantage of available third-party data sources that provide detailed information about a given market opportunity and customer base, including demographic data about consumer incomes, age, and education, Alteryx enables the integration of any data source for predictive analytics 6

7 as well as firmographic data about nearby businesses. Drive-time data has also become an important factor in understanding and modeling consumer behavior, since geographic distance alone won t account for traffic patterns, typical weather conditions, or the types of roads between a consumer s house and a restaurant or store. Integrating the right third- party data with predictive analytic capabilities allows organization to realize the maximum ROI from every internal data element. With Alteryx, much of this third-party data including consumer household, segmentation, demographics, population, firmographic, and spatial data is available with Alteryx Analytics. Organizations can now easily take advantage of these rich third-party data sources to better understand local markets, competition, and consumer behavior. Moving Predictive Analytics to the Front Lines While predictive analytics was the near-exclusive domain of high-end statisticians and data scientists as recently as a few years ago, an increasing number of organizations are now realizing the value of pushing predictive analytics to the front lines, to agents in the call center, clerks in the stores, and technicians in the field. The benefit? A customer service agent handling a call could see the customer s future value, as well as her likelihood to churn while the interaction is transpiring, enabling the agent to take appropriate action instantly to retain the customer. At the same time, a more user-friendly generation of predictive and statistical tools, based on open source technologies, has gained popularity both inside and outside higher education. Companies that benefited from college graduates having at least some familiarity with predictive analytics and statistical tools, like SAS and IBM SPSS, are now seeing individuals with a background in a new generation of tools, such as the R analytical language. In fact, recent studies predict that SAS and IBM SPSS could virtually disappear from academia by 2015 if current trends continue. 4 With each passing year, more and more graduates will enter the workforce with a solid understanding of R and the predictive algorithms and techniques that R makes possible. By leveraging the R language at the core of its predictive engine, Alteryx provides a wealth of analytical power that is both approachable and familiar to the incoming information workers of today and tomorrow. Alteryx puts powerful predictive analysis into the hands of decision makers 4 R4Stats.com, Will 2015 Be the Beginning of the End for SAS and SPSS?, May 9, 2012, com/2012/05/09/beginning-of-the-end/ 7

8 Rather than requiring deep knowledge of the underlying statistical techniques and forcing users to understand and create 4GL code, Alteryx provides a graphical drag-and-drop environment where business users can easily integrate popular statistical techniques with just a few clicks. This approach makes powerful predictive analytics far more accessible to a broader set of business users. About Alteryx Alteryx is the leader in data blending and advanced analytics software. Alteryx Analytics provides analysts with an intuitive workflow for data blending and advanced analytics that leads to deeper insights in hours, not the weeks typical of traditional approaches. Analysts love the Alteryx analytics platform because they can deliver deeper insights by seamlessly blending internal, third party, and cloud data, and then analyze it using spatial and predictive drag-anddrop tools. This is all done in a single workflow, with no programming required. Thousands of customers, including Experian, Kaiser, Ford, and McDonald s, and 200,000 + users worldwide rely on Alteryx daily. Visit or call Alteryx incorporates R directly into the data and analytic workflow, allowing both easy access to R s capabilities as well as the ability to import existing models or analysis created using R, thus extending the value of an organization s existing investments in R. Finally, the integration of R s predictive analytics into the Alteryx platform also allows rapid, large-scale data access, the ability to introduce additional analytical capabilities, such as spatial analytics on top of predictive models, and the ability to easily move from a desktop model to an analytical application deployed in the cloud without needing to involve IT. In Alteryx, R s analytical power is dramatically more accessible to business users than legacy predictive tools, enabling front-line employees to reap the benefits of self-service predictive analytics. Conclusion It s long been said that change creates opportunity. Recent changes in the business, technical, and user landscape are creating tremendous opportunities for organizations to get a deeper understanding of their market and their customers and enabling them to make a radical, 180-degree change in how they make business decisions. Rather than rely on historical performance data to anticipate the future, which incorporates a lot of guesswork and gut feel analysis, companies can now harness the vast information value in social media, Big Data, and third-party data feeds to truly look forward to the future and out-maneuver the competition with accurate and agile predictive analytics. Armed with Alteryx Analytics, today s data analysts can transform their organizations, using all the data, platforms, and analytics they need to understand their business and anticipate future business outcomes. Because Alteryx makes predictive analytics accessible to a much broader base of business users than ever before possible, every employee in the organization from the executive offices to the front lines, and even those without deep analytics training can better understand their companies markets, customers, and competition. 230 Commerce, Ste. 250, Irvine, CA Alteryx is a registered trademark of Alteryx, Inc. 8/15