✓ In data rich environments, look to see whether a large amount of natural variability exists in the data to identify the condition you are searching for. Additionally, deter-mine whether the data spans a long enough time period to contain the information you need.
✓ Use classification techniques to separate the data into classes belonging to the response variable, which is usu-ally a Yes or a No class.
✓ Using regression techniques is recommended when the response variable is numeric or continuous. Regression is best used when attempting to make a prediction rather than a classification.
✓ Grouping data is used as an alternative to predicting based on data. Grouping can be composed of two catego-ries: records and fields. Records are similar to rows in a database table while fields are similar to columns.
✓ In situations where data is grouped by records, a variety of tools including cluster analysis, association rules, and nearest neighbors is usually the best option.
Using the correct techniques identified in the preceding list helps the analyst apply the best predictive procedures to the problem being solved.
What to Look for in Predictive Analytics Tools
Quality predictive analytics tools provide a wide variety of predictive procedures and algorithms to support all the data characteristics that customers encounter. However, before purchasing any predictive analytics software package, have your software evaluators catalog the predictive procedures and algorithms supported by the software and make sure that they match their data characteristics and will indeed benefit your analysts.
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Developing a set of required criteria and nice-to-have’s is a wise approach when evaluating any product, including predic-tive analytics tools. You want any tool to be powerful enough to do the job required and easy enough to use effectively.
Providing predictive analytics horsepower
First, consider the processing capabilities of the analytics tool. If the tool can’t supply the necessary predictive analytics procedures and algorithms to perform the necessary tasks, the tool will fail to provide the results needed. One set of analytics tools that packs the punch needed by competitive companies is available from Alteryx (see Figure 4-2).
Preparation
TS ARIMA TS ETS TS Plot
TS Compare
Figure 4-2: Alteryx capabilities and tools for predictive analytics.
In Figure 4-2, you can see how some of the tools in Alteryx, like data understanding, time-consuming data preparation tasks, and predictive modeling techniques such as forecast-ing, clusterforecast-ing, and scorforecast-ing, aid the analyst in the predictive analytics cycle. Alteryx provides a prebuilt library, which it
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continually enhances, of powerful predictive analytics tools that leverage the power of the R programming language.
These tools are available for immediate use by analysts.
Predictive analytics tools, such as those in Alteryx, simplify the user’s navigation of the analytic cycle. Much of the dif-ficult work is automated and simplified by predefined pro-cedures and tools. Combining the analyst’s business and data knowledge with easy-to-access data sources, predefined procedures and tools, and graphical workflows, the path from preparation to prediction is streamlined and simplified.
Easy integration with key data sources
Data is what feeds the analytic processes, and any tool must have easy integration with the data sources required to answer critical business questions. Alteryx supports rapid data integration and enrichment from these sources:
✓ Structured data sources such as traditional databases and data warehouses already within the company
✓ Unstructured, fast-moving, and ever-changing Big Data sources such as social media input and the Cloud
✓ External third-party data included from vendors such as Experian and Dun & Bradstreet
A great deal of the analyst’s time is already spent on data identification and preparation, so any analytic tool must make the analyst’s job simpler and faster. Alteryx makes integration with multiple data sources fast and easy without the need for exhaustive work by IT professionals and data scientists.
Driven via a friendly and intuitive interface
A requirement of moving predictive analytics from dedicated IT professionals, data scientists, and statisticians is that the resulting predictive tool must be readily usable by users
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without these backgrounds. Specifically, the tool must be easy to use and intuitive so that analysts can drive the predictive analytics process quickly and efficiently from their desktops.
Alteryx tools bring the power of predictive analytics to analysts by:
✓ Simplifying and automating, or semi-automating key analytic processes
✓ Leveraging drag-and-drop workflows to create relation-ships, access data, and build processes
✓ Incorporating prebuilt predictive analytic procedures, libraries, and easy-to-use tools to provide immediate access to powerful analytic algorithms
✓ Supporting the easy customization of R developed tools
✓ Providing easy, graphically driven access to commonly used functions via an intuitive gallery interface
Users will not use a tool if they are intimidated by it, which is not the case with Alteryx’s tools.
Alteryx Analytics Deployed via Analytics Gallery
A predictive analytics tool that successfully combines powerful predictive analytics capabilities with an easy-to-use interface for the analyst is Alteryx Analytics deployed in the form of an Analytic Application via the Alteryx Analytics Gallery.
Being deployed via the Alteryx Analytics Gallery, the analyst can search for, create, and even securely share powerful analytic applications. Providing the analyst with a gallery of powerful analytic applications ensures that the company gets the benefits of analytic processing quickly and easily.
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As shown in Figure 4-3, users are greeted with a friendly gal-lery of applications that is easy to navigate.
Figure 4-3: Exploring the Alteryx Analytics Gallery.
From the gallery interface, analysts can easily access preex-isting analytic applications or develop and share their own analytic applications via the Alteryx Designer. Developing complex processes and workflows can be daunting for non-IT people, and long, complex processes are a hindrance to agility and efficiency. An analytic app removes all this complexity by making analytics available anytime and anywhere with just a few clicks.
A powerful feature of the Alteryx Analytics Gallery is the abil-ity for analysts to download and open analytic applications in the Alteryx Designer tool before they run the application. This allows the analyst to review and modify the application to fit the analyst’s specific requirements. Figure 4-4 provides details of the Predictive Inventory Analysis application available in the Alteryx Analytics Gallery.
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Figure 4-4: Examining details of the Predictive Inventory Analysis application.
A powerful feature of Alteryx Designer is the ability to open and modify the underlying workflow for applications. In Figure 4-5, the Predictive Inventory Analysis application is opened in Alteryx Designer for review and modification.
Figure 4-5: Opening predictive analytics applications with Alteryx Designer.
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As you can see in Figure 4-5, the graphical workflow is both functional and intuitive. Building a new complex workflow or updating the workflow for existing applications that previ-ously took a team of data scientists and statisticians many hours to create can now be done quickly by an analyst.
Alteryx Analytics deployed via the Analytics Gallery gives the analyst the ability to quickly and easily use predictive analytics applications to benefit the business.
To learn more about Alteryx predictive analytics, visit www.
alteryx.com/predictive-analytics.
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Chapter 5
Ten Things to Consider with Predictive Analytics
In This Chapter
▶ Being successful when implementing predictive analytics
▶ Ensuring the usability of predictive analytics technology
▶ Getting the most from predictive analytics in a real-world business environment
U
nderstanding the fundamentals of any technology is essential to getting the most out of that technology.Beyond understanding the fundamentals, knowing the tips, tricks, and special techniques of how to best use a technology will pay further dividends once you’ve implemented and put it in use.
This chapter looks at ways to maximize your predictive analytics investment and to ensure that the implementation within your business is highly successful.
Integrating Predictive Analytics with Big Data
Big Data is, well, big, and so is its impact on business. Big Data provides companies with a wealth of information, and its impact cannot be overstated. Companies realize the benefit of Big Data and are positioning their IT departments to take advantage of this emerging data paradigm.
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Companies need to include predictive analytics technologies whenever they develop Big Data programs to help maximize the value of these programs — and the time to include such analytics is at the start of the Big Data project, not at the end.
Embracing Social Media Input
Social media has taken society by storm and while specific sites come and go, social media will remain relevant and a large part of people’s lives. Various IT products already plug in to social media, but the overall social media analysis tech-nology is still relatively young.
When evaluating predictive analytics toolsets, you need to consider their ability to incorporate a wide range of social media input. If an analytic tool doesn’t support social media or is hardcoded to limited social media input, that tool is likely missing a big part of the data picture. Choose analytic tools that fully embrace social media and can add additional social media input as society’s social preferences evolve.
Accessing and Integrating Data from Multiple Sources
We’ve all heard the saying that “there are two sides to every story” but in data analysis, there are many more sides to the complete data picture. Data now comes from third-party data brokers, social media, technical devices such as inventory control tags, and of course traditional internal databases. All of these data sources are valuable and rel-evant to predictive analysis.
You need to be sure that any predictive analytics tool you use can easily access and integrate data from multiple data sources. Furthermore, the technology not only needs to access multiple data sources, but its integration also must be simple and practical for the business user. If data is not easily accessed, that data will often be ignored. Drag-and-drop visual workflows that allow the easy inclusion of data sources are good features to have in predictive analytics tools.
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Looking to the Cloud
Cloud computing is changing the way people use computers and will continue to do so for the foreseeable future. Cloud computing software offers companies greater flexibility and lower costs.
Predictive analytics tools should be built from the ground up to leverage cloud technology. While a predictive analysis tool may have an installation footprint on users’ workstations, integration into the cloud for data access and storage is a good indicator of the tool’s capabilities. When predictive ana-lytics tools have cloud access, they gain processing and data access capabilities not found in traditional computer architec-tures. When evaluating predictive analytic tools, evaluators should ensure that cloud capability is built into the tool as a core component.
Finding Customers through Spatial Analysis
No one catches fish in an empty lake so why open a business where there are no customers? Before a company opens in a new location, a great deal of market research goes into where customers live and shop and which competitors are in a specific area.
Smartphones and other mobile devices are routinely equipped with location tracking devices. Geotagging is the process of adding the users’ locations to pictures, Short Message Service (SMS) texts, social media posts, and Rich Site Summary (RSS) feeds. The result is that Big Data now contains location data about potential customers as they go about their normal lives, shop at stores, and comment about prod-ucts they like or don’t like.
Predictive analytics tools can tap into Big Data containing a treasure trove of data about where customers live, shop, what they like to buy, and what they don’t like. Leveraging this cus-tomer information allows for more accurate targeting of sales and marketing campaigns while reducing the risk of opening new business locations.
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Making Agility Your Strength
Computer technology can accomplish almost any impres-sive feat, but the downside is usually the time and resources necessary to complete the project. IT has long suffered a well-deserved reputation of being expensive and taking too long to meet the needs of businesses in a competitive environment.
The expense and long time frames have impacted every area of IT including traditional predictive analytics tools.
Fortunately, improvements in hardware and software processing capabilities, cloud computing architecture, the emergence of Big Data, and more flexible software design have allowed predictive analytics to move from slow and expen-sive to agile and obtainable for the common business user.
Companies that shed their old predictive analytics tools and embrace the new generation of predictive analytics tools will experience a considerable advantage of speed and agility over their competitors.
Leveraging Your Business Analyst
Business owners often make the big decisions for a company, but the operational business and data analysts are usually the people who know what data is most important to the company. These people who are close to the data are the true experts for their particular business, and giving these people the best predictive analytics tools will yield great benefits for a company.
Providing the experts closest to the data with powerful tools to enable their creativity transforms them into business and data analysts. Business and data analysts are able to meld their deep knowledge of the business with Big Data via easy-to-use tools and explore new opportunities to benefit the business.
However, the key to this empowerment is that the predictive analytics tools must be practical and intuitive for non-IT users.
Tools, such as those provided by Alteryx, make predictive ana-lytics usable by analysts and ensure that the true data experts are getting the full benefit of predictive analytics.
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Enabling Decisions Anytime, Anywhere
Good ideas and business opportunities don’t always come during normal business hours, and golden opportunities are often fleeting. Businesses that empower their people with agile technologies to quickly explore new opportunities and take advantage of market conditions will have a powerful advantage over their competitors.
Predictive analytics tools that are deployed via the cloud and enable analysts to get the right answers to business questions in just a few mouse clicks are what companies need in order to be competitive in a continually changing market.
Tools such as Alteryx Analytics are cloud-enabled, access Big Data quickly, and are easy to use for non-IT people. These key technological features ensure powerful predictive analytics technology is available any time a potential business opportu-nity presents itself.
Seeing What’s Behind the Curtain
A key driver of modern predictive analytics technology is simplicity — so that non-statistical savvy business users can utilize it. For the majority of situations, the easy-to-use screens, wizards, and graphical interfaces meet the user’s needs.
However, in some cases a greater degree of control beyond the easy-to-use features is required by power users.
Unfortunately, many modern tools lock the user out of advanced settings and programming options because the underlying code is proprietary. This results in the users not being able to do what they need to do with their tools.
Fortunately, Alteryx predictive analytics is built on the open source programming language R (refer to Chapter 2). The impact of using the R language is that more technically-savvy power users can write their own R program code and import
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that code into Alteryx to meet their needs. Alternatively, they can edit existing Alteryx tools (which are written in R) to customize the tool for their particular business need. The capability to update and supplement the R language used by Alteryx means that typical business users still enjoy an easy-to-use toolset while more advanced power users are not constrained.
Making Big Data More Manageable
Big Data by definition is large, quickly evolving, and can often become overwhelming. The unstructured nature of many Big Data input feeds (such as social media posts) makes harness-ing the data extremely challengharness-ing for a database professional attempting to use traditional tools designed for structured data. For a nontechnical business user, attempts to divine useful information from unstructured Big Data in its raw format without appropriate tools would likely end in frustra-tion and failure for the user and the data being ignored.
Modern predictive analytics tools are designed to streamline the relevant information from Big Data into a format that ana-lysts can easily use. Tools such as those provided by Alteryx remove the complexity with Big Data and provide the analysts feeds into Big Data with minimal effort. You don’t have to depend on having a team of data scientists to capture, format, and cleanse the data before giving it to the analyst; Alteryx automatically does the complex Big Data work for the user.