Author Archives: TechnologyAdvice

Big Data, Business Intelligence And The Programming Language “R”

Big Data, Business Intelligence And The Programming Language “R”

Why Your Next Business Intelligence Hire Will Use R

As anyone working in the field knows, business intelligence is an evolving landscape. New technologies, combined with a rapidly expanding marketplace, are disrupting legacy solutions and established platforms.

Despite such growth, however, there have been a few constants, such as the R programming language. As other technologies gain popularity or fade away, R has steadily grown into a standard for data exploration. This will only continue with the latest wave of medium and small business BI adoption.

R’s longevity stems from its perks, many of which come from its history.

R was created to take advantage of superior parallel processing abilities in newer systems. Before this, SAS was the default data language. SAS was created before parallel processing was commonplace, however, so even though it now supports it, the functionality isn’t ingrained in the language. Because of this, SAS analysis functions fail to take full advantage of parallel processing power in the way that R does.

Pleasing Graphics

business

One of the most immediate benefits of R’s parallel processing functionality is the ability to create aesthetically pleasing graphics. With only a small amount of extra code and preparation, R allows you to create navigable graphics from data analysis results. The graphics are presentable enough to export and use in a report, or to quickly gain new insight into a data set.

Along with such tangible benefits, R is also receiving some much-deserved recognition. Companies that previously fought against the language, such as IBM and SAS, have since acknowledged the value of R and started implementing support for it in their software.

If such news comes as a surprise and you’re now considering the implications of R usage among major BI vendors, it should be noted that this doesn’t undermine the efforts of companies such as Revolution Analytics. Rather, such adoption should bolster providers of standardized R packages and modules in what is otherwise an unstructured open source environment.

That said, R adoption can lead to some issues. There’s a steep learning curve with R, which means retraining a SAS expert could be difficult. Companies looking to take advantage of the new R integration from IBM, SAS, or Revolution Analytics’ toolset might instead look for applicants already familiar with the language.

Fortunately, there are plenty of statisticians trained in R use, and they come in the form of recent graduates. Being open source, R is ideal for educational use in colleges and scientific research. Many undergraduates and post-graduates leave college with sufficient R experience to join your company and begin BI work. This is made even more convenient by the fact that many R capabilities are open source (aka free) and thus can be downloaded and installed in a day.

With adoption of R by a wider audience (including 6 of the largest enterprise software vendors), it’s time for companies to look harder at what the language can do for them and their own evolving business intelligence efforts.

By Keith Cawley

How To Find Data As A Small Business

How To Find Data As A Small Business

Small Business – Big Data

You’ve probably heard the buzz about the benefits of business intelligence (BI) technology for small to medium sized businesses. But to use BI, you have to have data. Fortunately, you don’t need that much.

big-data-bi

Sure, “big data” is a hot topic for enterprises, but, unless you count product sales in the hundreds of millions, “small data” is often plenty. It’s not like having fewer data points means your data isn’t informative. Value comes from connecting multiple data sets, of any size, to discover correlations.

In reality, you likely have at least one of the following four things in your business model, and any of them will suffice as a starting point for insightful data analytics.

Website
Web data comes in the form of visitor counts, location information, visit dates and times, bounce rates, mobile phone or desktop, click behavior, and more.

Point-of-sale (POS) system
POS systems record purchase quantities, purchase times and dates, groupings (peanut butter with jelly), use of discounts and coupons, etc.

Customer Relationship Management (CRM) software
CRM software not only keeps all your customer information and in some cases your interaction with those customers, but are also capable of importing external data sets of potential customers in your market.

Foot traffic
If you have a storefront, people walk into your store. And if people walk into your store and don’t buy anything, then any data you could have collected from them is lost, unless you have a “people counting” tool. These come in the form of cameras or sensors and can track how many people enter and exit your facility, whether they come in groups or individually, and can even be set up to follow a customer’s path through the store.

Shop-Tech

Comparing and combing different data sets often reveals unexpected patterns in customer behavior. One example of such comes from the Point Defiance Zoo & Aquarium, which was trying to increase their ticket sales and market efforts. After implementing a business intelligence tool to track online sales numbers, they discovered 32 percent of their ticket sales occurred between 8pm and 4am. Using this information, the Zoo was able to target specific demographics, and tailor their offers in order to increase sales.

Here’s a few tips to help you get started on leveraging your own company’s data resources.

Start Tracking Website Analytics
If you don’t have a website, free do-it-yourself tools such as Weebly and Squarespace are easy enough for anyone to set up. These platforms often also set up web analytics for you automatically, but Google Analytics is also a great option for tracking your site activity.

Collect All the Data You Can
Larger organizations can naturally be more hands on, but there are plenty of passive forms of data collection. For starters, try accessing public data sets (data.gov, sba.gov, etc.) to find broader market trends that you can compare to any internal data you collect. Examine software you already use (POS, CRM, ERP, etc.) for data you can extract. You can also experiment with online questionnaires for voluntary customer opinions (aka data). A variety of easy — and free — options abound for surveying customers, including Formstack and SurveyMonkey.

Organize Your Data
Every person, department, or company will need to experiment in this area to find out what works best for them and their analytics goals. Research what BI software others in your industry are using, then test out different options. If you aren’t ready for a full-service BI platform, it’s usually best to input data in Excel or a rival spreadsheet application to keep your data consistent and organized. This will also allow you to quickly important your data into BI software later as your company matures.

Discipline in managing execution and expectations is necessary in the early phases of data strategy, especially when it comes to ensuring any strategy aligns with your key performance indicators and business goals. It’s important for a company to monitor how much time data-related tasks take for those involved, leading to a potential cost/benefit analysis to make sure the strategy is delivering desired results based on its time and expense. Long term patterns often offer the best actionable insight, so results can’t be expected overnight.

However, making an investment and formulating a strategy around data analytics can lead to more informed and targeted decisions for your business overall.

(Image Source: Shutterstock)

By Keith Cawley

The Business Benefits of Cloud CRM

The Business Benefits of Cloud CRM

The Business Benefits of Cloud CRM

From software deployment, to mobility, to wearable technology, cloud computing has transformed seemingly every aspect of modern business. Research indicates cloud applications and platforms will be at the forefront of IT spending by 2016, with half of all enterprises implementing cloud solutions by 2017.

One of the quickest markets to connect with the cloud is customer relationship management (CRM) software. In fact, cloud purchases are set to represent 50 percent of all CRM deployments this year.

The advantages of traditional CRM software are well known: tracking sales interactions and identifying patterns can increase profits, and lead to a better, more personalized experience for the customer. But combine it with the advantages of cloud computing and the business benefits are substantial. Let’s take a look at some of the ways this duo is making a positive impact on business technology.

startups

Lowered Costs

One of the most widely touted benefits of cloud computing is lowered upfront costs. SaaS solutions are subscription-based, and usually charge per month. This eliminates the need to purchase licenses and hardware, and can decrease maintenance costs. The vendor takes care of software updates, which are included in your monthly subscription. Additionally, a cloud-based CRM requires no local installation, so it can typically be up and running in a matter of hours.

Improved Productivity

In addition to reducing costs, cloud CRM can lead to increased employee productivity, specifically among sales teams working remotely. Since the software and data are hosted in the cloud, workers can access the CRM outside the office. Meaning sales and marketing teams can work anywhere, anytime. Cloud mobility also facilitates virtual workforces and multinational teams, providing a scalable solution for growing businesses.

Cloud CRM software can also improve productivity through collaboration and project management features. When you compare CRM software, you’ll quickly find that some solutions offer more functionality than others. While not every business will need a CRM with multiple add-ons, companies with mobile employees can benefit from being able to instantly communicate, manage tasks, receive notifications, and share files without a local network infrastructure.

Advanced Data and Analytics

Many businesses are turning to cloud CRM for business intelligence. On-premise solutions generally require manual uploading, syncing, and backup, whereas cloud solutions sync in real time. Cloud CRM provides an up-to-date picture of customer data, sales pipelines, invoices, and email.

Having access to unified company information allows businesses to organize data and gain accurate insights about inventory, customer leads, and profitability. Cloud-based analytics can integrate information from multiple sources, then use data visualization or predictive modeling to support better, faster decisions. Speed to insight, as well as data sharing and collaboration is a critical benefit of cloud CRM.

Employee Buy-In

employee-buy-in

As businesses shift to cloud deployment, SaaS solutions are starting to focus on simplicity. Although vendors are adding smarter tools to their CRM solutions in order to appeal to a wider audience, usability is becoming more important than ever.

If SaaS software isn’t easy to use, people won’t continue to subscribe to it month after month. An intuitive interface and effective dashboard help keep most solutions user-friendly.

Furthermore, customer retention is the key component to a SaaS vendor’s long term success. A subscription-based business model means that purchasing software isn’t a one time deal. Cloud providers are more concerned than ever about turning businesses into long term subscribers.

This means that getting their customers onboarded smoothly is their number one priority. Many cloud providers offer free trials, training and learning materials, and ongoing customer support. These resources not only help your company get started, but ensure your employees are well-trained and using the CRM properly.

Cloud CRMs offer numerous benefits. Companies of all sizes can use them to forecast trends, improve efficiency, and increase the bottom line. Cloud computing offers a scalable and affordable CRM solution that enables mobility, real time insights, and native integrations. The business benefits of a cloud-based CRM could be just the competitive advantage your company needs.

(Image Source: Shutterstock)

By Keith Cawley

Self-Service BI (Business Intelligence) 101

Self-Service BI (Business Intelligence) 101

BI (Business Intelligence) 101

Non-technical users are increasingly jumping at the opportunity to get real-time business insight from data without the help of the IT department, whether they’re a small business without an IT staff altogether, or a high-ranking executive unwilling to request a report that will take a week to be completed.

What is self-service BI?

Self-service BI adheres to several definitions. Primarily, self-service BI is the democratization of data. It allows business users (such as executives or managers) to access and glean insight from data without the assistance of IT, or a team of analysts.

Within this definition, there are two main ways self-service BI is commonly accomplished. Often, self-service BI software offers simple, pre-built reporting templates and ad hoc reporting functions. In more robust software, users are able to apply what-if scenarios with little more than a GUI interface to mediate between their actions and the database queries taking place behind the curtain.

The benefit of simpler software is that any business user can pull reports without assistance. The downside is that more technical users may be disappointed by the lack of customization and drill-down options available when creating these reports. More complex products allows for all the drill-down, additional data sources, configuration, and customization a user could want, but may be too feature-heavy and confusing for a laymen interested in a quick report.

How can self-service BI help a business?

business

The main benefit of self-service BI is that it allows small companies or individual departments to increase efficiency without further tasking their IT or development teams.

If business users are able to directly access data on their own time, they will be able to make quick, informed decisions, and with practice they’ll be able to tailor reports to their needs. Cutting out the back-and-forth with IT minimizes wasted time (on both sides) and removes communication barriers. Additionally, the freedom to manipulate data on their own will allow business users to explore the available information and experiment with the various reports that can be constructed from it. Knowing what’s possible with the data can improve the applicability of the reports to the BI users’ need. It will also increase the users’ understanding of what the reports mean.

IT departments also benefit from the implementation of self-service BI. Allowing users to generate their own reports eliminates, or greatly reduces, time spent on simple report creation and opens resources for more advanced analysis efforts. While IT may need to be involved in the implementation (to configure the data sources), it will mean a lighter BI workload for them going forward.

Who wants self-service BI?

The clearest case for self-service BI software is in small business environments. Small businesses may not have a dedicated IT department, so there may be no one in the company capable of advanced data analysis and management. Many self-service BI providers offer preconfigured integration with common data sources (CRM, POS, Accounting software, etc.), such as those most likely used by small businesses. Self-service BI tools are, by design, simple enough for an average business user to learn and operate. This software is capable of covering small businesses’ data analysis needs, and capable of helping with data management.

Enterprise companies can also benefit from such software. Data-driven company cultures work to back up every decision with clear-cut numbers. Such decisions can be from marketing –which promotional medium has been most successful in recent months and should be continued — or from sales –what times of day are employees closing the most deals in various regions — or even manufacturing. Just as a small business can use BI software to analyze progress, forecast possibilities, and change course appropriately, enterprise managers can direct their teams with greater efficiency. C-level executive that need a quick overview of the company’s key metrics may also enjoy having access to a tailored BI dashboard that they can drill down into to monitor specific departments or business metrics.

How capable is self-service BI?

Self-service BI tools have increased greatly in their capabilities. Options range from restricted, template-based report generation to advanced analysis.

If the intended audience is business users, they will need the ability to use pre-built report templates and dashboards, perform ad hoc queries, and share their results. Such tools provide “wizards” to guide users through the reporting process, suggestions for data attributes in columns and rows, and text search rather than point-and-click user interface when selecting attributes from otherwise complicated diagrams.

Power users, on the other hand, are looking to perform what-if scenarios, apply metrics and hierarchies to the underlying data model, explore attribute relationships, and perform open ended operations (where one doesn’t know exactly what one intends to find).

Current self-service BI solutions offer most of these capabilities.

Tableau software is often cited for its excellent data visualization, but it also suffices as a self-service BI platform for casual users. Visualizations and dashboards are completely customizable but come with suggested forms and guides. Dashboards are ideal for many business users, because once a dashboard has been configured to display certain metrics, it will update in real time without further instruction. Using such tools, actionable insights can be gained immediately.

On the other end of the self-service BI spectrum is IBM’s Cognos Insight, which maintains the capability of IBM’s vast business intelligence offerings within an interface that’s fully capable of being operated by a business power user.

What’s the future of self-service BI?

future-bi

Self-service BI offerings will increase in functionality as more emphasis is placed on improving the interfaces, and as business users become more educated about data, and its applications.

Business intelligence is becoming more ubiquitous as case after case demonstrates what can be accomplished by data-driven decision making. More departments and more business people will continue to request access to self-service BI tools, and one appropriate response would be for self-service BI providers to build apps and tools for delivering the required reporting capabilities to the appropriate individuals, such as the initiative from Information Builders.

Mobile BI applications are also on the rise. Mobile compatibility opens the door for data analysis away from a desk, or on cheaper, lightweight devices. Access to BI applications on lighter devices also eases the implementation process. IT won’t have to outfit every new self-service BI user with a more powerful workstation. It can be as easy as installing an application.

Business intelligence is only now realizing its potential to provide non-technical business users with the power to make informed decisions. The needs of advanced users, casual users, and executives alike can all be met with the right platform. Ad hoc queries, what-if scenarios, mobile compatibility, data source management, and easy to understand interfaces will eventually become ubiquitous among BI vendors. Then the movement that has already begun to democratize the power of data analysis across entire organizations will reach an informed completion.

By Keith Cawley

5 Considerations You Need To Review Before Investing In Data Analytics

5 Considerations You Need To Review Before Investing In Data Analytics

Review Before Investing In Data Analytics

Big data, when handled properly, can lead to big change. Companies in a wide variety of industries are partnering with data analytics companies to increase operational efficiency and make evidence-based business decisions. From Kraft Foods using business intelligence (BI) to cut customer satisfaction analysis time in half, to a global pharmaceutical company using predictive analytics to increase employee retention and meet market demands, data analysis is creating new opportunities for businesses to gain competitive advantages.

By the end of 2013, around 64 percent of organizations had already invested in or were planning on investing in big data technology, a six percent increase from 2012. While data analysis can be a powerful tool when properly handled, some companies just aren’t ready to add business intelligence to their competitive arsenal. Data analytics insight isn’t as straightforward as purchasing and downloading software. There are a lot of considerations that must be made before a company invests in BI.

How do you know if your company is ready for a BI system? By assessing where you currently stand, where you want to be, and what you need to get there, you can more confidently determine if your company should invest in data analytics tools. These questions can guide your assessment.

Do you have something you want to discover from your data?

Before you invest in business intelligence software, you should know what you’ll be using it for. Collecting data and establishing a system for analysis isn’t productive if you don’t know what problem you want to solve.

Consider areas of your company where the current process isn’t as efficient or effective as it could be. How effective are your current marketing campaigns? Do you want to gain insight into your customers spending patterns? Are you looking to improve the efficiency and quality of production? Deciding what questions you want answered prior to investing in a BI solution helps you select the appropriate partner for your company.

Do you have data to work with?

data-bi-sicentists

To perform data analysis, you have to have data; enough relevant and trustworthy data for your analytics to be dependable. If your company hasn’t already acquired sums of data or lacks access to workable information, you first need to determine if you can afford to and have the ability to aggregate such information.

This can quickly become expensive. Beyond the cost of labor for the hours spent organizing and cataloging information, data storage itself is often costly. Large enterprises can spend as much as 40 percent of their IT budgets on storage infrastructure – an average of $25 per gigabyte per month. Typically, these front end costs pay for themselves because of the increased insight data analysis provides. Regardless, the costs of data aggregation and storage should be thoroughly considered before moving forward.

Do you have the budget for BI software?

The price range for business intelligence software varies greatly depending on your needs as a company. Some BI vendors offer data warehousing, which can become expensive but is a good option for companies with a larger budget that require data storage and analytics. Other BI vendors offer visualization systems, both on-premise and in SaaS form. Because visualization systems come in a variety of price ranges, your company will likely be able to find a solution that fits your budget.

But the software cost is only part of the overall expense. In fact, the rule of thumb estimate for the cost of effort and services is 5 times the software cost.

Adding these expenses together, the total cost of a single business intelligence report could end up being around $20,000. This estimate reveals how expensive performing multiple reports can become. In fact, the average cost of business intelligence software for a department is $150,000. This estimate can change depending on the size and depth of the project, but it’s important for your company to thoroughly understand the full array of costs involved with data analytics prior to investment.

Do you have someone who can work with your data?

Data analytics aren’t going to appear out of nowhere. While many BI systems are intuitive, they still require user interaction and management. For best results, your company should have a data analyst or data scientist who is responsible for managing data and performing analytics. Having a single point of contact for analytical decisions will help avoid departmental confusion. Additionally, this person will be able to devote continued time and resources into monitoring and creating reports.

If your company can’t afford a full time data analytics employee, then you should select an existing employee (preferably one with data experience) to manage the BI software and act as the voice for projects. Without having a designated person capable of working with the data, you won’t be able to utilize the full capabilities of your BI solution.

Are you ready to take action?

At this point you’ve gathered data, identified the problem you want to solve, invested in BI software, and performed insightful analysis. To make all of this investment worth it, you have to be prepared to act quickly and effectively.

Implementing a new project or campaign can be expensive upfront. With your newfound data insight, you have the knowledge necessary to effectively change operations in your organization. It’s important to be prepared with the resources necessary to implement this change.

For example, Eurac, an international brake disc manufacturer, utilized Logi Data analytics to improve their reporting and manufacturing system. The intent was to make short term improvements, but also to consistently use the system to realize the company’s long term goals. What they experienced was an immediate ROI of more than 50 percent due to the reduced number of ERP licenses needed, in addition to crucial insight for improving operations.

Data analytics through business intelligence can be a powerful tool to improve the efficiency of your company, but not every organization is ready to responsibly integrate a BI system. By asking yourself these questions, you can better determine if you’re prepared for the influence of data analytics at your organization.

By Keith Cawley

Cloud Infographic – Wearable Tech And Preventative Healthcare

Cloud Infographic – Wearable Tech And Preventative Healthcare

Wearable Tech And Preventative Healthcare

There are so many exciting new opportunities available to utilize wearable technology in the future.  Areas such as nanotechnology disease monitoring, crowdfunding to wearable accessories are some excellent examples of the potential. Estimates vary, but appear to suggest that the market will produce between $14-50 Billion over the next few years.

Included below is an infographic provided by TechnologyAdvice which takes a closer look at Wearable Technology and Preventative Healthcare.

Some of the biggest findings are:

  • 74.9 percent of US adults do not use a fitness tracker or smartphone app to track health, weight, or exercise
  • Lack of interest (27.2 percent) and cost (17.7 percent) are the most common reasons for not using a health wearable
  • 48.2 percent of non-tracking adults would use a free device provided by their physician
  • 57.1 percent of non-trackers said the possibility of lower health insurance premiums would make them more likely to use a fitness tracking device

TA-study-wearable-technology

Ten Tips For Successful Business Intelligence Implementation

Ten Tips For Successful Business Intelligence Implementation

Business Intelligence Implementation

The cost of Business Intelligence (BI) software goes far beyond the purchase price. Time spent researching, implementing, and maintaining your BI investment can snowball quickly and mistakes are often expensive.

Your time is valuable – save it by learning from other businesses’ experiences. We’ve compiled the top ten tips on successfully implementing BI software from professionals who have already taken the plunge.

1. Prioritize your goals

Know your options and match them to your business goals,” says Boris Kontsevoi, President and Founder of Intetics. “For example, some BI platforms are free, but take a longer time to properly setup (up to 4 weeks). Others are subscription-based, but can be installed in a week. Do you care more about the cost or time?

2. Recognize your non-negotiable criteria

Harold Leusink, CEO of Peritas Solutions advises, “Before you even start looking at solutions, separate out your ‘musts’ from your ‘nice-to-haves’.” Peritas Solutions, a consulting firm that has been helping companies find insight in their increasingly large data sets since 2001, finds that “your musts are non-negotiable, and so should be the first thing you talk to vendors about. Your ‘nice-to-haves’ give you a set of criteria you can use to objectively judge any solutions that passed through your musts filter.

3. Utilize built-in tools first

Opt for built-in analytics,” says Christy Delehanty, Content Lead at PandaDoc. “Instead of piling apps on apps on apps, try to make use of the analytics built in to the tools,” suggests Delehanty, “often, these dashboards provide the simplest peek at actionable data with the least set-up on your end.”

4. Clean data only

Make sure you data is clean,” warns Jamie Lin, CEO of Gizmo Global. “This is obvious, but the biggest issue during implementation. Cleaning your data before you implement makes the entire project easier.”

Chandra Siv, General Manager of Data and Analytics Solutions for North America at Mindtree, agrees that addressing data quality is a must. Siv adds that “the confidence level and trust in the data used for decision making is a critical success factor.”

5. Identify key metrics beforehand

Before implementing business intelligence software, determine what data you need and what format you want it in,” adds Gina Cerami, Vice President of Marketing for Connotate. “Look for a technology solution that can deliver clean data with actionable insight. Web extraction and monitoring solutions go hand-in-hand with business intelligence and fuel informed decision-making.

Know what you are trying to show before you start,” says Jon Mills, Director at Paige Technologies. “It is easy to get caught up in the rabbit holes of correlation instead of causation if you don’t have clear metrics in mind before you start.”

6. Start small – choose a few goals to focus on in the beginning, then add more

Michael J. Smith, CEO for Raster Media, advises companies to “focus your BI integration on one or two business objectives initially.” In Smith’s experience, “this will speed up the integration and allow the integration team to focus their efforts rather than being overwhelmed with delivering results for dozens of business objectives. Additional goals can be added once the initial integration is complete.”

7. Don’t ditch currently effective processes without reason

Evaluate which tools and functionalities will actually benefit your company and ensure your entire team is using only those that you’ve determined are valuable,” says Sam Zietz, CEO of technology company, TouchSuite. “Although every tool in BI software was added to that solution (because) there is a need within the industry, many businesses already have successful processes in place that make those tools obsolete. In this case, implementing those tools might actually work against your company, particularly if some team members are inputting information in one system, and others within the new BI solution.”

In order to maximize productivity, Zietz says to “make sure your team is clear on which tools should be utilized and which should not be accessed within the solution. If applicable, you may want to consider putting administration locks on those systems within the solution that you do not want accessed.”

8. Make the technology work for you, not the other way around

Make sure that you align business activities with corporate strategy,” says David Reischer, Chief Operations Manager for LegalAdvice.com. According to Reischer, “the key is to extract useful information when needed.”

9. Empower end users by simplifying the toolset and infrastructure

You’ll never be able to gather all the requirements from the users of BI so they need to be empowered to create, change, and filter reports in order to meet their BI needs,” says Craig Abramson, Marketing Director for Third Wave Business Systems. However, “if the infrastructure is too complex then data anomalies are inevitable,” Abramson warns, “complex toolsets take away the user’s ability to be self-sufficient.”

10. Don’t just stop at a more intelligent business

BI is about better intelligence, but then what?” asks Stuart Easton, CEO of TransparentChoice. Easton says “that intelligence is fed to a group of people to make a decision and that’s where the value generated by better intelligence gets diluted by poor decision making practice. Without addressing better decision making, any investment in BI is going to have a very limited impact.”

The best way to avoid making costly mistakes when choosing BI software is to do your research. Check out the features, demo the products if you can, and make sure to evaluate them using defined criteria.

Do you have any additional tips on implementing BI software from your organization’s experience? Become part of the conversation in the comments section below.

By Keith Cawley

When To Use Supervised And Unsupervised Data Mining

When To Use Supervised And Unsupervised Data Mining

Data Mining

Data mining techniques come in two main forms: supervised (also known as predictive or directed) and unsupervised (also known as descriptive or undirected). Both categories encompass functions capable of finding different hidden patterns in large data sets.

Although data analytics tools are placing more emphasis on self service, it’s still useful to know which data mining operation is appropriate for your needs before you begin a data mining operation.

Supervised Data MiningData Mining

Supervised data mining techniques are appropriate when you have a specific target value you’d like to predict about your data. The targets can have two or more possible outcomes, or even be a continuous numeric value (more on that later).

To use these methods, you ideally have a subset of data points for which this target value is already known. You use that data to build a model of what a typical data point looks like when it has one of the various target values. You then apply that model to data for which that target value is currently unknown. The algorithm identifies the “new” data points that match the model of each target value.

Now let’s clarify that with some specific demonstrations:

Classification

As a supervised data mining method, classification begins with the method described above.

Imagine you’re a credit card company and you want to know which customers are likely to default on their payments in the next few years.

You use the data on customers who have and have not defaulted for extended periods of time as build data (or training data) to generate a classification model. You then run that model on the customers you’re curious about. The algorithms will look for customers whose attributes match the attribute patterns of previous defaulters/non-defaulters, and categorize them according to which group they most closely match. You can then use these groupings as indicators of which customers are most likely to default.

Similarly, a classification model can have more than two possible values in the target attribute. The values could be anything from the shirt colors they’re most likely to buy, the promotional methods they’ll respond to (mail, email, phone), or whether or not they’ll use a coupon.

Regression

Regression is similar to classification except that the targeted attribute’s values are numeric, rather than categorical. The order or magnitude of the value is significant in some way.

To reuse the credit card example, if you wanted to know what threshold of debt new customers are likely to accumulate on their credit card, you would use a regression model.

Simply supply data from current and past customers with their maximum previous debt level as the target value, and a regression model will be built on that training data. Once run on the new customers, the regression model will match attribute values with predicted maximum debt levels and assign the predictions to each customer accordingly.

This could be used to predict the age of customers with demographic and purchasing data, or to predict the frequency of insurance claims.

Anomaly Detection

Anomaly detection identifies data points atypical of a given distribution. In other words, it finds the outliers. Though simpler data analysis techniques than full-scale data mining can identify outliers, data mining anomaly detection techniques identify much more subtle attribute patterns and the data points that fail to conform to those patterns.

Most examples of anomaly detection uses involve fraud detection, such as for insurance or credit card companies.

Unsupervised Data Mining

Unsupervised data mining does not focus on predetermined attributes, nor does it predict a target value. Rather, unsupervised data mining finds hidden structure and relation among data.

Clustering

The most open-ended data-mining technique, clustering algorithms, finds and groups data points with natural similarities.

This is used when there are no obvious natural groupings, in which case the data may be difficult to explore. Clustering the data can reveal groups and categories you were previously unaware of. These new groups may be fit for further data mining operations from which you may discover new correlations.

Association

Frequently used for market basket analysis, association models identify common co-occurrences among a list of possible events. Market basket analysis is examining all items available in a particular medium, such as the products on store shelves or in a catalogue, and finding the products that are commonly sold together.

This operation produces association rules. Such a rule could be a statement declaring “80 percent of people who buy charcoal, hamburger meat, and buns also buy sliced cheese,” or, in a less “market basket” style example, “90 percent of Detroit citizens who root for the Tigers, the Lions, and the Pistons also favor the Red Wings over other hockey teams.”

Such rules can be used to personalize the customer experience to promote certain events or actions. This can be accomplished by organizing store shelves with associated items nearby, or by tracking customer movements through a website in real time to present them with relevant product links.

Feature Extraction

Feature extraction creates new features based on attributes of your data. These new features describe a combination of significant attribute value patterns in your data.

If violence, heroism, and fast cars were attributes of a movie, then the feature may be “action,” akin to a genre or a theme. This concept can be used to extract the themes of a document based on the frequencies of certain key words.

Representing data points by their features can help compress the data (trading dozens of attributes for one feature), make predictions (data with this feature often has these attributes as well), and recognize patterns. Additionally, features can be used as new attributes, which can improve the efficiency and accuracy of supervised learning techniques (classification, regression, anomaly detection, etc.).

Knowing your goals and the appropriate techniques to achieve them can help your data mining operations run smoothly and effectively. Different data is appropriate for different insight and understanding what you’re asking from your data analysts expedites the process for everyone.

(Infographic Source: New Jersey Institute of Technology)

By Keith Cawley

CloudTweaks Comics
M2M, IoT and Wearable Technology: Where To Next?

M2M, IoT and Wearable Technology: Where To Next?

M2M, IoT and Wearable Technology Profiling 600 companies and including 553 supporting tables and figures, recent reports into the M2M, IoT and Wearable Technology ecosystems forecast opportunities, challenges, strategies, and industry verticals for the sectors from 2015 to 2030. With many service providers looking for new ways to fit wearable technology with their M2M offerings…

Protecting Devices From Data Breach: Identity of Things (IDoT)

Protecting Devices From Data Breach: Identity of Things (IDoT)

How to Identify and Authenticate in the Expanding IoT Ecosystem It is a necessity to protect IoT devices and their associated data. As the IoT ecosystem continues to expand, the need to create an identity to newly-connected things is becoming increasingly crucial. These ‘things’ can include anything from basic sensors and gateways to industrial controls…

Cloud Computing Checklist For Startups

Cloud Computing Checklist For Startups

Checklist For Startups  There are many people who aspire to do great things in this world and see new technologies such as Cloud computing and Internet of Things as a tremendous offering to help bridge and showcase their ideas. The Time Is Now This is a perfect time for highly ambitious startups to make some…

Cloud Infographic – Cloud Public, Private & Hybrid Differences

Cloud Infographic – Cloud Public, Private & Hybrid Differences

Cloud Public, Private & Hybrid Differences Many people have heard of cloud computing. There is however a tremendous number of people who still cannot differentiate between Public, Private & Hybrid cloud offerings.  Here is an excellent infographic provided by the group at iWeb which goes into greater detail on this subject. Infographic source: iWeb

7 Common Cloud Security Missteps

7 Common Cloud Security Missteps

Cloud Security Missteps Cloud computing remains shrouded in mystery for the average American. The most common sentiment is, “It’s not secure.” Few realize how many cloud applications they access every day: Facebook, Gmail, Uber, Evernote, Venmo, and the list goes on and on… People flock to cloud services for convenient solutions to everyday tasks. They…

Cloud Computing and Finland Green Technology

Cloud Computing and Finland Green Technology

Green Technology Finland Last week we touched upon how a project in Finland had blended two of the world’s most important industries, cloud computing and green technology, to produce a data centre that used nearby sea water to both cool their servers and heat local homes.  Despite such positive environmental projects, there is little doubt that…

Four Reasons Why CIOs Must Transform IT Into ITaaS To Survive

Four Reasons Why CIOs Must Transform IT Into ITaaS To Survive

CIOs Must Transform IT The emergence of the Cloud and its three delivery models of Infrastructure as a Service (IaaS), Software as a Service (SaaS) and Platform as a Service (PaaS) has dramatically impacted and forever changed the delivery of IT services. Cloud services have pierced the veil of IT by challenging traditional method’s dominance…

Are Women Discriminated Against In The Tech Sector?

Are Women Discriminated Against In The Tech Sector?

Women Discriminated Against In Tech Sector It is no secret that the tech industry is considered sexist since most women are paid less than men; there are considerably fewer women in tech jobs; and generally men get promoted above women. Yet the irony is twofold. Firstly, there is an enormous demand for employees with skills…

Cloud Infographic – Guide To Small Business Cloud Computing

Cloud Infographic – Guide To Small Business Cloud Computing

Small Business Cloud Computing Trepidation is inherently attached to anything that involves change and especially if it involves new technologies. SMBs are incredibly vulnerable to this fear and rightfully so. The wrong security breach can incapacitate a small startup for good whereas larger enterprises can reboot their operations due to the financial stability of shareholders. Gordon Tan contributed an…

Ending The Great Enterprise Disconnect

Ending The Great Enterprise Disconnect

Five Requirements for Supporting a Connected Workforce It used to be that enterprises dictated how workers spent their day: stuck in a cubicle, tied to an enterprise-mandated computer, an enterprise-mandated desk phone with mysterious buttons, and perhaps an enterprise-mandated mobile phone if they traveled. All that is history. Today, a modern workforce is dictating how…

The Rise Of BI Data And How To Use It Effectively

The Rise Of BI Data And How To Use It Effectively

The Rise of BI Data Every few years, a new concept or technological development is introduced that drastically improves the business world as a whole. In 1983, the first commercially handheld mobile phone debuted and provided workers with an unprecedented amount of availability, leading to more productivity and profits. More recently, the Cloud has taken…

Connecting With Customers In The Cloud

Connecting With Customers In The Cloud

Customers in the Cloud Global enterprises in every industry are increasingly turning to cloud-based innovators like Salesforce, ServiceNow, WorkDay and Aria, to handle critical systems like billing, IT services, HCM and CRM. One need look no further than Salesforce’s and Amazon’s most recent earnings report, to see this indeed is not a passing fad, but…

How The CFAA Ruling Affects Individuals And Password-Sharing

How The CFAA Ruling Affects Individuals And Password-Sharing

Individuals and Password-Sharing With the 1980s came the explosion of computing. In 1980, the Commodore ushered in the advent of home computing. Time magazine declared 1982 was “The Year of the Computer.” By 1983, there were an estimated 10 million personal computers in the United States alone. As soon as computers became popular, the federal government…

The Future Of Cloud Storage And Sharing…

The Future Of Cloud Storage And Sharing…

Box.net, Amazon Cloud Drive The online (or cloud) storage business has always been a really interesting industry. When we started Box in 2005, it was a somewhat untouchable category of technology, perceived to be a commodity service with low margins and little consumer willingness to pay. All three of these factors remain today, but with…

Beacons Flopped, But They’re About to Flourish in the Future

Beacons Flopped, But They’re About to Flourish in the Future

Cloud Beacons Flying High When Apple debuted cloud beacons in 2013, analysts predicted 250 million devices capable of serving as iBeacons would be found in the wild within weeks. A few months later, estimates put the figure at just 64,000, with 15 percent confined to Apple stores. Beacons didn’t proliferate as expected, but a few…

Cloud-Based Services vs. On-Premises: It’s About More Than Just Dollars

Cloud-Based Services vs. On-Premises: It’s About More Than Just Dollars

Cloud-Based Services vs. On-Premises The surface costs might give you pause, but the cost of diminishing your differentiators is far greater. Will a shift to the cloud save you money? Potential savings are historically the main business driver cited when companies move to the cloud, but it shouldn’t be viewed as a cost-saving exercise. There…

Using Private Cloud Architecture For Multi-Tier Applications

Using Private Cloud Architecture For Multi-Tier Applications

Cloud Architecture These days, Multi-Tier Applications are the norm. From SharePoint’s front-end/back-end configuration, to LAMP-based websites using multiple servers to handle different functions, a multitude of apps require public and private-facing components to work in tandem. Placing these apps in entirely public-facing platforms and networks simplifies the process, but at the cost of security vulnerabilities. Locating everything…

Maintaining Network Performance And Security In Hybrid Cloud Environments

Maintaining Network Performance And Security In Hybrid Cloud Environments

Hybrid Cloud Environments After several years of steady cloud adoption in the enterprise, an interesting trend has emerged: More companies are retaining their existing, on-premise IT infrastructures while also embracing the latest cloud technologies. In fact, IDC predicts markets for such hybrid cloud environments will grow from the over $25 billion global market we saw…