Business Intelligence Blog from arcplan

3 Ways Analytics Help Retailers See Black Friday More Than Once a Year


retailers-black-friday-arcplanPeople do crazy things, especially during the holiday season. On Black Friday – and even on Thanksgiving evening – customers will wait in line for hours to grab the best deals and knock items off their wish list. Retailers offer ridiculous discounts on high-priced items and keep doors open 3 days straight to cater to buyers around the clock, then web retailers kick in their own Cyber Monday promotions. Black Friday has remained the number one shopping day for the past decade, accounting for most of the sales that businesses reign in during the holiday season. But smart retailers don’t have to wait until Black Friday to ramp up their bottom line. Your analytical or business intelligence platform can keep a pulse on operations year round and help increase sales before the 11th hour. Here’s how:

1) Use analytics for in-store promotions
Analytics can help guide store layouts by tracking which products perform best on an aisle shelf vs. an endcap in various cities, and what products should go on sale in a given month and region based on sales history and inventory levels. Using business intelligence, you can run scenarios based on historical data and make predictions about programs designed to drive in-store business. For example, if you increase the circulation of your direct-mail flyer, how much additional business can you expect it to drive? Similarly, if you offer in-store coupons for a certain timeframe, what kind of sales uptick can you expect? Essentially, you can analyze past customer purchasing behavior to determine how to best influence future purchasing behavior.

arcplan’s retail customers use our platform to track KPIs and run what-if scenarios to ensure that products are priced appropriately. For retailers, one important KPI is the cost of goods sold (COGS) – the price paid for the product, plus shipping, handling and other expenses to get it ready for sale. By keeping track of COGS…

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3 Big Data Approaches Based On Your Available Resources & Infrastructure


big-data-approachesUnearthing previously unimaginable insights from massive data sets is the premise of all the big data hype. Over the past few years as more and more stories come out about how companies are finding competitive advantages in their data, big data has moved beyond the buzz. Enterprises are deploying big data projects at a faster rate every year, and even more plan to do so within the next 2 years.

The extent to which a company can take advantage of big data analysis is determined by the amount of resources and infrastructure it has available. The good news is that now the barriers to entry have been lowered, making it possible for more organizations to meet their goals to transform operations with insights gained from big data. Here are three approaches that companies of any size can take based on their particular situation.

One thing to note is that these are underlying infrastructure approaches, and that you’ll still need an analytic engine like arcplan on top in order to interact with, visualize and distribute your insights.

Lots of resources and lots of infrastructure

Before big data was “big data,” Teradata was the only game in town. They’ve been at it for so long and their functionality is so robust – some of their capabilities are second to none. Now other vendors like SAP (with HANA) and Kognitio have their own massively parallel analytic databases. They offer robust processing and querying power on multiple machines simultaneously, enable near real-time MDX (Multidimensional Expressions, for OLAP querying) and SQL (Structured Query Language, the standard way to ask a database a question) queries, and in the case of SAP HANA and Kognitio, are fully in-memory. Not surprisingly, Teradata and SAP HANA come at a high price, but for that high price, the insights you achieve can be very near the speed of thought.

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Better Planning Through Business Analytics


In past articles I’ve written here on arcplan’s BI Blog, I explored the role of spreadsheets in the planning, budgeting, and forecasting process and the importance of agility and accuracy. Today I would like to talk about the benefits of business analytics in the planning process when it comes to providing agility and accuracy.

When forecasts are consistently accurate, business leaders can have more confidence when making decisions and investments to guide the organization, as they have a good idea of how the organization will perform in the coming months. Agility is essential because volatile markets make it difficult for forecasts to reflect current business conditions. Therefore, Best-in-Class organizations are more likely than All Others to implement technology to enable both data access and the ability to utilize data to make predictive decisions (Figure 1). Fifty percent (50%) of Best-in-Class organizations have implemented an enterprise-level BI solution in comparison to 28% of All Others. These tools provide data in an easily consumable format so employees can find and utilize the data they need to make decisions. The Best-in-Class are also over twice as likely as All Others to have implemented predictive analytics. This technology helps to convert BI data into forward-looking forecasts.

Figure 1: Best-in-Class Technology Adoption


Source: Aberdeen Group, January 2013

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Build vs. Buy: Business Intelligence for Big Data Analytics


build-business-intelligence-arcplanWhy It’s a Bad Idea to Build a Business Intelligence Platform From Scratch

A friend of mine is a Python developer for a billion-dollar corporation. His team is building a custom call center reporting app that connects to the company’s cloud data storage via APIs. I’ve seen some of the application and while it’s impressive for a custom system, it’s mostly tables of numbers with the occasional pie chart. This is after 8 months of work, and the only people accessing the system are a select few big data scientists.

Believe it or not, a number of companies are doing this kind of in-house development of analytical platforms. All the hype surrounding big data has them convinced that they’re missing out on the action. Consequently, companies large and small are devoting huge amounts of time, money and human resources to developing custom business intelligence systems for big data (Google BigQuery, Hadoop, etc.) reporting rather than simply choosing a platform that already exists and is proven to work in a similar environment.

At the heart of this trend is a desire for big data to have a greater impact in the organization. Since it’s usually small teams of data scientists who are dealing with big data, their impact and effectiveness is equivalent to a small drop in a much larger body of water – their ripple effect throughout the organization is often minimal and short-lived. To extend the reach of big data in the company and get important insights out to a greater number of decision makers, a BI platform is a necessary next step – one that leverages big data insights in easily-digestible executive reports and dashboards.

Some companies are going down the road of custom BI platform development, but their efforts are no match for solutions like arcplan that are already available. Below is a list of what you’d need to do to build a BI platform from scratch. You’ll quickly see why the effort and expense aren’t worthwhile.

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Dynamic Data Visualizations


There’s a lot of discussion happening in the BI world right now over data visualization. On the one hand, you have analysts pushing the idea that data visualization = visual data discovery = self-service BI = advanced BI. I’ve seen Gartner and Aberdeen both touting the idea that data visualization and data discovery are the same and that they’re the key to unlocking analytics for more users in your enterprise.

On the other hand, you have organizations who think data visualization = dashboards. They want to present their data graphically, have some interactive capabilities like drill-down and drill across, and use advanced features like animated graphs and motion charts.

At arcplan, we offer our customers all types of data visualization, from sophisticated desktop and mobile dashboards to visual ad-hoc reporting. Today let’s examine some of the dynamic, interactive visualizations you can employ in your BI dashboards to enhance data visibility and tell stories that are more expressive than static charts.

MotionCharts_220Motion Charts for Trend Analysis
A motion chart is a dynamic chart that shows the flow of data across a dimension – for example, time. It’s a great way to look at large amounts of data at once to discover patterns.

For example, a sales manager may want to conduct a trend analysis for the company’s product line over the course of a year to analyze profits and losses for a set of product categories. A motion chart provides a more dynamic option than a table of numbers. By simply sliding the time bar along the x-axis, the sales manager obtains a visual of the fluctuations in the product categories over time. It’s the difference between reading a book and watching a movie on the same topic: though the information is the same, a visual aid allows some users to better absorb it.

Zoom Line Chart for Dynamic Drill-Down
Don’t be fooled by this ordinary looking line chart…

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