Business Intelligence Blog from arcplan

Types of Return on Investment


Our series on Business Intelligence ROI has explored the importance of ROI for BI projects, provided examples of the types of BI projects that never pay off, and evaluated the methodology for calculating BI ROI. We saw that if a project has measurable returns it is more likely to get off the ground and get you acceptance for future BI projects.

Many of you who are tasked to calculate the ROI of your BI projects were never taught such a thing in school, so let’s break down another element that will help you do your calculations: types of return. Here are 5 types you should evaluate:

1. Revenue enhancement
Simply put, your organization will generate more money as a result of doing your project. Shareholders appreciate these types of projects – you’re reaching the right group of customers who see value in your project – and are willing to pay.

An example of this type of ROI would be one of arcplan’s grocery chain customers – their arcplan BI solution ties together three separate IT systems (one for sales, one for ordering, and one for inventory) and allows them to get a handle on inventory shrink (the loss of products between the point of manufacture and the point of sale…think brown lettuce or rotten tomatoes). arcplan allows the right people to see how many tomatoes are stocked in stores, how many are coming in from the warehouse, and how many are selling. The system allows the grocery stores to sell more tomatoes since they have better-looking inventory and less rotten tomatoes since they’re only ordering the amount they need in each store.

2. Revenue enhancement/margin protection
This means that your organization will increase profits through better efficiency. This does not necessarily mean more revenue but just higher profitability as a result of streamlining your current process.

The grocery store example from above also fits this type of ROI. The same shrink avoidance system allows stores to not only sell more tomatoes, but also to throw out less, thus protecting their profits (less shrink = more profit).

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Collaboration – the Future of Decision Making?


Perhaps every generation says this at least once, but I believe we’re in the midst of a very interesting time. The world is getting more social everyday with Facebook, Twitter, and LinkedIn, where we can find old friends, colleagues and even relatives online with a single click. We may even find new people to follow through social media tools’ recommendations and can form relationships online and offline with them. Hundreds of millions of users are making decisions online all the time – who to follow, what content seems interesting, what topics to promote.

Our social media feeds make it obvious who to engage with about a particular topic – a friend may post frequently about sports and you can go to him with thoughts or questions – but that type of insight is not widely available at the place where we spent most of our time: work. We lack intelligence when it comes to the enterprise decision making process. It follows that we should apply the same principles of social media in our corporate environments to identify which colleague can help us make decisions. Applying social media functions that allow users to rate, tag, and comment about corporate content is the answer. Enterprises gain insight into the most used reports and dashboards at the company, report authors get instant feedback and enhancement requests from users, and users gain from the existing expertise of colleagues.

This idea has led to a new category of business intelligence software that Gartner describes as Collaborative Decision Making (CDM) and Collaborative BI. Gartner considers such platforms an emerging trend to fill the gap in decision support for tactical and strategic decisions most often made by knowledge workers.

“By 2013, 15% of BI and analytic applications will combine BI, collaboration and social software in decision-making environments.” – Gartner Group

BI vendors are following this path, creating matching solutions that serve as an interface to your wealth of corporate data. Is the market ready to deploy these solutions?

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Big Data in Retail – Big Ideas for Better Retailer Performance


According to industry forecasts, the world’s volume of data doubles every 18 months, and all forms of enterprise data will grow 650% over the next five years. The talk around big data is more than marveling at the mass of information we’re creating. As analysts and data scientists, we’re trying to find the good stuff – the trends, the data that allows us to make better decisions now and in the future, to predict the moves that will make our business more successful down the line.

Big data (explained in our previous article here) might be new to you, but I’ve seen some analyst reports referencing big data ideas as far back as 2001. However, the BI world is talking about it more and more as data volumes grow and we begin to see the potential knowledge to be gained in these data sets.

So maybe you’re thinking, how can big data benefit my company? It’s hard to think conceptually about it, so let’s take a look at some concrete examples of how companies are using big data today. We’ll start with the retail industry. Keep in mind that many of these ideas can be used on a smaller scale for retailers of any size.

Wal-Mart sifts through massive amounts of unstructured social media and search data to find out what products consumers are talking about. They use that information to set their ad buying strategy on sites like Google, with the goal of competing for e-commerce sales – currently dominated by

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The 5 Traps of Mobile BI


Mobile business intelligence is poised to skyrocket in 2012 and beyond. With up to 80% of users expected to access BI exclusively on their mobile device within 2 years*, mobile BI has become a critical part of many businesses’ IT strategy. As the desire for mobile BI grows, businesses are jumping rapidly into the pool – in some cases, without fully forming a long-term strategy or managing users’ expectations, which can lead to low adoption rates or ultimately project failure.

Businesses should avoid the following pitfalls as they dive into mobile BI deployments:

1) Expecting true feature parity. When users are introduced to mobile business intelligence, they may expect it to offer the feature richness they enjoy on their laptops or PCs. Unfortunately, mobile BI does not currently allow actions like “drag-and-drop,” so it will never be quite the same experience. To make up for this, mobile BI apps should leverage device-specific controls and gestures that allow for zooming in and out and should make use of large buttons and easy navigation to make the experience as user-friendly as possible. Preparing users to miss some features but embrace others is the way to ensure a smooth transition from desktop BI to mobile BI.

arcplan mobile BI2) Ignoring mobile design standards. Mobile device screen resolution necessitates BI application redesign – not always a full-scale redesign of an existing BI app, but at the very least adjustments to font sizes, charts, and buttons to accommodate a smaller screen size. In addition, an app for a smartphone will have different requirements than one for a tablet. While a 9- inch tablet can display an entire dashboard at once, a smartphone BI app should limit users to a list of reports that lead to individual charts. As mobile BI grows in popularity, we will undoubtedly see organizations design their dashboards and reports with mobile in mind, enabling even easier deployment.

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The Big Data Trend Explained: Big Data vs. Large Data


Acquiring thorough insight into your data and tapping into the needs and buying patterns of customers are growing needs for businesses striving to increase operational efficiency and gain competitive advantage. Throughout 2011, I noticed a heightened interest in ‘big data’ and ‘big data analytics’ and the implications they have for businesses. In August, Gartner placed big data and extreme information processing on the initial rising slope of their Hype Cycle for Emerging Technologies, so we’re just at the beginning of the big data trend. A recent TDWI survey reports that 34% of organizations are tapping into large data sets using advanced analytics tools with the goal of providing better business insight. The promise of big data analytics is that harnessing the wealth (and volume) of information within your business can significantly boost efficiency and increase your bottom line.

The term ‘big data’ is an all-inclusive term used to describe vast amounts of information. In contrast to traditional data which is typically stored in a relational database, big data varies in terms of volume, frequency, variety and value. Big data is characteristically generated in large volumes – on the order of terabytes or exabytes of data (one exabyte starts with 1 and has 18 zeros after it) per individual data set. Big data is also generated in high frequency, meaning that information is collected at frequent intervals. Additionally, big data is usually not nicely packaged in a spreadsheet or even a multidimensional database and often takes unstructured, qualitative information into account as well.

So where does all this data come from?

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