Business Intelligence Blog from arcplan

Mobile BI: Device & Data Security Concerns


Accessing information from mobile devices is becoming second nature for business users and executives who need to be connected to performance data 24/7. We’ve seen predictions from Gartner heralding 2012 as the year of mobile BI explosion, where employees will bring their own smartphones and tablet PCs into the workplace. As the number of organizations that have implemented (or are planning to implement) mobile BI increases, there are mounting concerns about mobile security. Lack of control of downloaded applications, lack of centralized server management, and virus protection are some of the concerns that come to mind as business users tote their shiny new personal tablets to work.

Let’s examine more closely how your IT team can handle these issues:

The Bring Your Own Device (BYOD) phenomenon. Understandably so, many of us (myself included) have begun taking our own devices to work. Tablets and smartphones can be remarkably efficient for business users on the go, and sometimes it’s just easier to have your personal and business information on the same device. Since the company doesn’t own the device, there is no legal way of controlling what apps an individual can download. However, exposure to malicious software (malware) can pose a tremendous threat to business information. One way to address this concern is to whitelist applications so users have a selection of applications to choose from that IT approves. Employees can still use their devices at work, but within IT-sanctioned limits. IT may also ask users to install a mobile security package to help detect and remove malicious applications.

Mobile device security. Data breaches are a very real threat to data stored on mobile devices. This risk may seem obvious, but accidents do happen. Employees may inadvertently leave their smartphone or tablet in a cab, or at a Mexican restaurant while on a business trip (the arcplanner responsible shall remain nameless), complete with company confidential information.

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Business Intelligence at Hospitals: Real-World Examples of Hospital Efficiency & Quality Metrics


As both the battalion chief of my local ambulance and rescue squad and a business intelligence consultant, you can imagine that healthcare analytics are near and dear to me. Plus, living in the Philadelphia region, it’s impossible to escape the news of numerous hospital closures every year. So at arcplan, I love working with hospitals and healthcare organizations to build reports and dashboards that track the metrics critical to their survival. All the way back in 2001, Paul Mango and Louis Shapiro of McKinsey & Company argued that hospitals are essentially a commodity business and therefore need to compete on the basis of operational efficiency. This sentiment rings true more than 10 years later, with skyrocketing medical costs, declining insurance reimbursements, and increased utilization by an aging population. Giving hospital executives (and physicians!) access to real-time data has never been more critical to hospital operations.

Hospital executives often report on financial, operational and clinical system metrics which are crucial to ongoing operations and management. The hospitals we’ve worked with often have an overarching goal to provide efficient, quality care to patients, and they need access to their existing data to make sure they are achieving that goal. Important metrics that roll up to the goal of “efficiency” include the average wait time for a hospital bed, physician productivity, nurse turnover rates and the cost per discharge. Metrics that roll up to a “quality” goal include average length of stay, re-admission rates and patient satisfaction. The only way to improve the quality and efficiency of care is to analyze current performance and identify areas for improvement.

One of arcplan’s customers, the largest private operator of healthcare facilities in the world, came to us when they were focusing on efficiency. For more than 5 years, they have used an arcplan-powered business intelligence system (with data from Oracle Essbase and Teradata) to track key metrics and make decisions that improve efficiency of care – specifically in emergency rooms. All of their ERs needed to reduce wait times, shorten lengths of stay, and avoid people leaving the ER without care and treatment. The goal became to have every ER patient seen by a doctor within 45 minutes of arrival.

So what metrics do they track to achieve this goal? Here are a few examples:

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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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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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