Business Intelligence Blog from arcplan

When Analytics and Collaboration Intersect


Fueled by the big data hype and the need to extract greater business value from data, investment in business analytics software is on the rise. Many companies have begun to tap into the potential of big data analytics and this number is predicted to increase according to recent reports by the International Data Corporation (IDC). IDC forecasts that the market will continue to grow at a 9.8% compound annual growth rate through 2016 to reach $50.7 billion. Perhaps to a less aggressive extent, interest in Collaborative BI is also on the rise, with top performing companies incorporating collaborative techniques to share knowledge throughout the enterprise according to Aberdeen’s extensive 2011 research report on Collaborative BI. The demands for agile insight and self-service are changing the landscape of BI, driving the need for Collaborative BI, which uses social functionality to improve business decision-making. Separately, the benefits of deploying analytical tools and taking advantage of collaborative techniques are appealing for any organization seeking streamlined operational success – but the payback of merging these initiatives could be even more rewarding.

Analytics is gaining traction in the BI arena due to the need to explore massive amounts of varied information (what we now call big data), extract valuable insight, and quickly deliver these insights to the users who need it. Initiatives geared toward improving analytics utilize technology that gathers and organizes data from disparate data sources and provides a platform for in-depth analysis, yielding benefits such as improved business operations and agility, increased sales, and lower IT costs. So it’s no wonder that organizations are making significant investments in the analytics market.

Collaborative BI, on the other hand, seems to be the new kid on the block…

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Mobile BI Strategy Checklist: Part II


In my previous post on this topic, I evaluated some of the necessary components for a successful mobile BI deployment. As with any project, planning is the most important step, so let’s continue today with 3 more items to add to your mobile BI strategy checklist.

4. Platform strategy
When working out your platform strategy, you need to consider the kinds of devices you’ll deploy your mobile business intelligence on and then what decisions will be affected by those devices. Ideally, your organization would have a standard mobile device rolled out to users, enabling centralized hardware, software, and data security. But this is the real world and that train has left the station. “Bring Your Own Device” (BYOD) is a trend for a reason. Before the term was coined, business users were using their own mobile devices to keep in touch with work while away from their desks, and they don’t want to carry separate work and personal mobile devices. CIOs and CSOs (corporate security officers) are beginning to tentatively accept employees using their personal devices, if only for the cost savings to the organization (going back to the ROI discussion from Part I of this article). One of the implications of this platform strategy is, of course, security concerns, which I’ll address in my next article.

5. Software strategy
This is an area that will be affected by your choice of mobile platforms. If you’re lucky enough to have a standard mobile platform at your company, then native mobile BI apps will be an option for you. These are applications specifically designed to operate on a particular device, like an iPhone or iPad. They take advantage of the native gestures of the device, like pinching and zooming. However, if you might possibly switch device standards or have one set of mobile BI users on iPhones and another on iPads, consider Web apps, which are device-independent applications that can be rolled out on another platform in the future with little effort. They run through Web browsers on smartphones and tablets, eliminating the need to create separate apps for different devices.

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Mobile BI Strategy Checklist: Part I


At every turn, we’re confronted with the reality that mobile BI is making its mark among modern organizations. Studies are confirming this, with TDWI‘s December research report revealing that 70% of participants see mobile analytics as an important part of their company’s BI strategy. Howard Dresner’s Mobile Business Intelligence Market Study found a similar number – 68% see mobile BI as either “critical” or “very important” to their business. And from my own experience with customers and prospects at arcplan, it seems as though everybody is jumping on the mobile BI bandwagon. Before diving head-first into your own mobile BI deployment, lay out a smart strategy that will ensure the project’s success.

Let’s consider the most basic (and important) factors of any organization’s mobile BI strategy: where the money’s coming from, who the project is aimed at, and what kind of BI applications are appropriate for mobile devices.

1. Return on investment
As with any other business project, your mobile BI strategy must have a discernible return on investment in order to get off the ground. In another article, we explored the 5 types of return on investment and the importance of categorizing a business project into one of these buckets. Revenue enhancement is one of the easiest forms of ROI to prove for a mobile BI project. Here’s an example: one of our customers is a company that tracks the effectiveness of pharmaceutical sales reps on arcplan-powered dashboards. The data has revealed that the average sales call for these reps is only about 3 minutes long, so every second counts. One company instituted a pilot program to switch reps from laptops to tablets, which start up significantly faster, to see if this would have a positive effect on sales. It worked – the switch increased the productivity of the reps in their meetings (allowing them to pull up research studies and email them to physicians quicker). This responsiveness on the part of the devices (and therefore the reps) has led to an average sales call duration increase of over 30%. Consequently, these reps have been able to increase the number of sales for the pharmaceutical company they represent. This pilot program proved revenue enhancement ROI and stakeholders gladly signed off on the larger project (tablets for everyone!) as a valuable investment.

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3 Ways Self-Service BI Aids Decision-Making


There’s a lot of talk about expanding the reach of business intelligence beyond the 15-25% of potential users currently using BI solutions. Certainly it’s one of our core missions at arcplan. With nearly everyone in an organization making decisions every day that affect the company, getting this remaining 75% of potential users to answer their business questions with data is imperative. The solution is self-service BI – tools that allow users to navigate and visualize data themselves to get the answers they need to make important business decisions on their timeline. In many ways, self-service BI is synonymous with user freedom since business users need not wait for the IT department to fulfill report requests, but instead are able to generate queries on their own and tailor reports according to their requirements.

We spent some time thinking about the ways self-service aids decision-making. Check out our list and let us know if you have more to add to it!

1) It gives users access to real-time information for faster decision-making.
Ad-hoc reporting, one of the tools under the self-service umbrella, allows users to create new connections between data not previously found in static reports and generate new insights on their own. According to Cindi Howson’s report, The Five Myths of Self-Service BI, executives and managers are a segment of users beyond power users and IT developers who derive value from ad-hoc reporting. If given the chance, a sales director for instance would use an easy ad-hoc solution like arcplan Spotlight to run a query of YTD product sales and compare performance across different regions, rather than wait a week or more for IT to deliver the same information to him. He could also save that query privately or publish it publicly, giving his entire team access to it for future reference. He could even select that report for automatic delivery via e-mail, where it will include the most updated data. The ability to access real-time data and create new reports on the fly means that business users get immediate answers to business questions and can make decisions based on current (versus outdated) information.

2) It addresses specific user needs for greater efficiency.
Self-service tools target the specific needs of users, allowing them to glean the most value out their BI and enabling more efficient decision-making…

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Designing Olympic “A” Standard BI Dashboards


With trials for the 2012 Olympic Games in London almost complete, as a diehard trackie, I can’t help but reflect on the amazing standards that athletes must meet or exceed in order to qualify for their respective events. For instance, the “A” standard for the men’s 100 meter event is 10.18 seconds – that’s faster than the time it would take for many of us to boot up our computers. The standard for women’s high jump is 1.95 meters or about 6 feet, 4.7 inches – so an “A” standard athlete could easily clear the height of a very tall person. Olympic hopefuls work diligently to meet (or exceed) these high standards. Likewise, in a quest for excellence, we in the business intelligence world should strive to improve the design of our BI dashboards – the ones that guide our daily decision-making. We should be reviewing their effectiveness at least yearly. To that end, we’ve compiled a simple checklist to guide your dashboards towards the “A” standard.

Whittle them down to only the most relevant and timely information. With all the excitement around big data and the need to analyze vast amounts of information in order to spot trends, it’s easy to be swept away in a deluge of data and be distracted from what really matters. As excited as you (or the users you serve) may be to display all kinds of new information, remember that some data is a distraction rather than relevant to the decision-making process. So be cautious of the information overload that can hinder the effectiveness of your dashboards. Each organization must determine what really matters to decision-makers (this will vary between them) and center dashboards around the metrics most relevant to each department.

Implement appropriate design. When it comes to dashboards, looks do matter. But dashboards aren’t just eye candy. They’ve become a standard point of reference for business managers and executives who need to monitor company operations – often at a glance – in order to make timely decisions. In a 2011 interview with Dashboard Insight, Stephen Few, author of bestselling books on dashboard design and data visualization best practices (and also inventor of the bullet graph), explains…

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