We hope our readers are enjoying the holidays!
We’ll publish more business intelligence, analytics and planning content in 2014, including articles on the business benefits of Responsive Design, mobile BI, cloud BI and data visualization. Don’t forget to sign up for our RSS feed to get articles delivered to your preferred reader or directly to your inbox.
Happy New Year from all the authors of arcplan’s BI Blog!
Sixty-four percent of organizations are already investing in or plan to invest in big data soon according to a recent Gartner report.* That equates to a huge number of individuals who now have to research how to embark on a big data deployment. The prevalence and benefits of big data analytics are undeniable, but there are some considerations to keep in mind before jumping in:
1) Identify a specific business need
Big data projects reap the most benefits when they address specific business needs. Having a use case in mind will help determine what data you need to analyze – social, machine or transactional data. Gartner recommends researching use cases and success stories in other industries; why not get inspired by what’s worked for others? Gartner analyst Doug Laney recently shared examples of big data at work in various industries: using big data analytics, the department store Macy’s was able to adjust prices in near real time for 73 million items based on demand and inventory; Wal-Mart was able to optimize search results and increase web checkouts by 10 – 15%; and American Express used sophisticated predictive models to analyze historical transactions and forecast potential churn. Once you’ve identified the analytic need not met by “small” data analysis, you have the first green light for considering big data technology.
In the wake of the BYOD movement, organizations are challenged to support multiple devices for accessing business information while providing the best mobile experience for end users. Seamless mobility is now an expectation for many knowledge workers who rely on smartphones and tablets to do their work. With arcplan 8, our latest release, we offer unparalleled flexibility for mobile business intelligence deployments for developers and users alike. arcplan 8 was designed with the principles of Responsive Design in mind. Developers can use our HTML5 client to build state-of-the-art BI applications that only need to be designed once, yet can be deployed on any mobile device. Let’s examine the 5 principles of Responsive Design – design principles that are simple and effective, and can be used as a guideline for developers to create responsive mobile BI applications:
1. Design with mobile in mind.
Designing with mobility in mind leads to a better user experience across all devices and platforms. When designing a BI dashboard application, think of the charts as modular tiles. These tiles will need to be rearranged depending on the device’s screen size and orientation, so it helps if they are designed with similar widths and heights. Desktop monitors and tablets in landscape orientation can accommodate all the tiles arranged in two rows, but smartphones and tablets in portrait mode will be better served by tiles stacked on top of each other so the charts are large enough to be understood without too much zooming.
2. Start with the smallest device first.
Business intelligence is the key to unlocking insights from data and empowering company leaders to make impactful decisions, act swiftly even in volatile market conditions, and plan strategically for the success of the organization. arcplan is celebrating its 20th anniversary this year, and BI has been around at least as long as we have. Over the last 2 decades, we’ve seen companies make similar mistakes – mistakes that undermine the success of their BI initiatives. Those new to BI should learn from their predecessors. Here are 5 common BI worst practices and how to avoid them:
1) Blindly buying technology without considering your analytical requirements
BI projects do sometimes fail; it’s not something anyone likes to talk about, but most of the time these failures can be blamed on a lack of requirements gathering. Vendors like us have to ensure that we understand our customers’ requirements inside and out in order to deliver a solution that will be successful and demonstrate concrete ROI. But the truth is, some companies don’t have a thorough understanding of their users’ needs before they start evaluating solutions. Too many organizations start “feature wars” with vendors and end up buying the solution with the most perceived bells and whistles – features they barely understand and will never have a use for.
This is a much of a problem for customers as it is for vendors; it’s our job to ensure that what we’re selling you will have value to your organization, and a lot of that comes down to understanding your users’ needs. But if you don’t understand your users’ needs, how can we?
The first thing you must understand before you try to purchase a BI solution is the analytical problems your company is trying to solve. Don’t get side-tracked by fancy bells and whistles that will not solve your business problems. Avoid the feature wars and make your shortlisted BI vendors prove that their solution is a match with a custom demo or proof-of-concept application.
2) Using BI as a gateway to Excel
Why 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.