In mobile business intelligence (BI) design, two elements are always in play. I refer to them as “utility” (not to be confused with utility in economics) and “impact.” At the micro level, they influence directly how we develop our mobile assets (reports, dashboards) in order to effectively deliver actionable insight through the mobile user interface and experience. At the macro level, they influence how we design and execute our mobile BI strategy.
Utility Is About Efficiency
Mobile BI is about faster, better-informed decision making through the use of mobile platforms. In this …
Why is the future so difficult to predict? It is easy enough to jot down a few paragraphs on a given future topic, say the future of the retail industry and the impact that big data will have on it, but it is very difficult to have any assurance that those projections will map to anything that actually happens. Part of the problem is that we tend to see the future as an exaggerated version of the present rather than a world in which fundamental changes have occurred.
There is an old story in futurist circles, probably apocryphal, about a …
In the last installment of this series, I described the three key steps that everyday business intelligence (BI) users typically go through when they consume data: Observation, Perspective, and Insight. These steps often take place in an ad-hoc manner without the same degree of precision and requirements that one expects in corporate BI environments. Nevertheless, everyday BI users follow a similar process to achieve the same end goal—insight through data for better-informed decisions.
Let’s take a look at a great example that demonstrates these three steps before I continue with our experiments in future …
In the last installment of this series, I described everyday BI users as data consumers who use technology to drive insight from diverse data sources. I want to further expand on this idea that everyday BI users are insight-driven data consumers, and articulate what I consider the three key steps to insight.
This final piece sets the stage for our analyses and experiments in the coming posts of the Everyday BI series.
Step One: Observation
In this first step, we’re re primarily occupied with gathering basic data to answer rudimentary questions. At this early stage:
A lot has been written on the 3 V’s of Big Data – Volume, Variety and Velocity. Yet there are two more equally, and perhaps even more important, attributes to consider—Value (business value to be derived) and Veracity (the quality and understandability of the data).
Big Data Value
Value starts and ends with the business use case. The business must define the analytic application of the data and its potential associated value to the business. Use cases are important both to define initial “Big Data” pilot justification and to build a roadmap for transformation.
This value is critical in the …
Surveys, questionnaires, and polls generate data, but survey data and hard data aren’t the same thing. I often see them treated in the same light in the context of answering business questions or delivering actionable insight, and with equal zeal and qualification. But there are definite differences.
Understanding the difference between data collected from surveys vs. data generated from transactions or operations is crucial. It will help us find the relevant answers to our questions and also save us a lot of time and money in the process.
There’s a science and methodology to developing effective surveys. Design and data …
Come holiday season and it’s normal for your promotional mail to increase four folds. But this holiday, I received a few weird offers—a hearing aid, a retirement community brochure, and a marketing call for an elderly alert system. Being in analytics myself, I wanted to understand the reason why these companies are targeting me since I assumed this didn’t result from mass marketing.
I started scanning my last few months’ purchases to understand the trigger and didn’t find anything. Finally, I found a website that listed my marketing data and this is what I found:
And the …
What is a business intelligence (BI) Strategy? Why should you care? The phrase has been increasingly used by organizations to recognize effective use of business intelligence and to take BI programs to the next level. Do you have one? If you are looking at your architecture slide, then let’s explore some of the myths around BI strategy.
Myth One: BI Strategy Is All About Technology and Architecture
BI strategy is often misrepresented as architecture diagrams with several data sources feeding into an enterprise data warehouse and shiny tools that access data from the data warehouse. The technology and architecture are …
In the first installment of this series, I described everyday BI as a concept that surfaces everywhere in our daily lives. Next, I want to portray the essential attributes of everyday BI users in order to set the stage for our analyses and experiments in the coming posts.
First and foremost, everyday BI users are data consumers who use technology to drive insight from diverse data sources. In some cases, they generate the source data by their actions, such as accumulating purchases, signing up for subscriptions, or making inquiries. In other instances, they may not have any control …
The use of mobile business intelligence (BI) as a framework to enable faster, better-informed decision making continues to expand as the technology advances and more users become mobile ready. Whether you’re planning a project for a business app or developing a strategy, it’s critical to gauge your mobile BI app’s readiness for a complete mobile user experience.
Here are five must-have features that are critical to delivering a complete mobile BI experience.
Offline Capability Is the Battery of Mobile Experience
Since mobile users are still sometimes left to run without any wireless connection, offline …