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 …
A Better Way to Classify Risks
There’s nothing new about classifying risks by category – strategic risk, operational risk, and so on. But I’m suggesting the strategy for managing risks is dramatically different for each quadrant. And we make mistakes when we use a response strategy that doesn’t match the risk type.
In my previous blogs, I illustrated the GRC Strategy Quadrant, which classifies risks based on the risk “appetite” of the business and the perceived risk level, and I explained Type A and Type B Risks in detail.
Today, I’m covering Type C …
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 …
In mobile business intelligence (BI) design, performance is one of the most critical elements of the mobile BI success formula. High quality content, reliable data, and mobile purpose are a must. However, none of that matters if the performance is poor—mobile users tend to be less patient about performance. Think about it for a moment. Unlike a PC users who may be chained to a desk, mobile BI users typically access mobile BI assets on the go and with less time to spare.
When we discuss performance in mobile BI, we often talk about two components: …
The increasing number of chief financial officers (CFOs) and their peers from Generation X and Generation Y that are now heading up finance functions are irked by poorly performing business systems that fall way short of the likes of Google and iTunes that deliver exactly what you want in seconds. So it’s not surprising that many have accelerated automation projects already underway to enhance core financial processes with technology solutions that lead to increased productivity and lower costs.
It’s the only way they can free up headcount from traditional finance functions like reporting, and treasury and transaction processing in order …
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 …
Mobile has become a key ingredient in the integration of business and technology. If designed and delivered effectively, it provides unparalleled convenience, speed, and ease of use. However, having the right mobile mindset is a prerequisite if you’re going to drive growth and profitability through the use of mobile solutions.
In its simplest and purest form, I define it this way:
Mobile mindset is the framework that enables organizations of all sizes to deliver the power of mobile through innovation and without disruption to the business.
Regardless of the size or the scope of the mobile engagement, a …
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 …