My daughter recently aced her SATs. She credited her preparation, but it was her overall perspective that struck me: “It’s not like it’s real life or anything.” Real-life decision making is hard. It’s almost always based on incomplete and unreliable data, contradictory “facts,” and yesterday’s news.
Yet decide we must, and the futures of our companies and our careers depend on it. Cash and liquidity would certainly make an interesting SAT test question: “80% of unit cash balances are available as of yesterday afternoon. How much Japanese Yen will we need in six weeks?”
To answer, we’d of course …
We often associate the use of analytics with running a successful corporation, but the truth is, its use goes way beyond that. Analytics is now increasingly playing a role in sports (Super Bowl, soccer, tennis), in the public sector and politics, and at nonprofit organizations.
City Year: Helping Students and Schools
City Year is a national nonprofit organization that mobilizes young adults to serve as full-time tutors, mentors, and role models in many of the nation’s highest …
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 …