Mobile Lives of College Students
Smartphones of every type proliferate college campuses. But, how do most college students use their smartphones? And, what’s the best way to reach these coveted demographic?
Find out.
Does Visualization Deliver Insight?
What good is a visualization tool if you can’t understand what you are looking at? How can you gain insight on information that looks foreign? Can you even consider a fancy dashboard as a self-service tool if you need to get help to de-code the data?
The Soft Stuff is the Hard Stuff
Most of us are technical. We like to be fact-driven. We embrace technologies of all flavors, including computer hardware, software, mobile devices, the Internet and social media. We tolerate opinions of others that differ from ours, but we prefer tangible, hard evidence that supports any position or argument. The problem is that organizations are made up of people, not just computers and equipment.
We like research studies and analytics to gain insights and foresights, as well as to solve problems and pursue opportunities. But, darn it, people get in the way.
Overcoming the Unwritten Rules of Budgeting
What Rules?
We all have personal ‘rules’ and beliefs that direct the things we do and how we respond to situations. Many of these rules are ‘unwritten’ – that is we follow them religiously even though they are neither compulsory nor explicit company policy. We do them because we do, quite often without thought as to their origins or whether they actually make sense.
To BI and Beyond: A BI Primer
While the first use of the term “business intelligence” was in a 1958 paper by IBM researcher Hans Peter Luhn, it was Howard Dresner in 1989 (later with Gartner) who defined the term and the practice as we now recognize it. Even I could have invented the concept in 1999, but it was Dresner’s talent that he recognized a decade earlier that the disparate data warehouse, analytic and reporting projects and initiatives needed to be unified under a single umbrella.
The fundamental problem that BI addresses is: scarce IT resources.
Surfing The Disturbance
Mike Tyson probably said it best: “Everyone has a plan ’till they get punched in the mouth.”
The future of business is the martial arts CEO, the jujitsu strategist. Far too many organizations approach business with an American football mentality, complete with scripted plays, huddles and time outs, but the real world isn’t quite so convenient and accommodating.
How Does Predictive Analytics Work?
As any fortune-teller will assure you, predicting the future is an art as well as a science. And that truism applies as much to business decisions as to any other aspect of life. So on the one hand we can say that predictive analytics is a branch of statistics in which information extrapolated from historical data is applied to the projection of future conditions. That’s the science side.
On the other hand, we can say that predictive analytics is using information you do have to compensate for information you don’t have (yet), in order to make better business decisions. That’s the artful part, and it can depend as much on intuition and imagination as on algorithms.
Bringing the two sides together successfully is “what’s new” in the practice of predictive analytics.
Are All of Your Customers Profitable to You?
It is no longer sufficient for an organization to be lean, agile and efficient. Its entire supply chain must also perform as the company itself does. If some of its trading-partner suppliers and customers are excessively high-maintenance, those suppliers and customers erode profit margins. Who are these troublesome suppliers and customers, and how much do they drag down profit margins? More importantly, once these questions are answered, what corrective actions should managers and employees take?
The ABCs of Enterprise Analytics
“Enterprise analytics” is a widely used term these days. As often happens, though—it’s being used in different ways, by different groups, for different reasons. Enterprise analytics can refer to any or all of these three concepts:
1. Access to analytics capability (so users throughout the enterprise can perform their own local analytics)
2. Access to enterprise-level analytics (so some users can see reports or dashboards that incorporate data from the whole enterprise)
3. Analytics platforms that can function at an enterprise level (working with multiple data sources and formats)
Consultants, business writers, software companies, and IT execs may all be using the term enterprise analytics to meet their own communication needs—so conversations can get a little complicated, and research can be somewhat confusing.
Analytics for Creating More Choices
Analytics’ goal should be to gain insights and solve problems, to make better and quicker decisions with more accurate and fact-based data, and to take actions. “Big data” with high performance computing is allowing organizations to deploy business analytics to have more choices and make better decisions. But the three challenges that Iyengar describes also apply to organizations. For commercial companies they seek a competitive edge typically through differentiated products and services increasingly targeting differentiated customers.
Since customer preferences are not static – they are making individual choices on what to purchase – business analytics are essential to detect what is changing. They are also needed for organizations to uniquely define themselves. Ultimately the best source to gain a competitive edge is to grow competencies in employees with analytics – creating a culture for analytics.