Data scientists who do mashups from disparate data sources are able to gain deeper insights into problems and develop more comprehensive approaches toward attacking them by having a richer data set to work from. In turn, data mashups can help companies become more agile and speed time to market.
Consider the use of customer data.
Big data and analytics are top corporate, top-three corporate or top-10 corporate priorities in their organizations’ strategic agendas, according to 65% of 1,469 C-level executives taking part in a recentsurvey by McKinsey & Co.
Forty-four percent of the respondents say that they’re generating more value from big data and analytics than their competitors, while only 28% say the same for social tools or technologies.
An even larger share of executives in healthcare and pharma (56%) and business-to-consumer companies (50%) say the same about big data and analytics.
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.
Which is worse? Being wrong or being confused?
Let’s start with some definitions. To make a wrong decision means you were mistaken and erroneous. Your decision was incorrect for the problem to be solved or opportunity that could have been realized. (There is also an immoral, unethical, and illegal connotation; but that is a different variation of a poor choice. To be confused means you are baffled, bewildered, and perplexed. You cannot be positioned to make a correct decision because your thinking is muddled and clouded.
Embracing analytics can resolve both conditions.
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?
“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.
We are so pleased to Gary Cokins, Rock Star of enterprise performance management, join our Contributors Team!
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.
While the rest of the world is focused on volume, velocity and variety, Big Data has a real challenge that is larger than distributed storage and processing and larger than sources and types of data.
It has a problem I’ll call the Big Lever. Put another way, “What do you do once you think you’ve engineered something meaningful? How will you pull the ‘business lever’?“
Two trains are on the same track 600 miles apart traveling towards each other. One train is traveling west at 50 mph and the other train is traveling east at 20 mph. How long will it take for the trains to impact each other?
8 hours, 30 minutes
8 hours, 34 minutes
8 hours, 42 minutes
The narrow correct answer is #2.
But an experienced analyst thinking out-of-the-box would ask (1) Who would ride on a train traveling 20 mph? and (2) Who would put two trains on a collision course?