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The Decision Factor offers insightful comments and observations on analytics—from views on new technology approaches and market dynamics to the latest industry trends driving demand for faster, smarter information analysis. This blog contains personal views, thoughts, and opinions from SAP employees, mentors, and friends working in the area of analytics. It’s not endorsed by SAP nor does it constitute an official communication of SAP.

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5 More Reasons Why More Data Doesn’t Guarantee Better Decisions

group studies data at table

In my last blog, I discussed 5 reasons why more data doesn’t guarantee better decisions. I had picked what I considered my top five from a long list of reasons. I must have hit a nerve with our readers because the response has been tremendous. So today I’ve incorporated some of that feedback into this list of five more reasons why, with all this data, better decisions aren’t guaranteed.

1. When It Comes to Data, Quality Comes Before Quantity

We seem to take quality for …

5 Reasons Why More Data Doesn’t Guarantee Better Decisions

business intelligence team studies paper and laptop

Data alone may not be enough to guarantee better decisions, but better decisions almost always start with data. Just capturing and storing data will not get us far. Disconnected and fragmented data can’t paint a complete picture because different segments linger in a detached state or in isolated buckets. Left disintegrated, they lack the necessary transformations to be turned into cohesive and compatible building blocks.

With all this data, why is it that we continue to make bad business decisions? I was asked this question the most in the business intelligence course I …

Retail Innovation – A Game of Checkers

game changer retail

The balance of power within the retail ecosystem has shifted many times over the years and will continue to do so. As the game of checkers goes, players methodically attempt to outmaneuver and manipulate towards the outcome of the game.

Within the retail ecosystem, it was the suppliers and vendors that dictated the rules of the game for retail early on. How products would be made available, and when and how they would be promoted, was decided by the needs of the retailer. In the mid-1960s, along came the “Big Box” retailers such as Wal-Mart, Carrefour, Target, and Kmart. …

The Test of Effective Cash and Liquidity Management

http://www.dreamstime.com/royalty-free-stock-photography-currency-account-transactions-superimposed-image15687007

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 …

Social Media – A Double-Edged Sword for Corporations!

Just recently, I noticed a couple of similar social media posts by friends. They echoed the same sentiment —extreme irritation at bad customer service by a large airline, which is notorious for its lack of attention to its passengers. Ironically, my friends were flying first class on the occasions they were mistreated.

Their accounts reminded me of a similar bad experience I had with the same airline. I looked up J. D. Power’s customer satisfaction rankings of North American airline companies, and sure enough, found our antagonists languishing near the bottom.

Social media is a great leveler—it enables the …

Analytics and Innovation Make a Difference for a Nonprofit

City Year

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 …

Mobile BI Design Framework: Impact and Utility

Mobile BI Design Framework: Impact and Utility

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, …

Subtlety, Complexity, and the Future of Retail

Subtlety, Complexity, and the Future of Retail

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 …

Everyday BI: My Stocks

Everyday BI: My Stocks

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 …

Everyday BI: 3 Steps to Insight

Everyday BI: 3 Steps to Insight

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 …