I have a Corvette that I like to work on for fun and relaxation. It gives me an excuse to learn something new and an opportunity to hone my troubleshooting skills. It can be a fun way to spend a few hours on a weekend.
A few weekends ago I was looking for a few parts for a small project. This was spur of the moment and really didn’t need to be done now (as the car will be stored soon for the winter). I found the parts I needed from a single company, but then something strange happened.
This website had my address, knew the two parts that I wanted, but failed to make the process easy and almost lost a sale. I needed to manually check five different store locations to see if they had both parts. In this case two of the five did. One store was about 5 miles from my house and the other about 20 miles away.
Just think how helpful it would have been for this website to use the data available (i.e., inventory and locations) and present me with the two options or better yet default me to the closest store and note the other store as an option. Using spatial features this would be extremely easy to implement. It’s the equivalent to the “Easy Button” that one office supply uses in their commercials.
Now, take this example one step further. The website makes things quick and easy, leaving me with a very pleasant shopping experience. It could then recommend related items (it did, but by that time I had wasted more time than necessary and was questioning whether or not I should start that project that day). The website could have also created a simple package offer to try to increase my shopping cart value.
All simple things that would generate more money through increased sales and larger sales. It would seem that this would be very easy to justify from both a business and technical perspective, assuming the company is even aware of this issue.
I frequently tell my team that, “People buy easy.” Help them understand what they need to accomplish their goals, price it fairly, demonstrate the value, and they make the rest of the sales process easy to complete. This makes happy customers and leads to referrals. It just makes good business sense to do this.
So, while geospatial technology might not be the solution to all problems, this is a specific use case where it would. The power of computing systems and applications today is that there is so much that can be done so fast, often for reasonably low investment costs in technology. But the first step getting there is to ask yourself, “How could we be making this process easier for our customers?”
A little extra effort and insight can have a huge payoff.
Being in Sales I have the opportunity to speak to a lot of customers and prospects about many things. Most are interested in Cloud Computing and Big Data, but often they don’t fully understand how they will leverage the technology to maximize the benefit.
Here is a simple three-step process that I use:
1. For Big Data I explain that there is no single correct definition. Because of this I recommend that companies focus on what they need rather than on what to call it. Results are more important than definitions for these purposes.
2. Relate the technology to something people are likely already familiar with (extending those concepts). For example: Cloud computing is similar to virtualization and has many of the same benefits; Big Data is similar to data warehousing. This helps make new concepts more tangible in any context.
3. Provide a high-level explanation of how “new and old” are different, and why new is better using specific examples that they should relate to. For example: Cloud computing often occurs in an external data center – possibly one that you may not even know where it is, so security can be even more complex than with in-house systems and applications. It is possible to have both Public and Private Clouds, and a public cloud from a major vendor may be more secure and easier to implement than a similar system using your own hardware;
Big Data is a little bit like my first house. I was newly married, anticipated having children and also anticipated moving into a larger house in the future. My wife and I started buying things that fit into our vision of the future and storing it in our basement. We were planning for a future that was not 100% known.
But, our vision changed over time and we did not know exactly what we needed until the very end. After 7 years our basement was very full and it was difficult to find things. When we moved to a bigger house we did have a lot of what we needed. But, we also had many things that we no longer wanted or needed. And, there were a few things we wished that we had purchased earlier. We did our best, and most of what we did was beneficial, but those purchases were speculative and in the end there was some waste.
How many of you would have thought that Social Media Sentiment Analysis would be important 5 years ago? How many would have thought that hashtag usage would have become so pervasive in all forms of media? How many understood the importance of location information (and even the time stamp for that location)? My guess is that it would be less than 50% of all companies.
This ambiguity is both the good and bad thing about big data. In the old data warehouse days you knew what was important because this was your data about your business, systems, and customers. While IT may have seemed tough before, it can be much more challenging now. But, the payoff can also be much larger so it is worth the effort. Many times you don’t know what you don’t know – and you just need to accept that.
Now we care about unstructured data (website information, blog posts, press releases, tweets, etc.), streaming data (stock ticker data is a common example), sensor data (temperature, altitude, humidity, location, lateral and horizontal forces), temporal data, etc. Data arrives from multiple sources and likely will have multiple time frame references (e.g., constant streaming versus updates with varying granularity), often in unknown or inconsistent formats.
Robust and flexible data integration, data protection, and data privacy will all become far more important in the near future! This is just the beginning for Big Data.
Two years ago I was assigned some of the product management and product marketing work for a new version of a database product we were releasing. To me this was the trifecta of bad fortune. I didn’t mind product marketing but knew it took a lot of work to do it well. I didn’t feel that product management was a real challenge (I was so wrong here), and I really didn’t want to have anything to do with maps.
I was so wrong in so many ways. I didn’t realize that real product management was just as much work as product marketing. And, I learned that geospatial was far more than just maps. It was quite an eye-opening experience for me – one that turned out to be very valuable as well.
First, let me start by saying that I now have a huge appreciation for Cartography. I never realized how complex mapmaking really is, and how there just as much art as there is science (a lot like programming). Maps can be so much more than just simple drawings.
I had a great teacher when it came to geospatial – Tyler Mitchell (@spatialguru). He showed me the power of overlaying tabular business data with common spatial data (addresses, zip / postal codes, coordinates) and presenting the “conglomeration of data” in layers that made things easier to understand. People buy easy, so that is good in my book.
The more I thought about this technology – simple points, lines, and area combined with powerful functions, the more I began to think about other uses. I realized that you could use it to correlate very different data sets and graphically show relationships that would otherwise extremely difficult to make.
Think about having access to population data, demographic data, business and housing data, crime data, health / disease data, etc. Now, think about a simple and easy to use graphical dashboard that lets you overlay as many of those data sets as you wanted. Within seconds you see very specific clusters of data that is correlated geographically.
Some data may only be granular to a zip code or city, but other data will allow you to identify patterns down to specific streets and neighborhoods. Just think of how something so simple can help you make decisions that are so much better. The interesting thing is how few businesses are really taking advantage of this cost-effective technology.
If that wasn’t enough, just think about location aware applications, the proliferation of smart devices that completely lend themselves to so many helpful and lucrative mobile applications. Even more than that, they make those devices more helpful and user friendly. Just think about how easy it is to find the nearest Indian restaurant when the thought of curry for lunch hits you. And these things are just the tip of the iceberg.
What a lucky day it was for me when I was assigned this work that I did not want. Little did I know that it would change the way that I think about so many things. That’s just the way things work out sometimes.