It’s a rosy future for companies that take advantage of what strategic data management can do. But the specter of needing a bunch of people handling hardware on-premises continues to make organizations hesitant to move forward with a new data strategy because of the cost implications.

Now all that technology is moving to the cloud, however, so a huge investment in any new hardware on top of the investment you’ve already made in your own internal data center is going away. Still, there is a cost to taking the plunge. But when compared to the benefits, the value of doing so speaks for itself.

Here are a handful of factors to consider when weighing the costs versus the benefits of implementing a big data strategy in your organization.

1. Compare the Dollars and Cents

In 2012 I conducted a study that compared the cost of managing data with traditional data warehousing assets, such as Oracle, to the cost of managing that same data with an open-source software framework, such as Hadoop. At the end of the day, including a 60% discount off list price for the hardware and software licenses for Oracle, the cost to manage 1 terabyte in a 16 terabyte configuration with traditional assets was $26K per terabyte compared to $400 per terabyte with an open-source framework.

2. Analyze the Total Cost of Ownership

The reason there wasn’t a mass exodus in 2012 from Oracle to Hadoop was because you have to consider the total cost of ownership. You have to ask, “Does my organization have the skills to manage this new technology environment? Is my existing Business Objects universe investment compatible with the back end?” In 2012, the answer was no.

Today, you can connect your existing Business Objects universe investment to Hadoop on the back end. Then, take all that data out of Oracle―expose it through HIVE tables where it can be accessed―and enable the environment to perform even faster than it can perform in Oracle for pennies on the dollar. Pennies! Why wouldn’t you do that?

3. Evaluate the Competitive Advantage

This goes something like this: “Well, if my competitor is running their data warehouse for $4-million a year on a legacy technology stack, and I can “lift and shift” my data warehouse to a technology stack that I can run for $40,000 a year, who’s going to gain a competitive advantage?”

4. Assess the Value of a 360 Degree View of Your Customer

In the TV series “How to Get Away with Murder,” they did a forensic analysis of the suspect’s cellphone data that was backed up to his computer, and got the other data from the telecom provider. Because of the GPS service on his phone, they were able to identify his entire route from one state to another, how much time he spent when he was in motion and how much time he spent when he stopped, when he started again, and how many minutes his phone was in a particular location. They were able to create a geo-spacial plot of his path and this was all using the data stream from his cellphone as he was just driving his car with his phone on his person.

This brings us to another important point when we think about data today. We’re living in a world of mashups. There’s opportunity to go out and subscribe to a Twitter feed, and mash that up with an email address linkage in a way that would identify my behavior and thought processes. All that lives in Twitter space or in my Facebook posts can be analyzed. Mashing up these many sources of data into a mega analytic platform capability has become something that is easy to accomplish, but not if you don’t have a strategy for how you’re going to manage the data.

Sam Walton’s objective with his fledgling Walmart stores was to always know what the customer wanted to buy and always have it on the shelves when he or she walked into the store. Back in the 80s Walmart used Teradata technology to build a database to collect all of the point-of-sale data, which was then used to calculate how many units they would need to ship to each store so they wouldn’t have to carry a surplus of inventory. The rest is history. The database actually became much more valuable to Walmart than the inventory carrying costs problem they solved using it. And now Walmart is a half-trillion dollar a year global company.

5. Gauge the Payoff of Higher End Analytics

Amazon is another huge data success story. As you know, they started as an online bookseller—and didn’t make much money selling books online. But what they were able to do is get consumers to go to their portal and interact and leave data behind. They were very successful in leveraging that data, and from that data they have grown into a company with over 100-billion dollars in sales―selling everything.

Amazon is using the highest end analytics―predictive analytics. In fact, they recently filed for a patent on an analytic model that can predict what you’re going to buy before you buy it! Predictive analytics tells them there’s a pretty good chance that you’re going to purchase a product in the next 24 – 48 hours. They’re so confident in the accuracy of their algorithm that they will ship you that product before you even buy it. Let’s say something from Amazon shows up on your doorstep that you didn’t order, but it’s something that you wanted. So you pay for it. This isn’t yet a production feature of, but keep your eye on the bouncing ball!

The Future of Strategic Data Management

The future belongs to those companies that really have a data game that is completely integrated into the foundation of how they do business in the marketplace. And because companies like Amazon know so much more and their revenue is so diverse and their ability to manage data is so significant, they are now even in the data hosting and data enrichment services business. They are selling their data and hosting apps in an infrastructure that exists because of their desire to manage data and ability to do it effectively.

If you look at where the venture capital partners are investing their money today, you’ll see that it’s in companies who are busy creating that layer of integration between the front end and the back end because they have determined that the benefits of having a big data strategy greatly outweigh any costs.