In this series, we’re examining the underpinnings, the infrastructure that gives rise to marketing technology. Today, we wrap up with a look at where we’ve been and what you should do next.

Choosing a Platform

As we explored, each of the three major providers of marketing technology infrastructure offers different solutions, depending on where we are in the hierarchy of analytics.

hierarchy by service.jpg

If the marketing technology problems we face involve large scale infrastructure issues with storage, processing, and computation, Amazon AWS is the best solution for us.

If the marketing technology problems we face involve heavy computation, multiple data imports, and vast databases, Google Cloud is the best solution for us.

If the marketing technology problems we face involve cognitive and predictive analytics, artificial intelligence, and the most challenging computation, IBM Bluemix is the best solution for us.

Hybrids

While centralizing our efforts on one platform makes life easier for developers and provides us with greater importance to the provider, we are also not confined to just one provider. If we have significant issues across the spectrum, we can combine the different platforms. We can, if we have enough technical talent, choose the best-of-breed individual services to weld together.

For example, Amazon AWS S3 is generally regarded as the high quality, low cost leader in storage and basic computation. IBM Watson’s cognitive computing powers are generally regarded as best-in-class. We can create connections between the two services to capture the benefits of both.

Dealing with Vendors

For a great many marketing technologists, constructing our own software for the most advanced functions will be out of reach due to resources or technical skill. That’s okay; there’s no shame in lacking those capabilities. Services like Google Cloud and IBM Bluemix contain the most advanced computational capabilities available to the public. Expecting the average marketer or marketing technologist to master what computer scientists are just releasing to the public is an unrealistic expectation.

What we can take away from this series is understanding the underlying technology of many vendors. When a vendor says they have ‘predictive analytics’, you can now ask what kind. If the vendor is nothing more than a few Google Cloud, AWS, or Bluemix APIs glued together but is charging a massive premium, you now have a better sense of whether you should pay that vendor or hire a developer to build your own solution.

If a vendor says they have ‘artificial intelligence’, you can now query them about which AI service they’re using. Are they using TensorFlow from Google? Are they using IBM Watson? These are the best of the best services, and affordable to boot. If a vendor refuses to tell you what AI they’re using, they’re probably not using the best-in-class; that’s fine as long as they’re not charging best-in-class pricing.

Conclusion

Think of these marketing technology infrastructures as tools in our kitchen. They’re appliances, frying pans, pots, and spatulas. Whether or not our kitchen turns out delicious food depends on the ingredients (data) we start with and our skills with the tools. Thousands of people own top of the line appliances and cookware, yet make terrible food every day.

Whether you build your own marketing technology software or buy it prepackaged is like cooking your own dinner or dining out. You know the premium you pay for dining out; whether the equivalent price in marketing technology is worth it to you depends on your ‘cooking’ skills with marketing technology.

In the long run, mastering these technologies will save your company money and time. You’ll better be able to evaluate vendors, know when to build vs. buy, and innovate more quickly.


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