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Six Predictions about BizOps, eCommerce, Machine Learning, and Amazon

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Six Predictions about BizOps, eCommerce, Machine Learning, and Amazon

The team at SOASTA provides their six predictions for 2017, including the emerging topic of BizOps.

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Yes, we all know. Lists of predictions are ubiquitous at this time of year. And they’re usually pretty unreliable. But not this one. Why am I so confident? Because I’m in the process of writing another post where I evaluate our predictions for 2016, and I’m feeling pretty good about our credibility. But more on that another day.

For this post, I talked with some of my fellow SOASTAns about what we see coming up in 2017. Here’s what we agreed.

1. We’ll All Be Talking About Bizops

The massive digital transformation we are in the middle of will compel business and technology teams to work closer together than ever before, and data scientists are the social glue who will make it happen. They will do it through the relentless discovery of strong correlations between business metrics and technical metrics. By the end of 2017, this new culture will finally be mainstream (although everyone will be playing catch-up with Amazon for years to come) and we’ll all be talking about “BizOps”.

2. Retailers Will Realize They’re All Competing With Amazon

Ridiculously scary, but true. Amazon has moved into a staggeringly broad range of markets – including music, cars, clothing, photo processing, and even lumber. No matter what kind of business you’re in, it’s safe to say that Amazon has its eye on it. And while people may love your brand, they’re not at all loyal to your website. If they can buy your products through Amazon Marketplace, they will. That’s because they trust that Amazon will give them a safe, fast, reliable customer experience. Amazon has set the UX bar extremely high, and shoppers today expect every retail site to reach that bar.

Read our Case study: How Lowe’s competes with Amazon on its own turf.

3. The Competition for Holiday Dollars Will Continue to Move Online

This past holiday season, Black Friday traffic exceeded Cyber Monday traffic by 21%. Most shoppers are tired of the crush of malls, and these increasingly savvy consumers know that in-store deals are often no better than online deals.

In-store shopping on Black Friday – and over the holidays in general – will continue to decline, hastened by the prevalence of mobile devices and ubiquitous free shipping. Retailers need to be ready to meet the rush. Shoppers expect sites to be always available, even during record-setting traffic peaks. And those record-setting peaks are going to occur even more frequently in the future.

Read > Black Friday weekend 2016: What we’ve learned so far about retail traffic and user behavior

4. We’ll Manage Performance Like We Manage Products

Prioritization will become key. As consumer expectations for speed go up, Engineering and DevOps teams will need to manage performance like a product. Using data from monitoring products and advanced analytics to shift from fire fighting the most recent issues or optimizing the slowest pages to identifying the key areas that need attention. Focus for development, testing and optimization, will be on the pages, use cases and demographics that create the most revenue.

Read > How to test smarter with RUM-based performance testing

5. Data Integration Using Data Api(s) Will Become More Common and Will Happen in Near Realtime

Traditional ETL and data warehouses have a lot of friction and entropy. Stream processing and data integration in the stream will peek its head into the market as a preview of powerful capabilities in the future. (Big Beacon and BlockChain are both examples of this.)

6. Machine Learning Will Move Out of the Lab and Into the Real World

Machine learning models will be trained and installed in production systems to predict outcomes and to detect abnormal events and system states. This will result in faster and more targeted responses to systems issues. Problem alerts will carry context created from Big Data to support quick diagnosis and remediation. Advanced alerting and web integration (webhooks) will tie all of this together to “machine assist” the diagnostic and remediation activity.

Read: How SOASTA and Google used machine learning to predict bounce rate and conversions

Agree? Disagree? Have some predictions of your own? I’d love to hear your thoughts.

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Topics:
machine learning ,amazon ,devops ,big data

Published at DZone with permission of Tammy Everts, DZone MVB. See the original article here.

Opinions expressed by DZone contributors are their own.

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