Decreasing Noise While Increasing Value

twitter volume

When discussing some of the latest Microsoft innovations built around Office Graph, I’m not surprised to hear end users that are split over the value of Delve and many of the other Office Graph-driven technologies coming from Office 365. While speaking at the recent SharePoint Saturday Silicon Valley at the Microsoft campus in Mountain View, California, I was approached by a couple end users who attended my Yammer administration and productivity session, and their feedback very quickly wandered away from Yammer and social and was more about the confusion around the user experience. I asked them a bit about how they got work done today, how they interacted with internal teams, how content was created and shared, and where they thought they were having trouble collaborating as an organization. Many of the problems that surfaced – finding the right content, finding the right experts, being able to quickly and easily automate some of their key business activities so that their work was not so manual and repetitive – are themes that we’ve heard for years, and which are not unique to the SharePoint story. I then went on to explain the power of the Office Graph to help them find content and expertise, and as the machine learning picks up on their working patterns, to help with automation.

I was reminded of this conversation while reading an article in Bloomberg Business by Sarah Frier and Brad Stone, Twitter Tries to Tone Down the Chirping, in which the authors discuss Twitter’s efforts to reduce the “noise” of Twitter so that you don’t need to spend so much time digging through the massive flood of messages that come through your network. As the article outlines, what Twitter has come up with their ‘Highlights’ solution is a curated “best of” digest that provides a twice-daily summary of what it deems are your most important tweets. The article also points out that Facebook made some similar “advances” in curating content, and last year reduces the newsfeed to updates from only the people with which you have most recently interacted with, or pages you have visited (mixed with paid advertising, of course). As with Office 365, these platforms are using machine learning to, arguably, improve your productivity by only providing the data you need, based on your patterns of usage and social interactions.

Now, I’m not comparing Twitter and Facebook to Office 365 – I’m just illustrating the parallels in innovations focused on improving the user experience (UX). While Twitter, according to the article, is using these advances as a way to make Twitter “look less intimidating” and to be a tool that a broader audience can use as their primary newsfeed (let’s be honest – they’re chasing Facebook), the root effort at work here is end user engagement. After the novelty of a new product or service wears off, if that product or service generates too much noise and does not provide some baseline productivity, people will stop using it, plain and simple. That’s the value add that Office 365 experience like Delve can provide that Twitter and Facebook struggle with – because reduction of noise on its own does not equal productivity. I’m receiving the Twitter digests, and while they’re interesting, they are also throwaway. If the emails stopped, I would not even notice. But each time I use Delve, the results get more accurate. Each time I search, each time I skip over results and focus on specific content or conversations, it learns and improves on my results the next time I go to my Delve page.

Christian Buckley

Christian is a Microsoft Regional Director and M365 Apps & Services MVP, and an award-winning product marketer and technology evangelist, based in Silicon Slopes (Lehi), Utah. He is the Director of North American Partner Management for leading ISV Rencore (, leads content strategy for TekkiGurus, and is an advisor for both revealit.TV and WellnessWits. He hosts the monthly #CollabTalk TweetJam, the weekly #CollabTalk Podcast, and the Microsoft 365 Ask-Me-Anything (#M365AMA) series.