Coronavirus has disrupted business and social interactions everywhere. From financial markets distress to lockdowns and travel bans, we are living in the golden age of remote work.
Will telecommuting live up to expectations as companies adopt work from home strategies?
Businesses face an unprecedented Global Unwinding from COVID-19 as operations indefinitely pause and layoffs accelerate across all industries. Economists warn that COVID-19 could have lasting economic implications with the International Monetary Fund cutting global economic forecasts for 2020.
Companies including Microsoft are shifting to remote work¹ as social distancing is everywhere in the United States.
Zoom, Slack, and Microsoft Teams for collaboration are making it easier to organize workflows online.
These software are setting new standards for remote working.
Despite the current COVID-19 crisis, telecommuting is increasing according to Pew Research and could further rise as digitization continues.
How are data science teams² responding to the remote work model?
In this article, I will explore remote learning and break down ways you can adapt to the COVID-19 situation.
Remote Work Standards for Data Science Teams
Working from home is becoming common in this digital world and the gig economy. With the COVID-19 outbreak not going away, you need to learn remote work procedures³ such as communication and schedule management to boost collaborations.
Here are measures data science teams can implement for the remote work model.
1. Establish Frameworks for Remote Work Model
The first step of a remote work model is to develop frameworks to govern online coordination.
Adopting the remote work model will pose challenges to companies as they adjust to the current COVID-19 outbreak. Operating procedures differ among organizations and affect the functionality of the remote work system. Focus is needed here.
Companies face collaboration problems including low morale and productivity during implementation.
Communication policy is critical as the implementation of remote work begins to guide teams based on the workflow system. Most times, poor communication hampers collaboration with team members unable to function. Remote working requires clear communication and data science teams should consider this in their framework.
Video communication procedures enable better collaborations and #datascience teams should harness the benefits of remote conferencing. Developing a warm culture such as discussing personal issues can make online discussions better. A human-centered policy can improve data science operations.
Teamwork discussions create a positive environment when operating remotely. Data science teams need teamwork discussions to brainstorm their working goals to benefit from online interactions.
A brainstorming session creates a personal touch with team members where people exchange ideas and plan their workflows. Unfortunately, most teams disregard this friendly touch when working remotely and fail to realize their objectives.
A friendly remote culture motivates teams to understand work expectations by adding flexibility in their workflow without feeling pressured. Personalized interactions on remote work are essential for data science teams because of creating the freedom that allows teams to be innovative and productive.
2. Establish working expectations and motivate your Team
Data science teams need freedom and flexibility to work according to schedule. Over-supervising your team is one threat to the remote model⁴ as this stifles motivation.
Talk to your data science team and learn what works best for them as you adopt the remote model. This communication is vital for ironing out issues and developing an online platform that suits your conditions.
Develop a close relationship with your data science teams to understand their experiences as remote working starts. Strict social measures online do not benefit teams and instilling a fun working culture creates more opportunities to become productive.
Facebook and Google are using the remote system to develop strong relationships such as brainstorming sessions before each working day begins.
Expectations⁵ matter for online remote work and the same applies to data science teams responding to the COVID-19 crisis. By setting objectives of #remoteworking, you will be preparing your data science team to handle all tasks without pressure.
Overloading your team during remote working is a recipe for bad performance because of affecting the positive work environment. Online remote models work similarly to office operations, which means focusing on expectation setting.
3. Develop a Social Chatting Culture
Remote working should be fun and what better way to handle the current lockdown than with a fun platform for work?
Data science teams need a social environment that motivates them to collaborate, handle challenges and increase productivity online.
A human-centric culture has shown to support online remote collaborations according to a recent ZDNet poll. Socialization brings the best in people and using this approach online offers immense benefits.
Data science teams handle complex tasks and creating social moments when remote working is a bonus for your workflow. A social culture for remote work is an added advantage for data science teams as people experience a productive environment⁶.
Everybody has a story at the office not related to work and the same needs replication for remote working. Too much focus on work issues can overwhelm teams and adding some social fun is important.
4. Issue Progress Updates
Unlike the traditional office model, remote working requires keeping the rest updated about the workflow. Everybody needs breaks when working remotely and scheduling your time is a good collaborative method. Remote workers⁷ who disregard the updates process compromise the workflow by keeping others in the dark.
You need to inform your team about your schedule and current plans. Leave a message whenever you need to communicate something or need clarification. Updates show your dedication to project goals and enhance motivation for your workflow.
5. Feedback and Transparency
Remote working fails whenever teams do not use feedback to understand working patterns. A feedback system is important for data science teams and offers solutions for current challenges and pinpoints areas that need improvement.
Technology Tools for Remote Working
Remote working depends on effective communication and using technology tools such as Zoom and Slack can aid productivity for data science teams as the COVID-19 crisis continues. There are many #technology tools for remote working and depend on preferences for your data science team.
Here are some collaboration tools data science teams can use:
Zoom is becoming a popular video conferencing tool for remote working and data science teams will find it flexible. The Zoom video feature allows people to talk remotely on both audio and video where participants share discussions. By using Zoom, companies are taking advantage of the current COVID-19 distraction to maintain their workflows and keep teams together.
Zoom is an ideal and stable tool for data science teams because of connecting many people at the same time. Unlike other collaboration tools, Zoom offers teams a socialization platform and keeping the workflow going. Data science teams can use Zoom to record and even communicate important news about their projects in real-time.
2. Microsoft Teams
Microsoft Teams enables communication and coordination in real-time. Video conferencing from Microsoft Teams consists of updates for boosting user experience. Data science teams need Microsoft Teams as they confront the COVID-19 outbreak to facilitate smooth operations and keep in touch with their colleagues.
File storage from Microsoft Teams aids data science teams to organize resources⁸ and update the information when needed. The remote working model consists of many files and by using this option, you will be harnessing the value of Microsoft Teams.
Are you looking for a remote working tool for sharing on desktop and great video conferencing features for your data science teams? Then GoToMeeting is also a good choice because of their web-based model that makes remote working a great experience. The real-time coordination offered by GoToMeeting is applicable for data science teams collaborating round the clock and implementing projects.
Data science teams need to share their project work constantly and Comet.ml contributes to workflows and enables sharing anytime. This flexibility combined with cloud integration makes Comet.ml a great tool for data science teams because of using insights to improve working patterns. Experimental reproduction from Comet.ml makes work easy for data science teams and using this platform generates productivity.
What are the challenges of Remote Working?
Despite the benefits of remote working, data science teams need to understand the downsides of remote working to become productive.
Loneliness is one problem that data science teams can face when collaborating remotely. As teams work online, sometimes it gets lonely and affects motivation. Data science teams need to create a social support system to handle loneliness⁹ whenever it comes.
Internet connection problems emerge when working remotely and data science teams face this challenge. Unlike the reliable office internet, remote working creates unreliable connections that could hamper smooth workflows. Be sure to check your internet connections before starting your session to enable a smooth experience with other teams.
Home distractions such as TV, Radio, or family members pose challenges to remote teams and the same applies to data science teams. During this COVID-19 isolation period, most people are spending time with their families at home and this means increased distractions.
Personal scheduling problems face data science teams and involve a lack of developing a disciplined approach to remote working. The reality is that remote working requires discipline and this could prove challenging to most teams.
Remote Collaboration is the Future of Work
The COVID-19 outbreak is teaching us that remote working needs new thinking. The outbreak has affected businesses and with most companies unprepared from this unforeseen event, remote working should be part of our workflows.
Data science teams need to communicate clear policies about remote models to continue working smoothly despite the current disruption from the #COVID-19 pandemic. Remote working comes with its own challenges including poor coordination and broken communication, which affect operations. To avoid this scenario, you need to set remote policies that suit your situation.
Take advantage of tools such as Slack, Zoom, and GoToMeeting for your collaboration efforts today as remote working becomes the norm. Be creative in your remote work models such as brainstorming sessions and small talk outside work when coordinating online. This will bring productive results.
¹Remote Working, ²Data Science Teams, ³Remote Work Procedures, ⁴Challenges facing Remote work, ⁵Setting Expectations for Remote Working, ⁶Productive Environment ⁷Remote Workers, ⁸Organizing Resources, ⁹Loneliness