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How do you define data-driven workflows and why are they important?

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Data-driven workflows are processes that use data as the main input and output, and that automate or optimize tasks based on data analysis. Data-driven workflows can help you improve efficiency, quality, and decision-making in your projects, teams, and organizations. In this article, you will learn how to define data-driven workflows and why they are important for workflow management.

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1 What are data-driven workflows?

A workflow is a sequence of steps or actions that transform inputs into outputs, such as a product, a service, or a result. A data-driven workflow is a workflow that uses data as the primary input and output, and that leverages data analysis to automate or optimize some or all of the steps. For example, a data-driven workflow for customer service could use data from surveys, feedback, and chatbots to categorize, prioritize, and respond to customer requests.

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Zozi Yousefi

Senior SharePoint Specialist | Business Analyst | Microsoft 365
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From what I've seen, using data to guide our workflows has made a big difference. We've been able to make smarter decisions and make our processes work better. It's like having a map that shows us where we're doing well and where we can improve. By looking at how things have gone in the past, we can also get a sense of what might happen in the future. It's helped us save money and stay ahead of the game. Overall, by utilizing data, we can even identify user mistakes to enhance our business strategy.

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Kalaiarsi Periasamy

The Process Wizard | I help SMB owners reclaim their Time?| Automation and AI agents | LotusMVP
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In my experience, I have seen that data-driven workflows are the new normal. As part of our business workflows, we have lead/customer data from multiple tools. It makes sense to personalise/customise user journey based on the data we have, for better User satisfaction rates.

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2 Why are data-driven workflows important?

Data-driven workflows are important to help you improve efficiency, quality, and decision-making. Automating repetitive or complex tasks, such as data entry, validation, and reporting can reduce manual work, errors, and delays. By applying data quality standards, rules, and checks throughout the process, the accuracy, consistency, and reliability of your outputs can be enhanced. Furthermore, data-driven workflows can provide data insights, recommendations, and predictions based on data analysis which can support better decisions.

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Clinton James
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Efficiency is achieved for ‘happy’ path and quality is a achieved through managing variations. It is important that exception flows can readily engage (delegate/escalate) manual intervention with the option to re-enter the workflow.

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Kalaiarsi Periasamy

The Process Wizard | I help SMB owners reclaim their Time?| Automation and AI agents | LotusMVP
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In short, Customer satisfaction. Sending a 'buynow offer' email to a customer who has already bought it, would definitely annoy them. Also, manual errors from the team's side could cause potential damage to the business' brand and cause customer churn.

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3 How to design data-driven workflows?

When designing data-driven workflows, you need to take a systematic approach that involves four main steps. Firstly, you should define your goals and objectives, such as what you are trying to achieve with the workflow and the expected outcomes and metrics. Secondly, identify your data sources and requirements - this includes the data formats, types, and quality criteria. Thirdly, design your data analysis and automation - this includes the tools, methods, and algorithms used to automate or optimize tasks. Finally, test and refine your data-driven workflow by evaluating its performance and effectiveness, as well as using feedback and improvement mechanisms to adjust and optimize it.

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Kalaiarsi Periasamy

The Process Wizard | I help SMB owners reclaim their Time?| Automation and AI agents | LotusMVP
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Set key outcomes as target. Design using appropriate tools like N8N, Make, Zapier etc. Ensure that errors are captured and notified. Be sure to test edgecases like timezone dependent data, run time exceeding scenarios etc.

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4 What are some best practices for data-driven workflows?

To ensure the success of your data-driven workflows, you should align them with your business strategy and goals, involve your team and users in the design and implementation, and monitor and update them regularly. Your workflow should support your overall vision, mission, and values and meet the expectations of stakeholders. Engage team members and users in the planning, testing, and feedback stages of your workflow. Make sure they have the skills, knowledge, and resources to use it effectively. Track and measure the performance of your workflow, using data to identify any issues or opportunities for improvement.

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Raymarlyn C.

IT Specialist, Army Shared Services Center, QA, Audit & Compliance Branch
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The final step in creating data-driven workflows should be adoption and adaptation. Ensure end users are following the new workflows and that they have successfully incorporated the use of the data into their daily processes.

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Kalaiarsi Periasamy

The Process Wizard | I help SMB owners reclaim their Time?| Automation and AI agents | LotusMVP
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Some of the good practices include: 1. Setting up a tracker system. This could be as simple as a Google sheet that tracks trigger times, run completion times etc. 2. Error flows should BE there. Send emails/slack notifications when things dont work as expected. 3. Batch process where possible 4. Queueing runs could cause latency, ensure that its within acceptable ranges.

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5 What are some examples of data-driven workflows?

Data-driven workflows can be applied to various domains and scenarios, such as marketing, sales, and human resources. For instance, a data-driven workflow for marketing could leverage data from web analytics, social media, and email campaigns to segment and target potential and existing customers. Similarly, a data-driven workflow for sales could use data from CRM, lead generation, and sales pipeline to qualify and prioritize leads, as well as forecast and close deals. Lastly, a data-driven workflow for human resources could utilize data from resumes, assessments, and interviews to recruit, hire, and onboard talent, as well as monitor and improve employee performance and satisfaction.

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Kalaiarsi Periasamy

The Process Wizard | I help SMB owners reclaim their Time?| Automation and AI agents | LotusMVP
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Some of the common workflows are: 1. 'Cart drop' flows 2. Upselling workflows 3. Web analytics that ensures that Product dashboard KPIs are updated regularly

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6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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How do you define data-driven workflows and why are they important?

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1 What are data-driven workflows?

2 Why are data-driven workflows important?

3 How to design data-driven workflows?

4 What are some best practices for data-driven workflows?

5 What are some examples of data-driven workflows?

6 Here’s what else to consider

Workflow Management

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