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The Importance of Using Robust Analysis to Understand Change

The Importance of Using Robust Analysis to Understand Change

Using your data to uncover information makes leaders more insightful about areas of the business you cannot always see.

Understanding new customer expectations and collecting robust data to manage the progress of your change strategy is vital, but all too often the gap between actual expectation and company perception of needs leads to business process changes that fail to deliver the promised results.

Deep and solid analysis can find the signals in the noise, and here’s how.

1) Collect the data.

One area crucial to managing business growth is delivering a product or service that's relevant and resonates with your customer. Here, data should provide insights that management and employees on the ground are unaware of.

This gold mine of information should hold truths that the business doesn’t see in day-to-day operations but will support growth and strategy going forward. There are always new insights to learn, and when data is collected, the process should be broad so the results are not influenced and truths are revealed.

For example, according to the 2020 global Salesforce report, 70% of customers say connected processes — such as seamless handoffs or contextualized engagement based on earlier interactions — are very important to winning their business. 84% of customers say being treated like a person, not a number, is very important to winning their business, with technology driving higher expectations for innovation.

The driving force for data collection should be to close the gap between customer expectations and customer experience. Customers judge and trust brands based on their experience as a whole, not just interactions with specific departments, and they demand consistency across touchpoints. Having robust data collection processes allows the organization to see new emerging and changing customer patterns, and potential areas to explore.

The diverse analysis of the data collection can spark internal questions about operations. As the data comes in and you understand what you need to fix, how are you analyzing what changes to make? Knowing what the customer wants is a necessity of business. Converting that into the expected experience is the next big challenge.

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2) Convert data into information.

Every organization evolves its own best practices to systematically and continuously analyze the processes unique to their organization, and how they meet and exceed customer needs. Rich data can become deep information that drives innovation areas such as product management, process improvements, and business culture.

It's all interconnected; when organizations are able to make the right links with that information, leaders feel more empowered to make decisions, because data-led decisions that have broad oversight and are paired with good governance and leadership are extremely powerful.

Data collection and conversion may seem daunting. There is a plethora of analysis process tools that can fast-track getting a clear definition of the “as-is” business processes, drawing from proven successful models rather than reinventing the wheel, or running the risk of missing touchpoints.

They work just as well in service-driven companies as in product production, helping to standardize core, goal-oriented workflows, and stress test weak points. These tools are vital in complex organizations but are also needed to keep nimble smaller companies on track, as well.

Gartner defines these tools as being:

Primarily intended for use by business end-users looking to document, analyze, and streamline complex processes, thereby improving productivity, increasing quality, and becoming more agile and effective.

When working with clients to break down processes and procedures to optimize current methods and add new customer approaches, the data is usually based on more than one variable.

If correlation means there is a relationship or pattern between the values of two variables, and causation means that one event causes another event to occur, this can clearly have a fundamental impact on success. Too many times the two can become confused and business decisions are made on the wrong assumptions.

Some of my go-to tools to get to the root of how structures meet customer needs are:

  • Best practice documentation.
  • DMAIC model (define, measure, analyze, improve, control).
  • Value stream mapping (value-enabling, value-adding, and non-value adding activities).
  • Regression analysis (statistical process for estimating and understanding the relationship between variables).
  • Gemba walks (visit the location and employees where the task takes place).

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3) Stage the information.

Data visualization is one of the most critical skills for any company. Used by global reaching companies such as Amazon and Nike, data visualization is:

The process of creating graphical representations of information, helping to communicate data in a way that’s easy for the viewer to interpret and draw conclusions.

This type of insight keeps leadership in the loop, regardless of distance from processes and people.

By presenting data in a format that is more instinctual for the human mind to understand and comprehend, people at all levels of the organization can work effectively and speak the same language. Being able to interpret and communicate effectively together on business-critical issues and strategies allows for a drastic reduction of errors.

Subsequently, we are more able to identify trends and patterns within large datasets, adding another layer of analysis. Giving this type of support and empowerment up and downstream within the company only strengthens the outcome and helps to build a trusting and strong culture.

According to MIT neuroscientists, 90% of the information transmitted to the brain is visual. No matter how good your data collection is, no matter how robust your analysis, if the information is not presented in a way that’s easy for people to understand, then it's a missed opportunity to find the connections, trends, and patterns that drive innovation and successful change.

Staging the information through powerful visual aids also enables you to bring your teams on board more quickly, allowing you to responsively convert data insights into company action and business value.

Using your data to uncover information makes leaders more insightful, and cross-discipline and cross-department deep-dive data analysis techniques are essential to close the gap between customer expectations and customer experience. Training your teams on how to reduce the noise and look for the real information that data presents ensures that everyone has a consumer-oriented approach to process and performance.

In our current environment of swift market change, the difference between businesses that survive and fail are robust analytical practices and resilience in process and change management.

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Audrey Hametner is a global leader in operations, governance, and risk management, drawing on over 23 years of experience as an international operations strategist and NED. A certified Six Sigma Black Belt with an honors degree from the University of Toronto and an MBA from Imperial College London, she has managed teams across the globe, including India, Europe, Middle East, USA, Canada, and Singapore. She is a regular keynote speaker and panelist on business platforms, specializing in best practices that are relevant and responsive to changing customer demands.

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