Managing Transformation and Changes Using People Analytics

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Managing Transformation and Changes Using People Analytics

We are living in a world that is changing at a lightning pace and overloaded with information, continual technological revolutions, and uncertainty, like the phase we all are going through with Covid-19. The consequences are radical. All these changes are transforming the way we live, how we communicate with each other, how we create and share knowledge, how we do our personal stuff, and even how we manage a relationship.

Challenges and transformation
Leveraging people analytics beyond common practices
Using people analytics in organizational change
Case Study: Data-driven change with people analytics
On a final note

Challenges and transformation

The global Covid-19 pandemic has challenged companies to manage their enterprises in new ways. Organizations are not only experiencing enormous scope changes to their daily business but also a restructuring of the economic order. Many organizations are now forced to transform their business, organization, and way of work for their survival and existence. Working with distributed teams is a very good example of such a business transformation that applies to most of the organizations today.

The biggest challenge is that organizations must also operate in these changes at the same speed. As a result, they need to continuously adapt the way they managed the business before. As the world has become so unpredictable, adoption has become the only way forward in the fast-changing sectors of any economy. So, it is important now for organizations to do business with new resilient, and adaptive ways to engage within their ecosystem and deliver economic value.

Although this is challenging, it is also creating an opportunity for organizations to transform themselves with progressive strategies that can sustain for the long-term. In order to transform, organizations cannot just focus on technological enablement and processes, they need to rethink their organizational strategy, leadership, culture, and talent. Organizations must mount on activating, aligning, motivating, and equipping leaders to inspire and drive change.

Moreover, engaging and empowering workforces is also crucial as transformation is made up of the most complex system of all – people. Today, organizations need analytics-based insights to help them manage and track signs of progress of these complex organizational changes, changing behaviors of workforces, leadership competencies, and business results.

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Leveraging people analytics beyond common practices

People analytics is not only necessary for the HR organization. Other business functions have also recognized that they need people data to analyze and plan, and to make evidence-based decisions on people functions like joining, leaving, performance, pay, retention, engagement, succession, learning, leadership, etc.

In addition, the data can also be leveraged to deliver the highest-quality customer service, bring innovation, and help transform the business. Organizations that make data-driven decisions about their people achieve higher performance, better results, and a superior return on outcomes than those that don’t.

Although people Analytics is the 2nd most popular trend to watch as per LinkedIn’s Global Talent Trends for 2020, we haven’t seen any popular practices related to change, culture, and transformation (yet). Currently, companies are majorly leveraging people analytics practices in measuring employee performance, strategic workforce planning, identifying skills gaps, evaluating recruiting channels, assessing talent supply/demand, identifying flight risk to improve retention, and reducing bias in hiring/promotions.

According to LinkedIn still, many organizations are only at the early stages of developing their people analytics functions. There’s a steep learning curve from initial efforts to collect data in an organized way to capitalizing on insights for competitive advantage. 

People analytics is an evolving journey and it’s growing with the expansion of areas like organizational network analysis (ONA), cultural analytics, workplace analytics, etc. With these new applications and areas, organizations can gain insights from analyzing behaviors, values, relationships, workplace habits, and social sentiments of the workforce. This can help them in improving employee experience, future learning, and many other challenges.

Two years ago, I introduced Cultural Analytics and its use cases. I explained how culture is a key building block for any organization. Whether you consider talent acquisition, people engagement, business performance, or any transformation, culture is the centerpiece of it all. Using cultural data to identify the behavioral features of corporate culture and identifying highly aligned individuals can be key to the future success of any business.

This data also enables one to hire, develop, and promote using insight about an individual’s degree of alignment with the organization’s current or target culture. In many cases, we need to re-visit our current culture and drive it in a completely new direction. Cultural Analytics, which can be treated as a branch of People Analytics, is the use of computational and visualization methods to derive and leverage insight about shared values and believes in organizations. And change management for transformation and organizational culture change are the key use cases using people analytics for sure.

Using people analytics in organizational change

Organizational change related to transformation is not easy as it requires an adaptive strategy that’s well supported by leaders who can foster a culture of embracing change. The demand for an adaptive strategy is clearly seen from the constant changes within the workforce and workplaces, along with challenges faced by the organization due to unpredictable events like Covid-19. Capturing the right data is important to hypothesize, experiment, measure, and generate insight using exploratory data analysis in making better decisions for these changes.

Organizations need data to assess risks, progress, adoption, and usage related to the changes in leadership and culture when considering transformation. For organizational change, it is important to identify changing behaviors, ways of working and ongoing collaboration among the workforce. Instead of applying a checklist approach to change management, it is better for organizations to focus on listening to the voice of the employee and adopting this input to identify key drivers of change.

To start gathering information, the sources of data could vary depending on the needs, restrictions, infrastructure, and privacy design. There are different forms of data that can literally turn into some valuable insights when it has gone through the right analysis; real-time communication and collaboration data are most common within organizations in this case, whether it’s your email, online meeting tool, customer platform or even project management platforms.

There is always an opportunity to use survey data for change assessment, like engagement data, and other continuous listening techniques to enforce the outcome but depending on the needs. This data collected from different sources can provide some fantastic insights for better action-driven decisions when combined with organizational and HR data points. Figure 1 below shows the complete landscape that can be built to provide actionable insights to manage change and transformation.

Managing transformation using people analytics

 Figure 1 – The landscape for managing change and transformation using People analytics

Proper data engineering, correlation, and modeling are vital before we generate useful insights with the use of the right visualizations tools. They can provide us an accessible way to see and understand trends, outliers, and patterns in the data. It ads a big advantage when we can integrate these data segments within the existing business warehouse and leverage data mining capabilities to discover useful patterns. There has also been a significant use of organizational network analysis (ONA) tools to understand the human connections and their relationships, which provides a data-driven view of collaboration and information flow.

Lastly, when we are dealing with texts, mostly collected from these communication and collaborations data, there is always a possibility to use text analytics to convert unstructured text data into meaningful data for analysis, to measure opinions, feedback, sentiment analysis, and entity modeling to support fact-based decision making. Most of the data generated for the past years is unstructured data, mostly text but also images, video, etc.; this remains largely untapped by most organizations especially when it comes to HR and people data.

Employee text analytics with the use of Natural Language Processing science can be very useful to drive business outcomes. Overall, people analytics can be a real game-changer for organizational change by generating quantitative behavioral insights on what people do at work, how a transformation will affect their work, and how changes in behavior can improve business performance.

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So, when it comes to insights, this data can generate a meaningful analysis and metrics on workforce behaviors and ways of working including change behaviors, collaboration patterns, and communication efficiencies. These can help organizations to monitor the desired behaviors and target ways of working during their transformation. Another important aspect of any change and transformation is to understand the leadership role and behavior along with determining informal leaders and change agents.

For any successful transformation, it is always necessary to completely rethinking the organization’s design. These data points can also lead to a better perception of decision effectiveness metrics, current bottlenecks, and the organizational development scope which can significantly provide support to re-design the target organization and to keep it lean. In addition to this, there could be valuable insight to understand diversity and culture. Although culture analysis is altogether a separate topic, as I mentioned before.

Organizational activity metrics,  such as engagement, retention and internal job rotation frequency, can also be used to support the transformation. The most effective analysis, however, can be done when the insights generated with this data can also provide a clear picture of productivity, efficiency, and performance, and risk metrics which can help leaders to make better decisions on business outcomes when managing the changes. Depending on their transformation goals, organizations may generate different other insights with all these data.

According to BCG, people analytics can identify the wheel that most effectively improves performance and amplify the effects of a transformation. Their experience with analytics showed that the organization could improve customer satisfaction by 21% by increasing workforce retention, reducing attrition through better leadership, and empowering women to drive business. The right use of people analytics can uncover incredible actionable insights to get to the heart of the real business problem to be addressed.

Case Study: Data-driven change with people analytics

Many of us know Spotify’s agile methodology, which is a people-driven, autonomous framework for scaling agile while emphasizing the importance of culture and network. Spotify also introduces terms for teams such as ‘Tribes’, ‘Chapters’, and ‘Guilds’, and the objectives of these teams are a great way to promote teamwork, collaboration, and innovation, as well as giving team members ownership and a sense of enablement.

Figure 2 - Spotify’s agile methodology diagram
Figure 2 – Spotify’s agile methodology diagram

So, this methodology uses Squads, Tribes, Chapters, and Guilds, – the foundation of which is the Squad, which acts like a Scrum team. The Squad self-organizes and determines the best way to work, from Scrum sprints, to Kanban, to a hybrid approach. The Squad is single-product and single-project focused.

A product owner prioritizes and manages the backlog for the Squad while an agile coach works with them to accelerate transformation. A Tribe is a group of Squads that are working on a common area. The Tribe is co-located with the Squad and limited to 100 people. Chapters are part of a Squad and are a group of team members working together. Last is the Guild, a group of people with shared interests. Figure 2 above explains the relationship.

Time is Ltd. has provided a very insightful case study about their experience measuring the digital collaboration of an organization’s various teams with their favorite productivity tools like Slack, G Suite, O365, Zoom, and others which can save time, money, and enhance team productivity. During one of their client’s adoptions of Spotify’s agile methodology, they provide hypotheses, as shown in Figure 3, to identify the right KPIs to successfully measure the agile transformation. As it is crucial to be able to continuously monitor whether the implemented structures are also reflected in the workforce’s collaboration behavior.

Figure 3 - Key hypotheses for measuring the agile transformation
Figure 3 – Key hypotheses for measuring agile transformation
(Image Source: Time is Ltd.)

These key hypotheses have been chosen among others to test and were used as indicators while monitoring the progress of the organization during its agile transformation. The data was obtained through an aggregate of Office-365 calendar data that captured a significant part of the collaboration activity within the organization. More precise results can always be obtained by capturing communication data like email, chats, Jira, Slack, or Google Doc. Another data source has been used to get the organization’s data for the team.

Using the analysis of collaboration patterns using frequency analysis and socio-mapping (an ONA tool), the team was able to gain the below insights for the respective hypotheses above.

  • Change in interaction frequency between the original network of employees and the new agile designed network.
  • The proportion of attention time that agile people spend inside their respective squad and tribe over time
  • The behavior of employees in meetings including the number of attendees in the meeting, recurring meetings, meeting invitations, one-to-one meetings, meeting length, etc.

Figure 4 shows different graphs comparing the progress of agile transformation. For more details on this case study from Time is Ltd. please download the full version on via Data-driven Change Management: Measuring Agile Transformation Using Passive Data.

Graphs comparing the progress of agile transformation
Figure 4 – Graphs comparing the progress of agile transformation
(Image Source: Time is Ltd.)

These analyses are vital to measure the transformation and understand the need for organizational design or re-design. In the end, these insights help the organization to utilize the already existing data and identify new KPIs that can be used for more efficient and effective management of ongoing agile transformation. This is also applicable to manage other transformations and changes which are happening around us. Moreover, organizations can also explore metrics related to employee wellbeing, burnouts, engagements, productivities, and many others.

On a finale note

We have seen how people analytics helps to connect data to effective decision-making and how it draws insights from human behavior to help people and organizations perform better. People analytics is no longer a nice-to-have toolkit for HR. It’s a practice that organizations need to build so they can capitalize on the gained insights for competitive advantage.

It’s true that companies are going through constant changes and face challenges that stem from uncertainty, instability in the markets, digitalization, new ways of working, and globalization. People analytics can be of help thanks to its ability to bring in relevant data that will enable change management to answer many questions that are crucial for these constant changes that organizations are going through.

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