Do you know the difference between passive and active datasets for ONA? In the practical online training course How to Use Python for Organisational Network Analysis Bennet Voorhees outlines the basics of organisational network analysis, what it is and why you should do one. He takes you through the necessary steps of how to use Python to understand collaboration, networks and key influencers within your organisation as well as how to build network graphs and to visualise relationships. In this short bitesized learning video, taken from the course, Bennet helps us understand the difference between active and passive ONA.

What is ONA?

The simplest definition of Organisational Network Analysis (ONA) as described by Deloitte is that “Organisational Network Analysis is a structured way to visualise how communications, information and decisions flow through an organisation”. Wikipedia explains that “Organisational network analysis (ONA) can be used to provide an x-ray into the inner workings of an organisation — a powerful means of making invisible patterns of information flow and collaboration in strategically important groups visible.”

David Green describes Organisational Network Analysis (ONA) as a means of applying a fresh lens on relationships and behaviours both within and between teams, and how work really gets done in a business. It enables social capital to become visible and offers people analytics teams a supplementary and powerful data set to really understand productivity and performance within their companies.

Why is ONA growing in importance?

As organisations continue to embrace the concept of agile, cross functional and flexible working, we’re seeing the emergence of new teams and new networks being formed where the work is actually being done, which is information that is not captured in traditional organisational charts. They bear little resemblance to the actual web of people involved in executing and delivering on the work.

Conducting an ONA gives an organisation the power to generate the insights that determine how information flows through the business. Ultimately it can increase operational effectiveness by building an organisation that is structured to increase collaboration and the exchange of information between the right people.

ONA has the power to not only provide us with visibility in what networks exist and the cross functional work that is taking place, but it can also provide valuable insight to support organisational design. Making it far easier to establish team structures that unlock innovation, drive productivity and improve overall performance. It can transform organisations by identifying formal and informal leaders who can be counted on to facilitate change and help accelerate the realisation of transformation benefits, this is increasingly important as we navigate through this era of digitisation.

Now we understand the importance of ONA and why it’s growing in importance, let’s turn our attention to the difference between active and passive ONA.

Interested in learning more about Python, ONA and People Analytics? Take a look at our online People Analytics & Digital HR training courses on myHRfuture

What is active and passive ONA?

The effectiveness of an organisational network analysis is dependent on data available that describes the relationship between colleagues or between teams. There are two ways that we can collect the necessary information required to conduct such an analysis. The first is with an ONA survey, which is also often called active ONA.

Just as it sounds, you’d be required to send out a survey to people whose collaboration habits you’re interested in understanding, and you’d ask them about the relationships that they have with other individuals within the organisation.

The second way is a passive way of understanding relationships. And just as it sounds, it uses data that already exists within the enterprise, such as email logs, chat logs or in some cases even understanding who's collaborating on certain documents.

What is the difference between active and passive ONA?

The biggest advantage of collecting passive data is, as the name suggests, it’s passive which means it already exists. This information can be derived from email systems or from chat logs, and ultimately this data can be mined by simply having a conversation with your team members. This leads us on to the second biggest advantage of passive ONA data collection. And this is, because the data needed to derive the connections and network of connections that exist within a team or the organisation already exists thus it is not required to ask anything from anyone about their relationships. However, with that said there are downsides of passive data collection. The most common area of contention that arises is whether the data being used to generate the ONA has been obtained ethically. Have you obtained consent from your employees to analyse their relationships and connections in a passive manner? Bennet explains that it’s important to

“Think about a big brother type of a mentality when looking at information that exists without really telling anyone about the purpose of you looking at that information and what you're going to look it and what you're going to do with it.”

The second most challenging element to consider focuses on the amount of infrastructure necessary to both collect and process data on the flow of communication in a large enterprise organisation, especially when there are tens of thousands of employees. It may require large servers, investment in hiring resources such as data engineers or data scientists in order to process this data properly.

The biggest advantage to collecting survey data to structure your analysis is the ability to understand nuanced dimensions of relationships between employees. By asking people directly what they get from their relationships with others, it is possible to determine whether or not a relationship provides information to get work done, personal support when changes happen in the organisation or political support so that individuals feel like they have the support required to implement change or large initiatives throughout the organisation.

The biggest disadvantage to collecting survey data used to generate an ONA, is that it requires a large amount of effort to ensure that people fill out the information that is necessary to understand the relationships that exist, especially in surveys where we are looking at the collaboration between thousands or tens of thousands of employees.

In order to build a truly accurate picture we must ensure that the data provided is as complete as possible documenting as many of the relationships that exist within the organisation. An ideal response rate is approximately 80%, which means that we need to engage managers, have a very clear communication strategy about the intent of the analysis and to make sure that everyone is comfortable with the data that is being collecting.

Whether you decide to use active data, passive data, or both, one big consideration is around privacy. As with any people analytics project, and particularly with regards to using passive data sources such as email and calendar metadata, this means ensuring clear communication with employees (what data do you want to use, for what purpose and what is the benefit to employees), working closely with legal, IT and employee representative groups and establishing clear governance around data collection, access and storage, communication, and overall transparency.

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ABOUT THE AUTHOR

Manpreet Randhawa is the Head of Digital Content for myHRfuture.com. In her previous role as the Change Management Lead for People Planning, Design & Analytics at Cisco Systems, she was responsible for defining and executing on the change management strategy to successfully implement and sustain the digital and cultural transformation across the enterprise. Manpreet is very passionate about change management and technology and how to use both to transform the employee experience and prepare companies for the Future of Work.