Become more data driven by leveraging network effects
Brett Bivens of Venture Desktop and TechNexus wrote a very interesting article recently on the empowerment loops of products and services, and how they have fuelled the growth of some of the world’s most successful companies. Empowerment loops entail the productivity increases that arise when organisations make participation in an activity more broadly accessible. As more and more users or customers are given access and incentivised to take part, the network effect becomes stronger.
Network effects are the mechanisms by which every new user makes a product more valuable to every other user simply with their participation, ensuring that value is created for everyone involved, thus making that product more valuable to the business.
While the main focus of Bivens’ piece was on the business model of companies built on creating empowerment loops with their products and services, in this article we will discuss how such empowerment loops create tangible business value for the organisations actually leveraging these services.
In particular, we will discuss the impact of network effects on organisational data science and the management of data-based knowledge, and how tools like knowledge management platforms become more and more powerful within organisations when 1) more employees have access to them and 2) there are more ways they can actually use them.
Empowerment Loops in Action
Bivens discusses some examples of empowerment loops driven by companies like Nike, TikTok and Figma. Let us focus on Figma for the purpose of this article. The idea behind empowerment loops at Figma is that design is much larger than just designers.
In a separate article, Kevin Kwok elegantly breaks down how compounding network effects are what drive business value for companies using Figma. By bringing non-designers into the process is what gives designers a seat at the table of product and business decisions. Conversely, as more designers use Figma, they indirectly encourage even more non-designers to start using it. And again, the more these non-designers use it, the more designers are going to be willing to adopt Figma into their process. It’s a virtuous circle and a powerful compounding loop.
Design is much larger than just designers.
The value created for businesses adopting tools like Figma is bottom-up. It starts with an individual or a small team of designers using it within their company, but actions from any one of them can lead to wider-scale adoption. Such actions can be as simple as being sent a link to the new landing page design, where the recipients will have access to Figma’s collaborative features and its overall better user experience. They, in turn, encourage its usage and adoption across their other teams and projects and so on.
The wholesale adoption of a new design tool only for a company’s designers may not sound like a priority to management or to the executives at face-value, but as the product or service permeates its way through the everyday operations of a business, to the point that even the higher-ups are reviewing new designs on the app itself, then its value becomes unquestionable.
Empowerment Loops in Organisational Data Science
We have seen the impact that empowerment loops through network effects can have when it comes to product design. The value proposition of the tool for an organisation increases as more users around a business adopt it.
The same principals can also apply to data science. But what is organisational data science? Let’s take a moment to consider a company’s entire data pipeline, which involves:
1. Data Collection/Aggregation & Storage.
2. Data Engineering — systems infrastructure & database management.
3. Data Science & Analysis — data modelling & interpretation.
4. Presentation & communication of generated insights towards business value.
Most companies today have a dedicated team of data scientists, engineers and business analysts whose sole purpose is to collect & transform raw data into actionable insights for the business.
Ok, so where are the empowerment loops? Typically, the data team analyses and models data to generate data insights that can be applied to everyday business functions. In this sense, they are content creators. However, while the data team may be the masters of data, it is the other business agents across the various departments that are the individual domain experts. Creativity refers to the act of making data-driven decisions, and for this to happen at scale, a business should be driving content network effects.
Creativity is larger than just content creators.
Just like Figma allows designers to leverage the domain expertise of non-designers around the business, the data team should incorporate input from other business agents on both new and existing projects. This will ensure that data science is focused on business outcomes.
You hired smart people, now help them do what you pay them to do.
In this way, the data team is not controlling the narrative, but will continue to influence decisions made around the business by driving analytics-based actions at all levels of operation. However, attaining this level of empowerment loops requires companies to have the right systems in place.
Kyso & The Central Knowledge Hub
And this is where Kyso fits in. We help your organisation leverage its own data insights by keeping all your analytics knowledge in one place so the entire team can learn from your business data & apply these insights to decision-making in their respective roles.
Let us consider Microsoft, who owns Power BI — the idea is to provide democratic access to insights. However, what is possible with the data is quite limited. Now consider Github — limitless analysis, but there is very little democratic access. Kyso fits in the middle — making hard technical analysis that normally lives in Github accessible to the wider company, bridging the gap between technical and non-technical teams.
There is a network created within a business as a central discussion forum for data insights becomes more valuable the more it is used. Think of blogging platforms like Medium. A data scientist publishes a report to Kyso, where it is read, shared and collaborated on between different teams and departments.
The network effects discussed in the previous sections occur when such a system inspires conversation and unlocks the potential of an organisation’s analytics by allowing employees to discuss these results, ask questions and provide feedback to the data team. Everyone in the company is brought into the conversation, creating an empowerment loop. In another article on managing organisational data science we discuss the power of having such systems in place.
The technical team learns from these effects — they get feedback on what works and what doesn’t, they learn the types of projects and analyses that are needed around the business. And bringing non-technical data stakeholders into the process is what gives the data scientists themselves a seat at the table of product and business decisions.
As with Figma and product design, the value of the platform (in this case, the creation, distribution and discussion of data insights) to users increases with the number of other users using it. It makes the data team more efficient by breaking down the walls between data science & analytics and the other teams they communicate with.
Both traditional and modern companies recognise that employees need to be empowered with knowledge in order to do their jobs effectively & to stay ahead of the curve. Leveraging systems that drive network effects and encourage all teams to contribute to data-driven business decisions will allow companies to remove the bottlenecks that continue to exist at the intersection between data team and other business agents.
Knowledge management systems that allow more & more stakeholders to better use and interact with data insights, and facilitate wider communication and discussion of these insights across the business are the answer. Only then can your business truly leverage the powerful effect of empowerment loops with its data and data science.