July 19, 2021
Data communities are the key to your data-driven transformation
What are data communities and what are their benefits? How can they help with enabling data-driven decision making within organizations?
While more and more organizations embrace data analytics to improve decision making, few have succeeded in harnessing them.
Extracting the real value from data requires experts from different domains. To simplify, data teams are collecting, cleaning, processing data to produce insights and reports, while the business teams are relying on those to make decisions or perform further analysis.
But the data teams don’t have the business perspective to understand the data and the expected outputs, and the business teams don’t have the tech skills to deep dive into raw data.
That’s why it is critical, especially for data & analytics projects, to ensure that within the organization employees, contractors and suppliers communicate, share expertise, get alignment and understanding on common work, whether they are from different teams or departmeants.
The obstacles to effective communication within an organization include:
- Organizational complexity with numerous business entities (departments, divisions, brands, etc.)
- Information scattered across multiple tools and usually limited to specific groups or individuals
- Geographically dispersed teams
With the COVID-19 pandemic and the rise of remote workers, the disconnection has been exacerbated sometimes leading employees to be further moved away from business goals and priorities. It is a crucial step to unite a disparate workforce by optimizing communication, and data communities might be the right solution to enhance data management practices.
Data communities create an immersive environment for their members and make collaboration around data and analytics effective between them.
What are data communities?
Data communities gather people to collaborate around common interests and goals that involve using, manipulating, or processing data in some way. The members can discuss projects, request or provide help, exchange ideas or share resources in real time.
The members involved include employees within the organization but also experts or providers from outside. A community can be cross-functional with members from different functional areas such as sales, marketing, product, data or IT, and with different levels of expertise.
Different communities might gather different roles and profiles and have specific needs in terms of contributors and resources. However, they all need to rely on efficient and strong tools to ensure the best communication between their members and achieve their goals. They use a dedicated environment where conditions are created for users to gather, share and exchange digitally. Not only the resource sharing is facilitated but also the potential geographical constraints between users are erased.
These communities help with boosting efficiency and creativity within the organization, in order to generate insights, deliver projects, improve processes or innovate.
Different types of data communities
We can identify 4 main data communities within organizations, with different types of interests and challenges.
Data experts: gathering technical experts around their topic of expertise (e.g. machine learning, data engineering, dataOps, MLOps, etc.). As there are a few experts with rare skills within organizations, they often end up being scattered across projects, departments or countries, and as such it is a necessity to federate them to maximize their potential contributions and help each other. Their knowledge and support can be leveraged for expertise requests in various projects.
Data for business: gathering business users around how they can leverage data to impact their business area of expertise (e.g. HR recruitment or retention, customer satisfaction, sales forecasting, etc. - possibilities are very wide here!). In large organizations, those users end up being spread in different divisions or entities, each with their own context and specificities, but also sharing similar processes and common challenges. The ability to crowdsource ideas, get help or insights contributes to growing the data literacy and the ability to scale initiatives over time.
Data providers: gathering business and technical users, from within or outside the organization, around data sources that are either managed / operated by them, or corresponding to the business area. Those communities align on taxonomies, definitions and quality criteria on their domain, and can be leveraged for expertise requests in various projects.
Data projects: gathering all actors and contributors around a given project. While this is probably the most obvious one, it is also the most complex type of community in term of diversity and ensuring they are efficient and with a high density of communication all along projects in a critical factor of success. Those communities ensure alignment with expectations, the value on one side and technical excellence of delivery on the other side.
While they are distinct, all those communities remain complimentary and interconnected: members of a data project community also typically belong to at least one other community of expertise (data experts, data for business, data providers) and can act as “liaison agents” to leverage there help, insights or validation.
Why it's a good time to build a data community?
Most organizations have been acquiring the technologies to collect, store, process and analyze data. It allowed them to experiment with data and analytics.
However, most companies fail with moving towards industrialization, efficiently leveraging data into actionable insights and having tangible impacts on the business strategy.
Advanced analytics can drive value only if employees use them to make decisions. But adoption is often the biggest stumbling block in analytics initiatives. (McKinsey, 2020 - Source)
The success of data projects depends on the ability to embed data into operational processes, and that users trust and use them for insights and decision support. This transformation requires a strong collaboration and new data literacy capabilities. These capabilities allow users to read, interpret, use available data and, most importantly, to get value out of it.
Data communities materialize this approach, by gathering and animating people around the same interests and common goals, and building the right context for skills and literacy development.
By 2025, organizations that create a formal program for citizen development, analytics and automation will be far more agile than those that do not. (Gartner, 2021)
New ways of collaboration are rising online such as Kaggle, GitHub, TunedIT or Codalab. These communities bring together virtually thousands of members to collaborate around numerous topics especially around data and analytics.
Replicating their approaches to establish analytics communities within an organization can be a real advantage to help data users work collaboratively on solving analytical issues. It gives a chance to others to contribute to a project, and encourage them to teach, share with others and pass on information.
The benefits of data communities
It is important to align communities with common business objectives and expectations in order to federate the efforts and allow synergies. Being aligned and working on the same goals allow the organization to address business needs much faster and avoid wasting efforts.
By 2023, 30% of organizations will harness the collective intelligence of their analytics communities, outperforming competitors that rely solely on centralized analytics or self-service. (Gartner, 2021)
From an operational perspective, communities enable to efficiently deliver a solution or a project the right way and with the right expertise in order to achieve the 3Us:
- Useful: serves a purpose and allows a user to accomplish an objective.
- Usable: refers to the usability of the product/service so that it is efficient when it’s used (or performs at serving its purpose)
- Used: ensures the users will adopt the product/service. Even if it is usable and useful, a product is a failure without users. The first two criteria could be a waste of time if this last one is not achieved. The “used” criteria refers to the “buy-in” notion. To mitigate the risk and higher the chance of change adoption, organizations might want to include the concerned users in the building process. The buy-in will initiate the change among the teams.
From a long-term perspective, bringing together professionals provide lasting effects within the organization and promote a strong data culture.
Better expertise: having qualified talents is essential to success and is, therefore, the biggest concern for organization to harness the power of their data. There are few very specialized experts in certain fields and it’s a necessity to connect them wherever they are in the organization or in the world, and rely on their rare knowledge and skills.
Communities grow their expertise by referring to internal and external thought-leaders who can share best practices and inject ideas and inspiration.
Based on their specific focus, communities can be leveraged within the organization to provide expertise on methods, topics and products in their area.
Foster data literacy: having data communities within an organization is a great opportunity to promote data literacy among employees. As they encourage open communication between members or with external requesting expertise, communities are the place where employees can learn and ask freely their questions about specific topics. It encourages non-data professionals to develop their skills or learn new ones and stimulates active participation. Deploying data literacy allows to better exploit data at every level of the organization.
Boost innovation: communities are a place where technology and digitalization meet creative mindsets. As individuals gather around similar interests, this becomes a supportive environment to have innovative ideas and solutions emerge. Having shared goals provides community members with a sense of purpose. It unites and federates them around a cause while strengthening the
connection and motivating communication. It becomes a place where users can test new methods and practices. The exchange of knowledge gives more opportunities to explore for members.
What makes a strong community?
Whether you are initiating communities within your own organization or working to enhance those already set up, there are some characteristics to keep in mind to achieve success!
Tech support: a community concept will work only if users are sufficiently engaged, and relying on efficient digital tools is key to remove frictions, drive engagement and improve adoption. Having a central hub makes it easy to connect from anywhere, consult and share resources, trigger conversations. Data and information visibility helps to break down silos across teams and overcome geographical boundaries.
Diversity among the members: a community made up of members with the same skills and experiences will probably come up with similar ideas, solutions or conclusions. Putting together a group with various levels of experience, complementary skills set, different business contexts and even different point of views is a strong advantage to pull up new concepts and have a better understanding of the overall challenges, constraints and opportunities.
Integrated: maintaining thriving data communities is a continuous process. The more communities are integrated within core processes and become part of the employees' daily workflows, the more likely they will not be seen as an additional effort but instead as a value-generating activity. Efficient communities helps to break down silos across the organization, speed up communication cycles and creates a virtuous cycle of continuous improvement and learning.
Sense of purpose: as engagement is becoming one of the most important indicators in gauging work satisfaction it’s not just about what you do, but why you do it. Besides the company success, employee motivations can range from growing career, increasing salary to enhancing internal recognition. Identifying and articulating the purpose in which communities have a play will likely increase engagement and ensure both employees and the organization will benefit from animating and growing data communities.
As organizations are constantly improving their structure and processes to optimize their data-driven transformation, data communities might be the missing element.
Fully embedding data in everyday operations requires a shift in mindset in an organization. The goal to convert employees to new practices is a long process, including training and organizational guidelines.
This is even more challenging in the current context of the massive acceleration of distributed organizations and work from home.
It becomes more achievable with communities when people are much more likely to have a special emphasis on knowledge exchange and with the support of the community members, from within the organization or beyond. This allows to mitigate risks and uncertainty by sharing their skills, learn from each other about specific topics, address business needs faster and encourage employees to explore more opportunities.
Strengthening collaboration and developing a strong data culture are needed capabilities to have a successful data & analytics strategy, and data communities are here to help.
Looking for the perfect tool to support your Data & Analytics communities? YOOI provides a dedicated collaboration and sharing space for community members, in order to engage around common interests and projects.
With YOOI, you can build your cockpit to manage all your initiatives, from emergence to deployment. Fully connected with your ecosystem of tools to consolidate information, YOOI is providing alignment with the strategy, visibility on progress and risks, supporting animation of your communities.
Don’t hesitate to contact our team to learn more and schedule a demo!