Data Science comes equipped with enormous potential that can completely transform businesses and take them to a different level altogether. The field can generate insights on important parameters of performance and by helping businesses deliver superior customer experiences, it can significantly enhance the long-term sustainability of the business. That said, the key to realizing its full potential is contingent upon the ability of clients, users, or partners to fully understand the results. The majority of these stakeholders are non-technical in their backgrounds, making it difficult for them to comprehend the results from the larger perspective. To address this challenge, data professionals must focus on their target audiences, work on their communication techniques, and adopt a non-technical approach to keep things simple and uncomplicated. How all this can be achieved and what strategies can help in this regard; here's the complete lowdown:
1) Understand Audiences: A deep knowledge of the target audiences and what they expect from the data will help analysts to devise an effective strategy for teaching audiences from non-technical backgrounds. Focus on what target audiences are interested in - methodology, analysis, or end results and what they want to achieve with insights - growth, acquisitions, or cost cutting. The answer to these questions will help you understand the primary requirements of the client and accordingly, you can strategize your content, analysis, and format to best fit the needs of your audiences.
2) Focus on Clarity: Once the understanding of the audience is complete, next focus on what you want your audiences to accomplish from data analysis. Whether you want them to use insights for fulfilling strategic objectives, solving tactical issues, or exploring opportunities in new industry verticals. This clarity will help you to use appropriate models, simplify information, and communicate only those insights that are relevant to your audiences.
3) Avoid Jargon/Complexity: The primary motive of data science is to offer insights that aid audiences in effective decision-making. To accomplish that, communicating to audiences in a simple and uncomplicated manner is of utmost importance. Before interpreting the results, you should filter out irrelevant data and convey only pertinent information with the help of images, visuals, and infographics as these can be easily understood by non-technical audiences. You can also use descriptive terms such as mean and correlation to highlight important parameters and to further enhance the grasping power of audiences, the use of colours, stories, captions, and metaphors is also recommended.
4) Share, Involve, and Track: Keeping audiences involved by constantly sharing information with them is a great way to help them understand data science in its entirety. This continuous interaction also offers audiences an opportunity to raise queries, clear doubts, and offer feedback on the data analysis process. Further, steady interaction with users can greatly enhance the output by augmenting learning, suggesting improvements, or even initiating corrective action in case it's required.
5) Offer Contextualized Insights: Instituting the data analysis in a proper context is very important and even more so when insights are presented in front of non-technical audiences. To address this challenge, start with the broader context of the study and then come down to explaining what analysis is all about, what problems it solves, and how these insights are relevant. The use of regression, ranking, and rating models can help audiences understand the issues at the holistic level and their implications on stakeholders of value chains.
6) Provide Actionable Insights: A good analysis must result in actionable insights with proper emphasis on their importance, implementation urgency, and desired impact on businesses. As target audiences are non-technical, the use of real-world examples, testimonials, and endorsements can prove to be a great help in making them understand the situation in its totality. Further, supplementing the recommendations with well-defined timelines and schedules will double their chances of being fully understood by non-technical audiences among others.
Teaching data science to non-technical audiences needs a totally different approach. The key here is to keep things simple and uncomplicated and this is exactly what Edvancer has perfected with its years of research in the field of data science. The leading eduventure offers a range of data science courses and places a profound emphasis on delivering knowledge in an easy-to-understand manner. The courses on the platform are delivered by the best faculty members and by bringing together hands-on training, career assistance, and practical content, Edvancer makes sure that every aspirant scales new heights of success in their professional careers.
The article is authored by Aatash Shah, Founder and CEO of Edvancer Private Limited, India does not accept any liability for virus infected mails.
