Data has a wide range of applications and ideas can be generated by many people. Yet, in most organisations, data practitioners only comprise a small proportion of employees who may not have regular interactions with other business functions.
Data has a wide range of applications and ideas can be generated by many people. Yet, in most organisations, data practitioners only comprise a small proportion of employees who may not have regular interactions with other business functions.
Today, without basic knowledge of data, business users find it difficult to understand data analysts. They often feel that the pace of data projects cannot match the fast pace of the business. Due to communication breakdowns or mismatch in expectations, the original business problem may not be solved in a satisfactory manner. Data analysts also become frustrated because they find it challenging to communicate their results with colleagues who do not understand basic data concepts.
Such issues prevent companies from rapidly utilising data to deliver superior outcomes and adapt to changing conditions.
Wouldn’t it be highly beneficial for ANY employee, if they could unlock the potential of such powerful strategies and tools?
What if you could disperse or embed data expertise throughout the various functions of an organisation?
By educating employees, changing how tools are used and allowing employees to take on additional responsibilities, companies can place themselves in a good position to flourish in the years to come:
Continuous learning has never been more relevant, especially in today’s knowledge economy. Teaching the core concepts of data science, and some primers on using data tools will allow employees to become more self-sufficient. A good practice would be to create online courses or tutorials for employees. Alternatively, many vendors or consultancies (such as our own) have already created their own online educational academies or programs – these can also help improve the proficiency of teams at a reasonable cost.
As teams progress, there will certainly be some members who learn faster than the rest – these are opportunities for them to mentor others! Working on problems in pairs can also be another constructive way to accelerate learning and consolidate knowledge.
As teams get a better grasp of data concepts, the quality of communication should increase – business requirements can be more precisely translated into technical requirements and ideas can be exchanged freely without confusion or misunderstanding. Frustration should decrease – with understanding comes an appreciation of how difficult certain requests may be. Employees will change the way they interact with those responsible for end products of data science processes.
Data analytics teams should also be tasked to create more front-end tools that can be easily used by employees not familiar with coding. Keeping data tools confined within data teams may also not be optimal since no other team can access them. Data teams also end up shouldering the extra burden of being gatekeepers to these tools, should there be any request from other departments.
If most inquiries from other departments are relatively basic requests that can be completed with some rudimentary understanding of data science concepts, then it may be better to open up access to these data tools.
Once skills and tools have been introduced to an organisation’s employees, roles and responsibilities can be modified, and access levels to relevant datasets can be granted. Over time, as more team members also pick up basic coding skills, even employees from non-data teams can apply new knowledge to solve day-to-day problems within departments quickly.
The focus of data teams can then be shifted from helping teams with analysis and data-related tasks to building tools that enable others to do their data work better and faster.
With this scalable approach, progress can be made on a higher number of projects as compared to the previous approach where data science was a scarce resource.
Data is here to stay – companies would do well to ensure that employees are data-savvy and are able to work more efficiently and effectively with it.
Data analytics is a field that belongs to everyone and with some effort, employees can become “data people” within their own roles or teams. By doing so, new heights of innovation and productivity will be reached.
By day, Kevin is a proud Data Person at Ayudante, as a Data Analyst, he helps businesses across Asia Pacific grow by creating actionable analysis; by night, he’s a passionate life-long learner, running his own data science experiments, tinkering with new ideas.
Want to become a “Data Person” too? Check out Analytics Academy by Ayudante, and sign up to become a business ready digital data analyst in 20 hours.
https://www.ayudante.asia/analytics-academy
