Big companies have ignored small data projects to rapidly advance their businesses. In doing so, they have lost the confidence of employees. Vague analytics concepts among teams cause irrational fears, internal conflicts, and substantial budget constraints. Thomas C. Redman and Roger W. Hoerl’s Harvard Business Review article explains why organizations must prioritize small data projects.
Small Data Fact Checks
Unlike big data teams, a small data project comprises fewer team members dealing with controllable data points. Anyone in this highly focused team can access and utilize the available analytics tools. The team members can complete the assignment by working on a part-time basis. The organization, in return, earns $10,000 to $250,000 per annum. If a department with 40 teammates can complete 20 such small data projects annually, then the company makes lucrative returns.
Not only do the small data projects earn revenue, but they also improve in-house skills and build confidence. Employees finally understand the meaning of those data point numbers and enjoy their applicability.
Here are some small steps to help you introduce this data culture in the organization:
Include All: Make it a point to lead at least one of the small data projects annually. Your enthusiasm to learn data analytics will inspire others to follow suit. Identify redundant datasets and remove them as early as possible. Minimize the time wasted in waiting for meetings to begin or departments to send components to the other. Insist on proper handovers to reduce complications and loss of money and productivity.
Make a Formal Setup: Though small data projects are relatively easier to handle, encourage employees to follow standard etiquette and guidelines. Identify the business issue, collect relevant datasets and analyze them, define and implement remedies, and check your profit accounts. Move on to the next project and repeat the same steps. If you have experience in Lean, Six Sigma, data analytics, etc., you will have no trouble adapting to the framework.
Training is a Must: Undergo training courses to understand the nuances of these small data projects. Focus on different topics every day. Allow employees to put the knowledge to practical use per session for better clarity. Customize the case studies and examples according to the team. For instance, provide finance case studies for the financial department.
Gather Specialized Skills in At Least One Functional Area: Solve one problem through small data projects. Gather more experience in that area to become an in-house consultant in that area. You can be a data quality expert by measuring and enhancing data quality.
As you gain confidence, you can shift your focus to big data projects. You will see more data scientists solving complex organizational problems in the future. However, there will also be employees who can get used to such AI and data analytics tools. It is this where the small data projects will lead you too, but sooner.
To view the original article in full, visit the following link: https://hbr.org/2019/10/most-analytics-projects-dont-require-much-data
