Framing a proper organizational model is certainly important to improve overall productivity and accountability. In today’s business structure, the models are based on data analytics, functional role-based, project-based, and many more.
A model is a strategic plan that regulates the consumer’s role and internal team development of a company in simple terms. The objective is to operate a firm according to this to achieve growth and generate more revenue.
For an organization to operate successfully, it is essential to get the right insights about developing the proper structure. The source of income for any business is its customers. It is quintessential to take appropriate care of them by meeting their demands.
Here, Data Analytics can be of great help as it gives the organizations useful business insights to form a formal structure and develop a business plan that will help the overall growth.
This article will explore the organizational models and the necessity of Data Analytics for structuring them.
Types of Organizational Models
There are five fundamental types of organizational structures or say models in any company:
1) The Matrix Model
It is an active and dynamic operational model for organizations dealing with more than one product at a time. Every firm needs excellent implementation planning for product development, launches, and marketing campaigns.
Managers can get information from different team leaders existing in the company to check their status. Hence they can help the specific team to manage resources if needed to get the job done efficiently.
2) Project-Based Model
It is a dynamic model required for a company that is focusing on a particular project. It helps specific teams get specialized resources and even for those working on the same types of projects.
All the necessary resources have to need a proper assignment to the designated group.
3) The Line Organizational Model
It is a simple model or a hierarchy with a CEO at the top, followed by the admin and operations directors. Here, second-level persons do not communicate with each other, but they influence the company’s structure.
Under them, there are area managers that comprise their specific teams. The model makes sure that a person is at the top, following the management and delegation downwards. Hence, this is a highly rigid operational structure.
4) The Line and Staff Model
It is similar to the line model, but each management level has its staff. So they can communicate through their team. Hence, they can share staff to help the administrative requirements of the directors of different levels.
5) Functional Organizational Model
It operates similarly to the above line hierarchy. The only change here is that the manager can report to a person above him in the model and the other managers.
Hence, these models work to make sure that all the parties can get the right information. It is possible by collecting data from their subordinates. The model can also help a company to adopt any chances by avoiding over-specialization of any particular work.
How can NextBee help?
1) Business Drive
Our data scientists work with each organization’s unit like finance, operations, marketing, and more. And all of them are making their own decisions that work best for that specific department.
2) Centralized
Suppose – a company does not want to appoint many data scientists for each of their units, then choose a centralized approach. Here a few teams of data scientists independently structure their work for the entire organization.
They need to give their reports to one of our chief data scientists, i.e., CDS of the organization, who will finally decide on projects.
3) Matrix
Here, our data scientists can work just like business-driven, but they also need to provide reports to the CDS of the company. He or she can either verify their individual decisions or decide the best one for the company.
4) Hybrid
It is a complex model that is a combination of matrix and centralized models. Large industries must embed our data scientists at different levels of their business lines. We can better help to avoid the chances of co-ordination issues in-between structures.
Conclusion
After an outline of an organization’s operational process and model, one can implement it in the existing structure. If you realize that it is not working well at any point in time, it is good to restructure it with a new outline.
Consider both the existing model and significant data analytics reports and feedback from your company’s leaders. So, it can help you to find the gaps or errors.
If you came to know that the departments are not clear about the information flowing in the hierarchy, then it is best to apply the functional model. It can help them best by getting updates from their linked departments.
If you have found that a specific department lacks to complete their jobs on time, you need to focus on them. You need to apply a particular model only to that department based on your overall hierarchy.
The primary aim of using Data Analytics in choosing a model is to achieve a high level of accountability and productivity at each level.
A strategy must be flexible and need changes with time, either for organizational growth or marketing. It would be best to change with the changing trends to make sure your company is generating profits.
Hence, it would be best if you changed either a particular operational model or you might need to work on the entire corporate structure. Thus, by choosing the right organizational model using Data Analytics, you can build your company and its members stronger.
For any assistance regarding Data Analytics, feel free to contact NextBee. We will be glad to help you on your journey towards growth.
