Different size of organisation different way to use Data Science.

By arvind |Email | Oct 4, 2018 | 8682 Views

Realising that your company needs data science, not only because it is the buzzword or is in trend, but it can provide your company the necessary edge to stay steps ahead of your competitions. Many companies already have Data Science teams that provide a huge impact in the working of an organisation which can be the reason that separates them from other companies. Even after being aware that many companies are using data science and are gaining the competitive edge in the community very few companies are still following the path. 

Why companies shy away from Data science?
When it comes to data science and companies one of the big reasons of companies shying away from it is the disconnect organisations and data scientists have when it comes to the work both side priorities. While most companies have goals that do not require a lot of efforts like data cleansing these do not require complicated data science models, on the other hand most data scientists want to work on the most complicated problems in the business. There needs to be a proper flow of communication between the company and data scientists, Data scientists' needs to know why these easily completed goals/tasks are core part for organisations, and that data scientists can learn more about the companies working from these works. One can only make difference to an organisation only after knowing how it works (positive difference).

Data science important in different time of organisation size

1. Start-ups 
When it comes to start-ups the time money and people working are very limited, in this small team it is the responsibility of the data science team to give the company a solid data infrastructure, not only that they play a key role when providing a organisation with the awareness that will help the organisation move in the correct direction. To accomplish the above mentioned Data Science teams consists of Data scientists and data engineers that work together allowing the both groups to play on their strengths and drive data products and insights forward.

2. Medium size organisation
So now the money has increased the time as in experience too has increased and people working are more than limited. But that doesn't mean the end of problems. In a medium size organisation there is a scarcity of resources that you need to create products related to data. Because there are not enough resources companies usually separate the workings of a Data scientist and a Data Engineer, on rare occasions the companies might even have software engineers that can be used to take care of data collection and data acquisition, providing the much needed break between the skill set. All this a great way to help doth the professions to work and have complete focus on the field i.e. analytics and metrics providing the organisations how their product or offering is doing while still pushing the boundaries of developing new complicated models.

3. Large organisation
With large companies comes big money which indirectly relates to have works that get money enough so that they can focus complete on the portion the like and are best at with the minimal level of disturbance without having a single problem about things outside their domain. One of the traits large organisations share with medium size organisations is the work separation between software engineers and data engineers both focus on different parts of the spectrum.

The separation now occurs among the different types of Data Scientists.
1. Data Science, Analytics Engineers
These Data Scientists focus on:
(i) Taking large datasets and turning them into concrete conclusions and actionable insights. 
(ii) Communicating complex topics to a diverse audience.
(iii) Thinking creatively to identify new opportunities that will guide the organisation in the right direction.
2. Data Science, Machine Learning Engineers
These Data Scientists focus on:
(i) Developing highly scalable tools that leverage rule based models. 
(ii) Suggesting, collecting, and synthesising requirements to create effective roadmaps. 
(iii) Coding deliverables in tandem with engineering team.
(iii) Adopting standard machine learning methods to best exploit modern parallel environments.

Source: HOB