Big data is nothing but a large pool flooded with random information. Without the required expertise in churning this data into actionable insights, big data is nothing but just random information which has the potential of powerful insight, which is just lying dormant.
Lately, a lot of organisations from across sectors are recognising this and opening their doors to big data and unleashing its power. Thus, these are shining times which increase the value of a data scientist who not only understands the value of this already existing information but also has the skills to derive valuable insights from the data.
It is getting clearer by the day, that the value lies not in the data but on how the data is analysed and processed and that is the where a data scientist steps into the limelight. Organisation have started to understand the value of data science. However, most are still unaware the value that a data scientist holds in a company and the areas that they can impact.
When do Companies Benefit from a Data Scientist…?
Any data scientist usually holds an advanced degree either in statistics, math or computer science. It becomes imperative for them to have an academic knowledge of the same. And along with this they need to have good domain knowledge, where they can handle skills such as information management, visualisation, data mining, data analytics, knowledge in programming tools like, SAS, R programming language, Python etc…, it is also common for them to have knowledge in machine learning, infrastructure design, cloud computing or data warehousing.
A company generally benefits from the skills of a data scientist, when they have a need to crunch volumes of numbers, when they have or wish to possess ongoing operational customer information, or generally when they see the benefit from social media forums, market research or consumer research, credit data, or information acquired from third party data sets.
Seven methods or ways in which a Data scientist can Add Value to Any Business…..
- Partner with Management to make Informed Decisions: A major role of a data scientist is to become a trusted advisor to the management. By ensuring the entity that they are associated with, maximises its analytical capabilities. A data scientist will be responsible for measuring and tracking all performance metrics across all levels in an organisation, thus assisting the entity by facilitating an improved process of decision making through data analytics.
- Defining Actionable Goals: A data scientist can steer the direction in which the company should move, based on the data received. They can recommend certain trends based on which actions can be adapted which will help in customer acquisition, retention, engagement and thus, as a result, improve profitability. In doing so the data scientist aligns the organisation across levels in understanding the benefits of data analytics and further trains them in the effective use of the system to extract insight and derive action. An aligned staff can then better pay attention in addressing key business challenges.
- Identify Opportunities by staying Inquisitive: They have to question, analyse the current analytics system on a continuous basis. As only then will they be able to develop additional algorithms. As in Kaizen, their job is to add continuous improvement in the value that is derived from the organisation’s data.
- Data Driven Decision Making: They have to assist the organisation to take decisions with the help of facts, they add value by ensuring that the decisions are low risk, with a minimal margin of error. A data scientist helps with facts instead of intuition.
- Testing Hypothesis or Results: Setting the path with the help of actionable insights is only half the job done, after all, they are insights, low-risk decisions based on factual findings. The other half is to ensure that the organisation is steering in the right direction if those insights are actually true. It is essential to assess how those decisions have impacted the organisation. A data scientist is the one who measures the key performance metrics and quantifies the success.
- Target Audience: Key groups can be identified with precision by analysing the source from the data, with this in-depth knowledge organisations can segment their products or services by demographics and increase cost and profitability.
- Talent Acquisition: Recruitment is a daunting task, it generally requires scanning of multiple portals and CV’s. with the right algorithms, data scientist can help in the process of scanning in house or external portals and can further assist in data driven aptitude test screening, thus cutting down the cost in hiring.
Data science can definitely add value to any business in recent times. The above mentioned are only a few most commonly mentioned practices. Whether it is talent acquisition or strategic alliance with the leadership to make informed decision, it is true data science is here to help businesses function with profitability.