Different ways of achieving the MOV can be identified as different alternatives and these should be compared using financial models such as Payback Period analysis, Net Present Value (NPV) calculation, weight scoring models and risk analysis models to understand which alternative gives the most capital, which one emphasize higher margins or faster growth, and which capabilities are needed to ensure strong performance. In current context, organizational strategic planning has been supported by the emerge of Data Science.
Data Science can be identified as a set of interdisciplinary techniques that put data (Usually known as ‘Big Data’) to work to extract useful insights, predictions, or knowledge which is useful to drive the business. The whole process of processing Big Data using techniques, tools and practices is known as Business Intelligence (BI). Today BI has been implemented in a large variety of areas such as online shopping like Amazon, social networks like Facebook, professional networks like LinkedIn.
To become a Data Driven organization we need to be aware of the nature of data and the techniques which apply on top of data to deduce or extract vital information.
Data can exist at different levels of structure throughout any data ecosystem (Big Data source) and to decide on defining a level for a data set, we need to understand the costs and benefits of adding structure. Cost can be twofold.
The people costs: As the unstructured nature of the data, it will require a set of advanced data engineering techniques to map the extracted data patterns to business requirements which will need expensive analysis effort.
The time costs: Not like the traditional way of data processing where we collect requirements and define schema according to it, more time needs to be spent with unstructured data, preparing test data, evaluating it, and forming an agreement about whether it should be used in the decision-making process.
Working with fully structured data system, cause an organization to stick to the defined business path and it lacks addressing supply and demand fluctuation of business users. Working with structured data may give gaps between what business supplies and what user needs. On the other hand, stick only to unstructured data may affect the overall performance of the system as it will add overhead as it doesn’t provide standardized metrics or business limitations as with structured data. So as a perfect solution, it should look for a hybrid model which showcases both aspects of the data (structured/unstructured) and this can boost up data agility and performance at the same time. For an instance, all the data which managed under traditional Relational Database Management System (RDBMS) is considered as a structured representation of data while unstructured data can be identified with two major categories, human-generated and machinegenerated.
While sources like text files, emails, social media posts, websites, mobile data, are considered as humangenerated, satellite imagery, scientific data, digital surveillance, sensor data, can be considered as machine-generated.