UNSTRUCTURED DATA MANAGEMENT
Peoples desires and motivations are influenced by psychological, social, and cultural factors that require context and conversations for decoding, therefore data tells only part of the story, though a massive amount of user-generated data is used for innovations and new product development, it can only reveal new patterns, which act as a point to what people do, but not why they do it. An understanding of why they do it is important in innovation.
GROUNDED THEORY
Systematic inductive methods aimed toward theory development
The innovation landscape is rapidly changing, it is becoming more and more inclusive, co-creation or co-innovation has become critical, consumers require companies to tailor their products on individual customer experiences through flexible business processes. Grounded theory is a methodology for inductively generating theory, it involves investigating actualities in the real world and analyzing data with no preconceived hypothesis. We apply Grounded theory to capture the transformational spirit of innovation.
CRITICAL INDUCTION
An inductive approach for exploring qualitative data
We use an inductive approach to explore qualitative data, this approach allows research findings to be developed from the frequent, dominant, or significant themes inherent in the raw data, without the bias and restraints imposed by the researcher or the structure of methodology.  Quite often, key themes are obscured, reframed, or left invisible because of the preconceptions by the researcher, data collection methods, or data analysis procedures imposed by deductive data analysis.
The inductive approach helps in condensing extensive and varied raw data into a concise, crisp summary format, it helps to establish clear links between the objectives and findings, in a transparent and defensible manner.
UNSTRUCTURED DATA ANALYSIS (TEXT, PICTURES, SOCIAL MEDIA AND MANY MORE)
Knowledge is not always presented in a linear form
A significant amount of world knowledge is not organized in a structured way, a large amount of data is in form of text, pictures of even numbers which are not in a database form. We combine both machine learning approaches (Indictive theme analysis) and human interpretation (topic modeling). This can be applied in both quantitative and qualitative analysis.