Organizations constantly push themselves to move from insights to wisdom through domain knowledge, relevant experiences, and smart inter/intra collaboration. Moreover, these organizations understand how integrating knowledge with accurate information can provide valuable insights at the right time to the right people which is essentially required to solve the practical problems of humans, researchers, businesses and contribute to society as a whole.
The diagram below depicts the diagrammatical interpretation of the process from developing data into wisdom.
Source: Drawn by David Somerville, based on a two-panel version by Hugh McLeod
The pictorial representation of data is actually an amended version of the “DIKW” (data, information, knowledge, wisdom) model, which has been refined into the “DIKIW” model with the added component of either “Insight” or “Intelligence”.
DIKW Model: Information is defined in terms of data, knowledge in terms of information, and wisdom in terms of knowledge.
DIKIW Model: Information is defined in terms of data, knowledge in terms of information, Insight/Intelligence in terms of knowledge, and wisdom in terms of Insight/Intelligence.
This visual representation is able to illustrate the process of defining and refining insights for any industry. As a seasoned IP professional, I can draw the following conclusions from each phase of the diagram:
- Data: Unstructured form having a low value in raw format
- Information: Customized / Structured form by fixing certain details still having low value
- Knowledge: Driven and analyzed by the right resources creating medium value for the audience
- Insight: Driven by technology experts connecting dots for developing certain level of intelligence in the analyzed dataset creating high value
- Wisdom: Collaboration between the clients and technology experts every other day/week creating priceless value
So, what is the exact requirement to make a dent in the universe? Information? Knowledge? Insight? Wisdom?
To see how each analytical stage of the diagram above works, below is an example of parallel development in the hi-tech/semiconductor industry:
In April 2005, Gordon Moore stated in an interview stated that according to Moore’s Law*, the gradual delay in semiconductor chips will bring a new era of emerging technologies. Research and Development efforts have now shifted to novel generated technologies and computing architectures. Additionally, the long-awaited advancements in semiconductor technologies have finally arrived in the form of technologies such as 5G, 6G, autonomous driving, artificial intelligence/machine learning, IoT, quantum computing, cloud computing, AR/VR/MR, and industry 4.0 for smart manufacturing. Crucial data related to patents and STEM literature has become available through patent offices (PTOs) and other platforms, but why are only a few companies streamlining this data-gathering process?
The biggest differentiating strategy is to extract the available data/information through the right machines (databases), streamline the information via knowledge empowered by insights, and then further envision the outcome of the data by the organization and/or scientific experience with wisdom. The final step is related to the ‘mind’ factors, which make the available information more distinguishable and conscious.
|
Data |
Information |
Knowledge |
Insights |
Wisdom |
|
Machine |
Machine |
mind+machine |
Mind |
High stake Decisioning |
|
Raw nature, unstructured, comprehensiveness |
Standardized dataset covering macro details |
Niche details through smart tagging, technical and business information |
Connecting dots, thorough understanding of technology |
Collaborative path, identification of valuable portfolio |
|
Millions of Patents and Scientific Literature available in multiple databases ion emerging areas e.g., 5G, Automated Driving, Artificial Intelligence, Machine Learning, Quantum Computing, IoT, Cloud Computing, Industry 4.0/Smart Factories |
Around ~85% of filings in the last five years
Highest filings in US, China, and Japan.
A trend of filing in emerging economies |
Emerging technologies are broadly classified into: · Assisted Intelligence · Augmented Intelligence · Autonomous Intelligence Further, sub-classified into different nodes |
Companies aligning IP with commercialized products. Identification of ratio of organic to inorganic growth in patent portfolio |
Collaboration with client’s team and inventors to discuss the potential licensing opportunities by performing the patent strength analysis of the university portfolio based on proprietary methodology |
The beginning of each analytical assignment in any technical domain is initiated with a dataset driven by the right machines. In the age of artificial intelligence, most of the information is derived through databases, however relying on a single database can lead to a half-baked conclusion, hence relying on multiple databases can enhance the quantity and quality of the dataset and corresponding information.
Further tagging the required information according to appropriate taxonomy levels can generate insights relevant to both business and research & development. Taxonomy classification purely depends upon scientific knowledge of the industry, which can be gathered from review papers and core scientific literature published in renowned journals. Thoroughly understanding this literature ensures that organizations are aware of the challenges and problems in the technology landscape, corresponding solutions existing within the industry, and the invention’s implementation within the industry. An alternative way to classify datasets is through automated tools based on the frequency of keywords, patent classifications, and other bibliographic parameters. However, resist the further movement in providing insights to take any strategic decision. Therefore, the right balance of automation, exclusive analyses, and intelligence is required for contextualized output.
High strategic decisions for any organization are directly proportional to the actionable intelligence which have been derived from enriched data/information analyzed by technology experts and fruitful collaborations by industry experts. Advanced analytics can be helpful in identifying trends through identification and analysis of root cause factors and prioritize certain things based on the driven values, but the accurate insights when integrated with wisdom identifies and drives the next actions to address those trends.
In nutshell, not all data is information, and not all information is intelligence. The right mix of mind+machine integrated with market/industry knowledge expertise and experiences can do wonders. The approach outlined above shows the importance of leveraging the human elements (mind) in combination with the efficient databases (machine) for maximum effect through a collaborative approach of resonating the actionable intelligence with wisdom.
If you want to discuss how we can support you in turning the existing information into actionable intelligence integrated with collaborative wisdom, please do not hesitate to reach out to us at IPRD-Solutions@evalueserve.com.
*Moore’s Law: The number of transistors on a microchip doubles every two years, though the cost of computers is halved.
