By Lourival Monaco
Intelligence, not data itself, is what makes the difference when a decision is to be made. The most important step in the intelligence cycle is the processing and analysis of data and information. The analysis step transforms raw data and information into actionable intelligence. The key to this step is to take facts, figures, statistics and other quantitative data points and organize and interpret the data to reveal patterns, tendencies and relationships. The transformation of data and information into actionable intelligence is the heart and soul of good decision making.
Several concepts are fundamental for the data-to-intelligence-transformation process to be successful. These concepts permeate the various tools and techniques used to achieve the transformation, such as inductive reasoning, deductive reasoning and pattern recognition.
It also is important to understand that the tools used to collect, organize and store data and information may do a lot of the heavy lifting when the time for analysis comes. They not only will make this process faster, but they may already start the transformation. For example, a text mining tool may help you collect some textual data in a document or website about a topic. This same tool may also be used to analyze pre-collected content, providing, for instance, a count of how many times a word was used and the various contexts for the use of the word.
Methods For Transforming Data To Intelligence
Often, particularly in research-focused parts of organizations, there is inertia or pressure to use the latest and most complex method for transforming data to intelligence. It is critical, however, to maintain a focus on using the tool that is best suited for the job at hand. You don’t want to use a sledgehammer to tighten a screw, in the same way you are not going to use tweezers to bring down a wall.
The number of analysis tools and frameworks is practically limitless. We don’t need to go through them all, but some examples include:
Classical Analysis strategies and tools:
· BCG Matrix
· GE Business Matrix
· Industry Analysis (Porter’s Five Forces Model)
· Strategic Group Analysis
· SWOT Analysis
· Financial Ratios (ROA, ROE, ROI, etc.)
· Value Chain Analysis
· Scenario Analysis
Data Mining and statistical analysis tools:
· Class/Concept Description: Characterization and Discrimination
· Data Mining for Frequent Patterns
· Outlier Analysis
· Correlation Analysis
· Discriminate Analysis
· Factor Analysis
· Cluster Analysis
· Forecasting/Time Series Analysis
· Classification and Regression for Predictive Analysis
Visual analysis strategies and tools:
· Power Bi
Decision Modeling Tools:
· Business Experiments
· Sentiment Analysis
· Monte Carlo Simulation
· Linear Programming
· Cohort Analysis
Phew! And that is the short list!
The Role Of Big Data
Now, we would be remiss to ignore the modern-day role of Big Data in the data-to-intelligence transformation. To start, it is important to understand the definition of Big Data: “high-volume, high-velocity, and high-variety information assets that demand cost-effective, innovative forms of information processing for enhanced insight and decision making.”
Also, and very importantly, Big Data is not a “thing,” it is a “capability.” Therefore, while the dimensions of Big Data: Volume, Variety and Velocity are important descriptors of what makes data “big,” the more critical dimension of big data is Analysis, or the capability of transforming all of this big data into insights. In fact, one of the most sought-after degrees in higher education today is data analytics as this set of technical skills is what allows a company to truly leverage its big data to create differentiation in today’s marketplace.
“Big Data in agriculture is helping us better understand the factors that drive what has happened or is currently happening on our farms and in our food and agribusiness companies.”
Big Data in agriculture is helping us better understand the factors that drive what has happened or is currently happening on our farms and in our food and agribusiness companies. This is a powerful tool to provide insights into our decision making and helps us identify areas we should research further to learn how we can better predict what may happen in the future. Combining Big Data analysis with what is known as Small data (conducting scientific research in systematic way) is the true essence of converting data to intelligence. We have seen in recent times, for example, real-time big data analysis of weather patterns, soil conditions, and farm management practices, combined with small data associated with disciplined agronomic research to improve in-season farm management practices. This helps enhance production, improve plant health, preserve soil nutrients and maintain cleaner water systems.
Answering The Intelligence Needs
Regardless of the tool or strategy decided to transform our critical data sets, it is imperative that it achieve a simple and important goal: It has to answer the intelligence needs, or the questions that a decision maker must solve.
Once the right tool and strategy is employed, the data and information are transformed and we move on to the data transformation phase, the core of the intelligence cycle is done, but far from over. There is no value in the analytical efforts if they don’t reach the decision maker in a timely and appropriate manner.
DIAL Ventures, the innovation arm of the Purdue Applied Research Institute, tackles big problems facing the U.S. and the world such as food safety, supply chain efficiency, sustainability, and environmental impact. DIAL Ventures creates new companies that drive innovation in the agri-food industry which, in turn, makes a positive impact on our lives and lifestyles for years to come.
If you are interested in becoming a DIAL Ventures Fellow or Corporate Partner, contact us at email@example.com.