Data, information, knowledge, analysis, analytics, machine learning, artificial intelligence, business intelligence, data mining, big data … today’s buzz words are often used interchangeably; yet, each of these buzz words have a specific meaning and interpretation. Check the Appendix for the meaning of each of these buzz words!
The Knowledge Pyramid: From data to wisdom and intelligence
The concept of the knowledge pyramid is widely known and not new. Data transforms into information with analysis; which further transforms into Knowledge and Wisdom when analytics is applied to the information.
Challenges faced by businesses
As intuitive as this seems, the transformative process is not automatic!
Data is the fundamental unit and the foundation of the Knowledge Pyramid. The process of capturing, storing and categorizing data is a big step and requires understanding, planning and experience.
Yet, this is only the first step. Pure, unprocessed data is akin to noise. Data becomes useful only when some meaning can be extracted from it. Transforming data into useful information requires process and experience. The ultimate goal of business systems is to create a continuous stream of timely, useful information with alerts and notifications to assist in informed decision making.
Today, data is a commodity. All kinds of data – paid and free – can be easily procured. All businesses have access to all available data. Data is also generated by each business itself and this is a very valuable asset of the business. The challenge faced by businesses is extracting useful information in a timely manner from the sea of available data – and ahead of the competition.
Often data is not organized in a format that can be consumed and time is lost in pre-processing. Quality of data is another challenge which spawned the famous Garbage In Garbage Out (GIGO) concept. Data validation, verification, collection, organization, maintenance, backup, availability – all these steps play an important consideration even before data is processed.
Every single business process in today’s typical tech organization is generating data. A lot of this data may be scattered across different locations, different systems and separate platforms. Often, in spite of best intentions, availability of data collected in one department is not known to another department and opportunity to include data in knowledge systems is lost. And sometimes, there can be duplication of data collection, with one party not knowing that the same data is collected at another location.
A narrow knowledge pyramid: One of the common problems encountered in modern organizations is that the base of their knowledge pyramid is very narrow. This means that businesses are collecting and harvesting only a small column of available data. Much of the available data is not collected and therefore, opportunity to harness information is lost.
Represented in the image above, a narrow foundation of insufficiently harvested data leads to narrower rungs of information as you climb up the pyramid. Knowledge is compromised and consequently, the collective wisdom accumulation in the organization, limiting the intelligent, data-driven decisions.
Widening the base of the pyramid is, therefore, a basic step that modern enterprises need to consider in their journey towards transforming into the digital age and adapting process automation.
On the flip side, we come across situations where a lot of data is collected, but very little information extracted from it. This is yet another challenge. Technology can help harvest large amounts of data but extracting useful information is the ultimate business goal.
Poor data collection techniques, inefficient data storage and retrieval, GIGO, a lack of analytical systems and techniques can all result in a failure to achieve the ultimate benefits and system performance of automation and systems supported by decision support systems.
Fitting the Knowledge Pyramid with today’s buzzwords
Let’s look at how we can fit today’s technology buzzwords into the Knowledge Pyramid.
- At the base of the pyramid, we have the data foundation. Simple automation of collection and storage system can help capture better quality of data that can be validated and verified. Proper data collection techniques will facilitate better organization and management. The collection of large volumes of data, of wide variety of data types at a fast rate (velocity) – the 3Vs of Big Data processing will set up for of predictive analysis, user behaviour analytics and certain advanced data analytics and data mining methods that extract value from data.
- Reporting and visualization tools, data analysis and modelling tools and dashboards provide the extraction of information from the data.
- Business Intelligence systems provide the Knowledge layer. With predictive and prescriptive analytics, we can help pro-actively predict risks and send alerts and notifications via emails and text messages.
- Wisdom grows with time, as significant, pattern-based learning leads to systems becoming truly intelligent. While today’s enterprises continue to use technology-assisted decisions, with advances in Artificial Intelligence and Machine Learning, we will see more automation even at the top of the pyramid.
Is your business ready for this exciting new world of data, technology and automation?
- At CCR Technologies, we have been helping businesses take big steps towards improving their Knowledge Pyramid, using powerful Workflow Automation.
- Our Cloud Services help businesses move away from legacy systems and save on expensive hardware and software making web and mobile applications core to their automated systems process.
- We have successfully been using google maps and geo-sensing techniques and mobility solutions to capture location data in real-time and build applications to support businesses reduce cost and save time.
- Simple though these may sound, they go a long way in readying your business for the eminent Industry 4.0 revolution.
Don’t get left behind.
Reach out to us if you’d like to schedule a call with us to explore how our Workflow Automation Services can help you widen your data capture and data processing capabilities.
Appendix: Buzz words
Data is the very basic unit of measurable fact and it becomes information only when it is viewed in context after analysis.
Digital Data is a collection of storable and transmissible facts and measurable characteristics that may be collected using technology.
Information is data that is accurate and timely, specific and organized for a purpose, presented within a context that gives it meaning and relevance and can lead to increase in understanding and decrease in uncertainly.
Data analysis is a process of inspecting, cleaning, transforming and modelling data with the goal of discovering useful information, informing conclusion and supporting decision-making. Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names, and is used in different business, science, and social science domains. In today’s business world, data analysis plays a role in making decisions more scientific and helping businesses operate more effectively.
Data analytics refers to qualitative and quantitative techniques and processes used to enhance productivity and business gain. Data is extracted and categorized to identify and analyse behavioural data and patterns, and techniques vary according to organizational requirements.
Data mining is a particular data analysis technique focusing on statistical modelling and knowledge discovery for predictive rather than purely descriptive purposes.
Business Intelligence covers data analysis that relies heavily on aggregation, focusing mainly on business information.
Intelligence: the ability to acquire and apply knowledge and skills.
Artificial intelligence (AI), also called machine intelligence, is intelligence demonstrated by machines in contrast to the natural intelligence displayed by humans where devices perceive their environment and take actions that maximize its chance of successfully achieving its goals. Artificial intelligence is often used to mimic human cognitive functions such as “learning” and “problem solving”.
Learning: knowledge acquired through experience, study, or being taught.
Machine learning (ML) – a subset of AI, are a collection of algorithms and statistical models that perform a specific task without using explicit instructions, relying on patterns and inference instead in order to make predictions or decisions without being explicitly programmed to perform the task.
Data mining is an interdisciplinary subfield of computer science and statistics with an overall goal to extract information (with intelligent methods) from a data set and transform the information into a comprehensible structure for further use. Data mining is the analysis step of the “knowledge discovery in databases” process.
Wisdom is the quality of having experience, knowledge, and good judgment; the quality of being wise.