What is Continuous Intelligence?

The human mind consciously or unconsciously keeps learning and so do businesses run by group of people, formed to serve the people and profit earning. Technically every business process is data-dependent thus; continuous learning in-depth to gain intelligence is possible.

Continuous Intelligence as the name suggests is an ongoing process. It includes learning practices revolve around the current and historical data. The real-time analytics, creating new powerful business models to respond as designed for routine operations or special events.

Continuous Intelligence

CI is an AI-based interpretation of interpreting data and adding value to the business using this data. Business Intelligence and the tools are different from continuous learning because of AI & ML. The design of BI is not for handling complexities of digital advancements.

Need for continuous intelligence:

  • Severe competition
  • Increased customer expectations
  • To shift from mundane to exciting applications
  • Meticulous use of data from different sources
  • Utilizing data before its stale
  • Acquiring new multifaceted insights in business
  • Evaluate organizational level data-based strategies
  • Gap between insights and decision making
  • Ineffective feedback structures
  • Breaking the traditional use of data
  • Improvement in organization’s effectiveness
  • Channelizing the implied learning
  • Define actions explicitly
  • Solve ownership issues
  • Complement with the technical and cultural changes of organization

What can you do with continuous intelligence?

Benefits to Business:

  • Data Processing:  The devotion required to tackle the data and loads of data is not painless until you think you have achieved expertise. This pain certainly pays well. You keep drawing benefits at each level of data processing. Initial stage where gathering, cleaning, and formatting takes place, it’s usable to some extent. We learn to clean it, add power to it to get insights and finally the decisions control actions.
  • Recognize data patterns: Identifying the patterns and recognizing is a part of Machine Learning. It covers the acquired knowledge and study of statistical information. It helps to classify data and apply identifiers, develop new algorithms and create testing data.
  • Adding value to data: Ongoing learning process creates an understanding of converting the raw probabilities to rich possibilities. Execute better results by enhancing the creation capability.
  • Data creation and simultaneous analysis: AI and ML are in semi-developed stage yet has a lot to offer, storing and processing of data and timely analysis give you added advantage of introducing newer solutions.
  • Predict the customer requirements: The observations have a strong connection with intelligence. Even the customers are gathering lots of information as individuals. We as companies certainly need to find a mechanism that connects us to the customers to know their expectations.
  • Automate actions for speedy response: Intelligence is converting the mundane task to sharp features applying which you reduce the drudgery. Automation for the sake of automation has failed many. Finding unique requirements that were unattended can build minor features that add up to the overall efficiency of AI and ML.
  • Update the learning models: With the new insights, we can add the learning theory that you develop along the journey. Connect data points to generate new models that boost creativity.
  • Envision of business needs and growth: With the transformation that you are planning with continuous learning and the market trends, you build the capability to visualize the recently developed needs in business that add to continuous intelligence.
  • Develop relational learning with Artificial Intelligence: The training needed in AI needs base work of structuring the data and its response. AI is not self learning like ML; it gives you an opportunity to mould the uniqueness to make it commonly acceptable.
  • Establish the connection of real-time and historical data: The data sensitivity is not something that you know without learning via unanticipated situations. Data is proof of your activity and actions if recorded precisely. Analytics and the measurable you define can change the results and interpretations.  
  • Fill skill gaps within the organization: Continuous efforts in a particular direction and dedication for learning to empower self and others can help to fill skill set gaps. At the organization level, skill analysis is the most neglected exercise; of performance metrics. With gathered experience, we all tend to show interest in technological and non- technology based latest developments.
  • Focus on Stakeholders: The approach that was somewhat flexible earlier is now widely accommodating due to knowledge gained. Continuous Intelligence brings a change that is long-awaited by the stakeholders. It has always been challenging the management to find representatives that can pool resources of the technological and financial aspects for the betterment of organization’s growth.
  • Indispensable Tool that turns to an Asset: Learningis essential and continuous intelligence is an asset for the organization. Such investments never fail to reap fruitful harvests. The change is inevitable if we foresee then the vision that developed with our ongoing learning has been useful.
  • Not Restrictive but Relevant Learning: The significance of relevant learning is that it keeps us aware of the time, efforts, finances, and skills that are involved. We continue to look for value and add value to our functions.
  • Easy Mentoring:  Continuous Intelligence is a never expiring opportunity that can work wonderfully in favour of the organization. Planning for learning can be interesting if employee engagement and participation is involved.

Benefits to Team:

  • Incorporate the out of box thinking to enrich the work experience of the team: Changes that otherwise receive rejections will gain momentum with the unique thinking that supplements the acceptability towards experimentation. Co-operation from the team for cohesive effort towards continuous intelligence raises the work and delivery standards.
  • Motivational Effect on Team: We all have a trigger for upset and motivation, are we able to trace them? Will the motivational effect due to continuous intelligence be long-lasting? There can be multiple questions and excuses for not adopting something. If we draw benefits for some period in each department or on each individual, the collective effect is overpowering.
  • Managing the unmanageable: We strongly dislike but we all usually miss to plan for some or other stuff; resulting in havoc. Learning opens a door to new opportunities that would have been out of reach. The understanding that we develop over a period is a result of continuous learning.
  • Leadership Perspective: Change management is difficult but the ability to create change can happen with intention and leadership perspective. Prospective learning can progress eventually however; the initial thrust is the most important factor.

Benefits to Individuals:

  • Never Lack Ideas: Develop the ability to generate and implement ideas that are worth a try. Getting carried away with ideas is a common mistake. Save yourself from stepping on cactus if continuous learning has been your habit. Continuous intelligence saves all the turmoil of reverence to inefficient methodologies.
  • Builds Confidence: Invest to educate yourself well enough, to use confidently the continuous intelligence that you are building. Bring in the required energy on the platform. The contributors lead the show due to the confidence they have.
  • Improving Learning Techniques:  People have different thinking patterns, approaches towards learning, preferable methodologies and ways of learning. Constant improvement in these techniques helps in creating learning material, test cases, and research for internal use of the organization.

Create Easy Learning:

  • Data gives 360-degree view, we need to grasp the opportunity proactively. It creates awareness for actions and holdbacks if required.
  • Let yourself freely use intelligence to predict the change.
  • Making the smallest task of repetitive nature simple with automation
  • Streamline the process of data capture, filtering and making it usable
  • Learn from the past and continue learning in the present

Example of Continuous Intelligence:

Stock market a completely non-predictable place; it opens new imagination of studying and forming algorithms. The predictive analysis built on observations, past data and experience of utilizing that data is a clear indication of continuous learning. The charts and concepts of trading are based on factors like index, weighted average, etc., lets a user define the trade strategies. Algo-trading adds to the accuracy and speed of transactions.

Simply with the technical knowhow of Algorithm trading, one can know how powerful connection of data can assist. The timings as a key factor lead the software connecting the stock exchanges at the right point. The volumes in trading are extremely high and when the tools and charts are developed, the most obvious method is data. Clean data can lead to intelligent predictive analysis.

People following trends of stocks, or selective about the companies they want to deal with, have their preconceptions and targets to meet. Our solutions need to meet their expectations in terms of profitability, performance, and stability.

Building solutions that do self continue learning comes at a later stage when you are incorporating machine learning. Initially designing the unique solution or revamping the existing one is actually applying the intelligence.

Inspiration is the secret to Improvement that cannot happen by chance it takes a thoughtful effort. There can be self-taught learners, and some that receive training, the sole purpose is to remain relentless. A curious mind builds inquisitive nature leading to general development or specified.

Every industry whether manufacturing or service is surely up to unfolds this ability enhancer learning.

Cycle of continuous learning for continuous intelligence can have manageable loop holes if we do not fall prey to it. Actions suggest that experience magnifies the problems nevertheless increases ability to provide better solutions.

Build  New You Build Continuous Intelligence!!!

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