AI and MACHINE LEARNING
Artificial Intelligence (AI) is a system that has the ability to mimic human intelligence to a level where it is difficult to distinguish between human and mechanical ability.
Machine Learning (ML) is a method of letting AI calculate what is needed to achieve a specific goal without having step by step instructions.
Within analysis, AI and ML continues where Business Intelligence (BI) ends.
AI and ML can be applied in a number of areas to rationalize and streamline.
Better basis for decisions, now and then
How to integrate predictive analysis into the business
To take full advantage of artificial intelligence and predictive analysis, there are four elements that must be considered.
1. Right questions
Successful projects with predictive analysis begin with a good model to try. Even though we are going to use machine learning with algorithms that are to make their own associations between different data points, we need to define the business challenge that is to be achieved. This helps create clarity of the goal.
2. Correct data
The development in IT over the past decade has enabled us to gain insights from large amounts of data of unstructured data with greater precision. However, we need to process data to reach convincing conclusions. The next step after defining questions that you want answers with with predictive analysis is to find out which data is available and if it is sufficient to answer your questions.
3. The right technology
Computer technology is a fast-growing industry. Data is created at such a rapid pace that we continuously need to increase our ability to capture, store and do something with it.
Many of the leading standardized analysis tools have already launched predictive analysis tools, using different methods.
4. The right skills
To determine which solution fits your business best, it is more important than ever to have a team with expertise and experience in place to ask the right questions and identify the best solution.
AI and ML take over where BI ends
AI and ML can be applied in a number of areas to increase value and streamline.
Strategy for the business, products and services
The right basis for Strategy
Many businesses structure and manage extensive and strategic information. It is common to process large numbers and many parameters. The image quickly becomes complex, which makes control and operation resource-intensive.
Different businesses and departments seek the greatest possible outcome and efficiency. In order to achieve this, strategy and the opportunity to choose the right are required. It can be compared to a balance scale, striking it in the right direction increases the chances of taking the best overall decision in each situation.
Strategy work creates an action plan. The action plan is planned and budgeted to achieve the goals as efficiently as possible. With limited resources and time for strategy work, it is of the utmost importance to choose the right focus at the right time.
An important part of strategy work is to work to have as much digitized information as possible.
Strategy controls the choice of Tactics based on the best available information and conditions in Operation. Tactics leads the operational work, which is highly dependent on complete, accurate and updated information. Operational and tactical work comprises the overthrow of working hours in the ordinary business.
Have the right information
The goal is to have up-to-date and accurate information in order to have the best possible documentation when making decisions. Information consists of both quantitative and qualitative parameters that explain the state of the business from different perspectives. If the condition deteriorates, the risk of falling into a weak position increases, which can lead to a business risk. Traditional valuation model based on volumes, values, etc. and has difficulty managing other parameters.
Parameters often contain requirements that must be met. There can be a great risk if parameters fail, even stock market values can be at stake.
Most organizations have digitized their processes to some extent. Many use Business Intelligence (BI) to see the status and history where the system support is not enough.
Businesses lose many percent efficiency through various deficiencies in access to sufficient, accurate and up-to-date information.
Status and history are not enough information. Artificial Intelligence (AI) and Machine Learning (ML) take your analysis to the next level. By realizing proactive work, a comprehensive picture is given of where there are changes and measures should be put in place, which otherwise cannot be done because of the extensive complexity and lack of time and resources.
See the whole picture
The business also cannot be controlled effectively on individual parameters that BI-based solutions offer. AI and Machine Learning continually provide full overview and control of all objects with its parameters that give the opportunity to control and prioritize where the efforts should be carried out.
It is common for the information to be available in different systems. Compiling this complex information provides new conditions for managing and managing the issues that are linked to this information.
Machine Learning has a very large savings potential as the status changes over time and new parameters are added.
Machine Learning enables management of the business by the information going from passive to active and allowing action to move from reactive to proactive.
Frequent reading of state parameters through automatic signals and updates of changes can automatically lead to operational activities, from visualization to action.