What Is Data Analytics?
Information investigation and information mining manage to treat a bunch of crude information recovery and capacity measures (assortment and information stockpiling), cleaning and preparing (information cleaning), to construct information bases (informational index) fit to be treated through examination models (information demonstrating) to reach inferences that permit you to settle on educated choices on the data acquired (information-driven pick) or to expand knowledge.
For this reason, information investigation utilizes calculations, techniques and frameworks to extricate information from organized and unstructured data.
Types Of Data Processing And Analysis Methods
Data processing and analysis includes different methods and techniques based on two main types of data and related forms of research: quantitative and qualitative data and processes.
In Quantitative Analysis
Data are presented in numerical form and measurable by dimensions and metrics. These data are used for mathematical calculations, and statistical analyzes to answer questions such as: “How many are there?”, “How many times?”, “How much does it affect?”. These data can be verified and conveniently evaluated using mathematical techniques.
concerns all information that cannot be measured and can be traced back, for example, to verbal or textual content, emotions, opinions, beliefs and more. However, qualitative data can be entered into value scales and measured in the frequency of detection or similarity, so these data are still traced back to a quantitative measure. Analysis and qualitative data are also fundamental as their collection is exploratory and involves in-depth analysis and research.
The methods are mainly focused on gaining insights, reasoning and motivation, so they go deeper. The analysis techniques are based on two main approaches: deductive and inductive. The first approach is used when there is already a predetermined theory of the probable “input” expected from a sample population and aims to collect data that can support an idea to validate it.
On the other hand, the inductive approach is used when there is not much information on the expected outcome of a sample population and aims to collect data on a topic of interest and then identify patterns and correlations. The aim is to develop a theory to explain the patterns or “patterns” found in the data.
It underpins all data information. It is the simplest and most common use of data. Descriptive analysis answers the question “what happened”. The most extensive use of descriptive analytics in the business world is to track key performance indicators (KPIs). KPIs describe a company’s performance based on the chosen benchmarks.
After asking the central question about “what happened?”. The diagnostic analysis aims at the event’s causes. This is the role of diagnostic research. It uses the information found from the descriptive analysis and drills down to find its cause. Organizations use this type of analysis to create more connections and identify deeper patterns and patterns of behaviour.
Predictive analytics concerns the question “what is likely to happen?”. This type of analysis uses historical data to predict future results and is a step up from descriptive and diagnostic analyses. It uses statistical models that require different technology and operations.
While illustrative and diagnostic analysis is a well-established practice, predictive analysis is a new development trend that many companies still struggle to understand and adopt due to the need for investments in new analysis teams and the inability to train teams currently. However, this type of analysis is very effective in supporting decisions in critical areas such as risk assessment, sales forecasts, customer segmentation.
The fourth type of data analysis is the current industry frontier, which combines all previous comments to determine the course of action in a problem or decision. It is linked to both descriptive and predictive analytics and uses state-of-the-art data technology and practices.
Implementing this method is a substantial organizational effort. Companies must be consciously willing to put the necessary measures and resources before implementing a prescriptive analytics project. Artificial intelligence (AI) and machine learning (ML) are perfect examples of this type of analysis. These systems process large amounts of data to learn and use the data to make informed decisions continually.
Systems of this type, Business processes can be executed and optimized daily without a human being interacting with artificial intelligence. Systems of this type can process data and information not only on local databases (Database) but also in the context of Big data. Big data generally refers to data that exceeds typical local storage capacity. As a resource, Big data requires applied tools and methods to analyze and extract models on a large scale.
The most sophisticated prescriptive analysis systems can retrieve information from several interconnected nodes, correlating them through integrated analysis models that often use multiple methods and techniques simultaneously to achieve the purpose. Currently, such systems are the prerogative of large companies (e.g. Amazon, Facebook, Netflix, Google, etc.). At the same time, for other organizations, the transition to predictive and prescriptive analysis represents an obstacle that is sometimes impossible.
However, the market is constantly evolving, and many companies are developing systems and services that will make these systems available to small and medium-sized companies through the help of third-party supplier tools (e.g. SAP Analytics Cloud, Qlik Sense, RapidMiner, IBM SPSS Modeler, SAS Advanced Analytics and many others).
The Implementation Of The Data Analysis Models To Be Applied
Data analysis techniques involve several processes and activities that involve more than just analysis. In advanced projects, much of the work required takes place in advance, with a process that we can divide into three phases:
- Planning (Data Plan)
- Construction of the analysis model (Model building)
- Model implementation.
In each of these phases, we can identify at least three other sub-phases for each of them, as shown in the following diagram. Let’s now go to see the various sub-phases of a Data analysis project.
To get started, it is essential to understand what you are trying to solve. Defining the goal of the analysis is imperative. By aligning the function of the data analysis model that will be created with the company’s objectives and business strategy, the process becomes powerful and practical.
Once the goal is clear, one can begin to explore the variety of information and tools available to achieve it. This step establishes what data are available, from which they come, and their reliability, identifying the most appropriate approach.
With a clear understanding of the data source, the next step is to collect (Data collection) and clean (Data cleaning) the selected data in a universal format. The preparation process begins with classifying the data into segments such as product, case, behaviour, demographics, and more.
A sample is taken from the selected dataset and used to write the model’s code that you intend to develop. A model can be thought of as an equation composed of several variables based on the methods above (Monte Carlo Simulation, Regression Analysis, Cohort Analysis and others). During the initial creation phase, the underlying structure of the independent and dependent variables is formed to best suit the purpose.
Testing on the model is essential for its effectiveness before putting it into production. This phase involves running several statistical tests to detect errors, experimenting with alternative models, and checking if the dataset needs additional cleaning.
Once the functionality and accuracy have been tested, the model can be reported to the interested parties (stakeholders) for validation. Validation is an essential step in assessing whether the model will meet the project objectives.
Once the model is complete and validated, it can be put into production and used.
Scores and metrics can now be used for business decisions or processes. This enables the first phase of reporting on how the model has behaved in achieving the set objectives.
Over time, models will need to be adjusted and recalibrated. This activity is necessary to adapt the data analysis to the natural changes of the environment and the market, a continuous fine-tuning process to obtain more accurate and helpful information.
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