The different stages on the road to achieving data analytics maturity


Data Analysis is a process or effort to process data into new information so that the characteristics of the data become easier to understand and useful for the solution of problems, especially those related to research.

Data analysis can also be defined as activities undertaken to change the results of data from research into new information that can be used in making conclusions.

In general, the purpose of data analysis is to explain data so that it is easier to understand, and then conclusions can be made. The conclusions from the analysis of the data obtained from the sample are generally made based on testing hypotheses or conjectures.

Purpose, Types, Data analytics Procedures

Data analytics solutions is a job that requires special expertise. Yes, of course! If we make a little mistake in analysing the data, the results and the resulting interpretation will not be perfect or may even prove to be fatal to the decision making process. Now for that, we will share with you the notion of data analysis, the steps, and the type of data analysis that you certainly must know.

What is data analytics? 

Data analytics itself is a way to process data into information so that the characteristics of the data are easily understood and useful for the solution of problems, especially matters relating to research.

Data analytics can also be interpreted as an activity carried out to change the results of research data into information that can later be used to draw conclusions.

Data analytics is a very important part because it is through analysis that data can be given meaning that is useful for research problems. Data that has been collected by researchers will be of no use if not analysed first.

Some of the objectives of data analysis are to describe the data so that it can be understood, then to make conclusions or draw conclusions about population characteristics based on data obtained from the sample, usually based on estimation and hypothesis testing.

Analytics Maturity stages in business

Analytics plays a major role in the success of an organizations’ roadmap. An organization can drive actionable results through insights if good analytics are a part of their roadmap. Insights into predictions about the future, insights into history and insights into competitive differentiation can be achieved through analytics.

Analytics maturity can be measured using an analytics maturity model. Predominantly across organizations, you will see three levels of analytics maturity – Descriptive, Presictive an dPrescriptive.

  1. The simplest stage, which is descriptive analytics aggregates data into useful information to derive a summary or a conclusion. The use of descriptive analytics is to understand the padst and the present and to answer the most basic questions such as ‘how many?’ and ‘what happened?’. In this stage, processes, users and tolls are opportunity dependent it ad-hoc. The focus of projects in this stage will be on reporting, data visualization and business intelligence.
  2. This is the next stage or an advanced stage -Predictive Analytics. Predicitve analytics is the analysis of data to predict or discover future trends. In the predictive analytics stage, work happens around statistics, predictive modelling, advanced analytics and data mining. The predictive analytics stage will help an organization answer questions like ‘what would be the resultant change if X was changed? 
  3. The most advanced and transformative stage is  – Prescriptive Analytics. In this stage, an organization possesses the tools to analyze data in order to prescribe the ideal or best course of action. Machine learning is the best example where the algorithms and models learn and grow when exposed to new data. Prescriptive analytics will help answer questions like ‘for a said incident X, how can we learn from it and ensure that it is addressed automatically from now?’  

Data Analysis Stages in Qualitative Research Methods

A researcher chooses research using qualitative research methods, and it does not mean that a researcher cannot process numerical data through SPSS, which is usually done in quantitative research. But even these researchers prioritize quality research over quantity. If we compare the data analysis conducted in qualitative research, it is more complicated than the data analysis in quantitative research methods. And the following stages are carried out in qualitative research.

  1. Data collection: Namely collecting data to be analysed.
  2. Editing: Namely checking the clarity and completeness regarding filling out data collection instruments.
  3. Coding: Namely carrying out the identification process and process the classification of each statement contained in the data collection instrument based on the variable being studied.
  4. Tabulation: That is recording or data entry into the master research table.
  5. Testing: At this stage, the data will be tested for quality, namely testing the validity and reliability of instruments from data collection.
  6. Describing the data: Presenting in the form of a frequency table or diagram in various sizes of central tendency and dispersion size. Aiming at understanding the characteristics of sample data from research.
  7. Testing the hypothesis: Is the stage of testing the proposition whether it is rejected or accepted and has meaning or not. Based on this hypothesis later decisions will be made.

Types of Data Analytics in Research

There are two types of data analytics based on methods and methods, namely descriptive data analysis and inferential data analysis. Here are the reviews:

  1. Descriptive Data Analytics

Descriptive data analysis is an analysis technique used to analyse data by taking a picture of data that has been collected without generalizing from the results of the study. It is an analysis technique used to analyse data by describing or describing data that has been collected as needed without any intention of generalizing from the results of research. Included in the descriptive statistical data analysis techniques include presentation of data into graphs, tables, percentages, frequencies, diagrams, graphs, mean, mode, etc.

Included in the descriptive data presentation are:

  • Chart
  • Table
  • Presentation
  • Frequency
  • Diagram
  • and so forth
  1. Inferential Data Analytics

Inferential data analysis is a data analysis technique using statistics, the technique which is carried out by making conclusions that generally apply.

In general, this inferential analysis uses certain statistical formulas, which are results of the calculation of the formula, which will later be the basis in generalizing the sample for the population. It can be said that this inferential analysis serves to generalize from sample research results for the population.

Inferential statistics have a function to generalize the results of sample studies to the population. According to that function, inferential statistics are very useful for sample research. That is an explanation of inferential data analysis techniques.

This is a brief article about understanding different steps to achieve data analytics maturity from a business perspective and a technical perspective.; hopefully, this article was useful for you all. 

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