IAF s services help businesses make sense of the digital world by putting the customer first, being brand-aware, and producing measurable ROI from data.
Use your data to gain insights, automate tasks, enhance processes, and spark new ideas for growing your company.
When you use our Data & Analytics Services, you’ll be able to
- Modernize
- Standardize
- Optimize
- Visualize
- Maintenance
Data is a CRITICAL ASSET in today’s business world. Organizations rely heavily on data analytics to make quick and well-informed decisions, minimize risks, and maximize profits.
Require practical training with state-of-the-art equipment.
Privacy and data security concerns must be addressed.
We require a committed group to manage the continuing data analytics process.
Have to combine and synchronize unstructured data from disparate sources.
For large organizations that want to make better use of their data, IAF Business offers a wide variety of services and solutions.
IAF Business is able to analyze data and generate useful data insights because to its cutting-edge technology and sophisticated analytic methods. We use this information to assist our clients in establishing meaningful relationships with their customers.
Since client retention can have a significant impact on a business’ bottom line, our teams are committed to providing digital, commercial, and actionable solutions. Our on-time project completion will allow you to continue providing your clients with sound advice.
Steps in Analyzing Data
The steps of our tried-and-true data analysis procedure are as follows:
Business Understanding:
Many believe that data can be analyzed by using the data set, that the availability of the data set is sufficient to analyze any kind of pattern, but according to understanding, there is no data set for analyzing the data; all we need are the questions that define the data sets. This data analyzing process began with a question while working with business owners to understand what it does, what kind of decision they are going to make, and for what purpose the data is being analyzed.
Business Understanding:
Row data is queried to answer the questions, but this is not the row data set, instead, we need to call it row data because it is not exactly in the form where we want it to analyze. This happens after the question is defined and data is collected from various sources like a data warehouse, logs, and data sets.
Extract the Data:
This is the process of collecting information in preparation for further analysis. This is a pristine data set that will put us in charge of subsequent analysis. The information is retrieved from the database using SQL. the number of rows in the database that was queried to obtain the data was greater than 1,000,000. where data may be easily analyzed and manipulated with the use of database query languages like SQL. Learning SQL is a good initial step because it gives you access to the data.
Transform the Data:
Data transformation, the process of converting a dataset from one state or structure to another, is the groundwork of data integration, in which information from various sources has been combined into a coherent whole and is now in a form amenable to further analysis at its final destination. Extracting, transforming, and loading data is referred to as ETL. Recognizing and comprehending the data in its native structured or source format is an essential part of the data transformation procedure. Algorithms deployed using a profiling and data analysis tool are typically responsible for this. This process aids in determining the steps necessary to transform the data into the required format. Data transformation on huge or complex data at the source is typically possible in languages like R or Python.
Data Visualization:
The next step in exploring and evaluating data is to visualize it, after which we can form hypotheses or insights. As a data visualization solution, Tableau/SaaS makes it simple to extract insights and meaningful patterns from big datasets stored in both organized and unstructured databases.
Statically Analysis:
Summarizing the data and comprehending it in terms of models and graphs is a crucial part of data analysis. The data’s connections to the underlying real world are also explained. In addition to determining the statical relevance of a data collection, statistical analysis is used to spot patterns and trends for predictive analytics, which in turn aids in making important business decisions.
Data Model Development:
Industries are eager to implement models with predictive capabilities, and data model development includes articulating model goals, articulating the problem conceptually, and translating that into a computational model.
Modern applications play a crucial part in managing mathematical complexity, and the correct modeling allows you to generate a statistical model to reject any false or null hypothesis. Software as a service (SaaS) vendors like Tableau and SAS are making it simpler than ever for business analysts to construct models with automated predictive modeling tools. Analytics experts are constructing a predictive application model using machine learning algorithms sourced from open-source marketplaces or model-building APIs.
Recommendations/Report/Story:
In this last stage of data analytics, the analysis decision is summed up, and the story, report, and recommendations that result from the analysis process are represented. Tools like PPT, tableau, and SAS are used extensively at this stage to help with the report or story building, and the resulting report includes:
- Results that focus on the customer/industry.
- Business tactics and a hypothetical “decision tree”
- Priorities in company are determined.
- Consumers or users of the product being developed are identified.
- Measurable results provide the basis of the business case.
