The term data analytics broadly refers to the science of studying primary data to draw insights into it. A variety of methodologies are used to carry out data analytics, such as data mining, cleansing, transformation, and data modeling.
Many data analytics approaches and processes have been transformed into mechanical processes and techniques that deal with raw data that will be intended for human consumption.
Data analytics is one of the most intriguing and impactful technological topics, its purpose is to use big data to simplify things and come up with a unique purpose and strategy.
Data analytics gives a hypothesis and guesstimate of occurrences, assisting in the discovery of solutions that may be suitably disguised for a specific problem to arrive at the best conclusion and a persuasive solution.
Why is data analytics important for business growth?
The Data Analytics process covers many domains such as Healthcare, Insurance, Transportation, Manufacturing, Travel and Logistics, Energy and Financial services, and many more. Data analytics are used to forecast and handle interruptions, improve routes, provide proactive customer support, detect upcoming system failures, manage inventory systems, optimize pricing, and avoid fraud.
Multidisciplinary data analytics encourages internal and external stakeholders to interact and collaborate. For example, by leveraging the highly collaborative UI of modern analytics, data scientists may collaborate closely with customers to assist them in solving problems in real-time.
Data analytics empowers companies to track all available data, including structured, unstructured, authentic, quantitative, and historical information, to uncover trends and produce insights that are used to inform and, in some instances, automate decisions. The best solutions now enable the entire analytical process, from data access, preparation, and analysis through analytics operationalization and performance tracking.
Data Analytics Process
The following five phases of the data analysis process are iterative and must be implemented in the data analysis process by the data analysts:
1. Identify
The first step of the data analysis process is to determine, why data analysis? Starting with a specific goal in mind is a critical step in the data analytic process. Realizing the business challenges, fixing, and establishing well-defined goals makes it easier to choose the required information.
This requirement typically arises consequence of business-specific questions, such as:
- How to reduce the production cost without compromising quality?
- How to expand sales opportunities with available resources?
- How to know customer perfection on our product?
Once you’ve defined a problem, you must determine which data sources will best assist you in solving it.
2. Collect
Once a purpose has been established, it is time to begin gathering the data required for analysis. The data sources gathered can be qualitative (numeric) e.g., sales figures, or quantitative (descriptive) information such as customer reviews, all these data make this phase essential to understanding the breadth of analysis.
Each data set is divided into three categories: first-party data, second-party data, and third-party data. Let’s look at each one.
- First party – First-party data is information gathered directly from customers by the user or their company. It could be transactional tracking data or data from your company’s customer relationship management (CRM) system.
- Second Party – Second-party data is primarily made up of first-party information gathered from other companies. This could be obtained directly from the company or via a private marketplace. The main advantage of second-party data is that it is usually structured, and while it will be less relevant than first-party data
- Third-party – Third-party data is information gathered and aggregated from multiple sources by a third-party organization. Third-party data frequently (but not always) contains many unstructured data points. Many businesses collect this data such as users’ email addresses, mail delivery addresses, telephone numbers, social media manages, purchase history, and online activities and sell it to other companies in order to generate industry reports and conduct marketing analytics and research.
3. Clean
Once you are ready with the data collected, prepare to conduct the analysis. This entails cleaning and scrubbing the data and ensuring that its quality remains unaffected.
The following are some of the most important data cleaning tasks:
- Eliminating the issues – Eliminating the Errors, duplicates, and deviation problems that arise when data is pooled from multiple sources
- Avoiding unwanted data – removing non-essential data points and identifying non-relevant observations that are unrelated to the proposed analysis
- Enhancing data structure – Giving the data structure by resolving any layout issues or typos, as well as assisting in the simple mapping and maneuvering of the data.
- Taking Preventive measures – Once you’ve realized that some important data is missing while cleaning up the data, start filling in the gaps.
4. Analyze
At this stage, you already have a huge amount of information at your disposal. You’ve spent time cleaning it up. It’s the most well-organized it’ll ever be. Now it’s time to start snipping and cutting it up to extract useful information.
5. Interpret
Interpreting the results is the final step of the data analytics process is to share insights with the people involved after the analyst has completed their analyses and derived their conclusions. You must be able to present your valuable discoveries to decision-makers and stakeholders in a compelling and easy-to-understand manner if you want them to be implemented. It’s critical to make sure the insights are clear and consistent. As a result, data analysts frequently use reports, dashboards, and interactive visualizations to enhance their findings. This necessitates supplying all gathered evidence and ensuring that everything is covered in a thorough and concise manner.
Types of data analysis
Utilizing data analysis methods and techniques you look for patterns and connections that aren’t obvious, insights and predictions. Data analysis entails gathering all relevant data, processing it, exploring it, and applying it to uncover patterns and other insights
The various types of data analysis can be divided into four groups:
Descriptive analysis – Descriptive analytics helps businesses to understand what has happened so far. It is the basic and widely used type of business analytics, highlights and summarizes patterns in both current and historical data, then organizes and presents the information clearly and understandably.
Descriptive analytics is used to create reports, key performance indicators, and business metrics that allow businesses to monitor efficiency and other fashions. According to data analytics firms, there are a variety of businesses for which predictive analysis can be used. Including E-commerce, Sales, Human Resources, IT Security, Healthcare, and so on.
Diagnostic analysis – As the name implies diagnosis of the issue, it’s described the technique of asking the question to your data: Why did this happen? and focused on understanding, why a certain issue has occurred? Identifying the trends using descriptive analytics could be seen as a natural next step. Manual Diagnostic analysis can be obtained with an algorithm or statistical software like Microsoft Excel.
Predictive analysis – This form of analysis enables the analyst to detect future trends and forecast future growth based on historical data. Predictive analysis is commonly used in business to forecast future growth. It has recently evolved as a result of technological advancements.
For example, insurance companies frequently use historical data to predict which of their customers are likely to be involved in an accident. They use these records to raise the insurance premiums of those clients.
Prescriptive analysis – The prescriptive analysis is the final and most complex step. As the last step in the analytics procedure, it incorporates elements from all the other analyses we’ve discussed and allows users to predict future recommendations.
Businesses can use predictive analytics to help them decide which new products or business areas to invest in. For instance, consider algorithms that guide Google’s self-driving cars. Using the past and present data, these algorithms make countless decisions each second to ensure a smooth and safe ride.
Using a combination of descriptive, diagnostic, predictive, and prescriptive analytics, businesses can explain why something happened and predict potential future outcomes and actions.
How can Impelsys power up the businesses with a Data analytics process?
Harnessing the experience and expertise working with Enterprises, Impelsys Data Analysts & Tech Teams can provide tailor-made solutions for organizations that desire to integrate themselves with Data analytics. Impelsys has assisted Organizations in developing interactive, rich analytics-based reporting dashboards that paved the way for the decision makers to take timely corrective actions that saved time and resources resulting in significant savings and facilitating new avenues with revenue potential impacting their overall bottom-line.
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Authored by –
Rajan Pratap Singh
Project Leader
