All you need to know about data analytics

Data Analytics is actually a very simple process that’s often misunderstood and seen as complicated. Discovering hidden patterns, and undetected trends, finding correlations, and gaining insightful knowledge from vast datasets are all part of the process of data analytics, which is used to create business forecasts. Your company runs more quickly and effectively as a result. It involves the careful examination of large amounts of data that results in extracting meaningful insights for businesses. Find unseen patterns and consequently aid in making informed decisions.

At Eureka BI, We focus on Data Analytics/Analysis. That’s our main field and we excel at it. We are now the preferred and most reliable team for business solutions using insights and technology in Ghana. For almost 5 years now, we have been working with companies willing to learn from their data and to make strategic decisions based on evidence.

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What are the 5 steps in data analytics?

There are 5 main steps or processes involved with data analysis. They are discussed below.

STEP 1: GOAL(S) SETTING

Depending on the field or industry you are analyzing, your goal as a data analyst may span from trying to determine a trend so that. It may also be to get the right employee to take over a crucial position in an organization. Whatever you are trying to achieve through your research must be written out clearly.

STEP 2: DATA COLLECTION

During data collection, information on relevant variables is acquired in a predetermined, methodical way. Here are some methods by which data is collected –

Surveys.
Transactional Tracking.
Interviews and Focus Groups.
Observation.
Online Tracking.
Forms.
Social Media Monitoring.

STEP 3: DATA WRANGLING

At this stage, it’s necessary to remove mistakes and integrate large data sets. This makes the data more accessible and easier to analyze.

STEP 4: DETERMINE THE ANALYSIS

This is where you carefully study your data and go through processes like Estimation, Hypothesis testing, Parametric tests, and Comparison tests.

STEP 5: INTERPRETATION OF RESULTS

Finally, based on your findings during the previous stage, you interpret your data and the statistics that were analyzed in the previous stage. You go on to speculate and make predictions on what the study findings might imply for theory, practice, and future research.

What do I need to know about data science in relation to business?

Data analytics is significant since it aids in the performance optimization of enterprises. In contemporary times data analytics adds value to businesses. From insights and statistics across workflows and hiring new candidates to help senior staff at the strategic level. Data science has become very crucial and valuable to companies in various industries and hence the thirst for knowledge and efficient utilization of the field. Also, a corporation can use data analytics to improve business decisions and track consumer preferences and trends to develop fresh, improved goods and services.

Some key concepts in Data Analytics/Analysis

Data analytics typically examines and interprets data in order to derive meaningful insights and information. Here are some key concepts and techniques you need to know about data analytics:

Data Collection: The first step in data analytics is to gather relevant data from various sources.
Data Cleaning: Once the data is collected, it needs to be cleaned and preprocessed to remove errors, inconsistencies, and missing values.
Data Exploration: After the data is cleaned, it is important to explore and analyze it to identify patterns, relationships, and trends.
Data Visualization: Data visualization is the process of presenting data in a visual format such as charts, graphs, and maps to help understand and communicate insights.
Statistical Analysis: Statistical analysis involves applying statistical techniques such as regression analysis, hypothesis testing, and machine learning to extract insights from data.
Predictive Modeling: Predictive modeling involves building models that can predict future outcomes based on historical data.
Data Integration: Data integration is the process of combining data from different sources to create a unified view.
Big Data: Big data refers to extremely large data sets that are difficult to process using traditional data analysis methods.
Data Privacy: Data privacy is the practice of protecting personal and sensitive data from unauthorized access or use.
Data Governance: Data governance is the set of policies, procedures, and standards for managing data assets throughout their lifecycle.

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Can I teach myself data analytics?

The answer is yes, you can educate yourself on the foundations of data analytics. Yet to do that, you’ll need to make time to study data analytics independently by utilizing the tools at your disposal. Eureka Bi has made it easy for you to teach yourself to become a data analytics expert.

Data Science is a fast-expanding field that is driven by the need to make sense of massive amounts of data and with the correct skills and training, data analytics can be a powerful instrument for business and scientific decision-making. We have just discussed reasons why data analytics is important in business today and we hope this information was helpful. Don’t forget to contact us, at Eureka BI, if you are interested in learning more about data analytics, ERP, business intelligence, artificial intelligence, and machine learning.

All you need to know about data analytics