Gone are the days where businesses relying solely on gut feelings to make decisions. The technology-dominated era has drastically increased the amount of data and hence, the requirement for data analytics and visualization.
Data analytics are crucial in lowering business costs, retaining customers, and increasing overall business revenue. But beginners often get intimidated by the amount of information related to data analysis. However, this guide has covered everything you need to know about data analytics and visualization for successful business growth.
Before diving into more details, let’s get started with the meaning and core benefits of data analytics.
What is data analytics?
Data analytics involves collecting, organizing, and transforming raw data into valuable insights for intelligent business decisions. Identifying patterns, trends, and insights helps businesses reach their fullest potential with the optimum utilization of business resources. Businesses use various approaches to data analytics to identify the root cause of challenges and devise relevant strategies.
Businesses in every industry benefit from the successful implementation of data analytics. For example, retailers can use customer behavior analysis to run personalized campaigns and gain a competitive advantage. It can also help in fraud detection and prevention for the banking and healthcare sectors. Similarly, data analytics is crucial in increasing overall performance and reducing manual burden, irrespective of the industry.
Benefits of Data Analytics
We already mentioned how data analytics remain suitable for every business. So here are some significant benefits to solidify our statement:
- Helps in product development based on trend analysis
Data analytics help in product development based on foreseen market requirements. It predicts future trends and gives companies a competitive advantage by offering advanced features and updates beforehand.
- Improved ROI from advertising and marketing campaigns
Data Analytics helps to prioritize how to reaches out to potential prospects with enhanced advertising/marketing campaigns. The data collected from previous campaigns help in understanding past performance and interaction. Based on data analytics and interpretations, teams devise better marketing strategies.
- Provides a personalized customer experience
Data analytics help in keeping up with the changing customer preferences. Tracking your target audience’s tastes help in providing personalized offers. The data generally comes from major channels of interactions, including website, social media, surveys, and email campaigns.
- It ensures effective collaboration and quick actions.
Various teams come together to analyze data and make quicker decisions. Using the right tools and platforms leads to efficient data visualization for better understanding. Quick actions regarding certain matters ensure you deal with anomalies before they become significant organizational challenges.
Types of Data Analytics
Businesses must know about the different types of data analytics to make the right choice. Here are 4 major types of data analytics implemented by organizations:
1. Descriptive Analytics
Descriptive analytics remains the most preferred type of analytics worldwide. It aims to break large chunks of data into smaller units to derive relevant insights. All the data accumulated from your website and social media channels go through descriptive analysis. It helps organizations understand the reasons behind past business decisions’ successes or failures.
The implementation of descriptive analytics includes mining real-time and historical data from various sources. It then works on generating actionable insights to improve future approaches.
Use case
Sales reports remain one of the most common examples of descriptive analysis. It helps understand the extent of conversions based on different approaches used by the sales team. Based on the insights, the company modified its sales approach to convert more customers.
2. Diagnostic Analytics
Diagnostic Analytics aims to solve significant business challenges by identifying the root cause. It tries to understand the reason behind specific issues to prevent them in the future. It’s used by businesses to conduct trend analysis and identify correlations between variables.
The primary goal of the diagnostic analysis is to go through data to understand why something happened. Many businesses use the blend of descriptive and diagnostic data analytics to establish a solid data-driven decision-making model.
Use case
The HR department in companies often uses diagnostic data analytics to understand a sudden surge in employee turnover rate. Information from various sources, including reasons given in exit interviews, general industry salary packages, etc., are analyzed.
Based on the conducted analysis, companies find root causes like low salary packages or lack of retirement benefits. Therefore, the derived insights help modify company strategies to retain current employees.
3. Predictive Analytics
Predictive Analytics emphasizes forecasting to predict future trends, changing customer preferences, and competitor strategy analysis to modify current strategies. The historical datasets are analyzed using ML models to identify future trends.
For example, predictive analytics help reduce reduces high customer acquisition costs. Businesses suffer fewer costs in retaining customers than in acquiring new ones. Using predictive analytics, companies succeed in identifying the reasons for customer dissatisfaction. Therefore, you can take appropriate steps before your churn rate increases.
4. Prescriptive Analytics
Prescriptive Analytics aims to provide various solutions to the identified problems. It also provides detailed information about the outcomes associated with every solution. Businesses come across many viable solutions to complex issues using this type of data analytics. Furthermore, the implementation generally occurs after predictive analytics to suggest actions based on identified trends and factors.
Lead scoring remains one of the top examples of using prescriptive analytics. It ranks leads based on the likelihood of converting. You can assign scores to your leads and personalize campaigns based on the enhanced chances of conversion.
4 Steps To Conduct Successful Data Analytics
Now that we are aware of the benefits and types of data analytics, let’s look at the simple process of implementing it:
Data Collection
Data collection is the systematic accumulation of structured and unstructured data required for processing. Awareness of the specific data objective makes it a smooth process. Next comes the type of data you plan to collect. Many researchers focus on both qualitative and quantitative data collection.
Focus on using a blend of methods for data collection, including surveys, interviews (primary data collection), and existing sources like financial reports, sales reports, etc.
Data Processing
Now you have a bunch of scattered data ready for interpretation. But transforming it into understandable formats remains crucial to generate valuable insights. The process involves eliminating incorrect or incomplete data, transferring it to a data warehouse, and processing it for interpretation. Some notable formats include videos, graphics, simplified texts, etc., for enhanced understanding.
Data Analysis
The step involves using tools to generate valuable insights from the processed data. All the collected data is interpreted together using various types of data analytics to draw conclusions. Furthermore, data visualization techniques are essential in identifying and interpreting hidden insights.
Some of the top data analytics techniques involve regression, factor, segmentation, cluster, and time series analysis.
Data Interpretation
The final step of the data analysis process focuses on drawing informed conclusions based on the conducted analysis. It involves creating in-depth reports and dashboards to present the data for simplified understanding. Furthermore, it explains the modifications required in future approaches for enhanced results.
Top 6 skills required for data analytics
The tools used in data analytics vary based on your role, the company you choose, and the industry in general. Here are some essential skills one must have to thrive in data analytics:
Data Cleaning
remains one of the prominent skills which makes up most of the tasks handled by data analysts. It includes extracting data from various sources to identify missing and disorganized data. Next comes preparing the raw data for further analysis.
Data Exploration
refers to identifying trends and patterns in data. It remains essential as the upcoming trends often help businesses develop a competitive edge.
Statistical Knowledge
plays a crucial role in interpreting the collected data. It ensures you avoid logical errors and conduct systematic analysis.
Data Visualization
helps in understanding the collected data quickly. We respond better to charts and graphs than to numbers spread on a screen. Knowing how to present data in a visually-compelling format is another critical skill you must learn.
Communication Skills
(written or verbal) ensure you can explain your observations and insights to the team. It’s essential to have excellent communication skills to collaborate easily with others.
Problem Solving Skills
are vital for data analysts to deal with inconsistent or incomplete data. Companies consider working with individuals who approach a problem innovatively.
Various applications of data analytics
Here are some applications of data analytics in marketing and healthcare.
1.Marketing: Behavioural Analysis & Conversational Marketing
Data analytics play a crucial role in generating insights helpful in modifying current marketing tactics for improved engagement and conversions. Customer behavior is crucial in devising successful marketing strategies—data analytics help collect customer data like past purchases, product views, clicks, etc.
It identifies platforms where your customers interact the most, like websites, social media, and emails. All this information helps target the right platforms with strategies that proved to work in the past. It also assesses the required degrees of personalization.
Chatbots are a great example of conversational marketing. It uses data related to past conversations, browsing history, purchases, emails, etc., to initiate and continue a personalized conversation. Bots can quickly process a vast amount of data related to different customer segments. Therefore, customers access personalized conversation at all times (24/7 availability).
2.Healthcare: Predictive Analytics
Data analytics has proven records of improving healthcare efficiency. Predictive analytics helped hospitals identify and deal with ICU bed shortages during the COVID-19 pandemic.
The Lousiana Department of Health and Hospitals refrained from having a specific number of beds in the hospital. Using data analytics software, they predicted the possibility of fatalities. The software helped the hospital get a real-time look based on the collected patient data analyzed regularly. Therefore, they utilized resources only to the required extent and avoided over or underutilization.
Data analytics in Healthcare is also used for Fraud Detection and Prevention, Drug Discovery, Genomic Studies, Clinical Risk Prediction, and more.
Data Analytics Vs. Data Science Vs. Business Analytics
Data analytics, data science, and business analytics involve solving problems through the accumulated data. But here are some points of differences you must understand between the three:
Data Analytics
Aim: Examine a large amount of data for identifying trends and generating insights for enhanced business decisions.
Tasks involved: drawing insights from accumulated data and solving business challenges.
Example: identifying the reasons behind the failure of a particular marketing campaign or a sudden decline in conversions.
Knowledge required: Working knowledge of mathematics and statistics.
Data Science
Aim: research and identify unknowns using a combination of tools, algorithms, and machine learning principles. Full-fledged coding knowledge separates data scientists from analysts.
Tasks involved: building frameworks and automation systems to solve complex problems.
Skills required: machine learning, software development, programming languages, data mining, etc. Getting involved in data science requires an advanced degree and tech knowledge.
Business Analytics
Aim: finding solutions to specific business problems using the accumulated data.
Tasks involved: the process involves using different tools, including reporting tools, data mining, visualization, and financial analysis tools, to solve specific challenges.
Example: identifying the need for an enhanced business model based on historical data.
What are the career options in Data Analytics?
Data analytics continues to become essential for businesses to grow, expand, and have the edge over the competition. Every business looks for technical experts that can help collect and transform raw data into valuable insights. With the right skill sets and knowledge, you can start your career in data analytics.
Let’s now look at two of the most preferred roles in data analytics for people with creative thinking and a love for numbers:
- Quantitative Analyst remains one of the most preferred career options in data analytics. It deals with researching and analyzing data using basic mathematical principles. Some notable skills required for the job are database management, programming languages, and communication.
- The position of a Business Intelligence Analyst remains suitable if you are searching for a technical role emphasizing programming. The analysts are exposed to many successful tactics used by leading businesses. Some skills required for the role include critical thinking, business knowledge, coding skills, and communication.
Wrapping up
Now that you know data analytics and visualization implementation, use it to your fullest advantage. You’ll be shocked to identify all the hidden insights fueling your business growth. Moreover, in a competitive world, you need to develop an edge with detailed trend analysis and insight generation.
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