What is Data Analytics? Importance, Advantages & Applications
Data Analytics can assist businesses in better understanding their customers, improving their advertising campaigns, personalizing their content, and increasing their bottom lines.
The benefits of data are numerous, but you can’t take advantage of them unless you have the right analytics techniques and tools in place. Let’s uncover the world of Data and Analytics altogether.
What Is Data Analytics?
It is the process of examining data sources to discover trends and draw conclusions about the information contained within them. It is increasingly being performed with the assistance of specialized systems and software.
Commercial industries use data science technologies and techniques to help businesses make better business decisions.
As a term, primarily it refers to a wide range of applications, ranging from basic business intelligence, reporting, and online analytical processing (OLAP) to various forms of advanced analytics.
In that sense, it is similar to business analytics. The distinction is that the latter gears toward business applications, whereas data analytics has a broader scope.
Why Is Data Analytics Important?
The initiatives can assist businesses in increasing revenue, improving operational efficiency, optimizing marketing campaigns, and improving customer service. Analytics also enables businesses to respond quickly to emerging market trends and gain a competitive advantage over competitors.
It enables organizations to analyze all of their data to identify patterns and generate insights. Organizations use it to inform and, in some cases, automate decisions, thus connecting intelligence and action.
Enabling businesses to digitally transform their operations and culture, becoming more innovative and forward-thinking in their decision-making.
It is critical because it allows businesses to improve their performance. Companies that incorporate it into their business models can help reduce costs by identifying more efficient ways of doing business and storing large amounts of data. A company can also use analytics to analyze customer trends and satisfaction.
What Are The Advantages Of Using A Fully Integrated Cloud-Based Data Analytics Platform?
- It ensures that accurate and on-time data collection at the source.
- Raises the data quality and data literacy of all employees.
- Provides the processing power required to transform raw data into meaningful information for decision-makers.
- Removes the client’s responsibility for security.
Why Is It Important For Companies To Invest In Building an Analytics Platform?
One of the most important reasons to invest in analytics is that it allows businesses to gain valuable insights hidden within massive amounts of data. The rapid growth of data, combined with the widespread availability of the internet, has resulted in a massive revolution.
Here are a few of the reasons why businesses should invest in analytics:
1. Enables to Engage with Customers More Effectively
Data analysis can assist businesses in identifying trends in client needs, purchasing habits, and social behavior. However, the main impediment to achieving this is a lack of supporting data, slow processing, or inaccurate data. It not only helps businesses derive useful insights but also aids in data acquisition.
2. Incorrect Data Can Cost Your Company Money
Companies must ensure that the data they have is correct before it is analyzed and used to make further decisions. Incorrect data can lead to incorrect analysis, which can be dangerous when implemented or used to make critical business decisions. You can also avoid data discrepancies by using the same.
3. Keep Ahead of Your Competitors
According to a recent survey, nearly 75 percent of companies worldwide have already invested or plan to invest in big data analytics. Because competitors are already utilizing this powerful tool and becoming more data-driven, this trend will soon become a requirement for businesses rather than an option.
4. Assist in Accelerating Your Company’s Operations
Report creation and publication are important aspects of business operations. The forecasting and planning teams then use the generated reports to make major business decisions. Having a technically sound analytics team in place will allow businesses to accurately forecast and implement decisions.
5. Will Increase the Security of Your Company
This is one of the primary reasons why businesses should invest in analytics. The threat of data breach arises as a result of the massive amount of data accumulated by companies, where the organizational data is vulnerable to hacking. Analytics investments can assist organizations in protecting their data from external threats.
6. Cloud-based Solutions Will Save You Money
When businesses use large data sets, they can easily choose cloud service providers for storage and computing power. Cloud-based solutions enable businesses to analyze large amounts of data without investing in hardware. As a result, the majority of businesses today use cloud-based solutions to store data sets.
7. Assist You in Identifying New Business Opportunities
This is one of the primary reasons why investing in analytics is critical. It assists businesses in identifying new business opportunities as well as new products and services. Social media sites, too, are leveraging collected data and sending targeted ads to customers based on their interests.
8. Aids in the monetization of data
Data monetization is expected to be a hot topic in the coming years. Companies already understand the value of data because it acts as “digital gold” in the industry. The organizations’ next step would be to maximize the economic benefits of the collected data with the assistance of external sources and customers.
What are the Applications Of Data Analytics?
It has its application in almost every field of human life. Everyone, whether in online shopping, high-tech industries, or government, uses data analytics to aid in decision making, budgeting, and planning, among other things.
A variety of fields use DA solutions including:
Predictive modeling aids in the detection of transportation issues such as traffic congestions. It aids in the synchronization of massive amounts of data, which are then used to design plans and strategies.
It is used to plan alternative routes, reduce traffic and congestion, and thus reduce the number of accidents and mishaps.
2. Logistics & Delivery
Different logistic companies, such as DHL, FedEx, and others, use analytics to manage their overall operations. They can figure out the best shipping routes, approximate delivery times, and track the real-time status of goods that are dispatched using GPS trackers using data modeling.
3. Web Search
When you search for something on the internet, web search engines such as Yahoo, Bing, and Google use a set of data to provide you with results.
When you press the search button, search engines use algorithms to deliver the best-searched results. The searched data acts as a keyword and presents all relevant information in a logical order.
Analytics assists the manufacturing industries in maintaining their overall operations by using tools such as prediction analysis, regression analysis, budgeting, and so on. The unit can calculate the number of products that need to be manufactured based on data collected and analyzed from demand samples.
Security Analytics is a method of dealing with online security that focuses on the examination of data to deliver proactive safety efforts. No business can predict the future, particularly when it comes to security threats. But by dispatching security investigation apparatuses that can dissect security events, it is possible to identify danger before it has a chance to affect your framework.
It is primarily used in adaptive learning, innovations, content, and other areas. It provides the estimation, collection, investigation, and detailing of information about students. To determine the specific circumstances for the purposes of comprehension and streamlining learning. Machine learning helps to create the conditions under which it occurs.
The applications in healthcare are used to channel massive amounts of information. Mainly to discover treatment options or solutions for various illnesses. This will not only provide precise solutions based on recorded data, but it may also provide precise answers for exceptional concerns for specific patients.
Military analytics applications bring together a variety of specialized and application-specific use cases. It enables chiefs and technologists to forge links between information investigation and fields such as augmented reality and psychological science, which are propelling military organizations around the world forward.
Financial Analytics Services
Financial Analytics is a field that analyses a company’s financial data in depth. It assists organizations in gaining insights into their financial data and taking action to improve performance.
Cloud Analytics Services
Cloud analytics refers to the application of analytic algorithms to data in the cloud in order to produce the desired result. It entails the use of scalable cloud computing in conjunction with powerful analytic software to identify patterns in data and extract new insights.
Supply Chain Analytics Services
The processes that organizations use to gain insight and extract value from the large amounts of data associated with the procurement, processing and distribution of goods are referred to as supply chain analytics.
Enterprise Analytics Services
Enterprise analytics refers to the process of having data, business, and process analytical capabilities throughout an organization. It also contributes to organizations’ ability to collect, analyze, and process analytical data in the majority of business functions.
Data Analytics Consulting Services
Analytics Consulting Services employs a variety of techniques to optimize various business intelligence tasks by leveraging existing data. These roles are balancing business and hardcore analytics with the goal of delivering value-added analytical solutions.
Business Intelligence Vs Data Analytics
For the sake of simplicity, the term Business Intelligence and Data Analytics is now used interchangeably. The reality, however, is that they are not the same things. Both have a distinct scope of work and necessitate a diverse set of skills in order to assist organizations in flourishing through data-driven decision-making.
With the field of Analytics gaining popularity on a global scale today, many businesses are utilizing various tools and technologies in this field to gain insights from their customers. Business intelligence, a term commonly used by analytical firms, is critical in visualizing customer data so that customer behavior patterns can be predicted.
Let’s differentiate between business intelligence vs DA on the basis of a few points –
The goal of Business Intelligence is to create dashboards and reports. DA, on the other hand, goes a step further by identifying correlations between different variables in order to determine the factors influencing the results.
Business intelligence will assist you in conducting a simple analysis to obtain an overall picture of the business operations whereas DA can help you gain intricate insights into business operations.
Business intelligence can be performed without coding because several tools allow for data visualization and dashboard creation via drag and drop. DA, on the other hand, necessitates the use of a programming language to perform complex analyses.
Nonetheless, because of their ease of use and quick turnaround time, BI tools are one of the go-to platforms for more straightforward DA requirements.
Business intelligence is mostly concerned with descriptive statistics, which aid in determining the mean, median, and average. To better understand data and find insights with predictive analytics, data analysis combines descriptive and inferential statistics.
Statistical analysis is also essential for uncovering critical insights that can have a significant impact on a company’s customer experience or revenue.
4) Data Type
Business intelligence is performed on structured data that is curated for analysis using tools such as Power BI and Tableau. DA, however, is not limited to tabular data; analysts can conduct analyses with text, audio, and video file formats.
Analysts can scrape structured or unstructured data from websites using libraries such as ‘requests’ and ‘beautiful soup.’ It is very common here to use unstructured data to uncover insights.
5) Data Quality
Data Warehousing is required for Business Intelligence because it transforms data to improve the quality of information for streamlined Business Intelligence. However, Data Analytics is not always reliant on Data Warehouses for analysis.
A DA professional can collect information directly from Data Lakes or other disparate sources. Data Wrangling is a common task for Data Analysts but not for Business Intelligence professionals.
Every day, companies all over the world generate massive amounts of data in the form of log files, web servers, transactional data, and various customer-related data. Furthermore, social media websites generate massive amounts of data.
Companies should ideally use all of their generated data to derive value and make important business decisions. With Thinklayer you can start your journey in the world of Analytics with a data-driven approach for your business.