Big data influences major decisions businesses and governments make. In this article, we want to discuss: What is Big Data Analytics. Also, in what ways is big data shaping business decisions? Some drawbacks may come with handling large data sizes. You’ll understand how you can manage those challenges.
What is Big Data Analytics?
To start, let’s answer the question, “What is Big Data Analytics?” Big data is a term that describes larger and more complex data sets. These datasets are so voluminous that traditional data intelligent tools can’t process or manage them. However, voluminous data sets are used in decision-making and solving problems.
Gartner defines Big Data as high volume, high-velocity, and/or a wide variety of data assets. There are three types of big data. They include structured, unstructured, and semi-structured data. Structured data is high-organized and in a fixed format. In contrast, unstructured data have no fixed format and are unorganized. Lastly, the semi-structured data contain both data types.
A Brief History of Big Data Analytics which Everyone Should Know
What is the history of big data? The idea of big data is new. However, the origin of large data sets goes back to the 1960s and 1970s. Then, there was the development of databases and data centers.
In 2005, People started to realize how many data users generated through online platforms like Facebook. Yahoo created Hadoop that same year. Hadoop is an open-source framework for analyzing big data sets. The open-source framework contributed to the growth of big data. It made it easy to store and work on those data sets.
Roger Mougalas coined the name Big Data in 2005.
In the years that followed, to date, there has been an enormous increase in data. Users are still generating data every second. Thanks to the internet of things (IoT) devices and social networks.
Cloud storage has also become popular in recent years. Businesses and developers can save costs by using the cloud for data storage. The cloud offers scalability and accessibility. The next section on what is big data highlights the importance.
Why is Big Data Important in Today’s Era?
The importance of big data comes from how much value it provides. You can have a large volume of data, analyze them, and get insights. Big data processing helps different organizations develop new strategies and promote efficiency. The information from data analytics can help:
- Reduce cost
- Save time
- Develop new products and optimize the offering
- Lastly, make smart and informed decisions.
Furthermore, industries like healthcare are not left out. What is big data in healthcare?
- Data analysis can help in identifying the cause of an illness. Also, it helps in the treatment and prevention of diseases.
- Big data companies like Apple and IBM are using big data in designing new healthcare applications. Devices like the apple watch can help monitor your health and activities.
- Big data is also helpful in medical researches, finding new treatment, and identifying disease trends.
- Personalized healthcare
Big data analytics can help a business owner in some of these ways:
- Detect fraud
- Customize product offerings
- Detect root causes of failure
- Manage and recalculate risk portfolios
Next, let’s look at the advantages of big data in shaping the business decision.
5 Fundamental Ways Big Data Analytics Is Changing Business Decisions
What is Big Data Analytics changing for businesses? Big data influences important decisions most businesses make today. The more data a company collects and can analyze, the more data they have. This data forms the basis of the steps the company takes. Here are the five ways big data is molding the course of business decisions:
- Business Valuation: Gone are the days when you measure the success of a business by assets alone. Today, company data are also one of the indicators of success. The way a company channels data and makes decisions from it will affect the growth.
- Company Reputation: Companies can create an operational strategy from big data. Measuring the analytics from social platforms and websites helps a company understand how the public views them. They can use this information to rebrand or improve marketing strategies. This data can also help in enhancing customer services.
- Data Collection Software: Big data processing and analytic are only possible with collection software. There are different data collection software available. However, they do not have the same performance. Software that automates the process of data collection, and offers a detailed report, adds more value.
- Revenue: There are many uses of big data in generating revenue. When a company has access to the right data, it can identify new opportunities. Also, big data helps businesses improve their transactions. With big data, a company can promote their services to the right market. Thus, increasing revenue.
- Productivity: Companies can increase productivity and reduce operation costs by using big data. By finding ways to automate different processes, a business can increase output. A company can work with a team of analysts or use the software in analyzing business processes.
Advantages and Disadvantages of Big Data Analytics
The increased volume of data available has its advantages, as well as difficulties. Companies that have access to big data can conduct market research on their customers. Also, they can study the potential customers. This study will help them improve the product or services; and marketing strategy.
On the other hand, there are big data challenges too. Firstly, the data size can be overwhelming. Companies will have to analyze and find the right indicators. If the analytics isn’t appropriate, the business may suffer from bad decisions. Also, handling that data size requires expertise. Sometimes, you may need big data tools to analyze them. A Big data Engineer can help use these tools to get results. When you don’t have those, you may be unable to act on the data.
Furthermore, is big data dangerous? Yes, there are risks like privacy problems and security issues. However, there are data protection laws to help curb these. The next section on What is Big Data Analytics will help examine if your business is data-driven.
Is your Business Masquerading as Data-Driven?
It takes more than data and big data management tools to be data-driven. Your business may have analysts, quality data, and the right software. But, does that make it data-driven?
Being data-driven involves all the processes that ensure you make decisions based on details. Your company may analyze data, find several pointers, and not act on it. Some instances of how companies masquerade:
- Each team in the company is acting on different data or information. Thus, every department has a different strategy. The idea of big data analytics is to show a company which direction to follow. Stakeholders must agree on which data are useful and develop a common strategy with it.
- Also, when the decision comes first before details. Different company leaders make a decision, then look for data to support it. It should be the other way round. The insight from the data is what should guide company decisions. Leaders should learn how to share data with members of the company. Then, allow the experts to analyze it and discover insights.
Finally, it may require the right internal processes for your company to ensure a data-first approach. Other times, a cultural and value shift in the company is essential.
How to Overcome the Challenges in Managing Big Data
There are Big Data management strategies companies should employ to protect data from threats. These strategies involve different factors, like identifying data sources and how they utilize these data.
Furthermore, data management may involve more than one strategy. Organizations often have their data across different platforms. So, a data audit is necessary. Data audit can help identify the location of the exact data and how to act on it.
Also, to overcome big data challenges, its size, and how much resources they have come in. Besides, there are other solutions, including.
- Data Centralization: Centralizing data, above all things, will help reduce security and data breaches. Also, it will help with easy management.
- Automation: Automation involves using big data tools to manage data. Automating data management speeds up the process and also reduce cost.
- Measuring Progress: Finally, measure the success of your data management strategies. This measurement will help you understand what works and what doesn’t.
Characteristics of Big Data which a Data Receiver Should Know
We can’t provide an answer to What is Big Data Analytics without referring to the 5Vs. The 5Vs are the characteristics of big data. The five vs. of Big data include:
- Volume: Volume refers to the amount of data. One of the characteristics of big data is the size, hence the name. Due to the rise in digital products, there’s an increase in data size. The size of data collected always varies. For some, it could be tens of terabytes. For others, it could be hundreds of petabytes.
- Velocity: Velocity refers to the rate at which data is collected and, probably, acted on. Most internet-enabled products function in real-time. So, evaluating and working on data in real-time is vital.
- Variety: The third V, variety, refers to the different types of big data available. The rise in big data brings with new unstructured data types. Unstructured data types are different from traditional data formats and require preprocessing. Images, texts, videos are unstructured data.
- Value: The third V refers to the value of your data. The amount of data doesn’t matter if you can’t use it. Finding value involves analyzing it, recognizing patterns and insights.
- Veracity: What is big data veracity? Veracity refers to the credibility of the statistics and how reliable it is. Can it help your business make informed decisions?
The Bottom Line
Access to big data isn’t valuable if you can’t or don’t know how to apply it. Analyzing and gaining insights from statistics takes a lot of processes. It involves business users and analysts identifying statistics trends and patterns. The data from this analysis can help in planning and enhancing strategies.