by Analytics Insight
January 28, 2022
Big data analytics is mainly the process of thoroughly assessing big data and extracting useful information from it.
Customers generate a huge amount of data. Every time we open an email, tag someone online, use our smartphone applications, talk to any customer support representative, make an online purchase or contact a virtual assistant, the service providers and corporations collect that raw data. These huge, unorganized data clusters are called big data.
In simpler words, big data is an enormous volume of data, and these data sets come in various forms and from multiple sources. Data is like the backbone for any business organization — any integral part, just like other business applications such as board software, data rooms, financial applications, etc.
Yes, businesses understand the importance of data collection; they constantly hunt for more and more raw data. However, it is not enough. Corporations have to securitize raw data to convert it into more pragmatic information to get better insights and make informed decisions. This is where big data analytics comes to the party.
What is big data analytics
Big data analytics is mainly the process of thoroughly assessing big data and extracting useful information from it. The term “useful information” means identifying different patterns, links, customer preferences, market trends that can help organizations to make better and more well-informed decisions.
Generally, data analytics help businesses to evaluate sets of data and turn them into something useful. But, big data analytics is a bigger game because of more advanced analytics. Big data analytics boast advanced elements such as what-if analysis, statistical algorithms, predictive models, etc.
“Big data” has been a buzzword in the business fraternity since 2000. Business practices have evolved over time, and data collection has been a prime objective of any business organization. Big data analytics make sure that raw data is used to maximum effect. But the question is, how does big data analytics work?
How does big data analytics work?
Big data analytics refers to processing, cleaning, and analyzing massive quantities of raw data collected and turning it into a powerful asset for organizations. Here is how this process works.
1. Data collection
The data collection process varies in different organizations. Thanks to technological advancements, organizations can collect unorganized clusters of data from various sources such as POS (points of sale), social media, online surveys, customers’ buying patterns, customer feedback, cloud applications, mobile applications, mobile phone records, emails, etc. All the data is first collected to data warehouses in raw from whichever sources possible.
2. Data processing
Once data is collected and stored, data professionals step in to do data partition and configuration for analytical queries. Generally, there are two ways to process data, including:
- Batch processing processes large data blocks over time. The batch process is beneficial when a business has enough time between collecting and analyzing data.
- Steam processing processes a small data batch at once, shortening the delay period between collecting and analyzing data. The Steam process is more complex and expensive than a batch process and is mainly used when management has to make quick decisions.
3. Data cleaning
Whether big data load or a small chunk of data, it needs scrubbing and filtering to improve data quality. Any duplicate or irrelevant and improperly formatted data must be scrutinized, eliminated, or accounted for. Why? Because dirty or rogue data can be very misleading and will affect the quality of insights.
4. Data analysis
Converting big data into a usable format surely takes time. Once done, advanced analytics can transform big data into valuable and practical insights. Here are some common data analysis techniques.
- Data mining. It categorizes large datasets to determine patterns and links by recognizing anomalies and producing data clusters.
- Deep learning. Deep learning employs machine learning and artificial intelligence to find patterns and useful information from complex data clusters.
- Predictive analytics. This technique uses a business’s past data to assess patterns and make predictions for the future to minimize risks and threats.
Benefits of big data analytics
The ability to scrutinize data faster can be beneficial to any business organization as it helps businesses to answer important questions quickly.
Big data analytics is important as It allows organizations to use a huge amount of data from various sources to identify opportunities and risks, helping organizations to move quickly and efficiently. Some benefits of big data analytics include:
- Cost-efficiency. Helping organizations in identifying better and more efficient ways of conducting business
- Product development. Developing a better understanding of customers’ needs and desires leads to the development of better products.
- Market insight. Keeping track of market trends and customers’ purchase behavior.
It is safe to say that big data analytics now runs the business markets because the information is power in the digital era. Big data analytics is helping businesses to uncover the hidden truths below those massive datasets which they collect from different sources. Thriving in competitive business markets is just a dream without big data analytics.
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