Big Data Analytics: How It Works and Why It Matters
People generate massive amounts of data daily, and each year, the amount of data generated grows rapidly. It’s estimated that by 2025 the total amount of data created, captured, copied, and consumed annually will grow to more than 180 zettabytes.
Think of it that way, whenever customers visit your website or your retail store, buy from you, leave feedback, or review your product, join your email list, or get in touch with you in various ways, they leave a trace that can be collected, processed, and analysed. On top of that, there is data coming from mobile phones, apps, IoT devices, social media, and even fridges that have various sensors and are connected to the Internet.
But data doesn’t come only from your customers or outside your organisation; it is also generated within your company. Calls, emails, marketing campaigns, sales, and CRM systems all create enormous amounts of information that can give you a business advantage if effectively used.
However, globally only a small part of generated data is kept, and even less is analysed adequately by businesses. To stay competitive in today’s world, your company must be data-driven, yet simple data gathering is not enough. No matter how much data you collect, you only get value from it if you evaluate and act on your data.
What is Big Data Analytics?
By Big Data, we mean huge volume, high velocity, and immensely varied data that is too large and complex for processing by traditional database management tools.
Big Data Analytics, in simple words, is a process of examining vast datasets to uncover information that can help your company make informed business decisions. Large-scale data analysis uncover hidden patterns and find correlations, market trends, and customer preferences. With big data analytics, you can make better and faster decisions and model and predict future outcomes.
How Big Data Analytics works
Data streams encompass such a vast amount of information that Big Data Analytics is more of a combination of different technologies and techniques working together than a single do it all solution.
Before your data can be appropriately analysed, it must be collected, processed, and cleaned. The information collection process will look different depending on your business model and niche. The type of data affects how it is sourced, collected, and scaled. You can generally collect structured or unstructured data, with the latter being more commonly collected from various sources.
Structured data is stored in tabular formats - such as Excel sheets or SQL databases - that take less space. It is very scalable since it can be stored in data warehouses.
On the other hand, unstructured data is saved as media files or NoSQL databases, which take up more space. It can be stored in data lakes, making scaling difficult.
When data is properly collected and stored, the next step is to arrange the data correctly to get actionable results on analytical queries. There’s more than one way to organise data, and the method will depend on the task and your company’s resources.
We classify processing environments into centralised and distributed. Centralised processing happens when all the processing takes place on one computer system, for example, a dedicated host. Distributed processing, on the other hand, happens on multiple systems and is often required with vast datasets as it allows separating large datasets into smaller chunks and processing them in parallel.
Based on the processing time, there is batch processing and real-time processing. Batch processing is a slower process where large pieces of data are collected over time and processed in batches. It’s preferred where there is a need for accuracy over speed.
Real-time processing is used when speed over accuracy is preferred. It’s more complex and costly than batch processing. The final step before data can be evaluated is data cleansing. This is an important step that improves the data quality and ultimately leads to more accurate results. In short, the process can be described as scrubbing for any errors, duplications, irrelevant data, etc. Any wrong or irrelevant data must be removed or taken into consideration.
Types of Big Data Analytics
There are 4 types of data analytics:
1. Descriptive Analytics - “What happened?”
These tools tell you what happened by summarising past data and presenting it in a way that is easy to understand, for example, as simple reports or visualisations.
Descriptive tools seldom incorporate AI and machine learning techniques. A good example would be a sales report that shows you all the sales data from the past few years in a simple graph.
2. Diagnostic Analytics – “Why did this happen?”
Diagnostic tools answer why something happened and help you find the root cause of the problem. They are more advanced than Descriptive Analytics and often use techniques like data mining but don’t necessarily need to rely on AI and machine learning to be accurate.
Imagine that you are in the SaaS business, losing users. You could use diagnostic analytics to find out why your users are cancelling their subscriptions and then develop a solution to counter this problem.
3. Predictive Analytics - “What might happen in the future?”
Predictive analytics rely on data mining, AI, and machine learning to interpret current and historical data and predict the future. It’s widely used in many different sectors, from retail to health to finance.
In practice, predictive analytics can be used to predict market and customer trends or a soon-to-happen failure in a piece of machinery. It’s also used in credit scoring systems to help approve or deny loans within minutes.
4. Prescriptive Analytics - “What should we do next?”
Prescriptive analytics, in short, provides the solution to a particular problem. Based on data and considering all relevant factors, it helps you decide what you should do next.
Those tools are widely used in business. They help companies make data-driven decisions in sales and marketing, among others. One example would be lead scoring, a process of ranking the sales readiness of a lead.
Some of the biggest companies, like Netflix, use prescriptive analytics. Netflix employs this analytics to predict what viewers are most likely to watch and decide the optimal timing for those recommendations.
Benefits and Advantages of Using Big Data Analytics in Business
Large-scale data analytics helps organisations in many ways. It often has a tremendous impact on sales, customer service, marketing, and new product development, to name a few. Data-driven decisions lead to more efficient operations, higher profits, and happier customers.
Depending on your sector and business model, the benefits of deploying big data analytics vary. Most commonly, businesses use it to:
1. Drive Innovation
Innovative companies constantly look for new products and ways to improve existing processes and services. Vast datasets examination helps organisations interpret sales data, product performance, and customer feedback, improving existing products or leading to new product development.
Evaluating the collected company’s insights can reduce costs, identify new opportunities for revenue generation, improve efficiency, and lead to new business strategies and marketing techniques that will create competitive advantage.
2. Accelerate Business Transformation
Data streams analysis is also crucial for implementing operational strategies and facilitating business transformation. Evaluating and acting on your data can help you increase operational profitability and provide your company with new innovation-driven opportunities.
Companies that use big data analytics see vast improvements in many areas, including financial management, product development, and time/cost saving.
According to a recent survey, the COVID pandemic has speeded up organisations’ data-driven strategy, and 78% of IT decision-makers agree that collecting and analysing data will change the way their companies do business in the next 1 to 3 years.
Again, high-volume data can help you transform your business in different ways depending on your niche and business model. Examples include automating manual processes, thus saving hundreds of hours per year and reducing costs. Or, in the case of retail, learning and analysing customer habits to create recommendations based on purchase history.
3. Drive Better Customer Service
Data streams can also be used to improve your customer service. Companies commonly use big data analytics to improve customer experience and increase customer retention and royalty.
Analysing inputs from your website, social media, chatbots, sales, and marketing can help you solve customers’ problems faster or prevent them from happening in the first place. And in some sectors, just a small, 5% increase in customer retention can result in more than a 25% increase in profit.
4. Increase Security
Another critical area for big data analytics is IT security. In the past, security incidents were detected by two analytical techniques: correlation rules, network vulnerabilities, and risk assessment. They were good at detecting known bad behaviour but suffered from false positives and exposure to zero-day attacks.
Thanks to large-scale data, it is now possible to collect and evaluate all internal and external information to bring your IT security to the next level. Big Data solutions can identify personnel, network, or device behaviour anomalies. Thanks to machine learning, it is possible to stop zero-day malware.
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