What is Big Data Analytics: Why It Matters To You

Big data analytics has opened the door to better decision-making for businesses. Data comes in various sizes and formats, but with big data, there are increased possibilities for analytics. It’s safe to say that all businesses rely on some form of data to operate. However, many businesses struggle with the application of big data to transform it into useful insights. 

With the aid of software automation tools and their often inherent analysis capabilities, your organisation can benefit from big data in a variety of ways. We’ll get into everything you need to know about big data and how you can use it to boost your efficiency, lower costs and answer critical business questions. 

What is Big Data?

When data is too big for a traditional relational database (or in simple terms, a table structure) to capture and process within a tolerable time, then it can be considered big data. Big data can be defined as having any of the following characteristics: high variety (nature of the data, be it images, text, etc.), high volume (the quantity of the data), high velocity (the speed at which data is generated and processed) or veracity (the data quality and value). 

Big data can include both unstructured and structured data, but it is more likely used about unstructured data. Structured data is organised and easily searchable because it follows patterns. Unstructured data can be clumped as “everything else,” which can come from social media posts, emails, chat communication, audio, and video, etc. Data mining tools can be used to transform unstructured data into structured data. It’s necessary to utilise tools for unstructured data because they can often include crucial information. Not only is their value high when adequately understood, but unstructured data makes up for the majority of all data in existence, accounting for about 80% or more of enterprise data. 

The size of big data is continuously shifting and expanding because more data become available daily. This being said, big data does require advanced technology and techniques to clean, map, store, manage, and manipulate to retrieve insights. History of Big Data Analytics & What is Data Analytics

History of Big Data and What is Data Analytics

Data analytics is by no means a new concept. However, it is one that evolves and progresses as time, technology, and access to information expands. In the 1950s, businesses used data from spreadsheets, for example. The idea of big data analytics began to emerge in the 1990s before the term big data was coined. In the early 2000s, Gartner attached the 3 V’s definition to big data, namely volume, variety and veracity. 

Thanks to engineers from Yahoo in 2006, Hadoop was created, which is an open-source framework used to power big data applications on a platform. Herein lies the significant difference between traditional data and big data. After that, software companies and engineers have worked to maximise the efficiency of tools on the market to process, store, transform and utilise big data for business decision-making processes and valuable insights. 

Nowadays, organisations can run Hadoop clusters on the cloud, which means companies can call on big data analytics on an as-needed basis. It’s become a necessary tool for successful businesses. We will get into why. 

Big Data Analytics Types

The more data you have, the more significant opportunity you have to glean insights. Four basic analytical methods can be applied to big data, namely:

• Prescriptive analytics: Prescriptive analytics are used to help automate decision-making and actions. It uses neural networks, past data and heuristics to recommend the best action based on optimising outcomes. 
  

• Predictive analytics: Predictive analytics uses machine learning to recognise patterns and answer how and why questions as to what may occur given any choice of action. 
  

• Descriptive analytics: With incoming data, descriptive analytics can be used to answer what happened in the past. It gives a good overview of what already occurred to ensure that leaders make the best decisions moving forward. 
  

• Diagnostic analytics: To take descriptive analytics one step deeper, diagnostic analytics answer the questions as to why something happened. It involves the use of a dashboard and allows business leaders to eliminate analytical blind spots through the use of additional data and to pinpoint what actions should be taken. 
  

Importance of Big Data Analytics & Benefits

The collection of data in business is constant. But, if you have all this information with no way to use it, then you are likely wasting time, energy and space. With the use of big data analytics, your organisation can transform raw data and qualitative data into information to be used and applied to decision-making. 

Big data works in real-time, so it can expedite processes within an organisation and provide a new level of agility. Big data analytics easily allows a business to only focus on the data that is relevant to their current operations. This will enable companies to optimise processes, reduce costs, increase customer satisfaction and highlight areas for improvement. 

Businesses in virtually every industry can benefit from big data analytics. For example, in banking, big data analytics can help to detect fraud early or help a bank make informed financial decisions. For manufacturers, data analytics and machine learning can detect machine failures before they happen and give a company a competitive edge to optimise operations. In retail, big data analytics offers insights into customer behaviours and preferences, which can make it easier to increase a customer’s lifetime value or retain customers. The list goes on and on for what big data analytics can do for every business. 

Big Data Analytics Uses and Challenges

Anything in high volume can quickly become too much to manage. This is particularly true of data because it requires safe storage and immediate access. However, when data is coming into an organisation at record speeds with no predetermined process for collection, storage, or transformation, it can quickly become unmanageable. 

Many organisations report facing the following challenges when it comes to big data analytics (but don’t worry, software solutions like SolveXia can solve all of these challenges!):

• Growth: With the rapid amount of data a business collects daily, many organisations don’t know what to do with it. Not only are they unsure about where to store all the data, but the bigger question is what data is useful for insights. 
  

• Timely insights: The ability to analyse mass amounts of data quickly cannot be done with traditional methods. Before automation solutions, people had to transform data into usable information manually. The process was time-consuming, full of errors and also costly. This is because you’d need specialists or programmers to code the process. Now, you can accomplish timely insights using high-powered AI and automation solutions. 
  

• Storage: Big data takes up terabytes and zettabytes of space. Whether you choose to hold on that data on-premise or in the cloud, there is a price for the service. As such, it makes sense to know what data is worth storing and what is just considered noise. 
  

• Security: Every piece of data is valuable and can contain important information on behalf of your clients and business. As such, it needs to be securely protected and safe from being hacked. 
  

• Change management: Getting everyone on board to analyse data and standardise processes to do so promptly isn’t always an easy task because it often involves changing things from how they have “always been done.” 
  

However, suppose you can implement an easy-to-use automation solution. In that case, the entire team can benefit because it will remove manual labour in the form of time-consuming and repetitive tasks. Instead, the automation system can manage data collection, organisation, data mining and manipulation so that your team can focus on high-level analytical work. 

With access controls, you can assign user roles and ensure that any relevant stakeholder has access to the information they need to optimise their workflow. All data is securely stored in a bank-grade centralised system. 

How Big Data Analytics works and Key Technologies (Automation)

What goes on behind the scenes for big data analytics to work is complicated. But, the key technologies that power big data analytics (and automation) include:

• Machine learning: Machine learning is a subset of artificial intelligence that trains a machine to recognise patterns and automate models that can analyse large data sets quickly. Without being programmed, the machine can access data itself and use it to learn and enhance its algorithms.

• Data management: For data to be useful and analysed, it must be of high quality and well-governed. Data management is the umbrella under which all disciplines reside that work to manage data and make it a valuable resource. Data management systems establish processes that can be repeated ad infinitum to build and maintain the quality of data. 
  

• Data mining: With large amounts of data, data mining helps to find patterns and only focus on the information that is relevant to assess to answer complex business questions. Data mining works by pulling additional information from the data at hand. So, it’s a method to generate new data. It involves machine learning, database systems and statistics to work. 
  

• In-memory analytics: Instead of pulling data from a hard drive, systems can rely on data from system memory. This allows organisations to remain agile and quickly apply data to iterative and analytical scenarios. This Business Intelligence methodology increases the speed of querying data. 
  

Emergence, Growth, Trends & Best Practices of Big Data Analytics

Along with big data, big data analytics, technology, and best practices are changing every day to optimise the use of data for business insights. Some trends that experts have noted are taking place are:

• Deep learning 
• The use of in-memory analytics 
• Big data analytics in the cloud
• Increased predictive analytics
• SQL on Hadoop is becoming faster 
• Stronger NoSQL
• Machine learning
• Forecasting models 
  

When previously you had to hire a team of costly data analysts and IT experts to implement, setup, and train others, automation software companies are providing these benefits and more as out-of-the-box solutions. This means that without the need for coding, and intuitive features, you can leverage automation technology and tools to reap benefits immediately. 

Automation solutions collect data, store data securely, and can combine data from different sources into one centralised system. This means that you can say goodbye to disparate spreadsheets that run the risk of being saved and never used. When your team is equipped with an automation solution, you can set access controls. This way, anyone who needs to access timely insights and information can do so in real-time. The data is neatly displayed visually so that executives and stakeholders can use the insights from data for decision-making. 

The Bottom Line 

All business leaders want the same thing - to know that they are making the right decision. With big data and big data analytics and tools, you can take seemingly meaningless information and transform it into insights. 

Automation tools like SolveXia make it possible to store data in a centralised location, leverage data for models and processes, forecast future outcomes based on today’s decisions and adhere to compliance and regulations.