An Introduction to Big Data Analytics

Data has become one of today’s most important resources for businesses regardless of size. It’s invaluable when it comes to decision-making, so much so that big data is today a multi-billion dollar industry, and growing at an exponential rate.

The value of data isn’t new. In fact, collecting data and using it to make informed decisions have been around for millennia.

The history of data analytics

Statistics — where data analytics is based on — can be traced back to Ancient Egypt for the construction of the pyramids. Data collection has been used for centuries with governments collecting information for censuses, using trends and insights for planning and taxation.

By the 19th century, businesses have followed suit with the likes of Frederick Winslow Taylor and Henry Ford using data analytics to gain insights they will then use when making changes or improvements to their processes.

In the 60s, data analysis was shifting from being completely manual to being aided by computers. By the 80s, the growth of data gave rise to data warehouses. By the 1990s we were data mining; the process of finding patterns using large datasets.

Fast forward over 20 years, and the rise of big data as we know today is being used across industries from government and healthcare to retail and lifestyle. A multitude of tools are being developed constantly so we can collect, store, and assess data efficiently.

Key technologies in Big Data

Many techniques and technologies are involved in data analytics, enabling businesses to maximise their data and get the right kinds of insights.

Here are some of Big Data’s key technologies:

Data management: Before data can be analysed, it must first be high-quality. Processes must be established to maintain the quality of data. Tools like NoSQL databases and other master data management systems are used to ensure reliable and efficient data management that can be accessed by the entire enterprise.

Data mining: With the exponential growth of data, data mining is used to discover patterns and get information that can be analysed and used to inform complex business decisions. Data mining tools make it possible for data to be sifted immediately, providing only relevant insights to help companies make speedy decisions.

Predictive analytics: Data is processed using algorithms and machine learning to assess future outcomes. Through predictive analytics, businesses can determine if they’re on the right track, helping prepare them for the future and to discover, evaluate, or solve problems through predictive scenarios.

How we collect, analyse, and use data has evolved. Along with technology, the importance of data and its analysis has also evolved.

The importance of data analytics

Data analytics provides businesses with quantifiable information, enabling smarter decision-making through having the means to measure success and track goals over time. By analysing data, they can find new opportunities for improvement and growth.

Business moves are smarter, profits are higher, operations are more efficient, and customers are happier.

According to Whizlabs, the importance of data analytics is apparent through four perspectives: data science, business, real-time usability, and the job market.

• Data science: Big Data has variety, volume, and velocity with analytics being able to extract and prepare it to provide fresh insights for researchers, analysts, engineers, and businesses.
• Business: Businesses can keep their focus on customers by interpreting Big Data to provide them with their needs. This improves aspects of a business from efficient workflows and customer satisfaction to increased profits.
• Real-time use: Many industries benefit from Big Data analysis like banking, technology, energy, and consumer industries. The educational sector also benefits from data analytics in real-time through study.
• Job opportunities: Big Data and analytics have opened a wealth of lucrative opportunities in the data sciences, making it a promising profession. Those interested in data can gain opportunities like Big Data Analysts, Big Data Engineers, Solutions Architects, and more.

Making data-driven decisions

In order to survive in business, you must remain competitive. These days, where organisations use Big Data and powerful data analytics strategies every day, simply using your gut to make decisions will not get you the desired results.

Instead, you need to develop a data-driven culture.

Your business acquires data by the second — whether it’s from internal processes or customer input. You have the raw materials you need to get the answers and the best insights to inform your business decisions. These answers and insights are obtained through analytics.

Despite the value of data analytics, only 40% of businesses use data-driven strategies, with a majority still opting to trust gut instincts and experience. Making the most out of data technologies can be your strategic edge above the competition.

But how do you start making data-driven decisions? Here are five steps you can follow:

Step 1: Have a plan.

What goals do you want to achieve, or questions you want to be answered using data? This informs the kind of data that will be relevant, and the technologies and strategies you will use to analyse data.

Step 2: Identify sources of data.

Which area of your business provides the most valuable data that will help you achieve your business goals and strategies? Is it customer service? Operations?

Step 3: Target and streamline data.

With the amount of data you receive, it’s crucial to know which is most beneficial and worth investing your data management budget on. Focusing on relevant data reduces chaos and inaccuracies in data reporting.

Step 4: Collecting and analysing.

Determine the tools and technologies you will need to effectively collect and analyse data, as well as the skills and expertise of those that will handle data and analysis. Depending on the size of your organization, this can range from one person using Excel to a team of data scientists and executive level heads using a combination of technologies to acquire insights.

Step 5: Take action.

The presentation of data and insights can be crucial in helping decision-makers take the correct course of action. Visual presentation of data and analysis will help make them more clear, concise, and digestible. Decision-makers will be able to take quick action and improve the business faster.

Tools for data analysis

Whether it’s only one person doing data analysis or a whole team, there are indispensable tools that will help parse data and generate reports and statistics.

Two tools, in particular, are Excel and Tableau.

Excel

Microsoft Excel is a basic but powerful tool for data analysis used by many. It can perform simple to complex data analytics, helping users from creating simple pie charts with conditional formatting to more complex histograms, run SQL databases, search vlookups and pivot tables.

Tableau

Tableau features an intuitive interface and can do everything from data sourcing through any data source, preparation and exploration of data, as well as analysis and presentation. Tableau functionality includes data visualisation and even social media sharing.

Other tools for data science

Aside from the above tools, more complicated data may benefit from more robust tools like programming languages. Here are five of the most widely used:

• Python: one of the most popular data science languages, Python is easy to learn, has good visualisation capabilities and is scalable and faster than any other coding language. Data science and data analytics libraries also use Python. With its large community, it’s easy to find answers online too.
• R Programming: through data wrangling, R Programming is able to clean and process messy data for easier understanding and analysis. It has many tools for data visualisation, analysis, and representation and is compatible with machine learning. It is also open source, making it a cost-effective option for organisations regardless of size.
• Java: one of the oldest languages, Big Data tools are likely written in Java. It has a wide library with multiple tools for data science, enabling you to easily solve Big Data problems. It also has the JVM or Java Virtual Machine, a great platform for writing identical code for multiple platforms.

• Microsoft SQL: this is a relational database management server that can store and retrieve data. It has capabilities for different workloads, whether it’s a small single-machine application to a larger Internet-based one with multiple users.

• Scala: a high-level programming language that means “scalable language”. It is open source and like Java, runs on the JVM platform and can operate seamlessly, making it flexible and compatible with Big Data tools that also run on Java.

These are only a few of many programming languages that can aid in the analysis of data. Other languages like Julia, MATLAB, C++ and more have their own advantages and can be more preferable depending on your organisation’s needs and your data scientist’s expertise.

Through the use of tools and technologies for data science, businesses will be able to truly take advantage of data analytics.

Businesses will enjoy cost reductions through cost-effective storage options for large amounts of data, and data-driven improvements to make business more efficient. Not only will decision-making be better, but it will also be faster—even immediate.

Ultimately, data and analytics provide businesses with the ability to identify what customers want and the best way to give them exactly what they want.

Turn your data into actionable insights, and well-informed business decisions with our part-time Data Analytics course.