The Beginner’s Guide to Data Analytics

data analytics, The Beginner’s Guide to Data Analytics

As we move further into the digital era, we find ourselves in a more data-rich and data-oriented environment. Almost everything a company does today is influenced by data analytics in some manner. Many businesses have already begun to employ advanced analytics approaches to uncover new growth opportunities, reduce expenses, boost operating margins, make better human resource decisions, and create more effective budgets. Experts also believe that this effect will only intensify over the next few decades.

Despite its expanding importance, analytics remains a relatively new notion for many individuals, especially when it comes to buzzwords and essential concepts. At the same time, there is no shortage of professionals seeking to enter the analytics field.

In today’s post, we hope to inspire you to begin educating yourself in data analytics and use this to build towards a career in what is already one of the hottest skill-set of the 21st century.

Now, before we get started, let’s define data analytics.

What is Data Analytics?

So, what is data analytics? Simply put, it’s the process of examining data to find patterns and insights.

This might sound like something that’s only done by experts, but the truth is that anyone can do it with the right training. And that’s where you come in.

In this guide, we’ll teach you the fundamentals of data analytics including what it is, how to do it, and how to apply it to your business.

Check out Accenture’s video titled, What is Big Data Analytics?

Examples & Case Studies

Given how broadly data is utilised in the world today, there will inevitably be a number of intriguing use cases demonstrating how analytics has been used effectively by organisations.

Finance

The financial sector was an early adoption of data analytics. Data analytics is used extensively in the banking and financial industries to forecast market trends and analyse risk. Credit ratings are an example of data analytics that has a broad impact. Many data points are included in these ratings to estimate loan risk. Data analytics is also used to detect and prevent fraud, which helps financial organisations increase efficiency and minimise risk.

Sales & Marketing

Let’s say you’re a marketer promoting a new smartphone with a better camera than the previous model through an internet marketing campaign. You may begin by directing the ad to customers who purchased the prior version of the smartphone.

You use data analytics tools to filter through the data collected by your campaign as it runs. Many of these users are interested in photography, according to statistics on their interests. Using this data, you might fine-tune your ads to target customers who purchased the prior phone and enjoy photography. You may also be able to reach new audiences of individuals who did not purchase the previous phone but are interested in photography.

Healthcare

Data analytics is already widely used in healthcare. Predicting patient outcomes, distributing financing more effectively, and enhancing diagnostic procedures are just a few examples of how data analytics is transforming the healthcare business throughout the world.

Entertainment

The highly successful Netflix drama, House of Cards was created using data. They began by gathering large quantities of data from its customers, such as the kind of shows and movies they viewed and the most popular stars and directors. After calculating the figures, they discovered that director David Fincher and actor Kevin Spacey were among the most popular. They also discovered that the original British version of House of Cards was favourably accepted by these same people. The numbers favoured integrating these three aspects (the program, Spacey, and Fincher), prompting them to purchase the rights to the show and develop an American version.

Did all of this information work? The answer is an unequivocal yes! The program was a huge success, with Netflix gaining over 2 million new customers in the first quarter alone in the United States. They also received 1 million new customers from different regions throughout the world, and this rush of new members helped cover the show’s production costs.

4 Types of Data

Data was nearly entirely arranged in rows and columns and was virtually always in numerical form. We now have to deal with a broader range of data types, including text (reviews), pictures (photos on social networking sites), audio (phone records), and so on. This is classified as semi-structured or unstructured data. While the prospects are intriguing, these improvements introduce new hurdles to the data analytics process.

Structured Data

The goal with any type of data is to get it into a sufficiently structured manner. As a result, when we talk about data, we usually presume it’s in the form of a table with rows and columns. A row in a conventional data table is referred to as an ‘observation’ (relating to a single, recorded data point), whereas a column is referred to as a ‘variable.’

Within structured data, there are several data kinds that may be categorised into three categories: numeric, character-based, and date or time-based.

When most people think of data, they envision numerical data. It is divided into two categories: discrete and continuous. Discrete data is in full numbers and cannot be split down further. For instance, the number of consumers that visit a business on a specific day. Continuous data includes both whole numbers and fractions. This might include things like time, money, and price.

Following numeric data, character data is the next large category. This is text-based data. Brand names, for example, or gender categorisation as male and female, are examples of this. In general, the goal is to use some kind of mathematical process to transform the data from qualitative to numerical.

However, this is not always possible since not every character variable can be translated in this manner. For example, when categorising gender, you can assign men and females values of 0 and 1, respectively. Some character variables, such as a product or client name, are solely descriptive.

The third form of structured data is date/time data. Most statistical software treats date and time data as a different class of data since they might contain both character and numeric data. A mathematical operation is expected to be done on a specific date (for example, the number of days since the last purchase). The biggest problem with this sort of data is getting tools to recognise it as dates and times because it can be expressed in a variety of ways (with slashes, colons, words etc).

Semi-structured Data

Semi-structured data may have some structure, although it is frequently combined with data with no structure. Examples of semi-structured data may be found in XML files. These are comparable to HTML files. They do, however, contain information not just on how to show pictures or text on the web, but also about the type of item that was previously described. An address box, for example, will have an address, a phone box will hold a phone number, and so on.

Unstructured Data

As the name implies, this form of data is utterly devoid of structure. Unstructured data has considerably greater data requirements than structured data. When it comes to unstructured data, it is not feasible to automatically put it in rows and columns, and it would require significant processing to create some structure. This processing is rather intricate and dynamic in and of itself. For example, reviews on a website would each be written with a particular tone and style, which an algorithm would struggle with.

This type of unstructured data may be analysed in a variety of ways, including by inspecting the metadata and searching for specific items, such as a text string or a certain image. For example, many thousand photographs of men and women would be required for the algorithm to achieve a reasonable degree of accuracy. Because of the current surge of unstructured data, it is critical to understand how to deal with it as part of any data analysis process.

Mixed data

There are other cases where data is mixed, having both letters and numbers. As an example, consider the customer ID BG7634-21. Because it comprises both text and numbers, it is frequently stored as character data. In this situation, depending on the tool specs, the individual doing the analysis may opt to split the numeric or character parts.

In the end, all data, organised or unstructured, can and will be analysed. The main issue with the latter is that the storage and analysis methods differ greatly from those utilised for structured data.

4 Types of Data Analytics

data analytics, The Beginner’s Guide to Data AnalyticsSource: 4 Types of Data Analytics to Improve Decision-Making

Data analytics is classified into four types: descriptive, diagnostic, predictive, and prescriptive. These four classes answer everything a company wants to know, from what’s going on inside the organisation to what remedies should be implemented.

The four forms of analytics are often introduced in phases, with no one sort of analytics being superior to the others. They are interconnected, and each one provides a unique perspective.

Descriptive analytics

Descriptive analytics describes what has happened over a given period of time. Large datasets are summarised using these strategies to communicate outcomes to stakeholders.

For example, when you go to the grocery, there are certain goods you always buy. When you buy anything, such as a litre of milk, the information is entered into the store’s inventory system. Over a few months, the store may analyse this data to determine how much milk has been sold, providing the merchant with highly fascinating information about consumer and supply purchasing trends.

Simply said, this is descriptive analytics. All of the data comes from the customer’s purchasing basket, and it is simply sliced and diced and examined from various viewpoints in order to derive meaningful conclusions.

Diagnostic analytics

Diagnostic analytics is more concerned with why something happened. This requires more diversified data sources as well as some hypothesis testing. You investigate the reason using descriptive analytics insights. The performance metrics are also analysed further to see why they improved or deteriorated.

Predictive analytics

Predictive analytics assists in answering questions about what will occur in the future. These strategies use historical data to discover trends and predict whether they will reoccur. Predictive analytical tools give significant insight into what may occur in the future, and their approaches encompass a wide range of statistical and machine learning techniques, such as neural networks, decision trees, and regression.

Let’s look at another supermarket case. Have you ever wondered why milk is usually towards the back of a shop while magazines and chocolates are at the front? Retailers discovered via thorough research that when walking all the way to get your needs, you may be enticed to buy something else as well. Furthermore, chocolates and magazines are inexpensive impulse purchases, so you’re likely to place them in your basket right before you ring up the rest of your purchases.

This is how predictive analytics works in a nutshell: by recognising trends in previous data and then using statistics to generate predictions about the future. We basically try to fit the data into a pattern, and if the data follows that pattern, we can forecast what will happen in the future with some degree of accuracy.

Prescriptive analytics

Prescriptive analytics assists in determining what should be done. Data-driven decisions may be made utilising predictive analytics insights. In the face of uncertainty, this enables organisations to make educated judgments. Machine learning systems that can detect trends in massive datasets are used in predictive analytics approaches. The possibility of various outcomes may be determined by evaluating previous decisions and occurrences.

Prescriptive analytics emerged only about a decade ago and has since been shown to be a very popular and powerful tool with organisations all around the world. It may be used to analyse facts in the present and forecast what will happen in the future. Most importantly, it gives insights on what activities should be performed based on the available data, as well as the consequences of these decisions. Because of its incredible adaptability, prescriptive analytics is at the cutting edge of analytics technology.

Aviation is an outstanding example of an industry where this strategy is widely employed. Airlines are constantly seeking methods to optimise their routes for optimal efficiency, which may result in billions of dollars saved. As a result, the industry is continuously employing predictive analytics to uncover more effective methods of operating, which may keep airline expenses low while increasing revenues.

Terms & Jargons in Data Analytics

Let’s take a look at some popular terms you are likely to come across in the analytics space.

Big Data – Many people are perplexed by the term “Big Data,” owing to the fact that it is frequently used interchangeably with “analytics.” In reality, Big Data is a subset of analytics in general, which may be defined by the ‘3 Vs’ – variety, volume, and velocity.

Business Intelligence (BI) – It is commonly used to study data and derive conclusions from past occurrences. While many consider it a subset of analytics, a more appropriate definition would be that it is a component of the larger subject of analytics. The word ‘Business Intelligence’ is older and was popular long before the term ‘analytics.’

Relational Database Management Systems (RDMBS) – The majority of corporate data is saved in this manner. Tables represent information, and a “primary key” connects and understands the link between multiple tables. Oracle, Microsoft SQL, MySQL, and more popular examples

Structured Query Language (SQL) – This is a fairly common database query language. This might be used by a company to determine what its top items are in terms of unit sales in a certain place.

Data Warehouses and Marts – They are both instances of databases that are used and designed to generate reports. Data marts are often dedicated to a team, whereas data warehouses store data that is useful to the whole organisation.

Data lake – A data lake is very similar to a data warehouse, with the exception that data in a lake is considerably more “raw” than data kept in a warehouse. A data lake might hold any type of data, organised or unstructured.

Extract, Transform, Load (ETL) – It is a critical stage in the data processing. It is the process of collecting data from operating systems, cleaning it to meet procedural criteria, and then putting the clean data into the proper warehousing system.

The Benefits of Data Analytics

The value of business analytics in a company setting cannot be emphasised. It is used to investigate and comprehend previous trends, as well as to forecast and enhance future business processes.

Although the applications of data analytics are diverse, they may be employed in a variety of organisational activities. Call centre managers, for example, analyse their team’s performance data to optimise resource allocation, HR managers utilise personnel data to forecast attrition, and marketers use sales and marketing ROI data to determine the best allocation of their marketing budget.

As the relevance of data analytics grows, organisations must implement data analytics more quickly. Here are four advantages of data analytics.

1) Improved decision making

Companies may employ data analytics insights to guide their decisions, resulting in improved outcomes. Data analytics removes much of the guesswork from marketing campaign planning, content creation, product development, and other tasks. It provides you with a 360-degree perspective of your consumers, allowing you to better understand them and address their demands. Furthermore, using contemporary data analytics technologies, you may gather and analyse fresh data in real-time to update your knowledge as situations change.

2) Effective marketing

You can sell to your target demographic more successfully if you understand them better. Data analytics also provides important insights into how your campaigns are doing, allowing you to fine-tune them for the best results.

Using various data analytics tools, you may learn which audience groups are more likely to connect with and convert on a campaign. You may utilise this data to manually or automatically alter your targeting criteria or to build different messaging for different categories. More conversions and less ad waste result from better targeting.

3) Better customer service

Data analytics delivers insights into your consumers, allowing you to adjust customer service to their requirements, provide greater personalisation, and strengthen your interactions with them. Your data can disclose details about your consumers’ communication preferences, interests, and worries, among other things. Keeping this data in one place also ensures that your whole customer support staff, as well as your sales and marketing teams, are on the same page.

4) Efficient operations

Data analytics may assist you in streamlining your procedures, saving money, and increasing your bottom line. You waste less time developing advertising and content that don’t fit your audience’s interests when you have a better knowledge of what they want. This means less money spent and better outcomes from your advertising and content strategy. Analytics may generate income through increasing conversions, ad revenue, or subscriptions, in addition to lowering costs.

The Challenges of Data Analytics

While data analytics may give several benefits to businesses that embrace it, it is not without obstacles. Working with the proper partners and technologies may help organisations overcome these challenges.

Data collection is one of the most difficult aspects of data analytics. Businesses have a lot of data that they may possibly acquire, and they must decide what to prioritise. Collecting data requires tools that can gather data from website visits, ad clicks and other interactions and deliver it in a usable format.

Once you’ve collected your data, you’ll need a place to save it. This can take up a lot of space and contain a lot of various kinds of information. You must combine organised and unstructured data from online and offline sources, as well as internal and external sources.

You must also assure data quality in order for your results to be accurate. Furthermore, your data must be available and not compartmentalised so that everyone in your organisation has access to the same source.

Lastly, you need to have the knowledge and tools necessary to make sense of the data and provide insights. Arm yourself with relevant knowledge, skills, and tools with our highly rated data analytics course – Problem Solving with Data Analytics.

The Future of Data Analytics

Data analytics is a field that is rapidly expanding. And it’s hardly surprising given the volume of data created every day.

Businesses are looking to data analysts to assist them to uncover trends and patterns as they try to make sense of all this data. Data analysts are in great demand, and the future looks promising for people with this skill set.

So what does the future hold for data analytics? Here are a few things to keep in mind:

  1. The use of artificial intelligence and machine learning will continue to grow
  2. Data analytics will play a key role in fraud prevention and security
  3. Businesses will become more reliant on data analytics for decision-making
  4. The field of data analytics will continue to grow in scope and complexity

Data Analytics For Beginners

data analytics, The Beginner’s Guide to Data Analytics

After completing these two courses, you will be awarded a Professional Certificate in Data Analytics by Aventis Learning Group. This will certify that you will be able to confidently perform the entire process of data analytics from managing your data to presenting your data.

Problem Solving with Data Analytics

Introduction to Data Visualisation & Storytelling

 

For more information, you can get in touch with us at (65) 6720 3333 or training.aventis@gmail.com

 

Source:

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