What Is Data Analytics? [Beginner’s Guide 2022]

One man is holding a tablet, another one is working on his laptop, a woman is sitting above them inside a round infographic
One man is holding a tablet, another one is working on his laptop, a woman is sitting above them inside a round infographic

How can I grow my business?

How do customers interact with my newly launched product?

How do I increase the productivity of my employees?

How do I know if my marketing efforts are working?

We have just two words for you...Data analytics!

In this post, we will dive into this complex field and give you an idea

  • what data analytics is
  • why it’s important for your business
  • what it takes to be a data analyst
  • how brands are using data analytics technology to improve their work processes

and more.

Let’s get started!

Data Analytics Definition

Data analytics (DA) is the process of analysing, collecting, organizing and storing raw data to gain insights relevant to business operations.

This is done by utilizing different tools, techniques, frameworks and statistical analysis.

You can think of data analytics as a form of business intelligence (BI) used to

  • answer business-related questions
  • identify trends

and

  • solve issues every organization is facing (we will tap into those in one of the following chapters)

Armed with the insights of data, companies can make better, informed decisions about their audience, their organization as a whole and the industry.

💡 Did you know? While business intelligence usually deals with a more quantitative transactional assessment of the sales pipeline (e.g. sales numbers, deliveries), there’s also market intelligence, which offers a more qualitative and externally-oriented analysis of a company’s sales environment (e.g. future market trends like using augmented reality).

Interested in knowing more about this topic? Check out how market intelligence uses real-time data to generate revenue.

Some other concepts associated with data analytics are also data science, data analysis and data management (master data management).

To avoid confusion and learn how to differentiate between them, let’s take a look at the next chapter.

A computer display with various data in different colors

Data Science, Data Analysis & Co. [Important Terms Explained]

What Is Data Science?

Data analytics and data science are closely related disciplines, both dealing with big data in their own way.

Data science designs algorithms, statistical models and analyses to

  • make the collected data easily understandable
  • raise new questions that drive business innovation
  • draw predictions and conclusions for the future

To put it simply, a data scientist takes messy data from numerous sources and turns it into a story everyone could comprehend. 😊

Data analytics, on the other hand, is a component of data science designed to uncover the specifics of extracted insights.

If you want to find out how data science, machine learning and artificial intelligence (AI) are connected, check out the following video from 365 Data Science.

💡 Tip: In recent years, we’ve witnessed the emergence of another type of AI in marketing, used mainly to create video and audio hoaxes – the deepfake. Want to see how Keanu Reeves is fitting into the role of Forrest Gump? These deepfake examples will blow your mind. 🤯

What Is Data Analysis?

The terms data analytics and data analysis are often used interchangeably. What most people don’t know, data analysis is a subcategory of data analytics focusing on

  • examining
  • cleaning
  • visualizing, and
  • modeling

datasets.

Its aim is to circle out important information in raw data and use this insight to make vital decisions within a company.

💡 Did you know? Another term you might encounter when dealing with data analysis is data mining – the application of statistical methods to very large and complex datasets with the purpose of identifying new patterns. For example, if you want to evaluate the purchasing behavior of certain customer groups, you need to collect customer data and perform well-executed customer data management. Afterward, data mining is the way to go. When working with large data sets it is vital to perform data normalization to make sure you cut out all outliers.

Characters from the Simpsons dressed as dwarfs walking into a mine

Last but not least...

What Is Data Management?

Data management is the collection of measures, procedures and concepts to provide data for optimal support of the various processes in a company.

Data management is focused on areas such as:

  • data quality
  • data protection
  • data governance, etc.

You've understood how to master data management? Well done! 🥳

At this point, you’re familiar with the most important terms surrounding data analytics. It’s time to discover why it’s vital for your business.

What Is the Purpose of Data Analytics?

There are several reasons why data analytics is important for companies of all sizes.

We have listed some of the key benefits below.

💡 Tip: Consider adding data cleansing and data enrichment to your data analytics workflow to get more valuable insights.

Minimized Financial Loss 💰

We know that all enterprise solutions are directed toward minimizing financial losses. No big surprise there. BUT...Did you know that data analytics can help with that as well?

For example, a company wants to test how changes in pricing or product design would impact customer demand.

After collecting the sales data, data analytics tools come into play to determine and visualize how successful the proposed changes actually were.

Consequently, the decision-makers can choose whether to implement these changes or let them be.

A Beatles album cover displaying all four members, above them the sentence let it be is written

Personalized Customer Experience 🥰

When it comes to keeping your customers happy and giving them what they want, data analytics plays a crucial role.

Businesses collect customer data via various sources:

Here’s where it gets interesting!

By applying data analytics, you can create personalized customer profiles to gain insight into their behavior.

Think of a company with a physical and an online clothing shop. By conducting a real-time analysis of the sales and social media data, it can launch targeted marketing campaigns on the e-commerce platform to promote products that the customers are already interested in.

💡 Tip: To predict future customer behaviour and events you can also run a so-called propensity model based on machine learning technologies – such a statistical model is recommended if you want to unlock the full potential of personalized and data-driven marketing.

Enhanced Operational Efficiency 📈

Let’s stay in the world of fashion a bit longer...

Your organization is running a successful online clothing shop, cooperating with many different vendors. During the holiday season, the demand for products drastically increases and you want to ensure enough items are in stock.

Enter: data analytics!

With data analytics tools you’re able to gather data about the supply chain to see where it might come to, for example, production delays.

Consequently, you can replace vendors that might not be able to keep up and thereby enhance your operational efficiency.

Improved Risk Management ⛔️

Every business is facing risks, especially when it comes to

  • employee safety
  • legal liability
  • theft etc.

Based on insights gathered through data analytics methods, the company can easily understand those risks and take measures to prevent them.

If you are running a franchise, you could perhaps analyse which stores might be at a higher risk for theft. After having conducted the analysis, you can decide on the level of security needed in particular shops.

A security guard an Eric Cartman from the animated television series South Park are standing in an abandoned building and talking

💡 Tip: There are, of course, a lot of other uses of data analytics. It can also help you choose the best marketing automation software for your business needs etc.

We’re moving on to another important aspect – a data analytics strategy.

Developing a Successful Data Analytics Strategy [Tips]

Running a data-driven enterprise starts with a solid data analytics strategy. If you’re starting from zero, we’ve prepared three helpful tips to get you going.

1. Set a Clear Goal 🎯

What is your business goal?

With what kind of data do you want to measure your success?

Answering such questions is the first step toward developing a successful data analytics strategy. It will give you an idea, of which analytics models to use for your

  • revenue
  • marketing efforts

and other areas of focus.

2. Identify Key Stakeholders 🙋‍♀️

The next step is determining who will be using the data.

Some teams might need analytics to make data-driven decisions, while others – e.g. executives or investors – might only ask you to share progress and ROI for the company with them.

Knowing who will be involved in the process will help you define the key KPIs and arrange the data in a way that’s easily accessible to these people.

3. Focus on Metrics That Drive Growth ⬆️

Hoarding is bad in every sense – also when it comes to data. Just because you have a lot of it available doesn’t mean you have to use it all.

Instead, focus on three metrics that drive business growth and leverage the technology needed to gather these insights.

💡 Tip: Forget about vanity metrics such as likes or followers on social media. Instead, analyse the number of quality leads, revenue or conversions.

Keep on reading to learn how data analytics is put into practice.

A person is typing on a laptop, on the screen we see the hulu entertainment platform displaying different series and movies

Who Is Using Data Analytics?

Data analytics is used in different industries, such as

  • retail
  • healthcare
  • public sector
  • finance
  • entertainment
  • transportation
  • telecommunications

and many more.

Especially in areas of predicting market activity, calculating risks or preventing fraud, it’s of great importance - even more so when considering the industry 4.0 trend.

How Organizations are Benefiting from Data Analytics (Examples)

As previously mentioned, the use cases for data analytics in marketing and specifically in a digital world are almost endless – from predicting customer behavior to conducting a media impact analysis.

We want to show you how companies can benefit from using state-of-the-art social media analytics tools like the one from Meltwater.

Don’t worry, we will tap into its amazing features later on. 😉

Example 1: Data Analytics for Personality Profiling

AxiaOrigin is an organisation helping businesses achieve their strategic goals in an evidence- and data-driven way.

While working with a client, their task was to develop a personality profiling framework to understand personality characteristics from a psychological perspective.

A young woman is sitting in front of a computer working with the Meltwater social media analytics platform

The challenge – AxiaOrigin had to gather data to define the character of a specific region and analyse how people were using their native language on social media.

With the Meltwater social media analytics platform, they were able to

  • select specific time periods and locations
  • start a real-time analysis within minutes

and

  • extract was never before seen insights

The result: a personality profiling framework perfectly tailored to their client’s wishes.

Example 2: Data Analytics for Product Development

Brut. is a French digital-media firm producing news and videos for Gen Z and millennials. Their goal is to provide unfiltered information so people can form their own opinion.

Brut. needed help scanning social conversations around topics of interest to their audience.

Using the data available through the Meltwater data analysis software, the company could

  • analyze a specific social account’s data to uncover consumer insights
  • help inform journalists' subject choices
  • understand the real impact of their content strategy and adjust it accordingly
  • develop new products which would resonate with its audience

Check out what else the lead data analyst at Brut. has to say about his experience with our analytics tool.

Case study Brut x Meltwater

As you could see, data analytics serves numerous purposes. But did you know there are also different data analytics types?

Main Types of Data Analytics [Infographic]

There are four main data analytics methods.

An infographic showing the four main types data analytics, the descriptive, diagnostic, predictive and prescriptive

Descriptive Data Analytics 🖊

"What happened in the past?"

"What is happening right now?"

Those are the critical questions in descriptive analytics.

Accordingly, current and historical data from various sources are analyzed to describe the status quo and identify trends and patterns in the data.

Diagnostic Data Analytics 🔎

Diagnostic analytics is asking

"Why is this happening right now?"

The data used is often the result of descriptive analytics.

Factors or reasons influencing certain past performance values are to be determined with this method.

Predictive Data Analytics 🔜

In predictive analytics, everything revolves around the question

"What will happen in the future?"

Techniques from e.g.

are used to utilize the results of descriptive and diagnostic analytics and to predict future results.

Prescriptive Data Analytics 📣

Prescriptive analytics focuses on the question

"What needs to be done to achieve the goal?"

Prescriptive analytics enterprise solutions use

  • algorithms
  • application testing
  • machine learning

and other techniques to achieve the wanted outcomes.

Let’s dive into what a data analyst actually does on a day-to-day basis.

How Data Analysis Works (5 Important Steps)

We’ll walk you through the most important steps of data analysis.

Step 1: Set Specific Goals 🥅

Before you start collecting and analyzing big data, you need to understand what to do with it. After determining what business problem should be solved, you’ll be able to ask yourself the right questions and create specific as well as measurable goals.

Step 2: Collect the Raw Data 👀

At this stage, it’s time to define what data you need to answer those questions. First, check whether it’s already available in your organization (e.g. through employee surveys, performance reviews etc.). If some data is still missing, consider sourcing it externally.

Whatever you do, make sure that in the end you have a 360-degree view of the problem you set out to solve.

Tip: Use image recognition software to collect even more data. Learn more about how image recognition works.

Step 3: Clean the Data 🧹

The accuracy of your analysis will greatly depend on the quality of the data you are working with. Therefore, it’s essential to do some data cleaning.

Raw data is rarely usable in its current form and has to be “polished”. With data cleaning, you are

  • removing incorrect or unnecessary data (e.g. duplicates)
  • checking for inconsistencies or incomplete information

Step 4: Analyze the Data 🧑‍💻

After the data is in its final shape, it’s time for the fun part – analyzing it!

By implementing different data analysis methods, you’ll uncover patterns and get valuable insights.

Step 5: Present the Data 📊

If you want to inform the decision-makers about your discoveries, you’ll need to visualize the insights by selecting the most appropriate charts and graphs.

It’s important that the results are presented in manner that is easily understandable and compelling (e.g. with data storytelling).

As you can see, a routine of a data analyst is quite complex and not for everyone. Do you have what it takes?

What Are the Top 3 Skills for a Data Analyst?

We’ve compiled a list of hard and soft skills required to excel as a data analyst.

A scene from the movie Hangover where actor Zach Galifianakis is concentrating, numbers and calculations are appearing in front of his face

Mathematical and statistical affinity (as you will spend a lot of time crunching numbers)

✓ Knowledge of programming languages (e.g. Oracle, SQL)

✓ Excellent communication skills (once you’ve harvested the data for valuable insights, you have to share your findings with the relevant parties)

Can you tick all the boxes?

We are slowly approaching the end of our guide. However, there’s one more thing we want to get into to make your life as a data analyst easier – the necessary tools.

Must-Have Data Analytics Tools in 2022

If you want to conduct a thorough analysis, you will need the appropriate data analytics technology.

In 2022, these are the tools you can’t do without.

  1. Microsoft Excel & Google Sheets – to run basic queries, create pivot tables, graphs, charts
  2. Tableau – a business intelligence software to simplify raw data into visual dashboards, worksheets, maps, etc.
  3. Python – a programming language to process, merge and group data in various formats
  4. R – an open-source programming language to present and communicate the results of the data analysis
  5. SAS – a command-driven data analysis software to carry out advanced statistical analysis and data visualization

Let’s wrap it up with...

How Meltwater Can Help You Get Vital Insights from Social Media Data

Now the time has finally come to deep-dive into the data concerning your business. The Meltwater social media analytics solution serves as a single source of truth and has all the features you need.

A screenshot of the Meltwater social media analytics tool Engage displaying data about audience location and demographics.

✓ Export of insights via API (get more information about the Meltwater APIs)

✓ AI speech recognition

✓ 15-month historical archive of social media data

✓ Customizable, easy-to-understand dashboards and report building

✓ Consumer segmentation and behaviour analysis

✓ Brand, keyword or complex queries comparison

and many more.

To learn how our social media analytics platform can benefit your enterprise, fill out the form and get in touch. We’re excited to hear from you! 🥳

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