What is Data Analytics and How Will it be Used in the Future?

Wondering if data analytics is the future? In this article we explore the role of data analytics in today’s world and how it will continue to impact businesses in the future.

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Photo by Myriam Jessier on Unsplash

With the explosion of data in recent years, the ability to analyse and make sense of this information is crucial. According to a report by Research and Market

The global big data and business analytics market in 2022 was valued at US$294.16 billion. The market value is anticipated to grow to US$662.63 billion by 2028’

As businesses seek to gain a competitive edge, data analytics is quickly becoming the future of decision-making. And because of this strong outlook, The US Bureau of Labor Statistics predicts that the demand for data analytics jobs will grow by 23% between 2021 and 2031, much faster than the average of 5% for all other industries. 

But what is data analytics? How will it be used in the future? Will data analytics continue to play a key role in shaping the business landscape?

Data analytics has become an increasingly important tool for businesses in recent years, allowing them to make more informed decisions. More and more companies are starting to use data analytics to grow their business intelligence (the practice of deriving insights from data within an organisation). By leveraging data and analytics, these organisations are able to make informed decisions and stay ahead of the curve in their respective industries.

In this article, we delve into the future of data analytics and its potential for shaping the future of business. Here’s a quick breakdown of what we’ll cover:


What is data analytics?

Businesses today collect vast amounts of data on their operations and customers. Data analytics is the process of examining these large sets of data to uncover patterns, trends, and insights that can be used to inform business decisions. The figure below lays out the steps involved in a data analytics project.

Source: Author

1. Define the business question 

The initial step is to determine the business question, which can also be described as a “problem statement”. 

2. Gather the data

Once the objective has been set up, you need to work on gathering and arranging the suitable data. This can be either qualitative data such as interviews with customer groups or quantitative data such as user purchasing data on an app. This can also be internal data such as data generated from the  business’ website or external data from third-party sources which particularly focus on collecting data on specific sectors such as Kantar.

3. Clean and process the data

Before any analysis can be performed, you should clean and prepare the data. This involves removing any unwanted or redundant information. It is also important to address any missing values in the data which is very common. For example, in survey data, respondents might incorrectly fill up certain entries into questionnaires or might not know the answer to a certain question.

4. Analyse and interpret the data 

Once the data is cleaned, you can then start analysing the data to answer the question defined in the first step.This involves using a variety of techniques and tools, including statistical analysis, machine learning, and data visualisation, to extract meaning from complex data sets.

5. Visualise and share the results

Once the data has been analysed and insights derived, the last step in the data analysis process is for sharing insights with the relevant people who will usually be the decision-makers in the business. Who the insights are valuable for depends on the nature of the business question that the insights help to answer. It can be a product manager, operations director or even the CEO of the business who can use the information to take action. So the final step of understanding and communicating the results in an easy and clear manner is the most important of all for any data analyst. 


There are four basic types of data analytics, and these are:

  • Descriptive analytics

This describes what has occurred over a certain period of time. For example, increases in sales or higher page views in a particular month or quarter.

  • Predictive analytics 

This predicts what is likely to take place in the relatively near term based on previous data

  • Diagnostic analytics 

This analyses why a particular event (such as an increase or decline in sales) has occurred.

  • Prescriptive analytics

This suggests a certain course of action (e.g. a seasonal increase in stocking of a certain product) based on past data.


The importance of data analytics in business

Data analytics is becoming increasingly important in business as companies aim to stay ahead of the curve in their respective industries. By analysing large amounts of data, businesses can identify trends and patterns that can help them make more informed decisions about everything from product development to marketing strategies. Data analytics can also help businesses identify where they can improve their operations, reduce costs, and increase efficiency. Some of the key benefits of data analytics for businesses are:

Greater insight into target markets

Businesses can gain invaluable knowledge of their customers’ preferences, needs and behaviours when they have access to their customer’s digital footprint (the information about a particular person that exists on the internet as a result of their online activity). Companies can also find trends and patterns more quickly by analysing data collected from target markets. This data can come in from their internal sources such as their websites or mobile apps, and from third-party sources such as research agencies who collect market data. This allows them to develop specific products or services in order to meet these needs.

Enhanced decision-making capabilities

Data analytics also allows companies to make quicker and more informed business decisions. This means avoiding unnecessary costs of ineffective strategies, inefficient operations, misguided marketing campaigns or unproven concepts in the area of new products and services. Leaders can be more active in identifying opportunities because they can make decisions based on evidence rather than simple intuition or industry experience.

Greater ability to develop targeted strategies and marketing campaigns

In order to help the promotion be relevant to the correct audience, companies can also use data to inform their strategies and lead targeted marketing campaigns. Marketers can create personalised advertising that reaches new or further consumer segments and increase the effectiveness of their entire marketing efforts through analysis of customer trends, e.g. online shopping behaviour as well as analysing Point of Sale (PoS) transaction data.

Improved operational inefficiencies

Another significant benefit of data analysis is using the insights to increase efficiency in operations. Businesses can identify important patterns of behaviour in their customer data, which helps to optimise their product and services. Data analysis can also help them to identify opportunities for operational efficiency, reduced costs or increased profits through data analysis.

Greater ability to identify new product and service opportunities

When it comes to innovation, data analysis enables companies to understand their existing target audience, anticipate and solve any product or service gaps with new offerings that meet these needs. Companies may also monitor the activities of their competitors so that they are more competitive, by using information to measure customer feedback and product performance in real time.


The impact of data analytics in business

Data analytics has had a significant impact on industries across the board. From healthcare to finance to retail, businesses are using data analytics to gain insights into their operations, customers, and markets. For example, healthcare providers are using data analytics to improve patient outcomes and reduce costs, while retailers are using it to personalise the shopping experience and increase sales. In finance, data analytics is being used to detect fraud and manage risk. 

A Mckinsey survey shows that the use of customer analytics appears to have a significant impact on corporate performance (see figure below). For instance, for businesses who apply customer analytics broadly and intensively the probability of generating above average profits and marketing revenues is approximately twice as high as those who don’t. The impact on sales is even more pronounced: 50 percent of companies who champion customer insight are likely to have better sales than their competitors, compared with 22 percent of those who are weaker in customer analytics.


Source: Mckinsey Survey

A report by Kearney investigated the financial impact of analytics on ‘Laggards’ (organisations who make only basic use of analytics). It shows that they could increase their overall profitability by 81% on average if they were to increase their analytics maturity to the level of Leaders. It defines ‘Leaders’ as organisations with a clearly defined analytics strategy that aligns with their overall business strategy.

Figure 6: Returns are correlated with analytical maturity

Source: Kearney 2020 Analytics Impact Index report

The skills needed for a career in data analytics

Now that we have seen the importance and impact of data analytics in businesses, what does it take to be a data analyst? A career in data analytics requires a combination of technical and analytical skills.  Here are the eight most important data analyst skills:

Data cleaning and preparation

A data analyst will commonly need to retrieve data from one or more sources and prepare it for numerical and categorical analysis. Data cleaning also involves resolving missing and inconsistent data that may affect analysis, using programming languages like Python and R. 

Critical thinking and problem solving skills

In principle, when analysing data, it is essential to identify a business question and analyse relevant information in order to answer that question. This requires you to think critically about a business need. You will then need to then develop a practical approach to solving the question in hand.

Statistical knowledge

An essential skill for data analysts is probability and statistics. You’ll be able to analyse and explore data using this knowledge, which will help you make sense of it. In addition, it will help to understand statistics for the purpose of ensuring that your interpretation is valid and avoid common mistakes and logical errors. This YouTube channel provides a fun and beginner-friendly introduction to statistical topics useful for data analytics, like descriptive statistics (mean, median etc) and probability theory .

Creating data visualisations

Data visualisation makes it easier to understand data trends and patterns. People are visual creatures, meaning that we can usually figure out charts or graphs more quickly than they do with a spreadsheet. To help people understand more from what they are reading, we need to make clear and compelling charts enabling others to understand your discoveries. It also means avoiding things that are difficult to understand like pie charts or things that can be misleading like different axis values across comparable charts. This data visualisations course can teach you the basics, using the progamming language R.

Creating dashboards and reports

As a data analyst, you’ll need to empower others to use data to make key decisions. You can give others access to important data by creating reports and dashboards, removing technical obstacles. It could be a simple chart and table that contains date filters, or it could be a complex dashboard containing hundreds of interactive data points. There are helpful online courses by Datacamp and Dataquest to get you started on building dashboards through software such as PowerBI.

Writing and communication

When you work with your colleagues, communication is key. Careful listening skills, for instance, help you to comprehend the analyses that are required in your first meeting with business stakeholders. Written communication is also extremely important, particularly when you’re writing a summary of your analyses or explaining discoveries in the data collection process. Data analysts often need to present their findings to non-technical stakeholders. A skill that will advance your career in data is to communicate clearly and directly. It maye be a soft skill, but don’t underestimate it!

Domain knowledge

Domain knowledge covers topics which are of particular interest to the sectors and companies you work for. You might need to understand the nuances of e-commerce when you work at a company that operates an online store, for example. On the other hand, you might have to learn how specific systems work if you analyse data on mechanical systems. Whatever your role, you’re going to find it much easier to carry out your job if you get a good understanding of what you analyse.The good part about this skillset is it’s mostly expected to be picked up as part of the job.

Apart from the above skills, it is also important to keep pace with the in-demand tools in a rapidly changing job market. Excel for data analysis, and Tableau and Power BI for dashboards are the most important tools for a data analyst.

Source: 365DataScience

The future of data analytics

The future of data analytics is bright, with new technologies and tools emerging all the time. Recent advances in AI have brought in many new analytics tools to a data analyst’s toolkit such as ChatGPT. This trend of using machine learning and artificial intelligence to analyse data is likely to keep on growing. These technologies can help businesses identify patterns and insights that might be missed by human analysts, and can also help automate many of the tasks involved in data analysis. These changes mean that the role of human analysts will evolve and certain skills like critical thinking and making accurate judgements will grow in importance.

Data analytics is also likely to become more integrated with other areas of business, such as marketing and customer service, as companies seek to use data to improve their customer experience. With the explosion of data in recent years, businesses and organisations are increasingly turning to data analytics to stay ahead of the competition and make better decisions. 

So is data analytics the future? Absolutely. As technology continues to advance and more data becomes available, it’s clear that data analytics will play an increasingly important role in shaping the future. In the future we’ll likely see a growing need to streamline the process of data storage, analysis and interpretation. This would require collaboration between different teams across business. As such, it’s clear that as the amount of data and technology grows, data analytics will continue to play a major role in the future of business and technology.



Will data analysts be replaced by AI?

Answer: While AI and machine learning are becoming increasingly important in the field of data analytics, it is unlikely that they will completely replace human data analysts. AI can assist in data processing and analysis, but human analysts are still needed to interpret the data and make decisions based on it. Human analysts also bring a level of creativity and critical thinking that AI cannot replicate.

Is data analytics a promising career?

Answer: Yes, data analytics is a promising career choice for today and for the future. With the increasing amount of data being generated by businesses and organisations, there is a growing demand for professionals who can analyse and interpret this data to make informed decisions. Additionally, data analytics is a field that offers a wide range of job opportunities and career paths, from data scientist to business analyst to data engineer.

Will data science exist in 10 years?

Answer: Yes, data analytics is expected to be in high demand in the future. With the increasing amount of data being generated by businesses and organisations, there will be a growing need for professionals who can analyse and interpret this data to make informed decisions. What’s more, advancements in technology such as artificial intelligence and machine learning are expected to further drive the demand for data analytics skills.