Decoding Data Analytics: Informed Decision-Making in the Digital Age

Data is everywhere!

Every click, every interaction, and every touchpoint generates a stream of raw data. However, raw data in itself doesn't offer much value.

Enters data analytics – the process of analysing raw data to turn it into valuable information.

Data analytics helps businesses get more visibility and better understand their operations. It further offers detailed insights into customer experience and behaviour. That way, companies can make informed decisions in all aspects of business. Right from customer experience to product development, management, and supply chain.

Organisations that rely on data-driven insights increase profits by an average of 8% and see a 10% reduction in costs.

Read on to better understand data analytics by delving into key concepts, best tools, and real-world applications.

Understanding Data Analytics: Four Key Concepts

What is data analytics?

Data analytics is the process of analysing raw data to draw out actionable and meaningful insights. It can then be used to make smart business decisions. It helps to identify trends and patterns that inform you of something useful about a specific area of a business. For example, how certain consumer groups behave or how employees engage with a particular software or training course.

There are four different concepts of data analytics:

1. Descriptive Analytics

This concept examines historical data to deliver insights into past events and trends. It helps you to pull information from raw data and understand what happened or is happening.

For instance, an online learning platform can rely on descriptive analytics to learn about a specific course's performance in the past few months. It will help them to track course enrolments, course completion rates, and assignment completion.

Used by most businesses, descriptive analytics forms the crux of decision-making in product development, marketing and sales.

That said, data visualisation is a natural fit to show descriptive analytics. This is because graphs, charts, and maps can clearly show trends in data, spikes, and dips.

2. Diagnostic Analytics

Diagnostic analytics focuses on understanding why specific events or trends happened. It involves digging deeper into the data to compare trends, identify correlations, and determine causal relationships where possible.

Results from this concept of analytics help companies come up with accurate solutions for problems. It is often utilised to get to the root of an issue. For instance, a healthcare provider can analyse patient data. This will help them to identify factors contributing to readmissions in hospitals and put in place measures to fix them.

Most companies rely on machine learning techniques to perform diagnostic analytics. They process vast amounts of data to recognise patterns, detect anomalies, and uncover "unusual" events. This is all done in an efficient manner.

3. Predictive Analytics

This type of concept uses historical data to anticipate future outcomes. It's often used to forecast future sales, foresee customer behaviour, or estimate risk.

Many companies across different industries, such as manufacturing, use predictive analytics to improve business operations. With its help, manufacturing companies can track the performance and condition of equipment and predict failures before they occur.

4. Prescriptive Analytics

Prescriptive analytics is the data analytics method that helps decide the best course of action. This kind of data analytics examines results from other data analytics concepts and guides how to reach a specific action. It even considers existing scenarios and the result of each possible action. This way, it helps to make impactful decisions that are more likely to deliver results.

The concept relies on mathematics and machine learning and uses various statistical methods. It can even measure the outcomes of the decision in many future scenarios.

Top 3 Data Analytics Tools for Data-Driven Decision-Making

The following are the three best tools that will help companies keep up with the rising scope of data analytics.

1. Microsoft Power BI: Best for Data Visualisation

Microsoft Power BI is an interactive business intelligence and data visualisation tool. The software supports you to amass data from various sources. Then, you can use drag-and-drop elements to build and maintain real-time reports and dashboards.

Equipped with AI (artificial intelligence), an Excel integration, and many data connectors, you can easily gain relevant insights and share them across your team. Its integration with Microsoft tools and Azure makes it a good choice for companies invested in the Microsoft ecosystem.

Online training materials and tutorials offer help to use the software.

2. Tableau: Best for Business Intelligence

Another popular data analytics tool, Tableau, is widely used in business intelligence. It allows you to explore different data types and supports work on live data.

It lets you connect to cloud databases like Amazon Redshift and Google BigQuery. Tableau software helps create reports, dashboards, and stories using different graphs and charts. The dashboards and workbooks created can be shared with anyone. It supports large forms of data and runs on-premise and in the cloud.

There's a free trial available. There are different paid plans for organisations and individuals.

3. Looker: Best for Data Exploration

Looker is ideal for data exploration and offers value by providing fresh insights to businesses. It is a cloud-based data analytics and intelligence tool, part of the Google Cloud Platform.

The software integrates with existing tools to highlight previously unseen data relationships that help make more informed decisions. Its blocks feature lets you place reports where you want in your workflow.

Looker comes with automatic data model generation that scans data schemas. The software simplifies integrating various databases and developing customised KPI (key performance indicator) dashboard reports.

3 Real World Examples of How Brands are Leveraging Data Analytics

Today, many successful companies rely on data analytics to make better decisions. Let's look at three popular brands and how they use data analytics to define growth for their business.

1. Netflix

The popular streaming service Netflix uses data analytics to inform its content decisions. By analysing user data and industry trends, the company looks for content gaps and genres with high demand. This approach has enabled the service to produce popular shows like "Squid Games" and "Stranger Things".

The company has used advanced algorithms and machine learning to understand user behaviour and preferences. With that, Netflix personalised content recommendations for every user, increasing satisfaction and retention.

2. Amazon

Amazon is the leading e-commerce platform well-known for its personalised shopping experiences worldwide.

Data analytics plays a vital role in driving this personalisation. Amazon's recommendation engine leverages machine learning algorithms to analyse vast customer data, like browsing history, buying history, and preferences.

With those insights, the platform identifies which products customers often search for and prefers to show them relevant products, increasing the chances of sales.

3. Myntra

One of the most notable ways Indian online fashion retailer Myntra has used data analytics is the "My Stylist" program.

The program uses machine learning algorithms to deliver personalised fashion recommendations to customers. It does so by analysing their browsing behaviour and purchase history. It also considers factors such as age and body type alongside style preferences to curate a unique collection of products for every customer.

The "My Stylist" program delivered a high level of customer satisfaction and boosted loyalty to the brand.

Wrapping Up

Data analytics has massive business potential, whether providing insights into customer behaviour or every facet of organisational operations.

The ability to extract meaningful and relevant insights from data allows companies to move beyond intuition. They can drive improvements in business operations, management, recruitment, and customer satisfaction with concrete facts and evidence.

Moreover, various tools are available to simplify the data collection, analysis, and visualisation process. Once you understand data analytics, try out the listed tools, as they're user-friendly to improve your decision-making.

Remember, as technologies like AI and ML become mainstream, the scope of data analytics will significantly expand in the upcoming years.

Contact Growth Jockey to dive into the world of data analytics.

FAQs

1. What are the steps of data analytics?

The process of analysing data involves several steps. Here's a brief look into them.

• Determine the data requirements
• Collect data from different sources
• Organise data on a spreadsheet or other form of software
• Check and clean data to remove errors
• Analyse the data and find insights
• Visualise your data using dashboards and reports
• Share your findings across teams

2. How do you choose the right data analytics tool?

First, check whether the data analytics tools have the necessary features you need. These could include KPI creation, data visualisation, interactive dashboards, and reporting. Also, ensure that it allows you to export data from different sources. It must be interactive, offer integrations, and work with many devices.

3. What is the best data analytics software for SMEs and startups?

Some software to consider is Microsoft BI and Tableau. They both have received the top star ratings by Forbes in the best data analytics software category. However, the ultimate choice will depend on the business needs and requirements.