What Is Data Analytics? A Guide for Putting Your Data to Work

Have you ever made a business decision that you regretted later? Perhaps you launched a marketing campaign, stocked up a large quantity of goods, or changed your pricing levels just for it to fall flat.

Gut instinct is one thing, but information is knowledge, and knowledge is power. Data analysis converts raw information into meaningful information. With this, you will have the means to leverage your data to inform better decisions.

Data analytics is the act of gathering meaningful information from both structured and unstructured information. From this information, you can then enhance the performance of your company.

In this guide, we are going to explore the meaning of data analytics and how various industries leverage it to improve their strategy, operations, and customer service.

Data analytics is often compared to data science, and a distinction between the two is worth drawing. Both are related to each other, but they have separate skills to deploy and can impact your company differently.

Data analysis primarily examines past information to discover patterns, produce reports, and guide decisions. Trained analysts use intricate techniques to do this, but data analysis isn't limited to experts. Anyone can gather, organise, and review business information.

Data science goes beyond analysing the data and instead uses AI and intricate programming to discover deeper meaning from your numbers. Businesses can use data science and analytics to go beyond intuition and guide their decisions to improve their profitability and operational performance.

Data science is a broad category, with data analytics being a sub-category that simply refers to the practice of analysing data to answer specific questions.

Data analytics can automate processes, find opportunities (and threats), and enhance a company’s performance overall to keep you ahead of the game. Data will also provide you with a deeper knowledge of the customer base.

Here are five ways that you can use data analysis to inform smart decision-making at your organisation:

Have you ever wondered why some of your customers leave while others stick around? Predictive analytics is a way of processing your data that helps your business foresee your customer actions before they occur.

Businesses already possess a lot of customer information (everything from personal information to purchase histories). You can make the most of this data (like your customer success score and feedback) with the support of predictive analytics to leverage.

Analysing operational data can be an eye-opener. For instance, if you notice your projects tend to spend the most time in the design phase, it might be time to consider adding more design resources to your team to boost efficiency.

Intelligent AI agents can also help business operations by managing everything from costs to inventory, answering simple questions, and helping you come up with solutions for complicated issues, all while continuously improving your own performance.

Data analytics can also help you build stronger relationships with your customers.

Once you understand how your customers make buying decisions, you can craft personalised marketing strategies to resonate with them.

Some of the most effective ways you can harness data analytics to improve customer loyalty include:

• Segmenting customers
• Personalising offers
• Developing loyalty programs, customers value
• Measuring customer satisfaction

Data analytics is also a powerful technique for risk mitigation.

Understanding when risk is on the horizon by analysing your data will allow you to spot potential risks in advance. By assessing and identifying risks, businesses are in a better position to manage them.

Businesses can use data analytics to improve efficiencies and save costs. For example, retail and manufacturing businesses across Australia harness data analytics to fine-tune their inventory levels and reduce excess stock. This type of intelligent forecasting helps minimise food waste and storage costs, leading to significant annual savings.

Data analytics takes your data from different sources and turns it into powerful insights that you can use to make knowledgeable choices for your business. Following a systematic approach, you can uncover hidden patterns and trends, empowering your organisation to make bold changes.

Here are five basic steps you can follow to get the most out of your data.

Before you can start looking for trends, you need data. You could gather it from within your CRM platform, website traffic, customer feedback, social media, or even (if you have it) sensor data from smart devices.

Your data is a puzzle that needs to be assembled. Alone your data won't tell you much, but when ordered in the correct way, you'll be able to see the big picture. This includes removing clear mistakes, filling in gaps, and standardising formats.

Now that you have your data in a clear format, you’ll need a safe place to keep it. You don’t want important information sitting on employee desktops, so it’s important to find a database that your team will actually use.

Also, remember to think about the long-term application of the tool and pick a solution that will be able to grow with your business.

Now, it’s time to dig into the stats. Depending on the outcome you’re seeking to discover, you can start by analysing overall patterns within the information.

Alternatively, you can compare the performance of a number of products to one another or have AI software look at the information to provide forecasts of the future.

The real power of data analytics comes from using the insights to make better decisions, whether it’s optimising marketing campaigns, improving customer service, or reducing costs.

The best approach for analysing your data depends on your desired outcome. Here are the five key methods for examining your data to help you choose the one that suits your needs best:

A simple descriptive analysis merely looks at the information you've gathered to look for patterns. For example, a software company might look at its customer churn and discover that they unsubscribe after a decline in their usage of a company's service.

This would allow the company to know where they need to intervene to avoid the loss of other customers.

Diagnostic analytics takes your analysis further by investigating the causes behind the patterns in your data. For a B2B marketing agency, this could mean looking at your latest campaign platform and determining why one platform generated more leads than another.

Predictive analytics foretells the future by utilising historical data and machine learning. For instance, a bank might base a customer’s loan payment default chances on their spending behaviour in the past.

Prescriptive analytics is the next step to predictions by actually suggesting actions to take based on the information. For example, a bank can predict a customer’s propensity to default on loan repayment by examining their past spending behaviour and then giving them personalised financial advice to avert the default.

Cognitive analytics is the art of utilising your information to lead you to the best possible decisions for your organisation. Many companies today are using AI to perform functions such as analysing customer behaviour and making personalised product recommendations.

Data analytics can be useful across almost all industries. However, it is particularly helpful in sectors that run on data, like marketing, healthcare, finance, retail, sales and more.

If you want your business to make an impact, it is essential to understand your business's efficiency and ensure you are generating enough profit to sustain it.

Here’s how some industries utilise data analysis to enhance their operations and improve outcomes.

At the end of the day, marketers are there to get a product in front of the right customers. Without data analysis, they cannot ensure how successful they are at meeting this objective.

You can slice and dice marketing metrics in many ways, but at a bare minimum, marketing teams need to ensure their ad spend is lower than their sales revenue (ROAS). Marketers also use data analysis daily to determine their conversion rates through the sales funnel, engagement rates, and click-through rates.

Hospitals serve a large and diverse population, prioritising efficiency and patient care. Without the right data, hospital providers would struggle to predict patient needs, order consumables, and ensure that the right resources are available on shifts at any given time.

Beyond staffing, data analytics also plays a crucial role in early disease detection and personalised treatment plans. Models that examine medical histories, genetic data, and real-time patient vitals enable doctors to identify at-risk individuals earlier and recommend proactive treatments.

Financial institutions leverage data analytics to detect real-time fraud and assess credit risks. Banks use software to analyse customer transaction patterns, flag suspicious activities, and prevent fraud before it occurs.

For example, suppose someone who doesn’t often make large purchases suddenly spends a significant amount in a country they don’t reside in. In that case, the bank will proactively protect the customer by flagging the transaction for review. This shows how the bank uses data analysis to spot inconsistencies and protect against fraud.

Retailers operate within a universe of pricing strategies, customer preferences, and stock management that can either shatter or build profitability. Companies would struggle to forecast demand, maximise stock levels, or know the actual drivers of customer buys without the analysis of the data.

One of the most significant uses of data analysis among retailers is demand forecasting. Historically sold quantities are compared by the retailers with seasonality patterns to forecast the sales of products and the time of year they will occur.

For example, supermarkets use forecast models to stock up on peak-demand products like sunscreen in the summertime or influenza medicines in winter at the best levels to meet customer requirements.

Sales teams can have the assurance that they will perform well by relying on data-driven insights to surpass their quotas this quarter. Sales is all about forming strong customer relationships and bringing to the forefront new opportunities to produce revenues.

Data analytics have transformed the way that sales strategies are conceived and implemented. No longer do we have blind cold-calling with the hopes of receiving a response.

One of the finest applications of the usage of analytics in sales is lead scoring. Salespeople track KPI metrics like time on page, web visits, and e-mail opens and then prioritise the highest-intent leads to target their efforts on them.

Data can also be applied to find patterns of past sales, economic change, and seasonality to enable the sales team to forecast the movement of revenues with increased accuracy.

Customer service has evolved from reactive problem-solving to proactive, data-driven support experiences. Teams use analytics to understand customer pain points, reduce response times, and even predict issues before they arise.

One of the most notable applications is AI-powered chatbots and virtual assistants that look at customer conversations in the past, frequently asked questions, and support ticket histories to provide immediate, relevant replies.

Data analytics is imperative to inform companies if they are to remain competitive within the market, to base their decisions on knowledge, and to gain the optimal usage of their asset.

Through using analytical techniques like regression analysis, clustering, and predictive modelling, companies can uncover patterns in their operations, make changes for the better, and delight their customers with better experiences.

Regression analysis examines past patterns to predict the potential outcome of the future. For example, a company can use it to find out if the spending on advertising results in greater sales or the impact of price change on customer purchases.

Time series analysis examines data at a specific time interval that can aid in predicting patterns like seasonally varying demand, revenues or the consumption of electricity.

Clustering groups together with similar data is frequently applied to customer segmentation. For example, stores can segment their shoppers by purchase histories to target them in the future.

Anomaly detection identifies irregular patterns in data, helping businesses detect fraud, cybersecurity threats, or bottlenecks.

Natural language processing (NLP) helps computers understand and analyse text such as customer reviews and social media updates. It is then possible to leverage this information to calculate customer sentiment and track patterns of conversations.

Association rule learning establishes the relationships among the items of large datasets. It can also be used by retailers to understand patterns, like products purchased together frequently, to improve their cross-selling strategies.

A/B testing (experimentation) is a comparison of two versions of a webpage, ad, or e-mail to find out which version works best. It is commonly applied by marketers to maximise their rates of conversion.

Data visualisation translates complex sets of information into easier-to-comprehend graphs, charts, and dashboards that are easier to act upon.

Python and R are both strong programming languages that are frequently applied to statistical analysis, predictive models, and applications of machine learning.

SQL (structured query language) enables the user to access, manipulate, and analyse database-contained information that is structured.

Power BI is a software that is compatible with Salesforce to build real-time business intelligence dashboards.

If you aren’t a data analyst by trade, software like Tableau has been widely used to create interactive dashboards and visualise trends for more than 12 years. It’s an excellent option for exploring your data without requiring advanced technical skills.

Using these techniques and tools, you’ll be well on your way to getting the insights your team or business needs to make smarter decisions.

Data analytics is essential for businesses to understand if they are trying to stay competitive in the market, make knowledge-backed decisions, and get the most out of their resources.

Through using analytical techniques like regression analysis, clustering, and predictive modelling, companies can uncover patterns in their operations, make changes for the better, and delight their customers with better experiences.

Your business can begin to make smarter decisions that close more deals with Salesforce Data Cloud. On Data Cloud, it uses all your information to create personalised experiences for your customers that strengthen your relationships.

Your business can begin to make smarter decisions that close more deals with Salesforce Data Cloud. Data Cloud uses all your information to create personalised experiences for your customers that strengthen your relationships.