Data Analytics for Business: From Raw Data to Revenue

Most business leaders share one secret frustration: they've got data everywhere, but clarity nowhere. You'd think, with mountains of information collected daily, strategic decisions would be effortless. Yet, here they are—still guessing, hoping, and sometimes even crossing fingers.

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The issue isn't lack of data; it's the overwhelming noise created when data stays trapped inside scattered systems. CRMs whisper customer secrets, ERPs hint at efficiency hacks, and spreadsheets hold the potential for profit breakthroughs; but they're all talking separately.

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Profitable product ideas get shelved due to uncertainty and marketing budgets vanish on campaigns that didn’t deliver. It's maddening. But it’s fixable.

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Data analytics takes your raw, fragmented data and transforms it into clear, actionable insights. It bridges the gap between confusion and clarity, intuition and insight, guesswork and growth. And it’s the difference between companies that simply collect data and those that use it to drive smarter, more profitable decisions.

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So let’s explore how data analytics transforms businesses by improving decision-making, enhancing efficiency, and boosting profits. 

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Why is data analytics important for business?

Companies gamble millions on gut feelings—launching products they’re sure customers want, only to watch them sit unsold on shelves. Or worse, companies that burn cash blindly on marketing without understanding what’s actually driving sales.

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It’s frustrating because the data is right there. Yet, it stays locked in silos, fragmented across CRMs, ERPs, spreadsheets, and marketing tools. Instead of working together, these systems sit quietly apart, each holding pieces of the puzzle but never forming the full picture.

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Data analytics for business changes that.

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Businesses leveraging analytics make decisions grounded in reality. They see operational inefficiencies clearly—and fix them. They catch problems early, personalize experiences effortlessly, and, in doing so, become the kind of companies customers actually stick around for.

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“The estimated value of our data capabilities and outputs totaled about £3.2 million. Our most valuable category was Looker dashboards, contributing just over £1 million in productivity gains. For a single initiative, the standout was a digital splitter redesign experiment.”
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‍– McKinley Hyden, Director of Data Value and Strategy, Financial Times

How Financial Times Built Data Capabilities Worth £3.2M (and counting!)

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In fact, according to McKinsey, businesses effectively using data analytics are 23 times more likely to acquire new customers and 6 times more likely to retain them. That's no small advantage.

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Also read: Improving Gong’s customer retention with predictive data analytics 

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Here’s how data analytics reshapes the landscape of your business:

#1 Improves operational efficiency

Efficiency is the difference between success and failure. 

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I once watched a friend’s startup crumble, not from competition, but from poor inventory management. They guessed demand would spike in December, piled products high, and ended up broke in January.

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If only they used data analytics for business, they could have trimmed logistics costs and cut emissions while they were at it. 

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Also read: Driving Business Success with Data: Lessons from Data Leaders at Financial Times, Malt, WeWork, and Pleo 

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#2 Keeps customers loyal

Customers now expect—demand—personalized interactions. Generic approaches don’t cut it anymore. 

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Ever seen a Netflix recommendation and wondered if the app was reading your mind? It practically is.

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Netflix’s data shows exactly what you'll watch next, saving them $1 billion annually by keeping customers hooked. That's the kind of clarity you get from data analytics.

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McKinsey confirms companies that master personalization see impressive results:

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• 5-15% revenue growth
• 10-30% higher marketing ROI
• Up to 50% lower customer acquisition costs

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And they’re pulling in 40% more revenue than competitors.

#3 Detects fraud and reduces risks

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“Exiting bankruptcy was a significant milestone for WeWork, and much of that success was driven by the work done during the process. The use of data played a key role in achieving a successful turnaround and setting the stage for future growth.”

‍‍– Thomas Dodson, Head of Engineering and Data, Ex-WeWork

How data helped WeWork exit bankruptcy

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Fraud is a quiet killer of profits. Companies lose billions each year because they fail to detect unusual spending patterns or suspicious logins. According to PwC, fraud sucked $42 billion out of businesses in 2020.

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Analytics is the difference between losing your revenue to fraud and catching it before damage happens. You optimize pricing, marketing, and product strategies by analyzing purchase history and customer behavior, thereby increasing revenue per customer.

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#4 Improves revenue growth through smarter selling

Profitability comes from selling smarter. Pricing strategies, promotions, targeting: none of this should happen without solid data behind it. And yet, countless businesses do exactly that, hoping things will magically fall into place.

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But data-driven companies are 19 times more likely to be profitable. That’s because they're informed. And information beats guesswork every single time.

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Also read: 5 Mistakes Startups Make With Data And Analytics 

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All this is great but data won't translate itself into strategy. Someone needs to bridge that gap between raw data and actionable insights. And that’s what a business data analyst does.

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The pivotal role of a business data analyst

Your company generates vast amounts of data daily—sales numbers, customer interactions, supply chain metrics. Without proper analysis, it’s just noise. A business data analyst interprets this complex data, transforming it into clear, actionable insights.

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They use statistical tools, machine learning models, and visualization techniques to help companies:

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• Identify trends and patterns in customer behavior
• Improve operational efficiency
• Forecast market shifts and risks
• Optimize pricing, marketing, and sales strategies

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6 Key responsibilities of a business data analyst

The role of a business analyst sits at the crossroads of business strategy and data science, ensuring companies don’t just collect data but actually use it to increase efficiency, revenue, and competitive advantage.

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Here’s what a typical business analyst is responsible for:

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#1 Data collection and cleaning

Data is only useful if it’s accurate and well-structured. Business data analysts gather data from multiple sources, ensuring it’s complete, relevant, and free from errors.

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They use tools like SQL, Python, or Excel to clean and preprocess raw data. This step is crucial because incorrect or incomplete data can lead to misleading insights and poor business decisions.

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#2 Analyzing business trends

Understanding past performance helps businesses predict the future. Analysts identify patterns in customer behavior, sales trends, and operational performance to uncover opportunities.

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For instance, an analyst in retail might detect seasonal buying trends, helping the company stock up on the right products at the right time—preventing overstock or shortages.

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#3 Creating data visualizations

Numbers alone don’t tell a story—visuals do. Analysts create dashboards, graphs, and reports to translate complex data into digestible insights.

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Using tools like Power BI and Tableau, they present data in a way that executives and non-technical stakeholders can easily understand. A well-designed visualization can reveal insights that would be missed in raw data.

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#4 Building predictive models

Beyond analyzing what happened, businesses want to know what will happen next. Analysts use machine learning and statistical modeling to forecast sales, demand, and potential risks.

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For example, in banking, predictive models can detect fraudulent transactions before they happen, saving companies millions. In marketing, they help businesses target the right audience with precision.

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#5 Optimizing business processes

Efficiency is everything. Business analysts find bottlenecks and recommend data-backed solutions to streamline workflows and reduce waste.

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For instance, in manufacturing, they might analyze production data to pinpoint inefficiencies, cutting down costs and improving supply chain performance. Small process improvements can lead to significant financial gains.

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#6 Stakeholder communication

Insights are useless if they don’t lead to action. Analysts bridge the gap between raw data and business teams, translating insights into clear recommendations.

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They collaborate with executives, marketing, finance, and operations to ensure data-driven strategies are implemented effectively. Their role is not just technical but also strategic, influencing key decisions at every level.

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“We shouldn’t operate in silos—it’s collaboration that truly drives success. Our engine integrates both data and context, with invaluable input from our customers and customer success teams. By bridging these gaps, we can address the most critical challenges that our customers and company face.”

‍– Sriram Sampath, Head of Data Analytics, Pleo

Pleo's proactive data strategy for customer success

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Top 7 tools business analysts use to conduct data analytics for businesses

Business data analysts rely on various tools to extract insights and drive decisions. Here's an overview of seven top tools, including their introductions, pros and cons, approximate annual costs, and G2 ratings:

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#1 Microsoft Power BI

Microsoft Power BI is a business analytics service that delivers interactive visualizations and business intelligence capabilities with an interface simple enough for end users to create their own reports and dashboards.

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Pros:

• Seamless integration with Microsoft products
• User-friendly interface
• Affordable pricing

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Cons:

• Limited customization in the free version
• Steep learning curve for advanced features

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Cost: Approximately $120 per user per annum for the Pro version.

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G2 rating: 4.4/5

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#2 Tableau

Tableau is a data visualization tool that is widely used for converting raw data into an understandable format. It creates interactive and shareable dashboards that depict trends, variations, and density of the data in the form of graphs and charts.

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Pros:

• Exceptional data visualization capabilities
• Handles large datasets efficiently

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Cons:

• High cost for small businesses
• Requires training to utilize advanced features

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Cost: Starting at $840 per user per annum

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G2 rating: 4.4/5

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Also read: Best Business Intelligence Tools For Data-Driven Decision-Making in 2025 

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#3 Python

Python is a high-level programming language known for its readability and versatility. In data analytics for business, it's used for data manipulation, analysis, and visualization, thanks to libraries like Pandas, NumPy, and Matplotlib.

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Pros:

• Extensive libraries for data analysis
• Open-source and free to use
• Strong community support

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Cons:

• Steep learning curve for non-programmers
• Performance limitations with very large datasets

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Cost: Free

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G2 rating: 4.6/5

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#4 R

R is a programming language and free software environment used primarily for statistical computing and graphics. It's widely used among statisticians and data miners for data analysis and developing statistical software.

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Pros:

• Extensive statistical and graphical capabilities
• Strong community and package ecosystem

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Cons:

• Steep learning curve
• Memory-intensive operations can be slow

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Cost: Free

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#5 SAS

SAS is a software suite developed by the SAS Institute for advanced analytics, business intelligence, data management, and predictive analytics. It's known for its ability to handle large-scale data analysis.

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Pros:

• Robust statistical analysis capabilities
• Excellent technical support

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Cons:

• High licensing costs
• Proprietary nature limits flexibility‍

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‍Cost: Varies; typically starts from $8,000 per user per annum.

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G2 rating: 4.2/5

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#6 Looker

Looker is a data exploration and business intelligence platform that allows analysts to explore, analyze, and share real-time business analytics. It integrates seamlessly with various databases and supports collaborative data modeling.

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Pros:

• Web-based platform accessible from any device
• Customizable dashboards and reports
• Strong integration with Google Cloud services

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Cons:

• Pricing can be high for smaller organizations
• Requires SQL knowledge for data modeling
• Performance can be affected with large datasets

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Cost: Starts around $36,000 per annum

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G2 rating: 4.4/5

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#7 Amazon QuickSight

Amazon QuickSight is a scalable, serverless, embeddable, machine learning-powered business intelligence service built for the cloud. It enables users to create and publish interactive dashboards that can be accessed from any device.

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Pros:

• Seamless integration with AWS services
• Pay-per-session pricing model
• Built-in machine learning insights

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Cons:

• Limited customization options compared to competitors
• Some users report a learning curve for advanced features
• Dashboard performance can vary with complex datasets

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Cost: Pricing is based on usage; $216 per user/annum.

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G2 rating: 4.4/5

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Also read: 10 Best Data Management Tools in 2024 [Expert Picks] 

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It’s time to start making smarter decision with data

Business analysts often have the toughest job in the company. Because the tools for data analytics they're stuck with make their lives harder, not easier.

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Traditional analytics tools mean analysts waste up to 70% of their time just chasing down and cleaning up data. They spend hours bouncing between Excel sheets, ERP extracts, CRMs, and a dozen other siloed systems. And every hour spent wrangling data is an hour not spent solving actual business problems.

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It's a mess. And it gets worse. Those desktop analytics tools that seemed great initially? They crumble under the weight of enterprise-scale data. They're slow, inefficient, and frustrating.

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Worse yet, traditional analytics leaves analysts out in the cold when it comes to AI. They see the potential—smarter forecasting, automated insights—but they're stuck, without the necessary data engineering support to make it happen.

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So, how do you break this cycle?

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Enter 5X.

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5X solves the biggest challenges that analysts face:

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• Instant data access: 5X connects seamlessly with your ERP, CRM, and marketing systems, cutting data prep time from 70% down to just 20%. More analysis, less busywork
• Self-service analytics: Analysts don't need to rely on IT anymore. With 5X, they ask questions and get answers fast
• Enterprise-grade analytics without complexity: Forget complicated, bloated analytics software. 5X is built specifically for businesses, straightforward yet powerful enough to handle enterprise-scale data
• AI-powered insights: 5X isn't just analytics; it embeds AI right into your workflows. Analysts can uncover insights faster and deeper, spotting trends they might otherwise overlook

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Imagine what your analysts could do with 50% more time. Imagine how far ahead your business could be if insights came in minutes, not weeks.

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Let 5X bring your scattered data into one clear picture, so you can act fast and stay ahead.

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