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With big data abounding from organisations, people, and things, it’s no wonder that analytics is big for business. Considering the sheer amount of data people generate each day – from emails, tweets, online purchases and Google searches to even our watches, TVs, fridges and cars – we live in a truly connected digital universe that presents major opportunities for businesses to take advantage of. Hidden in all this data are valuable insights that can be used to grow businesses, improve performance and nurture customer relationships like never before. The challenge, however, is consolidating all that data from disparate sources and finding the insights.

That’s where business analytics comes in.


Analytics involves complex computer science, mathematical algorithms, statistics and machine learning to discover and interpret relationships and patterns in data that provide critical insights and knowledge.


Through the same analytical process, organisations can discover insights from their business data. These insights can help businesses make important data-driven decisions, become aware of bottlenecks or issues, and identify opportunities.


In today’s digital and on-demand economy, businesses that don’t make use of data analytics are sure to fall behind. The most successful companies in any industry use business analytics to monitor, optimise and improve every aspect of their business, from marketing, supply chain and sales to finances, customer experience and human resources.

“Leading organizations in every industry are wielding data and analytics as competitive weapons.”



In simple terms the following benefits can be reaped from business analytics:

  • Improved efficiency and productivity
  • Faster, more effective decision-making
  • Better financial performance
  • Identification and creation of new revenue streams
  • Improved customer acquisition and retention

With the rise of enterprise analytics, coupled with the impact of the COVID-19 pandemic, more and more businesses are relying on business-wide analytics to find new revenue streams, respond to new challenges such as forecasting demand, managing finances, identifying suppliers, retaining customers and empowering employees.

According to Gartner, analytics, business intelligence (BI), and data science are the most common use cases being accelerated due to the pandemic:

  • 94% of companies say analytics is important to their growth and digital transformation
  • 59% of organizations are currently using advanced and predictive analytics
  • 65% of global enterprises plan to increase their analytics spending in 2020

As businesses increasingly recognize the value of data-driven insights, the demand for assistance with writing papers on topics related to business analytics has surged. Many individuals and organizations seek professional help to navigate the complexities of data analysis, statistical modeling, and reporting. Whether it’s crafting research papers, case studies, or analytical reports, expert writing services provide the necessary expertise and precision to ensure accurate and impactful deliverables. With their assistance, businesses can harness the power of business analytics to drive growth, optimize operations, and stay ahead in today’s competitive landscape.


According to SAP Analytics Insights, there are four different types of analytics: descriptive, diagnostic, predictive, and prescriptive. Using these four types of analytics together enables business users to understand what and why something is happening, what may happen next, and what action to take.

Descriptive analytics

Descriptive analytics answers the question “What happened?”. This simple form of analytics uses values such as averages and percent changes to show what has already happened in a business. For example, a percentage increase in sales of a certain product. This is the first step of analysis, with the next step focusing on why this change has happened or how this value was achieved.

Diagnostic analytics

Diagnostic analytics answers the question “Why did something happen?”. It takes descriptive analytics a step further, using techniques such as data discovery, drill-down, and correlations to dive deeper into data and identify the root causes of events and behaviours.

Predictive analytics

Predictive analytics answers the question “What is likely to happen in the future?”. This branch of advanced analytics uses findings from descriptive and diagnostic analytics, along with sophisticated predictive modeling, machine learning, and deep learning techniques, to predict what will happen next.

Prescriptive analytics

Prescriptive analytics answers the question “What action should we take?”. This state-of-the-art type of analytics builds on findings from descriptive, diagnostic, and predictive analytics and uses highly advanced tools and techniques to assess the consequences of possible decisions and determine the best course of action in a scenario.


Business analytics is a broad field with many different components and tools. According to SAP Insights, some of the most common ones include:

  • Data aggregation: Before data can be analysed, it must be collected from many different sources, organised, and cleaned up. A solid data management strategy and modern data warehouse are essential for analytics.
  • Data mining: Data mining uses statistical analysis and machine learning algorithms to sift through large databases, analyse data from multiple angles, and identify previously unknown trends, patterns, and relationships.
  • Big Data analytics: Big Data analytics uses advanced techniques – including data mining, predictive analytics, and machine learning – to analyse massive sets of structured and unstructured data in databases, data warehouses, and Hadoop systems.
  • Text mining: Text mining explores unstructured text data sets such as documents, e-mails, social media posts, blog comments, call center scripts, and other text-based sources for qualitative and quantitative analysis.
  • Forecasting and predictive analytics: Forecasting uses historical data to make estimates about future outcomes, and predictive analytics uses advanced techniques to determine the likelihood these outcomes will occur.  
  • Simulation and what-if analysis: Once forecasts and predictions have been created, simulation and what-if analysis can test out different scenarios and optimise potential decisions before they’re made.
  • Data visualisation and storytelling: Data visualisations – like charts and graphs – provide an easy way to understand and communicate trends, outliers, and patterns in data. These visualisations can be strung together to tell a bigger data story and guide decision-making.


With augmented analytics at its core, SAP Analytics Cloud helps everyone in your organisation make decisions with a new level of confidence – without IT intervention or data science training. Everyday business users can harness newly accessible artificial intelligence and machine learning mechanisms to develop deeper insights than ever before. And you can deliver them in context, in natural language.

Even users with no special data-mining or visualization skills can automatically discover and display patterns and trends previously hidden from view. Analytics takes a quantum leap toward the conversational, automated, and predictive, helping users arrive at smarter decisions faster.


  • Enable all users to take full advantage of analytics;
  • Expedite the process of developing insight to improve business outcomes;
  • Eliminate human bias when uncovering and developing new insights;
  • Anticipate what will happen next.


  • Conversational queries that elicit best-practice visuals;
  • Natural-language text generation to explain visualisations;
  • Machine learning algorithms to detect key influencers of performance;
  • Automated discovery of patterns, trends, and outliers in data sets;
  • User-friendly, push-button functionality to build trusted and actionable predictions.


  • Spend less time collecting data and designing displays and more time making confident decisions;
  • Simplify access to critical information with natural-language processing;
  • Surface previously buried insights automatically with machine learning;
  • Empower analysts with convenient and accurate what-if simulation mechanisms;
  • Explore contextual recommendations for next steps.

An Intelligent solution such as SAP Analytics Cloud brings together planning and analysis, augmented analytics and business intelligence capabilities in one easily accessible application, eliminating the need to gather data from disparate tools and spreadsheets.

Contact MDSap to learn more about SAP Analytics Cloud and how it can be implemented in your organisation.

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