What is analytics?
Organisations, people, and things are generating massive amounts of data every day. In a 24-hour period, we collectively send 294 billion e-mails and 500 million tweets. We plug 3.5 billion searches into Google. Our connected cars generate a whopping four petabytes of data. Even our watches, fridges, and TVs are constantly creating and sharing data.
Hidden in all this data are insights that can trigger explosive business growth. The challenge is in finding them, which is where analytics comes in.
A basic definition of analytics
Analytics is a field of computer science that uses math, statistics, and machine learning to find meaningful patterns in data. Analytics – or data analytics – involves sifting through massive data sets to discover, interpret, and share new insights and knowledge.
What is business analytics?
Very simply put, business analytics is analytics applied to business data. It focuses on the business implications of data – and the decisions and actions that should be taken as a result.
The importance of business analytics
Today, the use of business analytics software is often the deciding factor distinguishing industry winners from losers. Leading companies use analytics to monitor and optimise every aspect of their operations – from marketing to supply chain – in real time. They rely on analytics to help them make fast, data-driven decisions, grow revenue, establish new business models, provide five-star customer experiences, empower employees, gain a competitive edge, and so much more. Companies without analytics – or without good analytics – are left to make decisions and do business based on gut instinct and experience alone.
Leading organisations in every industry are wielding data and analytics as competitive weapons.
The top business benefits of analytics are:
- Improved efficiency and productivity
- Faster, more effective decision-making
- Better financial performance
- Identification and creation of new revenue streams
- Improved customer acquisition and retention
Enterprise analytics is one of the fastest growing markets in the enterprise software space. Recently, this growth has sped up even more due to the COVID-19 pandemic, which has forced many businesses to find new ways to make money, cut costs, and navigate the turbulent “next normal.” According to Gartner1, analytics, business intelligence (BI), and data science are the most common use cases being accelerated due to the pandemic – blowing Internet of Things (IoT) and cloud applications out of the water. The problem-solving and predictive capabilities of analytics are helping organisations handle urgent, pandemic-related challenges such as accurately forecasting demand, protecting at-risk employees, and identifying potential supply chain disruptions.
of companies say analytics is important to their growth and digital transformation1
of organisations are currently using advanced and predictive analytics1
of global enterprises plan to increase their analytics spending in 20201
Four types of analytics
- Descriptive analytics
Descriptive analytics answers the question “What happened?”. This simple form of analytics uses basic math, such as averages and percent changes, to show what has already happened in a business. Descriptive analytics, also called traditional business intelligence (BI), is the first step in the analytics process, creating a jumping-off point for further investigation.
- 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 modelling, 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.
Common components of business analytics
Business analytics is a broad field with many different components and tools. 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 centre 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.
Examples of analytics
Analytics is used by businesses of all sizes, in all industries – from retail and healthcare to sports. Many analytics solutions are tailored to an industry, or to a specific purpose or line of business. Here are just a few examples of analytics today:
Traditionally, financial analytics was used for generating a standard set of reports. But now that finance has taken on a more strategic role with the business, financial analytics has evolved – combining financial and operational data with external data sources to address a wide range of business questions. These include everything from “Are we investing in the right opportunities?” to “How will our future margins be affected by the decisions we’re making today?”
Marketing analytics connects data from multiple channels – social media, Web, e-mail, mobile, and more – to give marketers comprehensive insight into how their programmes are performing. Users can mine millions of rows of data to improve the effectiveness of campaigns, hyper-personalise marketing messages, analyse sentiment on social media, target potential customers at exactly the right time, and much more.
Supply chain analytics
The explosion of e-commerce, increased market volatility, globalization, and other forces have made supply chains incredibly complex. Supply chain analytics helps organisations avoid disruption, keep goods flowing, and improve supply chain resilience and agility. They use real-time data from a wide variety of sources – including Internet of Things sensors – to optimise everything from sourcing, production, and inventory to transportation and logistics.
Modern analytics technologies
Today, nearly unlimited data storage and lightning-fast processing speeds have ushered in the age of artificial intelligence (AI) and machine learning. These technologies are “augmenting” analytics – making them infinitely more powerful than ever before.
AI and machine learning analytics can detect patterns, find outliers, and make connections in Big Data much faster and with far more accuracy than was previously possible. Through the cloud, they can tap into more data from more sources – including social media and Internet of Things sensors – and surface insights, opportunities, and risks that would otherwise remain hidden.
Machine learning algorithms can also automate some of the most complicated steps in the analytics process, which means relatively untrained business users – and not just data scientists – can wield advanced and predictive analytics. Natural language processing (NLP), a type of artificial intelligence, takes self-service a step further and allows users to ask business questions of their data (and get answers) in an easy, conversational way – just like typing a query into Google or asking Siri a question.
And of course, all of this is available on mobile devices – so users can get answers to ad hoc queries no matter where they are.
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Advanced analytics is an umbrella term for a type of analytics that uses sophisticated tools and techniques to autonomously (or semi-autonomously) explore data. These tools and techniques are typically beyond traditional BI capabilities and include predictive modelling, data and text mining, sentiment analysis, machine learning, neural networks, statistical algorithms, complex event processing, and more.
Big Data analytics is a type of advanced analytics that examines very large data sets – including structured, semi-structured, and unstructured data – from a wide range of sources. Using complex tools and techniques like predictive modelling, what-if analysis, and machine learning algorithms, Big Data analytics can surface hidden trends, unknown correlations, and other meaningful insights in data sets that are too large or diverse to be handled by traditional analytics.
Augmented analytics are analytics that have been “augmented” with artificial intelligence technologies, including machine learning and natural language processing (NLP). These powerful AI-driven analytics are not only capable of finding better insights, faster – they democratize advanced analytics by automating complex processes and allowing users to ask questions and understand answers with minimal training.
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