What is data analytics?

Data analytics converts raw data into actionable insights. It includes a range of tools, technologies, and processes used to find trends and solve problems by using data. Data analytics can shape business processes, improve decision-making, and foster business growth.

Why is data analytics important?

Data analytics helps companies gain more visibility and a deeper understanding of their processes and services. It gives them detailed insights into the customer experience and customer problems. By shifting the paradigm beyond data to connect insights with action, companies can create personalized customer experiences, build related digital products, optimize operations, and increase employee productivity.

What is big data analytics?

Big data describes large sets of diverse data—structured, unstructured, and semi-structured—that are continuously generated at high speed and in high volumes. Big data is typically measured in terabytes or petabytes. One petabyte is equal to 1,000,000 gigabytes. To put this in perspective, consider that a single HD movie contains around 4 gigabytes of data. One petabyte is the equivalent of 250,000 films. Large datasets measure anywhere from hundreds to thousands to millions of petabytes.

Big data analytics is the process of finding patterns, trends, and relationships in massive datasets. These complex analytics require specific tools and technologies, computational power, and data storage that support the scale.

How does big data analytics work?

Big data analytics follows five steps to analyze any large datasets: 

  1. Data collection
  2. Data storage
  3. Data processing
  4. Data cleansing
  5. Data analysis

Data collection

This includes identifying data sources and collecting data from them. Data collection follows ETL or ELT processes.

ETL – Extract Transform Load

In ETL, the data generated is first transformed into a standard format and then loaded into storage.

ELT – Extract Load Transform

In ELT, the data is first loaded into storage and then transformed into the required format.

Data storage

Based on the complexity of data, data can be moved to storage such as cloud data warehouses or data lakes. Business intelligence tools can access it when needed.

Comparison of data lakes with data warehouses

A data warehouse is a database optimized to analyze relational data coming from transactional systems and business applications. The data structure and schema are defined in advance to optimize for fast searching and reporting. Data is cleaned, enriched, and transformed to act as the “single source of truth” that users can trust. Data examples include customer profiles and product information.

A data lake is different because it can store both structured and unstructured data without any further processing. The structure of the data or schema is not defined when data is captured; this means that you can store all of your data without careful design, which is particularly useful when the future use of the data is unknown. Data examples include social media content, IoT device data, and nonrelational data from mobile apps.

Organizations typically require both data lakes and data warehouses for data analytics. AWS Lake Formation and Amazon Redshift can take care of your data needs.

Data processing

When data is in place, it has to be converted and organized to obtain accurate results from analytical queries. Different data processing options exist to do this. The choice of approach depends on the computational and analytical resources available for data processing.

Centralized processing 

All processing happens on a dedicated central server that hosts all the data.

Distributed processing 

Data is distributed and stored on different servers.

Batch processing 

Pieces of data accumulate over time and are processed in batches.

Real-time processing 

Data is processed continually, with computational tasks finishing in seconds. 

Data cleansing

Data cleansing involves scrubbing for any errors such as duplications, inconsistencies, redundancies, or wrong formats.  It’s also used to filter out any unwanted data for analytics.

Data analysis

This is the step in which raw data is converted to actionable insights. The following are four types of data analytics:

1. Descriptive analytics

Data scientists analyze data to understand what happened or what is happening in the data environment. It is characterized by data visualization such as pie charts, bar charts, line graphs, tables, or generated narratives.

2. Diagnostic analytics

Diagnostic analytics is a deep-dive or detailed data analytics process to understand why something happened. It is characterized by techniques such as drill-down, data discovery, data mining, and correlations. In each of these techniques, multiple data operations and transformations are used for analyzing raw data.

3. Predictive analytics

Predictive analytics uses historical data to make accurate forecasts about future trends. It is characterized by techniques such as machine learning, forecasting, pattern matching, and predictive modeling. In each of these techniques, computers are trained to reverse engineer causality connections in the data.

4. Prescriptive analytics

Prescriptive analytics takes predictive data to the next level. It not only predicts what is likely to happen but also suggests an optimum response to that outcome. It can analyze the potential implications of different choices and recommend the best course of action. It is characterized by graph analysis, simulation, complex event processing, neural networks, and recommendation engines.

What are the different data analytics techniques?

Many computing techniques are used in data analytics. The following are some of the most common ones:

Natural language processing

Natural language processing is the technology used to make computers understand and respond to spoken and written human language. Data analysts use this technique to process data like dictated notes, voice commands, and chat messages.

Text mining

Data analysts use text mining to identify trends in text data such as emails, tweets, researches, and blog posts. It can be used for sorting news content, customer feedback, and client emails.

Sensor data analysis

Sensor data analysis is the examination of the data generated by different sensors. It is used for predictive machine maintenance, shipment tracking, and other business processes where machines generate data.

Outlier analysis

Outlier analysis or anomaly detection identifies data points and events that deviate from the rest of the data.

Can data analytics be automated?

Yes, data analysts can automate and optimize processes. Automated data analytics is the practice of using computer systems to perform analytical tasks with little or no human intervention. These mechanisms vary in complexity; they range from simple scripts or lines of code to data analytics tools that perform data modeling, feature discovery, and statistical analysis.

For example, a cybersecurity firm might use automation to gather data from large swathes of web activity, conduct further analysis, and then use data visualization to showcase results and support business decisions.

Can data analytics be outsourced?

Yes, companies can bring in outside help to analyze data. Outsourcing data analytics allows the management and executive team to focus on other core operations of the business. Dedicated business analytics teams are experts in their field; they know the latest data analytics techniques and are experts in data management. This means that they can perform data analysis more efficiently, identify patterns, and successfully predict future trends. However, knowledge transfer and data confidentiality could present business challenges in outsourcing.

Data analytics improves customer insight

Data analytics can be conducted on datasets from various customer data sources such as the following:

• Third-party customer surveys
• Customer purchase logs
• Social media activity
• Computer cookies
• Website or application statistics

Analytics can reveal hidden information such as customer preferences, popular pages on a website, the length of time customers spend browsing, customer feedback, and interaction with website forms. This enables businesses to respond efficiently to customer needs and increase customer satisfaction.

Case study: How Nextdoor used data analytics to improve customer experience

Nextdoor is the neighborhood hub for trusted connections and the exchange of helpful information, goods, and services. Using the power of the local community, Nextdoor helps people lead happier and more meaningful lives. Nextdoor used Amazon analytics solutions to measure customer engagement and the efficacy of their recommendations. Data analytics enabled them to help customers build better connections and view more relevant content in real time.

Data analytics informs effective marketing campaigns

Data analytics eliminates guesswork from marketing, product development, content creation, and customer service. It allows companies to roll out targeted content and fine-tune it by analyzing real-time data. Data analytics also provides valuable insights into how marketing campaigns are performing. Targeting, message, and creatives can all be tweaked based on real-time analysis. Analytics can optimize marketing for more conversions and less ad waste.

Case study: How Zynga used data analytics to enhance marketing campaigns

Zynga is one of the world’s most successful mobile game companies, with hit games including Words With Friends, Zynga Poker, and FarmVille. These games have been installed by more than one billion players worldwide. Zynga’s revenue comes from in-app purchases, so they analyze real-time, in-game player action by using Amazon Kinesis Data Analytics to plan more effective in-game marketing campaigns.
 

Data analytics increases operational efficiency

Data analytics can help companies streamline their processes, reduce losses, and increase revenue. Predictive maintenance schedules, optimized staff rosters, and efficient supply chain management can exponentially improve business performance.

Case study: How BT Group used data analytics to streamline operations

BT Group is the UK’s leading telecommunications and network, serving customers in 180 countries. BT Group’s network support team used Amazon Kinesis Data Analytics to obtain a real-time view of calls made across the UK on their network. Network support engineers and fault analysts use the system to spot, react, and successfully resolve problems in the network.

Case study: How Flutter used data analytics to accelerate gaming operations

Flutter Entertainment is one of the world's largest online sports and gaming providers. Their mission is to bring entertainment to over 14 million customers in a safe, responsible, and sustainable way. Over the last several years, Flutter has acquired more and more data from most source systems. The combination of volume and latency creates an ongoing challenge. Amazon Redshift helps Flutter scale with growing needs yet consistent end-user experience.

Data analytics informs product development

Organizations use data analytics to identify and prioritize new features for product development. They can analyze customer requirements, deliver more features in less time, and launch new products faster.

Case study: How GE used data analytics to accelerate product delivery

GE Digital is a subsidiary of General Electric. GE Digital has many software products and services in several different verticals. One product is called Proficy Manufacturing Data Cloud. Amazon Redshift empowers them to improve data transformation and data latency tremendously so that they are able to deliver more features to their customers. 

Data analytics supports the scaling of data operations

Data analytics introduces automation in several data tasks such as migration, preparation, reporting, and integration. It removes manual inefficiencies and reduces the time and man hours required to complete data operations. This supports scaling and lets you expand new ideas quickly.

Case study: How FactSet used data analytics to streamline client integration processes

FactSet's mission is to be the leading open platform for both content and analytics. Moving data involves large processes, a number of different team members on the client side, and a number of individuals on the FactSet side. Any time there was an issue, it was hard to figure out at what part of the process the data movement went wrong. Amazon Redshift helped streamline the process and empower FactSet's clients to scale faster, and bring on more data to meet their needs.

How is data analytics used in business?

Businesses capture statistics, quantitative data, and information from multiple customer-facing and internal channels. But finding key insights takes careful analysis of a staggering amount of data. This is no small feat. Look at some examples of how data analytics and data science can add value to a business.

Data analytics improves customer insight

Data analytics can be conducted on datasets from various customer data sources such as the following:

  • Third-party customer surveys
  • Customer purchase logs
  • Social media activity
  • Computer cookies
  • Website or application statistics

Analytics can reveal hidden information such as customer preferences, popular pages on a website, the length of time customers spend browsing, customer feedback, and interaction with website forms. This enables businesses to respond efficiently to customer needs and increase customer satisfaction.

Case study: How Nextdoor used data analytics to improve customer experience

Nextdoor is the neighborhood hub for trusted connections and the exchange of helpful information, goods, and services. Using the power of the local community, Nextdoor helps people lead happier and more meaningful lives. Nextdoor used Amazon analytics solutions to measure customer engagement and the efficacy of their recommendations. Data analytics enabled them to help customers build better connections and view more relevant content in real time.

Data analytics informs effective marketing campaigns 

Data analytics eliminates guesswork from marketing, product development, content creation, and customer service. It allows companies to roll out targeted content and fine-tune it by analyzing real-time data. Data analytics also provides valuable insights into how marketing campaigns are performing. Targeting, message, and creatives can all be tweaked based on real-time analysis. Analytics can optimize marketing for more conversions and less ad waste.

Case study: How Zynga used data analytics to enhance marketing campaigns

Zynga is one of the world’s most successful mobile game companies, with hit games including Words With Friends, Zynga Poker, and FarmVille. These games have been installed by more than one billion players worldwide. Zynga’s revenue comes from in-app purchases, so they analyze real-time, in-game player action by using Amazon Kinesis Data Analytics to plan more effective in-game marketing campaigns.

Data analytics increases operational efficiency

Data analytics can help companies streamline their processes, reduce losses, and increase revenue. Predictive maintenance schedules, optimized staff rosters, and efficient supply chain management can exponentially improve business performance.

Case study: How BT Group used data analytics to streamline operations

BT Group is the UK’s leading telecommunications and network, serving customers in 180 countries. BT Group’s network support team used Amazon Kinesis Data Analytics to obtain a real-time view of calls made across the UK on their network. Network support engineers and fault analysts use the system to spot, react, and successfully resolve problems in the network.

Case study: How Flutter used data analytics to accelerate gaming operations

Flutter Entertainment is one of the world's largest online sports and gaming providers. Their mission is to bring entertainment to over 14 million customers in a safe, responsible, and sustainable way. Over the last several years, Flutter has acquired more and more data from most source systems. The combination of volume and latency creates an ongoing challenge. Amazon Redshift helps Flutter scale with growing needs yet consistent end-user experience.

Data analytics informs product development

Organizations use data analytics to identify and prioritize new features for product development. They can analyze customer requirements, deliver more features in less time, and launch new products faster.

Case study: How GE used data analytics to accelerate product delivery

GE Digital is a subsidiary of General Electric. GE Digital has many software products and services in several different verticals. One product is called Proficy Manufacturing Data Cloud.

Amazon Redshift empowers them to improve data transformation and data latency tremendously so that they are able to deliver more features to their customers.

Data analytics supports the scaling of data operations

Data analytics introduces automation in several data tasks such as migration, preparation, reporting, and integration. It removes manual inefficiencies and reduces the time and man hours required to complete data operations. This supports scaling and lets you expand new ideas quickly.

Case study: How FactSet used data analytics to streamline client integration processes

FactSet's mission is to be the leading open platform for both content and analytics. Moving data involves large processes, a number of different team members on the client side, and a number of individuals on the FactSet side. Any time there was an issue, it was hard to figure out at what part of the process the data movement went wrong. Amazon Redshift helped streamline the process and empower FactSet's clients to scale faster, and bring on more data to meet their needs.

How can AWS help with data analytics?

AWS offers comprehensive, secure, scalable, and cost-effective data analytics services. AWS analytics services fit all data analytics needs and enable organizations of all sizes and industries to reinvent their business with data. AWS offers purpose-built services that provide the best price-performance: data movement, data storage, data lakes, big data analytics, machine learning, and everything in between. 

  • Amazon Kinesis Data Analytics is the streamlined way to transform and analyze streaming data in real time with Apache Flink. It provides built-in functions to filter, aggregate, and transform streaming data for advanced analytics.
  • Amazon Redshift lets you query and combine exabytes of structured and semi-structured data across your data warehouse, operational database, and data lake.
  • Amazon QuickSight is a scalable, serverless, embeddable, machine learning-powered business intelligence (BI) service built for the cloud. By using QuickSight, you can easily create and publish interactive BI dashboards that include machine learning-powered insights.
  • Amazon OpenSearch Service makes it easy to perform interactive log analytics, real-time application monitoring, website search, and more.

You can start your digital transformation journey with us using the following:

  • AWS Data Lab – A joint engineering engagement between customers and AWS technical resources to accelerate data and analytics initiatives. 
  • AWS D2E program – A partnership with AWS to move faster, with greater precision, and a far more ambitious scope.

Sign up for a free account, or contact us to learn more.

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