What is Data Analytics?

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.

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 the storage and then transformed into the required format.

Data Storage

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

Data lakes vs. 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 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 non-relational 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 storage 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 continuously, with computational tasks finishing in seconds. 

Data cleansing

Data cleansing involves scrubbing for any errors, duplications, inconsistencies, redundancies, wrong formats, etc.  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. There 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.

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.

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. Let’s 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 like:

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

Analytics can reveal hidden information like 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 using Amazon Kinesis 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.