Big data analytics is an intricate process that involves the inspection of large and complex data sets, often termed as ‘big data’, to reveal underlying patterns, correlations, market trends and customer preferences.
This information plays a significant role in helping organizations make informed business decisions, thereby driving their growth and enhancing their performance.
The operationalization of big data analytics is facilitated by advanced analytics involving complex applications with elements such as predictive models, statistical algorithms and ‘what-if’ analysis powered by analytic systems.
Why Does Big Data Analytics Matter?
The significance of big data analytics in contemporary business operations is beyond measure.
Data-driven decision making, enabled by big data analytics, not only results in more effective marketing but also paves the way for new revenue opportunities, customer personalization and improved operational efficiency.
The ability to leverage big data analytics to gain competitive advantages over rivals can thus transform the way organizations function and contribute significantly to their success in the long run.
The Mechanics of Big Data Analytics
To leverage big data analytics, data analysts, data scientists, predictive modelers, statisticians and other analytics professionals engage in the collection, processing, cleaning and analysis of growing volumes of structured transaction data, alongside other forms of data that aren’t typically utilized by conventional business intelligence (BI) and analytics programs.
This process comprises four distinct steps:
Data Collection
This initial phase involves the collection of data from diverse sources, including but not limited to internet clickstream data, web server logs, cloud applications, mobile applications, social media content, text from customer emails and survey responses, mobile phone records and machine data captured by sensors connected to the internet of things (IoT).
Data Preparation and Processing:
Post-collection, the data is stored in a data warehouse or data lake, following which it must be organized, configured and partitioned correctly to facilitate analytical queries.
Thorough data preparation and processing subsequently result in higher performance from analytical queries.
Data Cleansing
This phase involves the elimination of errors or inconsistencies such as duplications or formatting mistakes from the collected data using scripting tools or data quality software.
This ensures the data is organized and in a state fit for further analysis.
Data Analysis
Finally, the collected, processed and cleaned data is analyzed with analytics software.
This can range from tools for data mining, predictive analytics, machine learning, deep learning, text mining and statistical analysis software, artificial intelligence (AI), mainstream business intelligence software, to data visualization tools.
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Classification of Big Data Analytics
Broadly, big data analytics can be classified into four categories:
- Descriptive Analytics: This form of analytics involves the creation of easily interpretable reports and visualizations detailing company profits and sales using readable data.
- Diagnostics Analytics: Diagnostics analytics facilitates the comprehension of the causes behind problems, helping companies understand why an issue occurred. It allows users to mine and recover data that helps dissect an issue and prevent its recurrence in the future.
- Predictive Analytics: Leveraging past and present data, predictive analytics aids in making future predictions. With AI, machine learning and data mining, users can analyze data to forecast market trends.
- Prescriptive Analytics: Prescriptive analytics provides solutions to problems by relying on AI and machine learning to gather data for risk management.
Tools and Technologies Powering Big Data Analytics
A variety of tools and technologies enable big data analytics processes. Some of the common ones include:
Hadoop: It is a popular open-source framework used for storing and processing large data sets and is capable of handling vast amounts of structured and unstructured data.
Predictive Analytics Hardware and Software: These process large amounts of complex data and use machine learning and statistical algorithms to make predictions about future event outcomes.
Stream Analytics Tools: These tools filter, aggregate and analyze big data that may be stored in various formats or platforms.
Distributed Storage Data: Replicated data, generally on a non-relational database, as a safeguard against independent node failures, lost or corrupted big data, or to provide low-latency access.
NoSQL Databases: Non-relational data management systems suitable for large sets of distributed data. These databases don’t require a fixed schema, making them ideal for raw and unstructured data.
Other tools include data lakes, data warehouses, knowledge discovery/big data mining tools, in-memory data fabric, data virtualization, data integration software, data quality software, data preprocessing software and Spark.
Shifting From On-Premises to Cloud-Based Big Data Systems
Initially, big data systems were primarily deployed on-premises, especially in large organizations that collected, organized and analyzed huge amounts of data.
However, with the rise of top cloud platform vendors like Amazon Web Services (AWS), Google Cloud and Microsoft Azure, setting up and managing Hadoop clusters in the cloud has become very easier.
Suppliers such as Cloudera now support the distribution of the big data framework on the AWS, Google and Microsoft Azure clouds, enabling users to spin up clusters in the cloud, run them as long as needed and then take them offline with usage-based pricing.
Big Data Analytics in Supply Chain Management
Big data analytics plays a crucial role in supply chain analytics.
By using big data and quantitative methods, decision-making processes across the supply chain can be significantly enhanced.
Big supply chain analytics extends data sets for more thorough analysis that surpasses traditional internal data found on enterprise resource planning (ERP) and supply chain management (SCM) systems.
Moreover, big supply chain analytics implements highly effective statistical methods on new and existing data sources.
Applications of Big Data Analytics
Big data analytics finds extensive application across different sectors of an organization, some of which are:
Customer Acquisition and Retention: By analyzing consumer data, companies can better their marketing efforts, increase customer satisfaction and boost customer loyalty. For example, personalization engines for Amazon, Netflix and Spotify offer improved customer experiences.
Targeted Ads: Past purchases, interaction patterns and product page viewing histories can be used to generate compelling targeted ad campaigns for users on both individual and larger scales.
Product Development: Big data analytics can offer insights about product viability, development decisions, progress measurement and guide improvements based on customer needs.
Price Optimization: Retailers can use pricing models that model data from various sources to maximize revenues.
Supply Chain and Channel Analytics: Predictive analytical models can facilitate preemptive replenishment, B2B supplier networks, inventory management, route optimizations and notification of potential delays to deliveries.
Risk Management: Big data analytics can identify new risks from data patterns for effective risk management strategies.
Improved Decision-Making: Relevant data insights can aid organizations in making quicker and better decisions.
Advantages and Challenges of Big Data Analytics
While big data analytics offers a host of advantages, such as quick analysis of large amounts of diverse data, better-informed decisions, cost savings, improved understanding of customer needs and better risk management strategies, it also presents a few challenges.
These include data accessibility, data quality maintenance, data security and choosing the right tools.
Additionally, organizations might face difficulties in filling skill gaps due to a potential lack of internal analytics skills and the high cost of hiring experienced data scientists and engineers.
The Future Scope of Big Data Analytics
As we move forward, big data analytics will continue to play a significant role in the market.
Already, enterprises have begun to leverage big data analytics in their operations.
Some of the future predictions for big data analytics are:
- A surge in cognitive analysis growth.
- Enterprises capitalizing on data for financial gain.
- The resurgence of open-source solutions in the market.
- Greater emphasis on data accuracy and security.
- An increase in demand for data scientists.
Given the value of data analytics skills, enterprises are shifting their focus to individual skill sets during recruitment and moving away from the traditional practice of focusing solely on degrees. Thus, the field of big data analytics offers plentiful job opportunities.
In conclusion, big data analytics, with its capacity to handle enormous volumes of diverse data, promises to revolutionize the way organizations operate.
With the potential to reveal hidden patterns, forecast trends, personalize customer experiences and inform strategic decisions, big data analytics will continue to be a key driver of innovation and business growth in the future.
