What is Advanced Analytics?


What is Advanced Analytics?


Advanced analytics is a data analysis methodology that analyzes business information from various data sources using predictive modeling, machine learning algorithms, deep learning, business process automation, and other statistical methods.

Beyond traditional business intelligence (BI) methods, advanced analytics uses data science to predict patterns and estimate the likelihood of future events. This, in turn, can assist an organization in being more responsive and significantly increase its decision-making accuracy.

Advanced analytics tools, which data scientists frequently use, combine and extend prescriptive and predictive analytics while providing various options for enhanced visualization and predictive models.

Why is advanced analytics important?

Advanced analytics is a valuable resource for businesses because it allows them to get more functionality out of their data assets, regardless of where they are stored or in their format. It can also assist in addressing more complex business problems that traditional BI reporting cannot.

A consumer packaged goods manufacturer, for example, may need to ask the following questions to create a contextual marketing engine:

  • When is a customer likely to exhaust their supply of an item?
  • What time of the day or week are they most receptive to marketing advertisements?
  • What level of profitability is achievable when marketing at that time?
  • What price point are they most likely to purchase at?

Advanced analytics can assist an organization in determining precise answers to those questions by combining consumption models with historical data and artificial intelligence (AI).

What are the advantages of advanced analytics?

Advanced analytics provides the following benefits in addition to enabling greater use of data assets and increasing decision-makers confidence in data accuracy:

  • Accurate forecasting.  Data analytics can more accurately confirm or refute prediction and forecast models than traditional BI tools, which still have an element of uncertainty.
  • Faster decision-making. With high-accuracy predictions, executives can act more quickly, confident that their business decisions will produce the desired results and that favorable outcomes can be repeated.
  • Deeper insight. Advanced analytics provides a deeper level of actionable insight from data, such as customer preferences, market trends, and key business processes, enabling stakeholders to make data-driven decisions that directly impact their strategy.
  • Improved risk management. Data analytics predictions’ higher level of accuracy can help businesses reduce the risk of costly mistakes.
  • Anticipate problems and opportunities. Data analytics employs statistical models to identify potential problems on the business’ current path or new opportunities, allowing stakeholders to change course and achieve better results quickly.

What are some advanced analytics techniques?

Advanced analytics can assist organizations in gaining a competitive advantage. The following are some examples:

  • Data mining.  Through data analysis, this process sorts through large data sets to identify patterns and establish relationships to solve problems.
  • Sentiment analysis.  This technique identifies the emotional tone of a body of text using natural language processing, text analysis, and biometrics.
  • Cluster analysis. This process matches unstructured data pieces based on similarities discovered between them.
  • Complex event processing.  This technique employs technology to forecast high-level events likely to occur due to specific low-level factors.
  • Big data analytics. This is the process of analyzing large amounts of structured, semi-structured, and unstructured data to discover hidden patterns, correlations, market trends, and customer preferences.
  • Machine learning. Machine learning advancements have dramatically increased the speed with which data can be processed and analyzed, facilitating disciplines such as predictive analytics.
  • Data visualization.  This graphical data presentation process makes data analysis and sharing more accessible across organizations.

What are some use cases for advanced analytics?

These are just a few examples of how advanced analytics software can help business processes.

  • Marketing metrics. Marketing organizations can use data analytics to create customized, targeted marketing campaigns while avoiding wasting money on ineffective strategies. Future outcomes analysis can also assist a company in identifying opportunities to upsell and optimize the marketing funnel.
  • Supply chain optimization.  An organization can use advanced analytics to factor in demand, cost fluctuations, and changing consumer preferences to create an agile supply chain that can quickly adapt to changing market conditions.
  • Risk management.  By examining specific data sets and data streams in real-time, data analytics can assist data scientists in identifying patterns that may indicate high levels of risk, such as payment fraud or insurance liabilities.
  • Business operations. Advanced analytics can assist organizations in streamlining and adapting their operations to better suit predictions on changing market conditions or trends, ultimately increasing revenue.

Analyzing advanced analytics software

A variety of advanced analytics platforms are available. Depending on the application, each has distinct advantages. These tools are classified into two types: open-source and proprietary.

Open source software

Many data scientists who work with machine learning and predictive analytics now use open-source tools. They include both programming languages and computing environments such as Hadoop and Spark. Users like open-source data analytics tools because they are inexpensive to operate, provide robust functionality, and are supported by a user community constantly innovating the tools.

Proprietary tools

Microsoft, IBM, and SAS Institute provide advanced analytics tools on the proprietary side. Most have required a solid technical foundation and an understanding of mathematical techniques.

However, a new generation of self-service analytics tools has emerged, making functionality more accessible to business users. Tableau, in particular, has become a widely used tool. While its functionality is limited compared to more advanced technical tools, it allows users to perform cluster analyses and other advanced analyses.

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