January 24, 2024

Advanced Analytics Guide: Definition, Benefits & Techniques

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Key takeaways

  • While basic analytics focuses on what happened, advanced analytics focuses on why it happened.
  • Data scientists build models that predict future outcomes, while data analysts analyze past data to aid decision-making.
  • Traditional analytics is reactive, and advanced analytics is proactive.

Advanced analytics is a sub-category under business intelligence (BI) tools with a slightly different purpose. BI tools are used to understand past events to improve current operations. 

Advanced analytical tools focus on predicting future events and executing actions that generate better business results. This article focuses on how advanced analytics is accomplished and the tools used to improve future business outcomes. 

Read more: Top Big Data Tools and Software for 2024

What is advanced analytics?

Advanced data analytics focuses on current events or actions and how businesses can improve future events or actions that are better than present-day business outcomes. Advanced analytics use statistics, math, artificial intelligence (AI), machine learning, and programming tools to accomplish this. BI tools may be limited to structured data only when analyzing past events.

Still, advanced analytics can use three categories of data—structured, semi-structured, and unstructured—to help predict future outcomes.

Advanced analytic software focuses on what will happen and, if something changes, how it will affect the future outcome. Whereas business intelligence looks at what happened and the events associated with what has occurred. 

Read more: Data Analysis Methods & Techniques

What are the techniques used in advanced analytics?

All business intelligence techniques or tools can be used with advanced analytics; however, some more complex techniques are explicitly used for advanced analytics. For example, Complex Event Processing (CEP) analyzes concurrent events across multiple systems to detect trends or patterns. CEP can detect abnormal behaviors and immediately trigger an action to minimize the threat. An example of CEP is fraud detection, which monitors transactions and flags suspicious behaviors.

A recommender system is another complex advanced analytical technique that uses past behavior analysis to predict a person’s likes or dislikes based on examples such as browsing history or the type of shows they watch on a streaming service. CEP and the recommender system use multiple advanced analytical techniques to generate predictable outcomes for each system. 

Some of the advanced analytical methods used in these systems are the following:

Big data analytics

Examines large volumes of structured, semi-structured, and unstructured data to reveal patterns not detectable by humans, such as market trends or customer preferences. Combining big data analytics with other techniques like statistical algorithms, what-if analysis, and predictive models are examples of what CEP and the recommender system use to produce outcomes. 

Machine learning

It is an AI technique that helps software applications predict new outcomes using historical data. Machine learning is used in recommendation engines and fraud detection.

Data mining

Data mining uses statistical models and machine learning to extract useful information from big data. Big data analytics collects, processes, and analyzes data from heterogeneous datasets put into one location that data mining uses. Data mining is primarily used for strategic decision-making, while data analytics gathers data from different sources in preparation for further analysis through data mining.

Data visualization

Allows complex information to be visually displayed to a target audience that can be easily understood.

The four pillars of analytics

What is written here is a subset of all the advanced analytical techniques available. The use of an advanced analytical method depends on what the user is trying to retrieve, and a variety of techniques are available to help users retrieve specific data to meet a requirement.

Statistical techniques

  • Regression analysis estimates relationships between dependent variables and independent variables.
  • Time series analysis is used to study data points collected over time.
  • Cluster analysis groups similar objects in the same cluster group in a diagram or graph.
  • Factor analysis reduces the number of variables into a smaller group, making research data more manageable to work with.
  • Cohort analysis groups similar data together based on a commonality and a specific period.
  • Sentiment analysis uses text analytics and natural language processing to determine a person’s attitude or emotions.

What are the different types of advanced analytics?

In addition to the four pillars of analytics that are considered advanced analytical techniques, we have already covered some techniques, such as data mining, big data analytics, and statistical or quantitative analysis. Text analysis and multivariate testing are considered advanced techniques.

  • Text analysis uses AI technologies to process human-written text.
  • Multivariate testing is a controlled experiment involving multiple variables to determine which combination generates the best outcome, such as a call-to-action (CTA) to make a purchase.

The advantage of advanced analytic techniques occurs when using multiple advanced data techniques to answer questions like why an event occurred, whether it will happen again, what will happen if something is changed, and what the data tells a business that it never considered. 

To be answered adequately, these questions involve multiple combined statistical and AI techniques. Combining a basic BI technique with one or two advanced data analysis techniques can be generally referred to as advanced analytics.

Are there industry-specific advanced analytical tools?

The advanced analytical platforms available on the market can meet numerous industry-specific business needs due to the variety of advanced analytical techniques provided within these platforms. Banking, healthcare, retail, manufacturing, and travel businesses may have unique requirements that advanced analytical platforms can meet. 

Combining AI and statistical techniques in an advanced analytical platform solution allows industry-specific companies to address different business concerns, such as banks processing data for a large customer base, healthcare using advanced data analytics to improve patient care, or manufacturing businesses improving quality control.

Each industry-specific business must find an advanced analytical platform that meets their unique needs.

Why is advanced analytics important to businesses?

Advanced analytics improves business operations in multiple ways besides operational efficiencies. Advanced analytics helps reduce data security threats by proactively identifying risk areas and mitigating or eliminating those risks. Advanced analytics software uses predictive analysis and statistics to help businesses make better decisions. 

Businesses can better anticipate customer needs, discover opportunities for improvement, make informed decisions about customer behavior and resource allocation, improve operations and products, and enhance risk management practices. By gaining significant insights into target markets, businesses can better understand customer preferences, help improve customer loyalty, and boost sales.

Do all business intelligence software solutions offer advanced analytical tools?

All business intelligence solutions come with statistical models and artificial intelligence tools. It does not necessarily mean all BI solutions have specific advanced analytical techniques or tools. Still, as stated earlier, an advanced analytical technique can be created by combining statistical analysis, machine learning, and other AI tools to find relevant data and help predict future outcomes.

What are the benefits and drawbacks of advanced analytics?

Advanced analytics can improve many facets of a business, including vital key performance indicators (KPIs) that keep a company competitive. This system improves every aspect of an organization’s business functions, from boosting revenue generation to reducing the surface of cyber threats.

Greater return on investment (ROI)

Holistically, when advanced analytics is applied in every aspect of a business, efficiency is gained, reducing operational costs. The cost savings due to efficiency are not the same as ROI generated. Still, the funds remain in the business coffers that can be re-invested into a project to create additional ROI. Businesses can also maximize revenues by analyzing marketing trends that increase customer growth or improve upon supply management that eliminates a bottleneck, leading to more significant ROI gains. Using advanced analytics results helps businesses make better decisions that improve ROI.

Predicting the future, anticipating problems and opportunities

Prescriptive analytics can use descriptive, diagnostic, and predictive analysis combined with a statistical method to predict future events, foresee a problem, or anticipate a new opportunity. Prescriptive analytics can even execute different business scenarios by modifying the data to produce better outcomes than past events, determine worst-case scenarios, better understand risk, reduce cost, and improve decision-making.

Provide deeper insight

Advanced analytics combined with other techniques that manipulate structured, semi-structured, and unstructured data can provide better actionable insight not typically foreseen when unstructured data is not included in an advanced analytics operation. Unstructured data in emails, social media sites, business documents, and web pages are value-added data sources that give businesses a holistic view of the organizations, leading to more accurate decision-making.

Improved risk management

Accurate advanced analytics results allow businesses to reduce risks by anticipating future risks and practicing enterprise risk management to minimize risk from an operational, financial, or strategic perspective.

Drawbacks of advanced analytics

A significant drawback of advanced analytics is the technical expertise required to use analytics effectively. Advanced analytics includes using unstructured data, real-time data, hypothesis testing, and data exploration, which require a certain level of expertise to use effectively. Other drawbacks include: 

  • Information overload: Accessing and using three sources of data to derive accurate results can be challenging.
  • Data quality issues: The accuracy and truthfulness of unstructured data can be suspect due to potential bias in the data.
  • Siloed data: Locating and verifying the accuracy of siloed data can be challenging to include in an advanced analytic operation.
  • No standard set of technologies: There is no standard practice for how data is collected, offloaded, and protected.

How is advanced analytics different from other types of analytical tools?

With traditional or basic analytics, the focus is on historical data, identifying patterns, and analyzing historical performance. While basic analytics can be reactive, advanced analytics can be considered proactive, using complex algorithms and techniques to predict future trends and behaviors. This system uses AI, machine learning, statistics, heuristics, automation, and predictive forecasting with complex datasets to produce deeper insight and predictions.

What are the outputs of advanced analytics?

Advanced data analytics outputs are focused on helping businesses make better business decisions. The decisions can focus on improving some aspects of a company, such as greater ROI, enhanced customer engagement, better marketing, improved productivity, and better employee performance that increases retention.

Advanced analytic techniques and statistics, including machine learning, predictive modeling, neural networks, and AI, are used to produce data-driven decisions.

What additional skills are required to use advanced analytics?

The job title commonly associated with a person technically competent at using advanced analytics is data scientist. Critical thinking and communication are also essential for a data scientist. 

Additional must-have skills for a data scientist are the following:

  • Programming: Need to know programming languages like Python, R, SQL, Java, or C.
  • Statistics: A comprehensive understanding of statistics and statistical analysis.
  • Mathematics: Must be proficient in advanced mathematics.
  • Data analysis and interpretation: Needed to analyze the data and interpret the results.
  • Data visualization: Visually prepare the data so a target audience can easily understand.

What’s next to get started with advanced analytics?

There is currently a data scientist shortage. Companies wanting to fully use advanced analytics should consider hiring a data scientist contractor for six months or more to train employees with interest and aptitude. Hiring college graduates with computer science degrees and sending them to a weekly training program over six months is another avenue to grow this skill set. From 2022 to 2032, the projected growth is estimated at 35% or 17,700 open positions annually.

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Frequently asked questions

Advanced analytics help businesses remain competitive and make data-informed decisions in a constantly changing business world.

If a business averaged an expected ROI of 15% for the past five years and fell short by 6%, advanced analytics would determine what happened and why it happened. The data collected and analyzed through advanced analytics would reveal why the business fell short by 6% and predict the best course of action to meet or exceed the expected ROI for the upcoming year.

Advanced analytics uses AI technologies, statistical methods, and predictive forecasting to predict or influence future outcomes that benefit businesses.

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