The technological advancements have certainly created room for cutting-edge industrial competition to prevail. One such prominent role players in taking technology to the greatest height are Advanced Data Analytics. The concept came into existence to enable analytic processing, precise data manipulation, and management. As a result, Advanced Data Analytics have reshaped the entrepreneurial outlook and approaches by making it more flexible, scalable, and refined to perform in-depth analysis. The self-sufficiency of the analytics provides a 360-degree overview of the data and establishes a better structure for root cause detection and problem prevention.
What is Advanced Data Analytics?
It is an autonomous data examination and analysis technique that uses sophisticated tools to implement business intelligence in the industrial ecosystem. Being an effective substitute for traditional business approaches, it helps to parse historical data, explore potential and analyze the alternatives to strengthen business decision-making processes. The advanced algorithm of analytics considers all significant aspects of the business from Data warehousing to legacy systems and facilitates the identification of relevant patterns, trends, root causes, and pinpoint issues. As a result, advanced data analytics is an effective technique to capitalize on opportunities and receive perks in the form of better conversions and successful calls to action.
It is observed as the self-serve advanced data analytics that allows automated introduction and maintenance of key metrics to go beyond restricted reporting and execution processes. With the adoption of unique and creative architecture users can discover problems and dive deeper to fetch suitable solutions. Thus, advanced data analytics is considered an umbrella term that encompasses the benefits of several other types of analytics and uses high-level data science methods.
Types of Advanced Data Analytics
Advanced Data Analytics serves as a roof for another analytics method which is known for its widely extended use cases. Besides the descriptive and diagnostic analytics which focuses on a retrospective aspect of the data, Advanced Data Analytics have been categorized into three major types.
1. Predictive Analytics
It is the most prominent type of Advanced Data Analytics where a variety of statistical techniques is used to predict future possibilities with high accuracy. Almost all industries across the world make use of predictive algorithms to streamline their planning and execution process. As a result, it is the suitable analytics to gain an advantageous edge over competitors, satisfy customers and explore new business opportunities.
2. Big Data Analytics
Advanced Data Analytics help to process and analyze data using the conventional approaches. Such data include videos, audio, social method posts, images, etc which serve as the core of big data. Large retailers and manufacturers use the tool to predict demand for certain products and services.
3. Prescriptive Analytics
Prescriptive Analytics is the next step after predicting the events. Here, the calculative steps towards implementing the strategies are taken. The complex data analytics feature uses both structured and unstructured data to create useful insights through applied statistics, deep learning, and other advanced methods.
Use Cases for Advanced Data Analytics
The concept of Advanced Data Analytics has extended use cases where basic to complex all aspects are sincerely taken into consideration.
|Optimizing the system||Every organization, be it manufacturing or service–driven ones includes a systematic process to attain final results. The AI-infused technique is used to identify and study patterns for forecasting application usage. The Information Technology, Healthcare, Education, Banking every sector have optimized their operations with the firm support of Advanced Data Analytics.|
|Providing customer service||Advanced Data Analytics is used in a variety of industries for improving customer service and experiences. Reportedly, the survey conducted by Willies Towers Watson indicated that 54% of the companies actively use advanced data analytics with 47% witnessing the new customer growth. Thus, the technique is a robust solution to retain customers.|
|Ensuring predictive maintenance||It is emerging as the largest Advanced Data Analytics sector which is responsible for increasing productivity, reducing maintenance costs and breakdowns. Thus, advanced data analytics is a way towards creating a healthy ecosystem where progress becomes a trend.|
|Assessing product and service development||Using Advanced Customer Analytics become business entities can analyze dynamic market scenarios which are driven by customer taste and preferences. Advanced data analytics considers all aspects from product development to delivery and service creation to rendering.|
|Developing recommendations||The recommendations are the basic step of creating a personalized business ecosystem where customers feel valued. Largely, social media platform makes the use of recommendation feature by considering the taste and preferences of the users.|
|Healthcare||Advanced data analytics has proved its worth in several aspects of healthcare where it is used from staffing to surgery. From basic to complex every function in the health care ecosystem revolves around AI-driven data analytics. Today, advanced data analytics support real-time alerting, critical surgeries, disease management, ER admissions, self–harm prevention, etc. in healthcare institutions. Thus, advanced data analytics promotes effective diagnosing, testing, and treatment.|
|Banking||Largely, advanced data analytics has a superficial role in the banking sector where it is used for strengthening customer service. Using the profound concept banks remodel the customer segmentation into a solution-driven approach which makes it comparatively easier to address all the pain points. Moreover, big data allows the bank to individual customer profiles and portfolios to ensure proper execution of the customer behavioral pattern and preferences. Some of the key factors which banks take into consideration while preparing data-driven customer portfolios are demographic information, life events, spending pattern, and service preferences.|
|Sales||The advanced data analytics help to develop customer-centric sale strategies which leads to better leads, conversion, and turnover thereupon. These advanced features of data analytics play an effective role in streamlining and channelizing organizational efforts towards a better sales deal by examining customer preference and buying behavior. Based on the descriptive, predictive, and prescriptive analytics companies can accomplish their desire goals in a short period because each step in a data-centric organization is taken after thorough research.|
|Supply chain||Supply chain management gets the biggest boost from advanced data analytics which focuses on increasing accuracy while planning. It helps businesses to predict future demand which helps to determine the supply statistics and avoid wastage. Moreover, advanced data analytics helps to understand the customer needs, based on which manufacturing strategies and processes are formulated. Thus, the effective implementation of data analytics increases the return of the investment made during manufacturing and supply chain management.|
|Finance||Advanced data analytics becomes useful in predicting revenue and cost in the organization. Based on the accurate predictions the finance sector develops potential value out of unstructured and cross-functional data which leads to improved reporting. Thus, advanced data analytics allows better control over finances and makes it highly flexible to adjust to the trends and market patterns. The accurate prediction for the potential challenges and risks enables companies to prepare suitable contingency reserves well in advance.|
|Manufacturing||Advanced business analytics helps the enterprise clients to take the competitive game to a whole new level where business ideas talk about strategies. The AI-driven decision-making creates room for smart automation and accelerating mining capabilities with the help of machines and deep learning. Hence, advanced business analytics add strength to overall customer experience, risk mitigation, revenue management, and operational supremacy with unique methodologies. The execution of advanced business intelligence in the business ecosystem begins from defining the business problem, followed by data preparation, content tagging, and ends with the overall process validation.|
The manufacturing process in every organization is concerned with two major aspects- quality and quantity. Thus, advanced data analytics act prominently in creating a better difference in the overall manufacturing process. It manages the asset life cycle using the appropriate prediction based on which suitable strategies are designed. Moreover, advanced data analytics works nearly with the supply chains, factories, data centers, worker productivity, and deploying smart infrastructure.
Advanced Data Analytics Techniques and Methodologies
The successful outcomes of Advanced Data Analytics are centered towards machine learning and deep learning algorithm in combination with diversified tools and techniques. The inclusion of analytical tools helps to refine the industrial operational and prediction process.
|Data Mining||It is defined as the process of discovering knowledgeable insights within the provided database. As a result, it serves as the firm backbone of Advanced Data Analytics in recent times. With the use of machine learning and measurement statistics, data mining helps to uncover patterns that result in developing a clearer picture of trends. The widely used data mining techniques are- regression analysis, anomaly detection, cluster analysis, associative rule mining, etc.|
|Plug ‘n’ Play Predictive Tools||The in-depth Advanced Data Analytics process gains immense strength from associative, clustering, classification time series forecasting. Using the tool organization can explore historical data and predict related future outcomes; As a result, the tool helps to assess the investment, pricing, training, sales, and partnership decision of the company.|
|Text Mining||On the other hand, text mining is attributed to discovering high-quality information from textual data on various applications and websites. Generally, the text mining software make the use of Natural Language Processing (NLP) to derive meaning out of sentences, phrases, etc.|
|Smart Visualization||With the help of the tool users can gather various data elements and convey them as a story. Further, it facilitates the identification of the root cause of the problem, understands trends, and provides a competitive edge through relevant information development.|
|Self-Serve Data Preparation||The sophisticated tool is designed to ease out the self-serving business environment. Additionally, business users are bestowed with powerful capabilities to explore, analyze, manipulate, monitor, and merge data sets with the help of data scientists. With successful SWOT analysis, business houses get the advantages of early utilization of opportunities and timely preparation for arising risk factors. As a result, organizations can achieve impressive TCO and ROI and empower their business environs.|
Examples of Advanced Data Analytics
The scope of Advanced Data Analytics is not limited to any specific industry types which makes it the prominent choice of major industry giants.
- Coca-cola used Advanced Data Analytics to strengthen customer retention and loyalty programs based on the proper market research. With the digitally-led campaign, the brand established its new and innovative mark in the industry and boosted its customer base.
- With over 100 million subscribers today, Netflix is certainly ruling the sphere. It all became possible with Advanced Data Analytics-driven targeted advertising using which platform sends personalized recommendations to the users.
- A UOB bank in Singapore made the use of Advanced Data Analytics to drive a risk management campaign. As a result of which, the financial institution gained competitive command over reduced calculation time. With the application of Advanced Data Analytics, an ideal risk calculation time is reduced from 18 hours to few minutes. Thus, the risk–analysis process got the accelerated momentum.
- Amazon Fresh and Whole Foods, the subsidiary of renowned brand Amazon is known for its innovation and product development which is a result of Advanced Data Analytics. The technique helped the company to leverage the potential of a large market by understanding the buying pattern, habits, and trends of the customers.
Advanced Data Analytics Companies
Several market players make it easier for the users to make the best out of advanced data analytics and generate better returns and turnover. Some of the prominent advanced data analytics companies across the world are mentioned below.
The advanced data analytics software from Oracle focuses on all relevant aspects of data which includes data mining, importing, building, and transformation. As a result, Oracle creates the fastest organizational path from mere data to developing insights. Additionally, it provides flexible deployment features to eliminate data duplication and ensure complete scalability and security. Thus, the fundamental of Oracle’s advanced data analytic Oracle is becoming the choice of various industries across the world.
The prominent company which is globally known for developing suitable data-driven applications for supporting the operational process has incorporated advanced data analytics features in its ecosystem. The advanced AI framework of data analytics holds great command over queries, machine learning libraries, graph processing, and streaming engines. As a result Spark help companies to make the most out of advanced data analytics and accelerate healthy organizational results which further lead to long–term sustainability.
The advanced data analytics software from SAS is all about innovation and healthy interaction with the industries. The SAS analytics solutions are meant to transform data into intelligent information. As a result, it helps to inspire customers and bring better Returns on organizations’ Investments. Moreover, it facilitates making bold discoveries into progressive market space and gain command over growing competition by circulating suitable alternatives. Over the year SAS has maintained and outgrown the users’ expectations and has served them with its excellent data-driven applications.
It is an advanced analytics consulting company that has created new standards in the data analytics ecosystem with its prominent offerings. The company provides advisory to public, private, and non –profit organizations to expand their operations and extract maximum benefit from their data assets. The data analytics expert at Bain & company collaborates with the users around and helps them tackle the daunting challenges with their advanced analytics techniques. These techniques focus on enhancing the overall customer experience and improving employee engagement for the perpetual growth of the company.
The renowned company delivers its expertise for organizational strategy formulation by analyzing the related data and channelizing them into the process. Mckinsey also provides advanced tools and techniques to create a better business model and generate predictive foresight to grab opportunities. The top-notch technical capabilities of the company provide a competitive advantage to the client companies and help them mitigate risk. Moreover, the advanced data analytics capabilities of Mckinsey allow companies to work closely in collaboration with key employees and a priority client base.
This German multinational software company provides data-driven applications to strengthen business operations and customer relations. Ideally, the company is globally known for its ERP product which is driven by data analytics and is creating huge impacts in the business world. The SAP applications are the most widely accepted advanced analytics tool which is making a healthy difference in transforming business operations towards more strategic aspects.
It is a cloud–computing service created by Microsoft to build, test, deploy, and manage business applications through the Microsoft Centers. It is a set of more than 200 products that bring new solutions to the organizational ecosystem. The majority of these software products is based on advanced data analytics algorithm and contributes largely in creating better business health by expanding the brand reach. The Azure infrastructure being introduced by Microsoft is the most potent advanced data analytics software and ensures thorough optimization and customization.
It is the next-generation service product that allows users to explore and interact with third-party engines like Python, R, Spark, etc. to facilitate real-time data calculations. Moreover, the advanced data analytics product allows people to spot questions and develop query-based insights. The external engines of the Qlik Sense are based on open APIs, connectors and support several business-specific functions. The open-source connectors of the Qlik also align with prominent languages like R and Python to develop additional interactive and customized applications to align with users’ requirements.
The advanced analytics tools and techniques from IBM deploy the concept of machine learning, predictive modeling, and data statistics to analyze business information. The company integrates business intelligence with automation efforts and helps organizations develop meaningful insights to transform business operations. Thus, IBM software is the hybrid approach of data analytics and data science which are both driven by prominent subsets of Artificial Intelligence – deep learning and machine learning.
The company provides advanced data analytics services to improve organizational capabilities and identify suitable areas of growth. As a result, an advanced analytics product from PWC helps to predict future events, prepare responses, mitigate risks, and grab progressive business opportunities. The PWC advanced analytics software is the result of data extraction and wrangling techniques which makes it the potent choice of industries across the world.
The company focuses on creating a deep mix of AI capabilities and analytics consulting. Based on these two prominent aspects, Deloitte captures, analyzes and manages data and help companies develop accurate prediction and strategies. The global network of Deloitte also contributes to expanding the strength of the client companies across the world. Being the industry expert company is quick in adopting technological updates and developing advanced data analytics software.
The advanced analytics tools of KPMG provide a more refined data structure and help to transform business ideas into strategic insights. It also facilitates transforming unstructured business data into a structured format and derives maximum value out of accurate data building. As a result, KPMG’s advanced data analytics technique support the sales, finance, executive management, marketing, human resource, customer service and all other crucial functions of the organizations across the world.
It is a column-oriented relational database whose primary function is to store and retrieve data as per the user’s request. This big data processing and analytics tool is a multi-model database where HANA stands for High-Performance Analytics Appliance. The talked SAP product is precisely based on advanced data analytics features which are developed and marketed by SAP SE. It’s vital prediction and memory storage makes it the globally accepted tool amongst the business ecosystem.
The advanced analytics offering of Microsoft goes beyond historical data aggregation and reporting of business intelligence. It focuses on statistical modeling, mathematics, and probabilistic techniques to facilitate automated decision-making and predictive processing. As a result, the renowned company is known for providing cognitive services to support healthy business journeys with its SQL-based data analytics services across different platforms.
The business intelligence aspect of Salesforce is focused on taking data analytics use cases to a new height. Thus, through its cloud-based platform, the company facilitates exploring data and developing quick queries. Moreover, the accurate and predictive Salesforce data help organizations to take instant decisions and developing precise business insights. The advanced analytics connectors of Salesforce also support Customer Relationship Data Management which is ideally the ultimate goal of every business entity.
This American software company is known for introducing data science and analytics products in the business ecosystem. Through its application advancements, the company can deliver the benefits of advanced data analytics to the companies associated with it. Fundamentally, the emergence of Alteryx’s advanced analytics tool has put immense power in the hands of business analysts and data scientists to experiment with innovation. Thus, Alteryx has elevated the application of business and artificial intelligence all crucial areas of the business.
Data Science and Advanced Analytics
The concept of data science and advanced analytics are two different sides of the same coin which combine to give a definite shape and meaning to the business ideas. It is a combination of mathematical and statistical modeling with helps organizations go beyond the traditional method of correlation and discovers actionable insights. When combined both these concepts which are driven by the AI algorithm help to disparate structured data from the structured data set. Moreover, it also considers wrangling and deploying the descriptive and rule-based intelligent data concept in all the reactive aspects of the company.
Besides these, the unmatched value in the data sets can also be configured with the collaborative application of both these crucial concepts and the experience of human expertise in the organization. Thus, the core use cases of data science and advanced data analytics in the business world are as follows – Data Acquisition, Predictive Analysis, Descriptive Analytics, Machine Learning, Robot process automation, Forecasting, and supporting econometric business modeling.
Advanced Business Intelligence
The autonomous data or content which uses sophisticated techniques and tools to discover business insights make precise decisions, and generate customized recommendations is known as advanced business intelligence. Typically, the concept is all about a gradual shift from traditional data aggregation and mining techniques to the completely AI-driven tools where prediction forms the base of prescription. Some of the core techniques used in advanced business intelligence are:
- Data / Text Mining
- Pattern Matching
- Forecasting and Visualization
- Sentiment Analysis
- Network and Cluster Analysis
- Graph Analysis
- Complex Event Processing
- Multivariate Statistics, and
- Neural Networks.
Advanced Analytics and AI
Artificial Intelligence is the umbrella term with two major subsets deep learning and machine learning. Both these subsets form the essential part of data science and data analytics which are dominating the business sphere in recent times. Besides leveraging support to big data AI-driven advanced analytics is responsible for delivering solutions for complex business problems making it easier to achieve organizational goals.
The effective implementation of AI and advanced analytics in the organizational setup helps to grab opportunities and acknowledge potential challenges in advance. The vast data system with scaled analytics provides cutting –edge delivery of accessible tool and techniques which suits the diversified business needs. Moreover, the widely accepted AI _ driven advanced data analytics is easy – to – implement and provides business analysts with the liberty to perform experiments and encourage smart automation with the least discrepancies.
Data Scientist in Advanced Analytics
The qualification and subject–matter expertise for all types of data scientists remains common but their roles and responsibilities vary with the organizational requirements. Yet there are some basic fundamental responsibilities of the data scientist which stands common in all business set up. The person is expected to be well–versed with data analytic and data science tools and techniques that serve as the basis of AI-focused organization. Moreover, a data scientist shall have command over R and Python as these two major languages are the core of data mining and analysis.
The data scientist for advanced analytics is the person who is trained to shape the technical and analytical future of the company and overall industry by using his/her subject matter expertise. The person acts as the key driving force towards extracting and using data in several creative ways for generating a new line of growth and development. Furthermore, the advanced analytics data scientist is the key performing member in the team for valuing and transforming business ideas into strategies by measuring manipulation and reporting data discrepancies on time. As a result, the basic job of the data scientist is to enhance the growth spectrum and reduce the risk factors by providing accurate predictions.
The expenditure incurred for the adoption of Advanced Data Analytics is an investment that yields better sales turnover and collective benefits to the organization. The models and algorithms of Advanced Data Analytics are prepared in a precise manner to suit the diversified needs of every industrial sector. Furthermore, the extended capacity of the users makes the adoption of technology the need of an hour and facilitates effective data disposal too. As a result, effective implementation of the appropriate data analytics process leads to efficient outcomes which help business entities to gain an edge over competitors and dynamic market conditions.
Topics in Data Analytics
- Advanced Data Analytics
- Clinical Analytics
- Credit Risk Analytics
- Cyber Risk Analytics
- Data Analytics for Customer Behavior and Customer Experience
- Data Analytics for Customer Journey
- Data Analytics for Fraud Detection
- Data Analytics for Human Resources
- Data Analytics for Logistics and Supply Management
- Data Analytics for Risk Management
- Data Analytics for Talent Acquisition and Management
- Data Analytics in Asset Management
- Data Analytics in Digital Marketing
- Data Analytics in Healthcare – Use Cases, Metrics, Techniques, Companies and More
- Data Analytics in Manufacturing
- Data Analytics in Pharmaceutical Industry
- Descriptive Analytics – Definition, Types, Examples, and More
- Digital Analytics
- Financial Data Analytics
- Financial Risk Analytics
- HealthCare Claim Analytics
- Insurance Risk Analytics
- Insurance Risk Analytics
- Population Health Analytics
- Portfolio Risk Analytics
- Revenue Cycle Analytics
- Risk Assessment Analytics