Advanced Analytics (AA) have had a transformational impact on global businesses. The emergence and rapid growth of AA has been the top business priority of CIOs, as it enables businesses to enhance their processes and operations. While data analytics refer to drawing insights from raw data, advanced analytics help collate previously intact data sources, especially the unstructured data and data from the intelligent edge, to accumulate analytical insights.
Gartner defines AA as “the autonomous or semi-autonomous examination of data or content using sophisticated techniques and tools, typically beyond those of traditional business intelligence (BI), to discover deeper insights, make predictions, or generate recommendations.”
Advanced analytics is an emerging choice in the finance and retail sectors as they help organizations authenticate customers, improve customer experience, and reduce the cost of maintaining acceptable levels of fraud risk, particularly in digital channels. The unification of these technologies helps mitigate such threats before there is any acute damage, thus enhancing compliance. This fraud detection capability is also helpful for brand marketers to distinguish successful campaigns and avoid extravagant spending.
Boston Consulting Group (BCG), in its study, revealed that consumer packaged goods (CPG) companies can lift more than 10% of their revenue growth through enhanced predictive demand forecasting, relevant local assortments, personalized consumer services and experiences, optimized marketing and promotion ROI, and faster innovation cycles — all through AA and other sophisticated technologies.
Leveraging AA will help organizations stay in the market as factors like data silos, competition into digital transformation and agility have been influencing them to rely on data-driven insights. AA can strengthen organizations’ ability to execute data-intensive workloads and at the same time, keep the HPC (High Performance Computing) environment adaptable, responsive and cost-effective.
AA data includes structured data as well as unstructured data (like videos, photos, and other media files, internet-of-things devices, and web data) that require transformation before it can be analyzed. AA uses complex modeling techniques/tools to process the data to predict future events or discover patterns that can’t be detected otherwise and then perform numerous functions, including correlational analysis, regression analysis, forecasting analysis, text mining, image analytics, pattern matching, cluster analysis, multivariate statistics, among others. This process can be used to answer specific business questions including the root causes to the issues, trends, forecasting, and insights or recommendations for smooth operations.
World Economic Forum states that the COVID-19 crisis accelerated the usage of AA and AI-based techniques to augment decision-making among business leaders.
A study by Forrester Consulting on behalf of Intel revealed that 98% of business leaders believe analytics are crucial in driving business priorities; and less than 40% of workloads are leveraging AA or AI.
Key drivers for the adoption AA include:
- Substantial use of cloud technologies
- Growing of enterprise data
- Growing demand of data analytics
Though AA leverages the organizations in analyzing the data and helping in speed decision making, there are few challenges in adopting AA, which include:
Upfront costs: Managing large-scale data can require substantial expenses, whether you’re working on an on-premises solution or cloud-based solution. A significant investment is also required to deploy the personnel (staff and developers).
Gathering unstructured data from distinct sources: Even though advanced analytics can unravel value by processing unstructured data, getting these data “cleaned” and integrated into your system is a complex task.
Sharing and collaborating: Security and logistical concerns can hinder the flow of data necessary for advanced analytics. This problem is only intensified for remote and distributed teams.
Using solid strategy, internal expertise and proper guidance, organizations recognize and address their analytical challenges and overcome the obstacles easily.
The volume of business data continues to grow exponentially as this data is generated through millions of sensors, connected devices, payment systems, cameras and other Internet of Things (IoT) applications. While many companies already use and operationalize business intelligence applications within their processes to leverage data, the true potential of data is still untouched in many organizations.
Advanced analytics and particularly predictive analytics can help organizations unlock the true value of organizations’ data by providing extremely accurate forecasts that incorporate changing variables and hypothetical circumstances.
Common advanced analytics techniques
Advanced analytics utilizes a wide variety of techniques drawn from statistical methods algorithms, to identify relationships and forecast trends. Different methods and applications include:
Key Benefits of AA
Gartner predicted that the global market for advanced analytics is expected to grow from $248.0 billion in 2019 to $281.0 billion by 2024, with big data and predictive analytics making especially substantial gains. Research and Markets predicted that global advanced analytics market size is estimated to rise at a CAGR of 25.7% over a period of 2021 to 2028. The eruption of the COVID-19 pandemic has triggered the adoption of analytics across businesses as part of the efforts to ensure business continuity and process optimization. The adoption of AA is growing traction in line with the advances in the latest technologies, such as machine learning, data mining, semantic analysis, neural networks, multivariate statistics, and the rising volumes of data generated by businesses.
Though costlier, businesses have been massively investing in AA to enhance decision making and increase competitive advantage. Key benefits include:
- Delivers more refined, detailed and accurate insights that enhance day-to-day business decision making and quickens hypothesis and prototyping through improved simulation and testing
- Promotes data democratization and the creation of citizen data scientists
- Improves the efficiency of data science-related processes
- Enables identification and exploitation of data monetization schemes
- Helps organizations better identify and address threats such as security breaches
- Boosts agility by enabling organizations to predict and proactively respond to future events
- Access to crucial, evidence-driven guidance in times of chance and uncertainty
- Provides a foundation for machine learning and AI development
- Helps companies solve challenges that traditional BI can not
- Based on likelihood, prescribes actions to result in better business outcomes.
- Provides businesses with a comprehensive understanding of the business, past, present, and future, to better identify and manage risk
Choosing the right advanced analytics solution:
Since many advanced analytics tools are available, it is not so easy to choose one as the best fit for your business.
ACI Infotech offers an entire set of tools that can effectively solve any number of business data problems. Our technology allows companies to use previously unexplored data to inform their decisions, optimize their processes, and take advantage of the highest level of business intelligence. Our Intelligent Decisioning approach uses advanced methods and tools to transform business decision-making in operational and customer-facing processes. It helps businesses make better decisions faster, even in times of great uncertainty. ACI innovation goes beyond insights to augment and inform the choices companies need to make.