Advanced analytics and Artificial intelligence (AI) in business applications enable organizations to explore new areas to better serve both internal and external customers. As AI aims to emulate the human ability to assess and draw conclusions from data, advanced analytics covers a wide array of analytics processes. The development of these technologies has not only an immediate impact on businesses and end-users, but also on the people that work in the field.
To garner the value of advanced analytics tools and AI algorithms require quality data. Without this, analytics tools and AI models won’t be able to make precise predictions and cannot deliver insights that can help grow a company’s business. In this regard, organizations must glean, clean, and standardize once data fed to analytics tools and AI models.
Traditionally, data analytics experts have spent their time performing tedious and repetitive tasks including devising data for analysis, making reports and dashboards, and more for use in models and business systems. However, they simply cannot find out every combination or variation of their data with traditional analytical and business intelligence tools.
Implementing AI and Advanced Analytics
Most enterprises today are capitalizing on the cloud for effective storage and utilization of semi-structured or unstructured data. Besides, most consider that the convergence of cloud computing, data analytics, and AI platforms will have a significant transformational impact on modern businesses. Although this convergence is still in its infancy, implementing them deliver inevitable and phenomenal advancements to organizations.
Essentially, as AI is driven by data, endeavoring data excellence on the front end can needlessly delay the deployment of AI. This is majorly owing to pouring massive capital towards data warehouses and data lakes without understanding meaningful insights or data-driven cultures that impact the bottom line of an organization. Creating business outcomes centered on AI and advanced analytics strategy ensures that enterprises are driving AI analytics toward their business objectives that really matter and that data is modeled based on these outcomes.
Moreover, companies using analytics to identify valuable business insights from various data sources can also have a tremendous impact on business outcomes. Advanced AI-driven analytics will give them a competitive edge over companies that are merely using traditional business intelligence tools.
According to a McKinsey report, AI techniques have the potential to create between US$3.5 trillion and US$5.8 trillion in value annually across nine business functions in 19 industries. Those include retail, healthcare systems and services, transport and logistics, travel, public and social sectors, CPG, automotive and assembly, advanced electronics/semiconductors, banking, insurance, basic materials, high tech, media and entertainment, oil and gas, telecommunications, agriculture, chemicals, pharmaceuticals and medical products, and aerospace and defense.
The report further noted that this constitutes around 40 percent of the overall US$9.5 trillion to US$15.4 trillion annual impacts that could potentially be enabled by all analytical techniques.
Comprehensively, using artificial intelligence and advanced analytics will have a significant impact on every aspect of a business, driving a competitive edge over peers who don’t have such technologies in place.
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