The recipe for success for most modern businesses has changed considerably over the last decade. In today’s quickly evolving business landscape, cookie-cutter approaches to business development strategies and execution rarely prove to be sufficient, and many organizations are now heavily reliant on the availability of real-time data and analysis to drive all aspects of their decision-making process.
However, while most businesses recognize that their current systems and processes contain key performance indicators (KPIs) and other useful information for decision making, the ability to discover, extract, and utilize all of these data points effectively is many times limited. This absence of skillset has left many organizations turning to data scientists and AI-driven tools and technology to help shape a more successful business future.
The Importance of Big Data in Modern Business
Gone are the days of gathering and extracting business data from one system or source. Now, relevant business data can live in multiple locations, whether residing in on-premise databases or cloud-based services and solutions. Big data is the collection of all of these data sources under one roof, giving organizations the ability to perform an in-depth analysis of all business areas.
Big data analysis plays a vital role in business development today and can be used to provide multiple benefits to organizations. Some of these benefits include:
- Enhancing organizational efficiency
- Helping to reduce overhead spending
- Identifying corporate risks and improving business continuity efforts
- Maintaining a competitive business
However, to capitalize on the benefits that big data can provide, organizations need the right tools and solutions to extract these valuable data insights. Artificial intelligence and machine learning technology have been fundamental in this process, accelerating digital transformations and adding intelligence and automation to the business analysis and improvement process.
How AI is Impacting Business Operations
While artificial intelligence (AI) technology isn’t a new concept for many, its application use cases in business are continuously being introduced and actively implemented. One example of industry segments benefiting from AI-driven technology is logistics operations and fleet management.
Logistics businesses have benefited from increased productivity and streamlined operations when utilizing newer technologies that power autonomous vehicles and facilitate near real-time GPS and onboard diagnostics solutions. AI-driven fleet management solutions also help track vehicles and assets, establish critical business benchmarks, and continuously improve how logistics organizations control their overhead costs.
The combination of deep data analysis and AI-driven technology has proved to be a profitable choice for any organization that manages a supply chain. By systematically analyzing transport times between production facilities, transport ships, delivery trucks, and distribution warehouses, AI technology can recognize and report in near-real-time cost inefficiencies across the entire supply chain. By synthesizing large pools of data faster than any human can reproduce, AI-driven systems can present long-term profitability risks to profitability other areas of concern to the appropriate stakeholders, giving them the insight they need to act quickly to improve operational efficiency.
Ultimately, AI helps managers more closely address their business needs and supports the adoption of new technologies. With the continuous use of AI tools and technology, fleet managers can successfully mitigate risk, track and communicate with staff, and even predict and prevent future business hiccups.
Getting Data Analysis Down to a Science
As businesses begin to identify different use cases for AI-driven technology and in-depth data analysis, it quickly becomes clear that the best course of action is investing in the staff and resources necessary to execute effectively. For many organizations, this organizational investment comes in the form of hiring a data scientist.
As the title implies, data scientists are tasked with managing the complexities of uncovering hidden business insights within big data sources. This requires a highly technical aptitude and professional experience in computer science, statistical analysis, and mathematics. These skillsets provide data scientists with the ability to make sense of multiple unstructured data sources and identify important trends residing in them and make them great high-level analytical thinkers, communicating the larger needs of a business as a whole.
When looking at the future of business technology, it’s important to understand how vital a data scientist’s role is and how impactful they can be to an organization. An experienced data scientist can not only provide the methodology and business modeling to support everything from cross-departmental functionality to longer-term operational efficiency, but they can also identify gaps in mission-critical processes and costly compliance risks.
More and more businesses have recognized the value of pursuing digital transformations, and many have already begun implementing progressive changes to support the evolution of today’s modern business landscape. Data scientists and the AI-driven tools they use to facilitate deep data analysis and business automation represent the future where most industries are heading. As organizations continue to rely on more advanced technology to sustain and grow their business, farming these systems for the near-limitless amount of data they hold is essential to remain competitive, profitable, and operationally efficient.