Being able to efficiently collect and manage digital information has become a vital business process across all industries.
There was a time when databases and massive collections of digital information were mainly the tools of researchers, actuaries, statisticians, and scientists. These days, however, even owners of small companies can access sophisticated datasets for the purpose of formulating business strategies based on real-time, actionable information.
Data analytics is being implemented in a wide variety of industries – for multiple purposes – more often than ever before. Companies are realizing that without this tool, their growth and success is limited. Data modeling and analysis gives companies access to insights about their growth potential that would have otherwise been hidden in their cluttered data files.
Here are examples of how some of the largest industries are using data analytics to benefit their success:
Data preparation and analysis has become a major aspect of modern curriculum design. Education is a field where digitization means results can be frequently and accurately measured. The academic performance of students can be compared across public school districts and socioeconomic conditions; combined with feedback from the students themselves, an efficient curriculum can be developed taking into account various factors. Education leaders can now pinpoint with great accuracy how students are doing, what curriculum areas are performing best, and in some cases, even predict how well students will do in a given course of study. With all of this information (and more) at hand, leaders in the education industry can also be prepared for budget planning, professional development of the faculty, optimizing the physical learning environment and technology within each department of their school, and much more.
Food Service and Hospitality
Customer feedback is vital to restaurant, hotel and tour operators. Earlier this year, a craft beer industry group partnered with a software developer to create a mobile app that will allow users to rate their experiences when visiting breweries across the United States. This effort combines marketing with real-time feedback that breweries can use to improve their operations. Companies in this industry are collecting an immense amount of customer data across all departments, incorporating customer feedback, marketing & sales, operating results, and supply chain information. Using a proper data analytics platform gives the decision makers the insights they need into this valuable customer data to properly make changes in each department, increasing customer satisfaction, revenue, and company success overall.
Investment banking firms have been improving their use of data visualizations for the benefit of their clients, and to uncover hidden or hard-to-see trends in financial statements. The power of digitization and social media opens up a much wider portfolio of research to investment banks, who can now see primary feedback from customers about the companies they are following. Portfolio managers are using data preparation and modeling techniques to create precise, predictive visualizations to convince clients of the long-term value of investment products. There is no end to the amount of insights within financial statistics that could be accessed with a strong data analytics platform. Especially with Artificial Intelligence assistance, even the smallest of details within this type of data could reveal powerful financial predictive statistics, that can in-turn support the entire success of the bank, investing firm, accounting firm, etc.
Technology and Software Development
Without a doubt, artificial intelligence is the most exciting advancement in the IT development field. A data analytics platform that doesn’t include the power and assistance of artificial intelligence is one with a fixed shelf life. Within the technology and software development industry, AI-powered data analysis is crucial for accurate planning, requirement analysis, design, integration, change control, and much more. Analyzing the abundance of information used to make these decisions can be tedious, and if done by hand could take months. Data analytics takes those tedious tasks and completes them in a matter of minutes, saving time and increasing efficiency of the project.
In the construction industry, data modeling has been traditionally used to improve workplace safety, manage costs, and ensure schedules are met. Modern analytics platforms use drones to film construction sites and transform footage into data that can be analyzed and turned into useful models by artificial intelligence software.
The goal is to make the workflow more efficient after making decisions about:
- Tool placement
- Equipment distribution
- Handling of materials
- Scheduling of crews according to skills and working conditions
Construction firms also use data to increase productivity. With artificial intelligence and machine learning systems, firms can turn the immense amount of data they have collected over time on projects to predict outcomes on other projects to gain a competitive advantage when estimating and bidding on construction projects. As these construction projects become more complex, big data may become the most important tool at a construction company’s disposal.
In the extremely competitive insurance industry, more and more companies are turning to big data analytics to outpace their competition. With data analytics, insurance companies discover innovative insights to help create value for their customers, reduce claims, and help actively monitor risks to maximize customer retention.
Real time data analysis is taking control of the technological advancement curve within the insurance industry. In the past, companies relied on analyzing historical information by hand to make decisions. Now, data can be analyzed in the same second that it is collected, all online, creating an extremely more efficient means of articulating calculations for better decision and prediction making.
One important area for data analytics in Insurance is Predictive Lead Scoring and Predictive Customer Value. Insurers are combining their own business data, enriched by outside sources, and Data Science tools like neural networks and advanced regression analysis, to define new ways of running their business, in real-time. Insurers are able to score, prioritize and organize their prospective clients and lead candidates in a very precise manner, keeping and working the best leads based on predicted customer lifetime value.
Data promises many benefits for the claims and risk parts of the insurance business as well. Environmental data, automotive data, health and behavioral data is being produced faster than ever before, and the challenge is collecting and digesting an endless rising tide of data while making sense of it at the same time. The idea of handling all of this analysis work with human hands and brains is rapidly becoming eclipsed by advanced AI-powered data analytics tools. AI-powered tools can handle many millions of times more data, much more accurately and cost effectively than human beings.
In the end, data science and analytics will continue to advance at a very fast pace, and they will continue to be adopted across all industries. The time to implement digital information solutions is now; companies that neglect to do this are at risk of falling behind the competitive curve.
Read next: What is the Difference Between Business Intelligence, Data Warehousing, and Data Analytics?