Data Analytics and Artificial Intelligence: A Long-Term Marriage?

Data Analytics and Artificial Intelligence: A Long-Term Marriage?

Many businesses rely on data analytics to help them improve customer satisfaction and lower their costs. But when paired with artificial intelligence, or AI, data analytics can further help your business. Here is some information about data analytics, artificial intelligence and how they can work hand-in-hand to benefit your business. 

What are Data Analytics?

Data analytics are pieces of data that are used by managers, business owners, and marketing managers to help them determine their customer base and how to grow their business. Data analytics can show you who visits your business, who is interested in your product, what price point your product can best sell at, how engaging your advertisements are and who your target customer should be. 

What is Artificial Intelligence? 

Artificial intelligence takes your data analytics and kicks it up a notch. Much of your data is achieved through actual testing. For example, if your target market is females aged 18 to 25, you may have tried to market to those who are younger and older to see what their interest was and failed. This helped you determine that those in this age group are most interested in your services. Artificial intelligence uses predictive behavior to predict that those who were older or younger wouldn’t have been most interested in your product. Ultimately, this predictive behavior helps the trial and error that goes into the pricey and time-consuming testing that goes along with data analytics

Data analytics and artificial intelligence work hand-in-hand because your data analytics are fed into the AI system to give a basis for the predictive behavior that AI is able to determine. Artificial intelligence relies on data analytics to provide a full and accurate assessment for your business.

How Can the Two Work Together?

Data analytics and artificial intelligence work hand-in-hand because your data analytics are fed into the AI system to give a basis for the predictive behavior that AI is able to determine. Artificial intelligence relies on data analytics to provide a full and accurate assessment for your business. 

What are the Benefits of Using Artificial Intelligence in Data Analytics?

The biggest benefits associated with using artificial intelligence in data analytics is the cost and time-savings. Artificial intelligence is able to more accurately predict consumer behavior based on your data analytics, helping to ensure that you don’t have to go through the trial and error process of testing new products or defining your target audience. This helps you to save both time and money, ultimately allowing your business to grow faster and more rapidly. 

Are you looking to incorporate artificial intelligence in your data analytics in the greater Atlanta, Georgia area? If so, we at Starr & Associates would love to assist you. We understand how to use the tools of business intelligence, analytics, and data mining to improve process efficiency and customer service, while also creating effective strategies for improving the end-customer experience and lowering costs. Contact us today to find out how we can help you. 

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Creating a Data-Driven Culture in Field Service

Creating a Data-Driven Culture in Field Service

Data-driven cultures are far-reaching. Nearly every industry is currently being disrupted through data analytics, and the field service culture is no exception. Many companies are utilizing a combination of unmanned drones, sensors, and data analysis to make field service more effective and comprehensive. Here’s what you need to know.

Predictive Field Service Management Models

What if you could predict when a system would go down? With big data, you can. Big data uses a combination of sensors and historical data to identify environmental conditions that could indicate a breakdown. This data isn’t a replacement for traditional field service techniques, but instead, it’s designed to augment field servicing. Big data can be used to tell field service technicians when there could be a critical problem, as well as to fine-tune the number of times technicians go out and when they go out.

Predictive field service models work hand-in-hand with the Internet of Things. Internet of Things devices are utilized to capture data in the field, and this data is analyzed. Big data isn’t magic — it requires tremendous amounts of data which is then used to view patterns. As an example, a certain heat signature might only occur just before an element breaks. These patterns can then be used to create a risk assessment for individual machines and equipment on the field.

A data-driven culture is a cost-effective, safe culture. Better data means technicians need to be on the field less and are on the field when they are most useful. Not only does this reduce field-related industries, but it also reduces the overall cost to a business.

Creating a Data-Driven Culture

A data-driven culture is a cost-effective, safe culture. Better data means technicians need to be on the field less and are on the field when they are most useful. Not only does this reduce field-related industries, but it also reduces the overall cost to a business.

Creating a data-driven culture begins with the right hardware and software systems. Companies must take care to outfit their on-the-field infrastructure with the right sensors and IoT devices and must utilize state-of-the-art software to capture and analyze this data.

Companies also need to change their core business processes to directly relate to and manage this data. The technology has to be integrated at all levels of their field servicing so that data brought in and analyzed has an ultimate impact on when service calls are made and how service technicians operate.

Integrating data into field service is the first step towards making a more effective, productive, and competitive environment. Companies can substantially reduce their overhead while also reducing their risk, by utilizing an ecosystem that is less likely to experience breakdowns, delays, or injuries. For more information about this type of solution, contact the experts at Starr & Associates.

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Big Data Strategy – How to Break Down Silos and Drive Organizational Change

Nearly every company goes through some organizational growing pains. One common development, departmental silos, occurs at some point. Successful companies break down a silo, a department that protects its information, keeping it from other departments. Silos cause growth breakdowns, so continued growth requires their eradication.

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