In an era of data-driven businesses and big data technology, it’s important that companies make smart use of the data they have collected to help them optimize their operations and gain insights into their customers and their market in general. That’s where data analytics and business intelligence come in. But what exactly are these two terms? And how can they benefit your business? Let’s take a look at what they are, how they work together, and how you can get started with them in your company today.
What is Business Intelligence
Business intelligence is the process of taking data, analyzing it, and then using that data to make business decisions. The term business intelligence is often used interchangeably with data analytics, but there are some key differences between the two. Data analytics is the process of collecting raw data from various sources in order to answer business questions about trends or customer behaviors. Data science is a sub-field of data analytics that typically incorporates more advanced techniques for understanding what's happening with data, such as machine learning.
What is Data Analytics?
Data analytics is the process of applying complex data science algorithms to generate insights that can be used to make better, more informed decisions. Data analytics is an umbrella term that covers three main activities: business intelligence, predictive analytics and data mining. BI is a subset of data analytics which focuses on extracting meaning from historical data in order to provide information for decision-making purposes.
Predictive analytics is the process of using machine learning algorithms to make predictions about future trends based on current or past user behavior or information.
What are the benefits of Business Intelligence?
Business intelligence is made up of data science, analytics, and business intelligence. Data science is the analysis of large amounts of data in order to find patterns or answers to problems.
What are the benefits of Data Analytics?
Data analytics is the process of collecting, analyzing, and interpreting data in order to make more informed decisions. Data science is the intersection of business intelligence (BI) with computer science, mathematics, statistics, predictive modeling, or other analytic methods. Data analytics benefits businesses by providing them with insights into how they can improve their bottom line. Data analytics helps business owners identify trends in company data that lead to increased revenue over time. This includes areas such as customer spending habits, best marketing campaigns for a specific product or service, or which products are most likely to sell on a particular day of the week.
What are the challenges of Data Analytics?
Data analytics is a complex, diverse field that offers an incredible amount of potential to help businesses make better decisions. BI can provide insights into how your customers feel about your product or service offerings. Data science can help you understand how to improve the quality of your products, reduce operational costs, or even create new business opportunities.
But despite these benefits, data analytics poses challenges for both small and large businesses alike: namely, the investment required in order to implement it, either in terms of time or money. For example, BI requires collaboration between IT staff and those in the business departments where data will be collected - two groups with different skill sets. This makes the process more complicated than simply purchasing a software package from a vendor.
How can BI & Data Analytics help your business grow?
Data analytics can be used to help your business grow by understanding customer needs, analyzing risks, or tracking key performance indicators. Data analytics tools can help you find the answers to some of your most important questions. You can use data science to access a wide range of information that your employees may not have the skills or time to dig into. Data scientists are experts in using advanced analytical techniques and data visualization capabilities to uncover insights in data that other people might not think of. Data scientists spend their days solving complex problems in fields like finance, healthcare, manufacturing, marketing, sales and many more!
The following is an example scenario of how data science could help a company grow:
A manufacturing company has been experiencing declining productivity levels over the past six months. Using predictive modeling with machine learning algorithms they see patterns between on-the-job accidents resulting in injury and decreased production levels over time.