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With over 25 quintillion bytes of data collected daily, different technologies have been established to help in extracting sense from data. Data Analytics vs. business intelligence are the major players in matters of data processing. These two terms are common in matters of data processing since they play a significant role during the data analysis process. They are also fundamental elements when running a modern business that uses data in decision-making.
Given that these terms are used synonymously, they raise many questions about whether they are similar. The simplest answer to this question is no, although there are many similarities between these two terms that call for explanation. It is no secret that many people confuse the difference between data analytics and business intelligence since they believe it’s the same thing.
There are a lot of elements that make these two completely different from one another that people need to understand. This article explains the difference between data analytics and business intelligence by outlining what makes them different from one another. Read on!
What is Business Intelligence?
Business intelligence refers to the set of technologies, tactics, processes, and architectures that are put in place to transform boring data sets into exciting data insights that aid in the decision-making process. When you look at this definition clearly, you will understand that business intelligence is used to help business owners find meaning from their raw data. It involves the use of tools, technologies, and other methodologies to ensure that data is easily readable.
This technology is specifically designed for companies using data to facilitate their day-to-day operations. It plays a fundamental role in extracting business insights that company stakeholders use to evaluate the progress of their daily activities. Companies operating in the modern world use this technology to assess some of the best decisions that need to be made to achieve their objectives.
Business intelligence involves things such as real-time monitoring, performance management, data and text mining, benchmarking, data analytics, and many others. All these activities elaborate more on the power of business intelligence in converting data into insights that decision-makers can use. Even though data analytics is part of business intelligence, it only covers a smaller bit of the entire process.
Application of Business Intelligence
Even though many people strongly believe that the ultimate goal of business intelligence is to streamline company strategies and the decision-making process, there is a lot more hidden behind the curtains. However, this does not mean that this is not true, but the reality is that business intelligence is capable of doing much more than that. The power of business intelligence boils down to enhancing the profitability of a given company.
The reality is that business intelligence can offer a lot more benefits depending on how you apply it in your daily operations. Keep in mind that this technology is highly flexible, and you can use it any way you want to generate your desired results. What matters is how you integrate into your daily operations and align them with your business goals. For instance, a social media company can use business intelligence to increase the number of clicks and attract more customers.
This means that the type of business you run will determine how to apply business intelligence to achieve your goals. Regardless of your business industry, business intelligence is specifically designed to increase productivity and elevate revenue generation. It has the ability to increase your profit margin and create a better environment for your business development. You can also Incorporate your customer acquisition strategies to help you nail your efforts to the right audience.
What is Data Analytics?
Data Analytics refers to the whole process associated with collecting, cleaning, storing, modeling, and querying raw data sets. The goal of data analytics is to generate insights that are used by business stakeholders to make critical decisions. This technology of data analysis is also applied in other fields such as education, science, and also in the government. When you elaborate on this definition clearly, you will realize that it resembles closely with business intelligence.
The significance of data analytics for decision-making cannot be overstated. By harnessing the power of data, organizations gain a competitive edge, better equipped to adapt to dynamic environments and seize opportunities. These decision-making insights, derived from comprehensive data analysis, serve as beacons guiding strategic directions and resource allocations. Moreover, they foster a culture of evidence-based decision-making, mitigating risks and maximizing returns on investments. Across sectors, embracing data analytics not only enhances operational efficiency but also fosters a culture of continuous improvement, driving innovation and sustainable growth.
What you need to keep in mind is that data analytics only focuses on the major components of the analytics process. On most occasions, this technology is used in business environments, but it is not a comprehensive business tool. This means that there are some major components that business owners can require that are not part of data analytics. Even though data analytics comes with additional tools, such as data dashboards, you should always consider them as add-on features.
When you see this technology and its accompanying features, always be mindful of where and how to apply them. Also, it may lack some useful components that you need to use in running a business. This is triggered by the fact that data analytics is not an all-in-one business tool.
What is the Difference Between Data Analytics and Business Intelligence?
Considering the explanation shared above, data analytics and business intelligence are similar in most cases. These multiple similarities can make you confused when it comes to differentiating between the two terms. Well, there are various elements that make data analytics vs. business intelligence completely different from one another. Let’s look at some of these differences!
Structured vs. Unstructured Data
During data analysis, there are various scenarios where you need to use structured and unstructured data. Business intelligence is specifically designed to use structured data during data analysis. This includes data that is stored in warehouses, tabulated in spreadsheets or any other systems that are used to organize data. This type of data is mostly used when you want to generate dashboards or data reports.
On the flip side, data analytics also uses structured data, although the entire process starts by using unstructured data. On most occasions, data analytics uses real-time data that is generated from different departments within an organization. The main responsibility of a data analyst is to collect this data from different sources and analyze it using charts and graphs such as waterfall chart, bar chart, line chart and comparison charts.
Tidy vs. Messy
During data analysis, there are different activities ongoing that are conducted by different tools. Data analytics is known to carry out messy work such as data mining, data cleaning, development of algorithms, and many others. All these are major responsibilities that are given to data analytics which have the capabilities to drill down and acquire all the essential information needed to achieve a given goal.
Business intelligence is only used to conduct tidy tasks such as generating clear data dashboards. It also takes part in data reporting and other data monitoring activities that help to generate clear insights that are used for decision-making. Business intelligence generates data that is easily consumable and can be used by stakeholders in making key decisions.
Uses Insights Vs. Creating Insights
The primary goal of data Analytics is to convert raw data into insights. This technology performs the hardest task of collecting data from different sources and analyzing it to acquire useful information. The insights collected by business analytics are then used by different organizations and companies to make decisions. These insights are also acquired by business intelligence to achieve its goals.
Business intelligence is designed to support the decision-making process using data insights. This technology only comes into the picture during the decision-making process helping business stakeholders to make actionable decisions based on the available insights. Note that the insights used at this point are collected by business analytics in the initial stages.
Non-Technical Audience vs. Technical Audience
Business intelligence is mostly used by business stakeholders and all other non-technical audiences. If you want to communicate technical data insights to a non-technical audience, business intelligence is the key to your objective. It is utilized by major stakeholders in a company, such as the chief executive director, financial director, chief Information director, and many others.
Data analytics is only applied by the technical team, such as data analysts. It is also used by data scientists, computer programmers. All other individuals who have a detailed understanding of the technical world. This is the reason why data analytics is not used when creating data reports.
Final Takes
Data analytics and business intelligence are two different terms referring to different components. They all play a major role in creating a data-driven environment and help in the decision-making process. Data visualization has a significant role to play across all these technologies since it helps in converting boring datasets into attractive visuals. Whenever you apply business intelligence in your company operations, you will automatically need to implement data visualization to make your data communicate sense.
With business intelligence, you will get access to all business technologies required to propel your brand to the next level. Before utilizing this technology in your daily operation, ensure that you understand the art of data visualization. How it can transform your data into attractive reports. By understanding the difference between these two, you will be capable of identifying when and why to apply each technology.
Brittany
