With the importance of harnessing data to inform strategic business decisions, organizations need a variety of processes in place to ensure quality data is optimized for making data-driven insights. The two “big data” concepts necessary for mission-critical decisions are business intelligence and data analytics.

Although these two concepts sound similar, they serve different purposes — and more importantly — they are best when used in tandem. In this article, you’ll learn the difference between business intelligence and data analytics and how to use both together for the best results.

Business intelligence vs. data analytics

People often use the terms “business intelligence” and “data analytics” synonymously, but in fact, they are not the same.

Business intelligence

Business intelligence refers to tools and systems that analyze corporate data in ways that facilitate decision-making. Data analytics, on the other hand, is the process of transforming unstructured, raw data into a functional format.

In every industry, business stakeholders want to track key performance indicators or KPIs. These KPIs might measure marketing success, customer satisfaction, or sales. Reporting on and visualizing these metrics is key to making big decisions.

Business intelligence involves the use of complex technologies to make strategic, data-driven decisions based on the aforementioned KPIs. BI platforms help stakeholders retroactively analyze company data to identify pain points, challenges, and opportunities. Certain BI tools empower end users to create their own reports and dashboards to evaluate department-level KPIs and increase productivity.

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Data analytics

The data companies collect is not always pretty. Free text, audio files, images, and webpages are all rich data points, but are difficult to interpret without structure. Businesses use data analytics to convert raw data into a format stakeholders can use.

Overall, data analytics enables end users to give unstructured data accurate meaning. Data scientists configure unstructured data based on the type of business analysis stakeholders want to perform in the future. Business requirements may require data scientists to cleanse or transform raw data.

Three differences between BI and data analytics

Although BI and data analytics sound similar, they offer different outcomes. It is important to understand the differences between each term in order to apply them appropriately in a business context.

Implementation

Data analytics implementation typically comes before BI implementation. Companies use data analytics to establish a data model prior to making data-driven decisions. Data analytics implementation may involve installing data storage, cleansing, or transformation tools.

Data analytics

The data companies collect is not always pretty. Free text, audio files, images, and webpages are all rich data points, but are difficult to interpret without structure. Businesses use data analytics to convert raw data into a format stakeholders can use.

Overall, data analytics enables end users to give unstructured data accurate meaning. Data scientists configure unstructured data based on the type of business analysis stakeholders want to perform in the future. Business requirements may require data scientists to clean or transform raw data.

3 differences between BI and data analytics

Although BI and data analytics sound similar, they offer different outcomes. It is important to understand the differences between each term in order to apply them appropriately in a business context.

Implementation

Data analytics implementation typically comes before BI implementation. Companies use data analytics to establish a data model prior to making data-driven decisions. Data analytics implementation may involve installing data storage, cleansing, or transformation tools.

Contrarily, business intelligence is implemented to assess progress towards organizational goals. Data analytics is usually already in place so that BI systems are implemented to monitor and make predictions based on clean, structured data. In essence, data analytics implementation supports business intelligence.

Debugging methods

Stakeholders viewing a BI dashboard might notice something is off. The analyst debugging this report might adjust the report query to show more suitable metrics. Alternatively, the analyst might modify the historical data source feeding into the report. BI is only debugged via the historical data or as a result of new end-user requirements.

Conversely, debugging data analytics necessitates modifications to the data model. Upon further investigation, data scientists might determine that a data model is inaccurate. This could mean that the manner in which unstructured data was formatted was not in line with business requirements, or the business requirements have changed.

Purpose

The purpose of business intelligence is to present corporate end users with actionable information. This intelligence could be in the form of reports, dashboards, or monitoring alerts, but all are predicated on historical data. CEOs, CFOs, or CIOs are most interested in business intelligence because the structured data makes it simpler for them to make decisions based on what happened in the past.

On the contrary, the purpose of data analytics is to prepare unstructured data for quantitative analysis. While business intelligence is focused on historical data, data analytics can view unstructured data in real-time, and even predict how it might change in the future. Data analytics can help data scientists perform predictive analytics

Difference

Business intelligence

Data analytics

Implementation

Data visualization tools

Data storage, cleansing, transformation tools

Debugging Methods

Depends on historical data and end-user requirements

Depends on data model

Purpose

Decision-making based on what happened

Providing clean data to determine what will happen

 

How to use data analytics for better business intelligence

Although there are distinct differences between data analytics and business intelligence, the two concepts are even more robust when used in conjunction with each other.

More and more companies are zeroing in on data-driven decision making. Simultaneously, there are more and more opportunities to collect raw, unstructured data. This conversion means there are infinite opportunities to use BI and data analytics together to normalize data and set up predictive analytics.

BI teams can work with data scientists to implement analytics frameworks that drive decisions based on real-time insights. With both teams situated closely, debugging for both BI and data analytics can occur in tandem and accelerate reliable decision-making. Moreover, companies can start to automate BI or data analytics tasks to focus on even broader enterprise strategy.

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Big data and the cloud: Driving BI and data analytics forward

At its core, big data describes the extremely large data sets businesses acquire over time. Data points can come from customers, products, internal help desk tickets, even external factors like weather and the economy.

Gathering this data is great, but businesses need somewhere to store it. On-premises tools are capable of stockpiling this data, but the sheer amount of data that increases in volume every day suggests that on-premises data warehouses may not be able to perform that function. Therefore, most businesses are turning to cloud storage. The cloud makes data access and preparation easier, allowing stakeholders to increase their speed of analysis.

BI and data analytics will only continue to grow in popularity and value with the proliferation of customer data housed in the cloud. On top of the cloud, data analytics and BI tools decipher patterns quickly so that useful business intelligence can be into practice immediately.

Business intelligence and data analytics tools

Cloud-native tools are essential to harness the full power of both business intelligence and data analytics. Businesses accumulate data from social media, purchases, opt-ins, online reviews, and so much more.

This much data can overwhelm on-premises systems, causing data processing to be a long, drawn-out affair. In contrast, cloud systems distribute data processing loads in order to run computations more efficiently. Since data processing takes far less time, serverless computing can save businesses an enormous amount of money.

The best tools use big data analytics to encourage better business intelligence. One attractive feature in a big data analytics solution is the ability to connect to multiple business systems. Tools with pre-built connectors introduce new data source possibilities and eliminate the need for IT to create connectors from scratch.

In addition, data analytics tools should be relatively easy to use and maintain. You want data scientists and business analysts to be up and running quickly. Last, but not least, a good analytics tool will have integrated data quality and a mechanism for enforcing data governance. Data must be curated and tested before sharing with end users who will be making enterprise-wide decisions.

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Improving data analytics for competitive BI

Business intelligence and data analytics are both critical for companies to keep up with the data-driven world we live in today. Companies utilizing cloud-native, data analytics applications have a distinct advantage. BI and data analytics allow businesses to obtain structured data and use it to uncover business intelligence that was invisible in years past.

What is the first step towards developing highly synchronized data analytics and BI teams? A cloud-based suite of apps that can deliver integrated, scalable data with built-in collaboration tools.

Talend Data Fabric is a prime example. It amasses data across business systems (like ERPs, CRMs, etc.) and transforms it into consumable formats for data analytics and BI use. Gain a competitive edge by giving Talend Data Fabric a try today.