Business intelligence vs data analytics composes a duo that works behind the scenes to empower decision-making. They act synergetically to paint a complete overview of a business and support leaders to make informed decisions.
While business intelligence (BI) offers a panoramic perspective, data analytics (DA) digs into subtle details to discover hidden actionable insights. A perfect blend of historical know-how delivered by BI and future forecasts from DA provide organisations with the necessary tools to adapt and grow in a challenging environment.
Even if they serve different scopes, they complement each other and create the backbone of a powerful data-backed strategy.
What Is Business Intelligence (BI)?
Business intelligence is a sort of savvy wizard manipulating raw data to extract meaningful insights that leaders of a company can use to support their decisions. Practically, it helps to understand what’s going on and why.
It offers a panoramic overview of a business’s current and historical data. Summarises and visualises the past to depict the present situation and synthesises a business via reports and dashboards. Tracks KPIs (key performance indicators) and delivers real-time updates.
The business intelligence scope is
- To gather data and extract meaning from it.
- Report, analyse, and visualise data.
- Analyse trends and patterns and create a realistic snapshot of the business’s current situation.
It would be best to have tools and systems with certain features and components to obtain business intelligence. They should be capable of data mining, reporting, querying and dashboarding, among others.
Business intelligence tools aim to transform complex data into digestible and actionable insights. For this purpose, they compile data from a large variety of sources, clean and organise it and present it in a visually appealing manner.
Types of business intelligence:
- Traditional business intelligence. It uses data from internal sources like finance, inventory, sales, and operations. Such data is stored internally and analysed via SQL tools. Traditional business intelligence examples are financial reporting or sales performance analysis.
- Modern business intelligence. This type of BI leverages the latest developments in technology and delivers individual real-time access to data gathered from a plethora of sources. It uses machine learning to automate data collection, processing, and analysis.
And it allows businesses to explore and dissect data via charts and visual dashboards. Examples of modern business intelligence are predictive analytics and real-time reporting.
BI has different applications depending on the industry. For example:
- In retail, BI identifies consumer buying patterns and tracks inventory and sales results.
- In education, BI can be used to efficientise administrative tasks and processes and assess student performance.
- In healthcare, it is helpful in resource allocation and patient data analysis to improve treatments.
What Is Data Analytics (DA)?
Data Analytics is the approach of collecting, processing and analysing multiple and vast sets of data to extract valuable insights and patterns that can improve decision-making.
Data analytics delves into the future, using the potential concealed within data to explore patterns and trends, guide decisions, and design strategies for success. In a nutshell, DA leverages machine learning and statistical analysis to forecast the future.
It delves into scenarios that answer the “what if” question and indicate options to reach the desired outcomes.
Data analysis operates with a variety of tools. There are four classes of data analytics:
- Descriptive analytics. It summarises historical data and offers a clear overview of past events. Descriptive analytics examples are site traffic analysis or sales performance reports.
- Diagnostic analytics. This type is more advanced than the precedent as it examines past data to find explanations for why certain events appeared. It’s about revealing the “whys” behind the “whats”. Diagnostic analysis examples are customer churn and product returns analysis.
- Predictive analytics. It takes the historical data as a basis to preview future trends and opportunities. And it could be considered a crystal ball that anticipates what may happen in the future. Predictive analytics examples are demand forecast and client lifetime value prognosis.
- Prescriptive analytics. The next level of analytics goes beyond predicting outcomes and delves into suggesting the required steps to achieve a specific result. Prescriptive analytics examples are ad campaign optimisation or price optimisation.
Data analytics applications vary across industries:
- Finance – helps with risk assessment and fraud detection.
- Marketing – analyses customer behaviours to personalise targeting strategies.
- Sports – used to optimise sportspeople’s performance and efficientise team strategy in team sports.
Business Intelligence vs Data Analytics. How Are They Different?
Although BI and DA are complementary data analysis and management approaches, they are characterised by some key differences.
Scope and focus
Business intelligence: It revolves around processing current and historical data and provides insights into what’s happening with a business and its performance. These insights are used for decision-making. BI focuses on monitoring, reporting and delivering a panoramic overview.
Data analytics: The limelight is on predictive and prescriptive analysis, as it processes large and complex data sets to spot valuable insights, patterns, and trends. The scope is to use algorithms and statistical models to identify opportunities and offer strategic options for the future.
Purpose – data analytics vs business intelligence
Business intelligence. The primary purpose is monitoring and reporting. Most often, it’s like a sort of dashboard that reveals current trends and the level of existing metrics. And provides an overview of a business situation at a particular moment and data for running daily operations.
Data analytics. Its primary purpose is insights generation and forecasting. DA goes beyond the current status; it aims to anticipate trends and support data-backed decisions that impact a business in the long term.
Applications and users
Data analytics. Analysts, data scientists and leaders in charge of strategic decisions are the most common users of DA. Their task is to analyse data cautiously, dig into the finest details, and employ advanced analytics to uncover insights that indicate optimal long-term strategies.
Business intelligence. It’s usually found among the tasks of executives and operational managers to support them in daily operational decisions—and KPIs tracking.
Instruments
Data analytics. It generally requires knowing programming languages like SQL, R, and Python for data manipulation, modelling and analysis. And employs specialised tools with advanced capabilities.
Business intelligence. This doesn’t require such advanced knowledge as data analytics, as it needs more user-friendly applications like Excel, Tableau, or Power BI. Such tools are accessible to a broader audience and have an intuitive interface for producing personalised dashboards, reports and charts.
Data type – business intelligence vs data analytics
Data analytics. It employs structured and unstructured data from multiple sources like customer service, social media, and client feedback.
And applies natural language processing, sentiment analysis and machine learning algorithms to unearth not-so-obvious patterns and forecast the future.
It acts at a granular level.
Business intelligence. This approach only deals with structured data like sales or financial data extracted from various systems. It aims to offer reports and charts that are effortless to comprehend and interpret.
It acts at a more general level.
Methodologies
- BI. Employs reporting tools and query-based analysis to outline and present data and findings.
- DA. Its toolbox includes statistical analysis, machine learning techniques, and predictive modelling to make forecasts and identify strategic alternatives.
Business intelligence vs data analytics examples
In healthcare
BI may help with hospital operations and patient data monitoring.
DA may recommend tailored treatment plans for patients using predictive analysis models on their historical data.
In retail
BI may be responsible for tracking sales performance, optimising operational efficiency and identifying buying patterns.
DA may be in charge of anticipating future consumer trends and suggesting tailored promotional strategies.
So, what’s the difference between business intelligence vs data analytics? The short answer is not much.
The distinction between business intelligence and data analytics is more complex than it once was. Both terms are now used interchangeably to refer to the process of using data to make better decisions about your business.
Business intelligence is a term that encompasses data analytics. Data analytics is a process that forms the basis of business intelligence.
How Data Analytics Is Used for Business Intelligence
Business intelligence is all about taking your data and turning it into actionable insights. Data analytics is the first step in that process – what you do with the data afterwards determines whether it’s business intelligence or not.
Here are some examples of how to apply data analytics to your business strategies:
Use review data to improve customer satisfaction
Customers always leave reviews online, but keeping track of them all can take a lot of work. Data analytics tools can help you collect and process this data so you can make changes to your products or services accordingly.
For example, a SaaS business can use data analytics software to collect and review data from g2 and Gartner and measure sentiment by topic to discover customer pain points and opportunities for improvement.
Symanto’s text analytics software uses advanced natural language processing technology to get accurate customer sentiment data. It then clearly illustrates its findings through its interactive dashboard, allowing users to navigate the data quickly and make informed decisions.
Use social media data to analyse the success of your marketing campaign
Social media data is a valuable source of information for businesses. Data analytics tools can help you track and measure social media metrics so you can see how successful your marketing campaigns are.
Rudimentary social media listening tools will give you stats such as the number of likes and interactions your posts have received or the number of mentions of your brand on Twitter. But now, there are new and exciting ways to use social media data.
For example, Symanto technology can detect cues in comments and posts online that reveal key psychographic traits and tendencies.
From just a short Tweet, Symanto psychographics can predict whether the author is a loyal customer with an emotional connection to your brand or an at-risk customer who is highly likely to churn.
This technology has many potential applications, but one example is measuring campaign success.
After releasing a major campaign, you can use social media data to see its impact on your most loyal customers.
While engagement is a valuable metric for measuring brand awareness, it doesn’t tell you anything about who you’ve reached. With Symanto psychographics, you can measure the emotional response of your most loyal customers to gauge the true success of your marketing campaign.
Use employee data to improve retention
Employee data is another valuable source of information for businesses. Data analytics tools can help you track employee engagement and performance to identify issues early and prevent them from becoming more significant problems.
Data analytics software can use machine learning to predict which employees are at risk of leaving based on subtle changes in their communication.
For instance, are they sending fewer emails? In those emails, are they less engaged and less emotionally connected? Are they using more negative words?
This type of data is hard to process and make sense of without the help of data analytics tools. But with the right software, you can identify employees at risk of leaving, proactively address the issue and prevent it from escalating.
Does Symanto Offer Business Intelligence Tools or Data Analytics Tools?
Symanto AI analyses unstructured data and turns that data into easy-to-understand visualisations to help you draw insights from virtually any dataset on any topic.
Our tools are primarily used for business intelligence but are also much more versatile. For example, our technology and research have also been used to detect hate speech and misinformation and predict the success of crypto projects.
In short, Symanto offers tools for data analytics, which assist in gathering and processing business intelligence.
Does the distinction matter?
Well, yes and no. Suppose you want to know whether your business would benefit from a data analytics or business intelligence tool. In that case, it helps to understand that both essentially serve the same purpose.
You’ll need to look at the specific features of each tool to decide which will be most valuable to your business.
Get Started With Symanto
Discover more about the potential of Symanto technology for business intelligence and more. Get in touch and start making better decisions for your business in no time.
Synergies Between Business Intelligence and Data Analytics
BI and DA intertwine and together create a powerhouse for data-backed strategic decisions.
Even if they are distinct approaches, their paths overlap pretty frequently. Business intelligence delivers an exhaustive overview of current and historical data, and data analytics digs into the details to spot opportunities and advise on future actions.
Among others, they intersect at the juncture of the past that shapes the future.
Their fusion provides businesses with a holistic perspective of their environment. And drive transformative outcomes. Business intelligence supplies the foundational comprehension, and data analytics refines it with forecasts.
Combining DA and BI empowers business leaders with an extensive toolbox, enabling them to prepare for the future and foresee opportunities and strategies to profit from them.
For example, a bank might use the business intelligence approach to obtain an overview of the historical transaction data—and data analytics to identify patterns that reveal defrauding activities. Combining the two enables the bank to detect issues in real-time and prevent fraud tentatives.
Blending BI and DA findings streamlines the decision-making processes, offers companies a competitive edge and fosters innovation.
How Do You Choose Between Business Intelligence and Data Analytics?
Selecting the right approach depends on the particular requirements of a task and choosing the right tool from a vast array of possibilities.
Here are the factors that impact such a decision and the elements to take into consideration to ensure you pick the best option for your needs:
Factors
- Business goals. Clearly understanding your organisation’s goal is fundamental. If you are looking to depict past and current performance, choose BI. DA might be the best option if you need to identify future trends or improve strategies.
- Data type and complexity. For structured data, BI is the obvious choice. DA might be more helpful if you need to get insights from complex or unstructured data.
- Available expertise and resources. The resources at your disposal, like your tech stack and the team’s skills, impact the selection. In general, data analytics requires advanced technology and programming skills, while business intelligence is more user-friendly.
Considerations
- The chosen approach aligns with your business goals. BI is your first option if you are interested in short-term monitoring and insights. Are you interested in long-term strategy and forecasting capabilities? DA is the better option.
- Cost. Budget and return on investment potential are powerful elements that impact tool selection. In the short run, BI tools might be a better choice from a cost point of view. However, the benefits brought by DA might counterbalance the cost in the long run.
- Flexibility and scalability. Organisations should opt for tools that are able to adapt and evolve with their needs.
Examples of how strategic selection can drive success
In a manufacturing niche, business intelligence may be used to monitor production metrics in real time. And data analytics may be leveraged to streamline supply chain operations and preview machine maintenance intervals. This combined approach reduces downtime and increases efficiency.
In a retail niche, an organisation might choose BI to monitor daily sales and DA to forecast seasonal trends and spot client preferences. This mix allows the company to react quickly to short-term fluctuations and simultaneously strategise at scale for the future.
Business Intelligence vs Data Analytics Trends
DA and BI are in a fulminant evolution, propelled by technological advancements and emerging trends that reshape strategic decision-making.
Here are some trends that will impact their immediate evolution:
- Machine learning and artificial intelligence. They are revolutionising DA and BI. Machine learning algorithms enrich predictive capabilities to deliver improved forecasts in terms of accuracy. AI supports decision-making methods by automating insights.
- Embedded analytics. Integrating analytics in other applications enables users to access insights effortlessly in real time, making the most out of available data.
- Augmented analytics. Leveraging AI to automate the generation of insights and helping users to interact with data more intuitively.
As the customer exigences increase the business intelligence and data analytics tools will evolve to answer them and accommodate higher volumes of data and growing complexity. The main focus of businesses will be to leverage these technologies to earn a competitive advantage and drive innovation.
