Business intelligence and data analytics: the two buzzwords that you must have heard thrown around many times. Interestingly, the terms are often used interchangeably, but is there a difference between business intelligence and data analytics? A short answer to that question is no, both have many similarities.
Your business generates an overwhelming amount of data every day. To make better decisions, identify problems, and be profitable, you need methods and tools for transforming data into actionable insight.
The purpose of business intelligence (BI) and its subsets, business analytics, and data analytics are to understand and create insight from historical and contemporary data sets.
The difference between business intelligence and data analytics is only as good as the data that goes into it and only as smart as the people who use it.
The process of gathering business intelligence and using data analytics to make real business decisions is getting lost in translation as companies large and small take up the arms race of big data.
If you are stuck on the thought of the difference between business intelligence and scrambling for a “BI” solution, our team at Ascend Analytics can help you! How? We specialize in data and AI for business intelligence across industries. Experience data-driven growth and unlock your business’s true success.
The First Step Is To Develop a Road Map
If you want to upgrade the way your organization uses data, you need a plan, whether you start from scratch or move from spreadsheets.
The following steps will guide you through the difference between business intelligence and data analytics and the process of implementation:
1. Identify the problem(s) you hope to solve. Begin by clearly defining the problem and setting smart, measurable, actionable, realistic, and timely goals.
2. Determine which stakeholders will be involved. Proper planning can prevent poor performance. What information are they looking for? What are their plans for using it? This information can be obtained from what kind of data?
3. Make a list of the data you need and how you will obtain it. To make this work, you need to have a data management practice that allows for 100% pure quality data. Analytics are only as good as the data that goes into them.
4. Establish KPIs for measuring success. Measurement leads to management. If you want to measure the effectiveness and success of your rollout, you need an objective method. Identify key performance indicators that your team can rally around.
5. Make data actionable by setting up systems and processes. Reports should be automatically generated. Ensure that information is delivered to the right people at the right time through automated systems and processes and set action deadlines.
Key Differences Between Business Intelligence and Data Analytics
- In 1865, Richard Miller Devens published ‘Cyclopedia of Commercial and Business Anecdotes,’ the earliest book to use Business Intelligence. By analyzing his environment, Sir Henry Furness gained profit by using BI (Business Intelligence) to stay ahead of his competitors, according to Devens.
- Analytics is the process of turning unstructured or raw data into meaningful information for enterprise users. The use of data analytics is a common practice incorporated into various business strategies and procedures by many organizations around the globe.
- Business Intelligence is prevalent throughout many organizations as a tool for improving decision-making, analyzing business data, mining information, developing reports, and improving operations.
- The Data Mart or Data Warehouse is the only place BI is implemented using historical data. A key aspect of implementing data analytics is cleaning, modeling, transforming, and forecasting future data trends.
Eager to find more? Read our blog on the purpose of data warehousing tools in your business.
- A data analytics program is implemented when a business model must be substantially changed in a relatively new organization.
To make the right changes to the proposed business model, data analytics can help the users analyze historical and current data and predict future trends.
An organization implements Business Intelligence when it makes no changes to its current business model, and its primary objective is to meet organizational objectives.
The purpose of BI is to assist users in identifying and rectifying data management loopholes for efficient decision-making.
- Using Business Intelligence and Data Analytic tools can enable the implementation of reports. Nevertheless, business data and scenarios determine the reporting or visualization type developed.
In the case of a business scenario in which the client needs to generate ad-hoc reports and cope with current market trends, data analytics would be the best solution. A business may also prefer Data Analytics when forecasting future trends based on past data is necessary.
Additionally, if there is a need to track targeted sales delivery or to organize data to provide sales intelligence, Business Intelligence would be a better option for the client since it would allow them to pull data from the data warehouse and generate reports.
- The BI mechanism can only be debugged following the end user’s requirements and historical data provided. A data analytics model can be used to debug data by converting it into a meaningful form.
The Common Ground of BI and Analytics
- Business intelligence helps companies and departments meet organizational goals by analyzing ongoing operations. A data analytics approach can help companies transform the way they operate. A little data preparation can benefit both disciplines.
- The process of data analytics generally involves data modeling, which involves collecting, cleansing, categorizing, converting, aggregating, validating, and otherwise transforming raw data. For BI, clean data is also important.
- Once the data has been cleaned, it is stored in a format and structure that makes reporting easy. Data warehouses typically store data in columnar format, which are now frequently hosted on cloud infrastructure. All organizational reporting and BI and data analytics are based on the data in the data warehouse.
- A data warehouse forms the basis of both business intelligence and data analytics, with data piped in via an ETL tool.
Considerate Factors that Drive Decision
There is no one-size-fits-all answer here, and the decision must be based on the business requirements, budget, and parameters listed below.
1. Scope of the Project
- As far as the scope of work is concerned, business intelligence differs significantly from data analytics.
- The former aims to gain operational insights, while the latter performs various analyses. The idea behind Business Intelligence is to create dashboards and reports.
- By analyzing data, you can determine the factors influencing results by looking for correlations between different variables.
- You can, for instance, gain year-over-year sales performance with Business Intelligence. You can find out why there was a variation in outcomes using Data Analytics.
2. Coding Requirements
- An important difference between business intelligence and data analytics comes from the coding requirements of each.
- Data can be visualized, and dashboards can be built without coding using several tools.
- Programming is required to conduct complex analyses using Data Analytics. Professionals who want to uncover interesting patterns beyond Business Intelligence must learn programming languages like Python or R.
- Due to their ease of use and speed of turnaround, Business Intelligence tools remain one of the most popular platforms for simple Data Analytics.
3. Mathematical Skills
- The skills of linear algebra and probability do not need to be core to be a Business Intelligence professional.
- Using these skills, a data analyst can assess data in ways that cannot be performed without customized commands.
- Command-line features are available with Business Intelligence tools, but Power BI requires learning platform-dependent languages like Data Analysis Expressions (DAX).
- The scope of the Data Analyst workflows extends beyond business intelligence skills. Analyzing data comprehensively requires Math, which is integral to Data Analytics.
4. Statistical Skills
- In data analytics, statistical analysis is key to uncovering critical insights that can significantly impact a company’s revenue or customer experience.
- The majority of Business Intelligence is based on descriptive statistics. These statistics help find a given set of data’s mean, median, and average. You require statistical analysis like inferential statistics to go beyond a simple analysis.
- Predictive analytics can analyze data by combining descriptive and inferential statistics.
- Statistical analysis is also widely used when decision-makers are considering new features to help them make informed decisions.
5. Type of Data
- BI is the process of analyzing structured data using tools like Power BI and Tableau.
- It is not just tabular data that can be analyzed with Data Analytics; analysts can also analyze text, audio, and video files. Libraries enable analysts to gather structured and unstructured data from websites.
- Unstructured data is widely used in Data Analytics to uncover insights. By analyzing Twitter data, a Data Analyst can create word clouds to understand the sentiment of collected information better.
- In contrast, Business Intelligence is meant for leveraging tabular data for descriptive analysis, which limits the range of possible applications.
6. Data Quality
- Data Warehousing is important for Business Intelligence as it transforms the data for better information quality.
- Using Data Warehouses for data analysis is not a requirement for Data Analytics.
- A Data Analytics professional can collect information directly using Data Lakes or disparate sources. Wrangling data is routinely performed by Data Analysts but not by Business Intelligence professionals.
- Data quality must be improved before data analysts can begin to analyze it. As part of Data Analytics, data must be cleaned to be suitable for analytics, but this falls outside the scope of Business Intelligence.
7. Report Generation
- A business intelligence report is typically executed based on a specific time frame. The idea is typically to streamline regular reporting, even though ad-hoc reporting can also be done with it.
- However, Data Analytics offers a wide range of analytical approaches to maximize reporting effectiveness.
- By contrast, Business Intelligence generates standardized reports, while Data Analytics allows organizations to make advanced analyses.
Implications of Data Analytics
Since data analytics is a broad field, it can be applied to a wide range of fields. Here is a top few to begin with:
The Food and Drug Administration uses data analytics to improve the situation of food-related illnesses. With the help of big data technology deployed across labs across the country, the FDA can study patterns relating to food-borne diseases. By using real-time analysis, the FDA can curb the spread of contaminated foods faster.
Cybersecurity professionals can protect businesses from hackers by using data analytics. Experts can analyze data sets to detect cyber threats and develop strategies to counter them.
Using predictive analytics to analyze data sets can give product developers a better understanding of what customers want. Analyzing data allows companies to understand customer budgets better, features they value most, and purchasing behavior. Product managers can use this information to update existing product offerings or develop new products based on customer feedback.
According to experts, the data analytics industry is expected to reach over $250 billion in revenue by 2022. Business users will adopt data analytics more widely over the next few years. Machine learning and artificial intelligence will enable companies to provide specialized services using large data networks.
By partnering with Ascend Analytics, you can achieve the following (but not limited to) data analytics-focused results:
- Ensure that data is democratized, so that everyone has access to it.
- Making data-driven decisions and processes more efficient
- Faster identification of potential losses, security breaches, crises, and opportunities.
- Changes in data & important business metrics can be responded to faster.
Implications of Business Intelligence
Various business operations rely on Business Intelligence. Some examples are given below:
A company that owns a call center can benefit from Business Intelligence. Identifying phrases that encourage successful calls can be achieved with customer interaction analytics.
Using call center statistics, business managers can see how top agents make successful calls in real-time. This allows them to increase success rates by duplicating audio patterns and phrases.
The most widely used business intelligence tool is Google Analytics. The number of websites using Google Analytics in the United States exceeds 2.5 million. Metrics that are analyzed visually with this business intelligence tool include:
- Spending time on the page
- The type of traffic
- Bouncing rate
- Performance of a website
- User and session numbers
These metrics provide business managers with the information they need to make informed decisions about web content updates and website tweaks.
With automation and collaboration software integration, Business Intelligence tools will deliver faster results. Artificial intelligence is fundamental to the future of Business Intelligence. A new generation of BI tools is expected to provide answers tailored to each analyst based on their unique needs, thanks to AI.
Ascend Analytics can help you solve the following BI challenges:
- Data import and cleansing
- Data Mining
- Big data viewing and visualization
- User-friendly design for casual use
The difference between Business Intelligence and data analytics includes many roles and responsibilities to transform raw data into meaningful insight.
Data and Business Intelligence analysts are among the most commonly employed analytics personnel in organizations.
With Ascend Analytics, you can take advantage of a secure public cloud infrastructure with a full-stack platform, including data-centric services and solutions tailored for your needs.
In order to meet the customer’s demands for data science and AI, Ascend follows the following loop of rituals:
- Reframe the problem
- Compile raw statistics
- Data analysis
- Make an in-depth analysis of the situation
- Create a visual representation of your analysis’s results
By leveraging open-source technologies, enterprises can unlock new value from their data with Ascend Analytics.
Connect with us for a proposal and let’s talk further!