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Data Science VS Business Intelligence: Does Your Company Need the Science or The Intelligence?

Do you need the science of data or do you need the intelligence?

In this article, we will discuss what exactly data science and business intelligence are and how businesses can decide which one would be best for them!

So keep reading!

Data Science VS Business Intelligence: Does Your Company Need the Science or The Intelligence?

Do you need the science of data or do you need the intelligence? These are two questions that many business owners ask themselves when they are deciding whether to hire a data scientist or an expert in business intelligence. There is no simple answer to these questions because every company has different needs. In this article, we will discuss what exactly data science and business intelligence are and how businesses can decide which one would be best for them!

Data Science is a branch of artificial intelligence. It consists of the data and information that has been collected, analyzed, and interpreted to learn something new about it. For example, using social media posts to understand what people are talking about to make decisions on how businesses should advertise or be aware of current trends.

This type of work can include gathering data from sensors like cell phone cameras or digital grocery store checkouts which also help us with understanding consumer behaviour more deeply.

Business Intelligence is another term for decision support systems (DSS). These are computer programs designed to provide insight into past performance by analyzing vast amounts of stored data as well as predicting future outcomes based on patterns found in historical data sets. They use predictive analytics to identify the business analytics.

In a nutshell, Business intelligence is the use of data and analytical techniques to understand, summarize, and predict trends in data. This helps organizations make important decisions about hiring new staff members or changing their services offered.

Corporate business analysts work with a variety of software packages to produce reports that can then be used by executives for making strategic plans. These tools include reporting programs such as Crystal Reports, analysis technologies like SPSS or SAS, statistical modelling software like R or MATLAB and computer-based visualization applications such as Tableau Software’s Reveal product line.

Both of these concepts include data collection, modelling and information analysis. The difference between the two is that business intelligence is specific to business-related issues such as cost, profit, equipment utilization efficiency, etc., while Data Science answers questions like how various factors, such as social, geographical, seasonal, etc. affect the business as a whole. Data Science ties data together with algorithm design and technology to provide answers to the questions posed.

At first glance, both terms are quite closely related to each other and are used interchangeably, but in reality, they are different from each other.

As a widely used application area, business intelligence has existed for quite a long time, more than three to four decades, since the end of the 20th century. As a separate discipline got formalized as a separate discipline sometime in the 2010s.

Before describing the differences, Let’s consider the basic concepts common to each of these entities.

Data (if you use this word as a term) exists in its raw form, and the processed data is called information. It is also necessary to mention “Data Lifecycle Management (DLCM)” and data classification.

What is DLCM or Data Lifecycle Management?

Data that is initiated or processed, then created, classified, stored, accessed, processed, stored again, used and finally destroyed is called data lifecycle management.

Typically, data in the digital world is classified as

Structured

  • Data is visible and understandable
  • Very limited processing is required to interpret the data

    Weakly structured
  • Poorly structured, but not random
  • Have some correlation
  • A little analysis is required to understand the dataUnstructured
  • Random data
  • Requires a lot of resources and processes to transform it into meaningful data

Now that the basic terms are defined, let’s try to figure out which is which.

What is Data Science?

Data Science – is an interdisciplinary field, which is working on decoding and, if the term is appropriate, demystification of large data sets i.e. Big Data. It uses a combination of mathematics, statistics, computer science, machine learning, data analysis, and other related fields of research.

Data Science itself consists of 5 main stages (general view):

  1. Data collection
  2. Saving data
  3. Data processing
  4. Data analysis
  5. Reporting and presentation of results

What is Business Intelligence?

Business Analytics is a range of technologies and practices designed to collect, correlate, process, analyze and explore data related to a specific business. Also used to monitor business performance and improve business planning.

The role of a business analyst:

Business Analyst (Business Analyst) uses various forms of quantitative analysis, statistical, predictive, analytical modelling and iterative methods to interpret business data. Thus, using the results obtained, the company receives information about its past activities, which helps to develop business development plans. In addition, business intelligence also opens up opportunities for solving complex problems in business processes and thereby increases the profitability of the business through increased productivity and reduced losses.

There are four types of business analysis:

1) Descriptive analysis – This form of analytics answers the question “What happened?” and is the main form of analytics that does not require high-end tools, and can be done manually with a minimal set of tools, for example, in Excel.

2) Diagnostics – Here the focus is on the question “Why did this happen?” This answer is sought using analytical tools such as drill-down, data analysis, and correlations.

3) Predictive analytics – At this stage, the future appears in analytics, since this type of analysis is associated with finding an answer to the question “What will happen?” It uses statistical and mathematical tools.

Predictive analytics can be further divided into the following subcategories:

  1. Predictive modelling (What’s next?)
  2. Root Cause Analysis (RCA) (Why This Happened?)
  3. Data identification and correlation – data mining
  4. Forecast (What will happen to this trend if it continues?)
  5. Monte Carlo method (Simulation determining how this will happen?)

4) Prescriptive analysis  – This is a moment of action from a business point of view. This is where the question arises: “What should a business do?”, And primary recommendations in the style of “Do it this way.” For this purpose, optimization and modelling tools are used.

How do Data Science and Business Analytics Compare?

Data Science covers many interdisciplinary fields such as computer science, mathematics, statistics, data analysis and programming, artificial intelligence, machine learning, neural networks, and deep learning to solve complex problems consisting of large datasets.

And given that Business Analytics is used to solve specific business problems using optimization, modelling, statistics and mathematics, then, as part of the overall process, Business Intelligence can be called part of Data Science to some extent.

Main Differences Between Data Science and Business Intelligence

  Business Intelligence Data Science
Skills The specialist is required: knowledge of mathematics, skills in modelling, optimization and statistics Requires interdisciplinary skills in computer science, programming, statistics, data analysis, mathematics, artificial intelligence, ML, DL, and neural networks
Data Usage Using business data Using large datasets called Big data
Data Type Used Only structured data is used All 3 data types are used
What is it Used For Used to obtain business intelligence on business operations, revenue generation, forecasting sales, increasing productivity, reducing costs and rejects. That is, it is used to solve specific business problems. Used to get answers about user behaviour and solve very difficult problems. That is, it is used to identify trends and create behavioural patterns.
Stages or Types Four types of business intelligence – descriptive, diagnostic, predictive, and prescriptive Five steps – collection, storage, processing, analysis and reporting
Industries Applicable in industry, marketing, technology, retail and finance Applicable in education, technology, finance and e-commerce
Mission Critical Decisions Used to make critical decisions Data Science results are not used to make critical decisions
Basic tools Statistical analysis Programming
Using Statistics Statistical data exploration is the foundation Data is studied, including using statistics
Instruments MS Excel, databases and specialized statistical software packages Programming languages. Python, AI, algorithms, ML, R programming, Hadoop, DL algorithms, neural networks, etc.
Kind of Work Normal work requiring many iterations The work includes a lot of research and a lot of data extraction work
Functional Area Business-specific job required Cross-functional and interdisciplinary work required

 

Essentially, Business Intelligence vs Data Science boils down to what kind of information your company needs most: raw data from sensors on social media posts which might not have much meaning if it isn’t sorted or structured data that has a lot of insights.

This distinction is more than just academic; it can impact the way your company does business. If you require data from social media posts, for example, then Data Science may be what you’re looking for.

However, if you want to conclude why a customer has stopped purchasing or changed their services offered and deciding how to market differently as a result – which requires lots of structured data – Business Intelligence might make more sense.

How Businesses Could Benefit From Data Science?

Data science provides insights into customer behaviour that can improve their relationship with customers and create loyal relationships. Or it could be used to analyze customer behaviour and designing your product’s experience based on customer behaviour.

How Businesses Could Benefit From Business Intelligence?

Business intelligence helps a company to understand what is happening in its marketplace which can help them design new products or attract more clients. It also allows them to make better decisions on how they should allocate resources for marketing campaigns, advertising budgets, inventory management and much more.

Which do you need most: Data Science VS Business Intelligence? The answer depends on your needs as well as your overall strategy! If you’re looking for structured data – like databases of previous transactions – then it’s time to start considering an investment in Business Intelligence software (or consulting). Conversely, if you are looking to get insights from unstructured data for designing a new product, data science could be your best bet. 

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