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Although they are two closely related concepts, Business Intelligence and Big Data are not the same thing. They are not even interchangeable. In this article, we compare Business Intelligence and Big Data to see their main differences.

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Differences between Business Intelligence and Big Data

What can we expect from a solution like Business Intelligence (BI) and Big Data? BI or Business Intelligence software helps companies make decisions based on data and metrics. But what does Big Data have to do with it?

Nowadays, there are many companies that use data as a resource. They rely on it to support strategic decisions that help to grow and improve the business. In this aspect, both Big Data and Business Intelligence work together on that data. However, they do not do it in the same way, since we can find differences between them.

But before we look at the differences between Business Intelligence and Big Data, we should give a small definition of both. This will give us enough context to explain their differences.

Also read our Data Center Manager and Big Data Engineer Job Description

What is Big Data?

difference between business intelligence and big data

Big Data it is a set of technologies and tools that allow us to manage and process large amounts of data at high speed and in real-time, whether they are structured, semi-structured, or unstructured.

The data come from various sources (smart devices, sensors, social networks, websites, etc.). However, it is not so much the quantity as the quality of the data that matters. In other words, they are usable to generate relevant ideas and make good strategic decisions.

Therefore, when we talk about Big Data, we are referring to a large volume of complex data that is difficult to manage and analyze without the right tools.

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What is Business Intelligence?

BI is the combination of software applications, infrastructure, and practices that make it possible to access and analyze the information collected by companies. Info they then use to improve decision-making processes.

It is through BI and its tools that we can carry out quality analysis of the data obtained from Big Data.

With Business Intelligence tools, companies can make decisions based on data already processed and treated to convert them into information.

Analyzing the information

The analysis of all this information makes it possible to obtain new data and exploit previously collected information. These processes are useful to:

Generate new information from the analysis of existing data, e.g. for demand forecasting or people classification methods.
Identify alarms or exceptional situations to review/study in order to take appropriate action.

The value obtained by these companies translates into:

Cost savings
Speed
New products and service
The anticipation of the competitors
Better operational management, etc.

Now that we know what they are, we can see the main differences between Business Intelligence and Big Data.

Also read our Business Intelligence Analyst job description

Main differences between Business Intelligence and Big Data

Comparing these concepts is like talking about two worlds. They are still under exploration, but which constitute the closest reality for companies. Both allow extracting the value of information in totally different but complementary ways.

BI is a set of business management techniques that enable companies to make decisions based on data; Big Data, on the other hand, are the tools that can obtain, store and process data.

In other words:

Business Intelligence provides access to data sets that are already organized and stored so that the user can easily navigate them,

Big Data focuses on massive processes for a large volume of data, with very different organization and origins, in order to obtain new information.

This means that with Business Intelligence, companies can carry out analyses and draw conclusions, produce reports, graphs, maps, tables, etc. with 100 per cent detailed information. With Big Data, the opposite is true.

In short, we can summarize the main divergence between the two as ‘innovation and discovery vs. questions and answers‘. Some of the processes that business intelligence uses to deepen data are: the use of software, the feeding of knowledge systems, the transformation of data into actionable intelligence, etc.

Another difference concerns the type of data with which both methodologies work.

In Big Data all types of data, structured or unstructured, are collected.

Business Intelligence, on the other hand, only works with structured data, which has previously been stored in a database hosted on a server (also called a data warehouse), which allows it to work with offline data.

Business Intelligence and Big Data do not store data in the same way

Big Data & Business Intelligence Data storage

Speaking of data storage; we have already pointed out that Business Intelligence stores data in a database hosted on a server and this must be done prior to processing and analysis.

Big Data, in order to work at the speeds it does, must use several servers to store the large volumes of data, i.e., it must use distributed file systems in nodes to store the information, such as Hadoop. These systems, which are much more flexible (they allow data to be stored without labelling) and more secure, since if one of the nodes fails, the information will be replicated on other nodes.

Data are not processed in the same way

Data processing is not carried out in the same way either.

As we have already mentioned in the previous point, Big Data uses a system of files distributed in nodes, which allows parallel processing of data, thus optimizing the speed at which data is handled. It does this by executing several instructions at the same time, comparing the results obtained, grouping and analyzing them before presenting the final solution to users.

In Business Intelligence, queries must be made to the database to obtain the solutions sought.

Also read our Data Scientist: Job Description

They also differ in how they perform data analysis

Big Data and Business Intelligence Data performance

If Big Data can store and process structured and unstructured data, it also has the tools to be able to analyse and visualise large amounts of data, regardless of its type and origin. This is particularly useful for companies, since the vast majority of data currently collected comes from various sources on the Internet and only around 20% is structured.

In addition, Big Data has the ability to work with data from the past as well as in real time, which makes it possible to make more accurate predictions.

Business Intelligence, since it can only work with previously stored, processed, classified and converted data, always works with data from the past.

**Main differences between Big Data and Business Intelligence**

The professional profile is not the same either

Big Data VS Business Intelligence professional profiles

Finally, Big Data and Business Intelligence also differ in the type of professional profile dedicated to each speciality.

On the one hand, the professional profiles for Big Data usually include mathematicians, computer engineers or statisticians. In addition, they belong to the technology department and report to the CTO (Chief Technology Officer).

The data analyst is the leading expert for all business database operations. They assemble and process data in order to evaluate business activity and make appropriate recommendations. Their work enables them to ‘make the data speak’ by interpreting it.

Read the entire job description of the Data Analyst

On the other hand, professional profiles for Business Intelligence come from fields such as business administration, economists or marketing, although they may also include engineers or technicians.

For example, QlikView is a Business Intelligence platform facilitating self-service data interpretation. Thus, the QlikView solution enables big data analysis to be transformed into actionable insights. As a consequence, the role of the QlikView developer is to prepare prior data processing to adapt the tool to the business needs and the activities of the company.

Meanwhile, the role of the IT Business Analyst aims to bridge the gap between the various operational departments and the IT department.

They are usually found in the company’s management department and report either to the CSO (Chief Strategy Officer) if there is one, or directly to the CEO.

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Why Business Intelligence is important

Businesses are undergoing the shift towards digital transformation. More and more businesses are seeing the need to invest in data analytics solutions for one simple reason: information is power.

Through them, we can have full control of data and increase business insight, as well as:

Save time and costs.
Improve the service offered to customers.
Facilitate the consultation of data.
New business opportunities.
Obtain verified, transparent and reliable results.

Having exposed these advantages, we can affirm that having a BI platform is essential to achieve business success.

Although each company is a different world, all of them can find competitive advantages in BI. Solutions focused on business intelligence are no longer seen as a simple tool focused only on large companies, so more and more SMEs are becoming interested in its technology.

What you shouldn’t do in Business Intelligence

Here are the things you should avoid at all costs when it comes to BI:

Choosing a technology that does not meet your business requirements, needs, or problems.
Poorly defined software objectives in the planning phase.
Forgetting the role of the end user.
Lack of integration and protection of company data.
Leaving the back-end in the background and giving top priority to the front-end. They must be in balance.

Learn more about the most common mistakes to avoid in Business Intelligence

So, Big Data vs Business Intelligence, which wins?

The truth is that neither, because it is not a competition between the two methodologies. But rather they must work together to get the most out of data collection and analysis.

Thus, the Business Intelligence team will work together with the Big Data team. They need to establish the data to collect and then go on and analyze it. For its part, the Big Data team will look for patterns in the data to communicate them to the BI team.

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The next Business Intelligence challenge: real-time analysis

If BI wants to remain relevant and not be displaced over time by Big Data tools, it must take the next step and be able to have its own tools for real-time data analysis.

In other words, BI will also need to carry out analysis on unstructured data and achieve a system in which it is possible to detect and respond to situations that occur in the market in a quick and agile manner.

This does not mean that Business Intelligence will stop working together with Big Data. This is because the process of collecting and storing massive data will continue to fall to the latter. But it does mean that the former will have tools that allow it to analyze these data in real time. This without having to process, treat and store them in a database as it has been doing until now.

You can also read : 10 Business Intelligence Stats That Show Its Worth

We chat with Ton Badal, machine learning engineer at London-based DataOps start-up Synthesized; about pursuing a career in data science and the challenges of working with data.

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How did you get started in tech, what made you go for data science career and machine learning in particular?

Since school, I have had an engineering mentality, I’ve always had this problem-solving way of thinking. I’ve always enjoyed math and solving problems. In university, I studied telecommunications engineering and specialised in audiovisual systems, so the processing of audio, images, video and other audiovisual systems from a technical perspective.

There I started doing research in machine learning, AI and data science. I started discovering this super interesting world. After that, I was sure that I wanted to do a data science career. So I went for a master’s in AI. And that’s how I discovered this very, very interesting and challenging world.

What did you find to be the most challenging part of this process of learning data science as career and becoming a machine learning engineer?

When I started university, it was not a clear path yet. Eighteen or fifteen years ago, you couldn’t see the path of a data scientist from start to end. Data science sits between computer science and math. And, throughout my career, I’ve been closer to computer science than to math. But the challenge is that you have to know as much as possible from both worlds. But at the same time combine them as well as possible. So I think it’s been quite challenging to be able to unify both worlds.

What’s the best career advice you have ever been given?

This is not really a piece of advice that someone has given me, but rather something that I’ve seen people do. I’ve realised that, when I was starting to look for jobs and was looking for a career, I was kind of looking for anything. I felt like I was the only one selling myself. But at some point, you realise that it’s important that the company also sells itself to you. The company also has to be interested in the person who’s applying. It’s not just top-down, but also bottom-up. There has to be this mutual understanding. When I started looking for jobs, I didn’t care that much about that. But after a while, I realised that it’s really important to feel confident and be in a good environment. It’s crucial for your career development and for example a data science career.

So, I would recommend to everyone to not just get the first job and be very selective about what they want and what they seek to accomplish. Also, the people who interview you: you have to look at them and ask as many questions as you can about the company. It’s not only about selling yourself, but also about understanding the company and making sure that the step you’re going to take is the best one for you because that’s going to influence the rest of your career.

CV Writing Tips for IT Professionals

What’s your advice for those who want to learn data science?

If you want to learn something, the best way to learn it is to get hands-on, to find a project that you’re interested in. There are a lot of open source projects that require some help. For example, at Synthesized, we’re now going to open source a fairness package. If you’re interested in this field, you can collaborate on many, many different projects. The best way to learn computer science and data science is to get a project, get a data set. Sign up for a Kaggle Competition, for example, and try to solve it and get as close as you can to the top of the ranking.

Need tips on how to find a job in IT? Check out our IT job hunting guide.

What are the biggest issues with working with data these days?

First of all, there is the problem of ending up with a poor signal-to-noise ratio. The amount of data that you can find nowadays is huge. But, many times, this data contains a lot of noise. And, if you are not careful, you are just going to end up with just a lot of noise that renders it useless.

The second big issue is compliance, so GDPR, HIPAA, etc. If you have data that is not privacy-compliant or that is discriminating against some groups, that’s going to be not only useless, but it’s also going to be illegal to use. So you need to work closely with compliance teams. You need to spend time with the legal team to make sure that you make proper use of your data.

Finally, there’s the problem of data sets becoming data silos. More and more, to access data, you need a data engineer, a data scientist or a machine learning engineer — someone who can do the magic with the data. It’s getting more and more complex to access the data because doing so requires the knowledge of a data engineer or a test engineer.

How is Synthesized helping to solve these problems?

Synthesized has a core engine that is able to solve these problems by enabling users to easily access their data products in many different ways. So, for example, let’s take one of the problems that I was mentioning before: working with compliance and privacy. Our engine is able to generate data that is representative of the original data but is free from privacy issues and from even biases.

Another of the problems is related to infrastructure, to data silos. Current approaches are data warehouses and data lakes. There are some problems with these approaches, for example, the signal-to-noise ratio in the case of data lakes. There’s a lot of data in there, but it’s very difficult to use. But, the infrastructure problem is also there because the data is very centralised and you need a data engineering team to get to it. So what we’re working on is a new infrastructure called data mesh that aims to decentralise data access. It tries to decentralise all these data products so that each team can access the data independently. Both for internal and for external collaboration.

Can you tell us a bit more about your role at the company?

I’m very lucky to have been a very early employee of the company. I joined at a very early stage, and this meant that, although my official title is machine learning engineer, I’ve been able to touch a bit of everything.

However, my main role as a machine learning engineer is making sure that the core technology is as good as possible. But that also involves a lot of what a pre-sales person would do. So, going to the clients, asking them for requirements, and making sure that the product works well for them and is as tailored as possible to their requirements. But about also improving the product.

And there is also some marketing work involved, like developer relationships. We need to push into that direction because we’re a small company with very new technology and we need to make sure that we sell bottom-up, not top-to-bottom. We approach customers as machine learning engineers, as the nerds who sell to other developers, not as the marketing guys who are trying to sell something to them. Otherwise,+j the message doesn’t get through that well.

What’s next for data? Where do you see data science in, let’s say, five years from now?

I think that, right now, we’re in a very crucial moment for data. We are having all these privacy issues, fairness problems, and the users are more and more aware of this. So, we have to make sure that we have the best practices in place, that we make the best that we can with our data but still respect users. It’s going to be a very challenging time.

At Synthesize, we mainly work with structured data, but I think it’s worth mentioning unstructured data. What’s happening with OpenAI, GPT-3 or other generative models — what’s being done is amazing. It’s a very exciting time. I’m very, very excited to see what the next new thing is going to be.

You’ve been based in London for a while. What do you like the most about the London tech scene?

What I like the most about it is that there are a lot of people working on the same topic, and you can very easily meet people doing really interesting things. And that’s one of the most powerful things when you are doing research or trying to improve your product. Just talking to people, understanding their problems and just having a conversation about something that probably you don’t understand and you don’t even know about.

Discussing new tech trends with people at other companies, that can really help. You discover new things and go out of your usual boundaries. London is great for that because there are a lot of meetups. Well, there were before corona. But yeah, you can talk to and meet a lot of people. There’s this big ecosystem where a lot of things are happening and there’s so much to learn. I’m really happy to be living here.

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