What is Business Intelligence or BI?

The Business Intelligence (BI), in Castilian Business Intelligence or Business Intelligence, is the set of processes required to offer a software solution that allows us to analyze how our company is working. This knowledge will make us optimize this operation by making appropriate decisions.

Within the world of BI, there are multiple possible solutions. As each company has its own delusion, a completely valid solution for some companies may not be valid for others.

We can help the automotive sector to give an example. While for a company, the perfect solution will be a city car, for another, it will be a 4 × 4, for another a minivan, for another a truck, etc., there will even be one that will need a complete fleet of vehicles.

Within BI solutions, there are different options. By relying on rapidly developing technologies, these solutions cover new aspects and improve others. So those included first will give way to others that will emerge over time.

In any case, the most common solutions (all together or only some of them) are based on the creation of both predefined and customized reports, along with their automated distribution ( reporting or corporate reporting ).

Some solutions would be:

• The forecast results ( forecasting )
• Query tools for advanced users ( query ) including access to multidimensional cubes ( OLAP )
• Dashboards ( dashboards or scorecards )
• Special data warehouses ( data warehouse or datamarts ).

In fact, BI solutions are in the process of transformation. Encompass from the systems used for measuring corporate data and related to those who also support analysis, forecasting, and data discovery capabilities (reports Data Discovery ). In this way, we can see that Business Intelligence relies heavily on Big Data with data analysis and data development.

Business Intelligence Solutions To Improve Processes

Let’s take an example to see how these technologies are used; This example is that of a large company where all these functionalities are required, so it will help us understand where each of them fits in our companies. Imagine a business that has different software programs in use: an ERP, a program specially developed to control its production systems, a CRM, etc.

Although it is not always necessary to cross data from one system with all the others, it is done to some extent. This implies that it must be done externally. For example, in spreadsheets, advanced users collect data from different systems and cross it.

It is a very error-prone system, apart from being expensive (it always needs available personnel), and slow (this person has to prepare the type of information required in each case). BI solutions allow you to make it in advance: there are different types of solutions (for example, those based on metadata), but the best known are those of individual data warehouses.

These allow us to have these data available, having previously treated them to correct and/or discard errors (errors as shocking as dates from other centuries, poorly written geographical areas, etc. are shared). In addition to storing them with the previous groupings that are required (temporary, geographic, by type of business, others), so than any report, scorecard, etc., that needs them, will be much more efficient.

These special data stores have many more interesting functionalities. However, for some specific cases, they may not be the perfect solution (this post is not intended to be a treatise in this regard): as always, it will depend on each case, and for this, there are specialists in these technologies.

Use Of Data Depending On The Department

Once we have these data prepared and crossed, there will be users who will only need the development of a part of them.

For example, predefined reports with a certain cadence will be useful for commercial visits every week. Others, such as sales managers, will require aggregate data on product sales by vendors and geographic areas in addition to data for the correct monitoring of budgets and sales forecasts.

Managers or those responsible for objectives will need dashboards to follow the evolution of the company’s key indicators. In the hospital sector, they will be used to measure the use of beds or visits per patient. Others should be able to cross data to provide knowledge and discover patterns that modify or create new ways of working. The example of beers and diapers is famous: as having young children, you cannot go out so much, they tend to consume more at home, so they got close to each other.

The Advantages Of Business Intelligence Solutions

But why are BI solutions required? What do they contribute, and how do they help us? From the company, years ago, the focus was on having management programs that improved day by day. These programs are the so-called ERP ( Enterprise Resource Planning ).

It should be borne in mind that there are very diverse and more or fewer functionalities. They allow us to control aspects such as delivery notes, orders, invoices, customers, products, manufacturing, accounting, sales, to payroll.

There are also other programs, such as CRM ( Customer Relationship Management ) to control sales activities. With the use of all these computing solutions, a lot of data that used to be wasted is stored but can be used. In fact, ERPs or CRMs tend to have a series of predefined reports to provide us with information about part of them (sales reports, for example). The good news is that they offer more and more options than those contemplated in the BI world.

In any case, they are not usually very complete, and they are also assembled according to the data they include. If we use different computer programs in our company, they will only be valid for your data, so you cannot have a joint vision. Furthermore, the structure in which they reside is not optimal for their exploitation. That’s where Business Intelligence solutions come in to solve all these shortcomings and limitations.

Business Intelligence Success Stories

Toyota Motor Corporation

The Toyota Motor Company is not the first time that it has been at the forefront of innovation in the global business sector. We highlight it because the automaker incorporated information management software that allowed it to reduce production costs, achieving a higher number of clients.

Bridgestone Firestone

Another example of a motor company that uses BI is Bridgestone Firestone, who uses information management software for the distribution of its products and for the logistics of receiving and purchasing raw materials exported from different parts of the world.

Feeding chains

Also, large supermarket chains such as Wal-Mart or restaurant chains such as Wendy’s and Ruby Tuesday and Friday’s use Business Intelligence for decision-making based on the data provided by customers, being able to maintain loyalty and maximize sales.

Within the school, we have students trained in new technologies such as Business Intelligence, Big Data, or Artificial Intelligence. On this occasion, we follow the trend of the labor market with training in BI. Discover everything you can learn.

Also Read : 5 Ways To Protect Your Business Data

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But it also poses a new challenge: take advantage of the enormous volume of data, detect those that are useful from the great variety that exists, control them at the necessary speed, and have knowledge about their veracity. Surely you know what Big Data is, but do you know the 5 V’s of Big Data? We explain them to you!

Big Data, From Data To Valuable Information

Big Data is one of the fundamental keys to improving corporate governance. And it is that more data is generated in two days than in all our contemporary history. According to the consulting firm Gartner, in 2020, there will be more than 25 billion devices connected to the Internet, which suggests that the volume of data contained in Big Data will grow exponentially.

The digital transformation of structures, processes, and tools allows the Big Data environment to grow by leaps and bounds every day. Due to the rapid evolution of technology and the habits and behaviors of society, companies are now in need of collecting, managing, and analyzing large amounts of data that, thanks to Big Data, can convert into information.

A precious source of knowledge about clients, competitors, the environment, etc., with which you define better strategies, to achieve your objectives, and obtain competitive advantages.

The 5 V’s of Big Data 

Big Data is made up of five dimensions that characterize it, known as the 5 V’s of Big Data. Let’s see what each of these aspects consists of:

# 1 Volume

Traditionally, data has been generated manually. Now they come from machines or devices and are generated automatically, so the volume to be analyzed is massive. This feature of Big Data refers to the size of the amounts of data that are currently generated.

The figures are staggering. And it is that the data that is produced in the world for two days is equivalent to all that generated before 2003. These large volumes of data that are produced at any time pose significant technical and analytical challenges for the companies that manage them. 

# 2 Speed

The data flow is massive and constant. In the Big Data environment, data is generated and stored at unprecedented speed. This large volume causes data to become out of date quickly and to lose its value when new data appear.

Businesses, therefore, must react very quickly to collect, store, and process them. The challenge for the technology area is to store and manage large amounts of data that are continuously generated. The other fields must also work at high speed to convert that data into useful information before it loses its value.

# 3  Variety

The origin of the data is highly heterogeneous. They come from multiple supports, tools, and platforms: cameras, smartphones, cars, GPS systems, social networks, travel records, bank movements, etc. Unlike a few years ago, when the data that was stored was extracted, mainly, from spreadsheets and databases.

The data that is collected can come structured (they are easier to manage) or unstructured (in the form of documents, videos, emails, social networks, etc.). Depending on this differentiation, each type of information will be treated differently through specific tools. The essence of Big Data resides in, later, combining and configuring some data with others. 

Each type of information is treated differently, through specific tools, but then the essence of Big Data lies in combining and configuring some data with others. It is for this reason that the degree of complexity in the data storage and analysis processes increases.

# 4  Truthfulness

This feature of Big Data is probably the most challenging. The large volume of data generated can make us doubt the degree of integrity of all of them since the great variety of data causes many of them to arrive incomplete or incorrect.

This is due to multiple factors, for example, whether the data comes from different countries or if providers use varying formats. These data must be cleaned and analyzed, a continuous activity since new ones are continuously generated. Uncertainty regarding the veracity of the data may cause certain doubts about its quality and availability in the future. 

For this reason, companies must ensure that the data they are collecting is valid, that is, that it is adequate for the objectives that they intend to achieve with it.

# 5  Value

This characteristic represents the most relevant aspect of Big Data. The value generated by the data, once converted into information, can be considered an essential aspect. With this value, companies have the opportunity to make the most of the data to introduce improvements in their management, define more optimal strategies, obtain a clear competitive advantage, create personalized offers to customers, increase relations with the public, and much more.

To be aware of all the opportunities that can be extractedicle through the application of Big Data, it is necessary to understand what are the main elements that add value to it and make its implementation at the business level a safe bet. And you, where do you think the success of Big Data lies? Do not hesitate to comment and give us your opinion.

Related Article: Small Data: Big Data For SMEs

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What Is Small Data?

Before entering the definition of Small Data, it is essential to emphasize that Big Data gave the key to making smart and correct decisions. He minimized risks and, most importantly: thanks to him, we can predict consumer behavior and be at the exact moment when they need to satisfy a need.

If we Google the term “Big Data” we will find different types of concepts, in which we will see words such as: “massive”, “large scale”, “large data sets”, “huge amounts of data”, “Petabytes”, “Exabytes”, among others. 

Do you know the amount of information an Exabyte contains?

The answer is a trillion gigabytes, an unimaginable amount of data for us mere humans, but not for a machine. Large companies such as Google, for example, often talk about Petabytes and Exabytes of information very frequently, and it is normal for the amount of data they collect. On the other hand, if we lower the scale and start talking about SMEs, the common thing would be to speak about Gigabytes and Terabytes.

The needs of giants like Google increased over time, so at one point they considered what to do with so much data and how they could take advantage of it, leading them to understand that if they analyzed all the information they collected They were able to understand the market better and create customized strategies based on that data to meet demand better.

It sounds great what can be achieved with Big Data’s concept, but SMEs can be overwhelmed. They come to think that they do not have the necessary tools to obtain and organize this enormous amount of data. But let’s do a little exercise: let’s change the term from Big Data‘s to Small Data; the concept of the 5V’s fit correctly for an SME, the only thing that changes is the volume of data and the tools that we will use. 

Applying Small Data?

1. Collect data from different sources
2. Analyze the data obtained and give value to them
3. Interpret the data until you have a clear vision of who our customer is and what their needs are.
4. Design personalized strategies based on what we know about our consumers. Strategies that help us improve our processes, products, services, etc.

What Tools To Use In Small Data?

In the cloud, there are many tools to collect and analyze data efficiently and at a low cost or even for free. For example: 

• Google Analytics
• Heatmap
• Mailchimp
• SurveyMonkey
• Alexa
• Similarweb
• Adwords
• SEOSiteCheckup

All the above tools collect and analyze data, although we must choose them based on business needs. We must investigate them, find in them what works for us and discard those that will not be useful to us.

For retail businesses, for example, GFK is a company that is in charge of studying the market of major technology brands such as Sony, Samsung, LG, among others, and offers a market analysis and statistics service, free of charge, to change to be provided with a sales report on certain product categories with a specific format; They give you different options, and you choose the one that suits you best.

Small Data Specialized Tools

There are other specialized tools to integrate the data collected from different sources. These tools are of great help when the time comes to consolidate and analyze the information; you can do it from a single platform. 

Differences Between Small Data And Big Data

We can conclude that Big Data and Small Data treated the same, only varying the amount of data and infrastructure necessary for the treatment thereof, being synonymous with the Business Intelligence, and this is where comes a specific phrase: l or not Measure cannot be improved, if you are an SME, it is time to start taking this concept seriously and creating an action plan for its implementation.

And if you want to train and be a real Big Data’s expert to differentiate your professional profile. You can become a real data scientist!

Related Article : Big Data And Cloud Computing As A Starting Point In The Business Model

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