But this development is also the consequence of technical advances which now allow not only to collect a much larger quantity of data, but also to store and use it. This is the area of ​​Big Data, now accessible even to small and medium-sized businesses.

Here are 3 trends that will impact Business Intelligence in the coming years and that could convince you to adopt a BI solution for your organization.

1. Ergonomics and simplicity of interfaces

One of the main obstacles to the development of BI in companies is undoubtedly its apparent complexity. This is presented as reserved for analysts, managers and decision-makers capable of deciphering the tables and feeding the solutions with the “relevant” data. Those times are over. Because Business Intelligence is now intended for all employees . BI solutions now offer the possibility of customizing management tools.

Thus, from a standard tool, it becomes possible to offer a large library of indicators , so that everyone can compose the dashboard that suits them. It is sometimes called BI in “Self-Service”. It therefore becomes possible for any manager, whatever his sector of activity and his role within the company, to leave aside the traditional but obsolete spreadsheets, to turn to a BI solution, without having to “undergo” a training for several months.

With a BI now “integrated” into the ERP solution, there is much less risk of distorting the data than with external tools that can lead to errors, or even display results that have nothing to do with reality.

With this new facility, it is more and more common to see BI solutions used within the Finance, Purchasing, Marketing and Sales departments, which are the first concerned with the analysis of figures and decision-making. But the use is also spreading for new departments, such as logistics, production or even Human Resources.

2. The Analytics Power of the Cloud

In the mind of the average user, BI involves 2 technical prerequisites: having a large amount of data to exploit and a powerful IT infrastructure capable of processing this data. Some SMEs may even tend to stop there, thinking that they have neither and that BI is not for them.

It’s a pity, because it is easy today to sweep away these 2 obstacles. As far as data is concerned, the digital transformation of the company and especially of its customers leads to the production of an exponential quantity of information, often poorly exploited or even neglected. This data is accumulating and just waiting to become an effective resource for your company, whatever its sector of activity.

Storing them is no longer a problem in itself, with the advent of the Cloud: it is no longer necessary to invest in on-site infrastructure because it is possible to use the infinite and secure storage capacity of the Cloud. This is an answer that is all the more appropriate since a lot of data is produced outside the company , in the Cloud.

In terms of processing capacity, again, the Cloud is an answer. Some organizations are still reluctant to equip themselves with expensive infrastructures that would only be used occasionally (which moreover remains to be demonstrated).

But the Cloud allows this additional occasional power which can make it possible to regularly analyze the mass of data that you will have collected. One more reason to fully exploit this intangible resource to feed a BI solution that will allow your teams to manage and anticipate effectively.

3. The preponderance of mobility

Google recently pointed this out: the number of consultations of its search engine from mobile devices has exceeded the number of consultations from computers. This is proof, if one were needed, that we have fully entered the era of mobility. Smartphones, tablets and laptops with touch screens are now more common than desktop computers.

In terms of Business Intelligence, this has 2 direct consequences. On the one hand, the amount of data produced is much greater and more relevant, due, for example, to the geolocation of users. On the other hand, employees spend less time at their desks. This does not mean that they should be less informed. BI solutions integrating mobile consultation have reached a level of maturity that allows mobile workers to do analysis away from their company.

Furthermore, mobile terminals have reached or even surpassed traditional computers in terms of power and display quality. Two essential conditions that will allow the development of “mobile” Business Intelligence.

Also Read: Do You Know The Business Intelligence Tools?

The post The 3 New Pillars Of Business Intelligence appeared first on Tech Today Reviews.

In this article, we analyze different tools that apply this methodology and clarify many of the concepts related to the entire Business Intelligence environment.

What tools does Business Intelligence use?

It is known as Business Intelligence (business intelligence) to the use of a set of strategies and functionalities that allow intelligent business decision-making based on knowledge.

The information taken into account for this decision-making will be collected in multiple ways and its sources will be both internal and external. Therefore, applying Business Intelligence consists of analyzing and processing the different data available to the company.

Typology of tools in Business Intelligence

There are countless tools on the market that facilitate business intelligence, but, in sum, they can be classified as follows:

• Monitoring and data collection: they are used to extract and collect all the data that can be used to analyze the market, the competition, the stakeholders.
• Data management: they allow the purification of the data and its correct transformation into a multidimensional database for later use.
• Reporting: its function is to capture, simplify and adapt the data for its application. They are those that allow, after a study, analysis, and understanding of the data, its graphic representation in the reports.

The 5 most used Business Intelligence tools on the market

The market offers multiple options and choosing one or the other will depend on factors such as the size of the company, its specific needs, budget.

The most used today are the following:

Microsoft Dynamics 365

It offers a set of business applications that allow you to manage the company’s data set (sales, marketing, services, operations, and commerce) to have a global vision of it. This comprehensive perspective allows decisions to be made based on the actual current situation of the business.

IBM Cognos Analytics

It brings together a set of functionalities that allow you to monitor, analyze and make business reports to carry out correct decision-making.

Tableau

The platform offers three packages of data analysis tools to apply business intelligence: for the individual analyst, for teams, and with embedded analytics. All of them have the same purpose, to turn company data into useful information for decision-making.

Sisense

This analytics platform is used to create, integrate, and deploy online analytics applications using interactive dashboards, self-service analytics, or white-label business intelligence applications.

It allows you to integrate data models, manage them, analyze them and transform them graphically on any business page.

GoodData

Monitoring platform that allows the data to be collected, prepared, and distributed after centralizing it through simple dashboards and reports.

Business Intelligence Glossary

Getting lost in the universe of applications and platforms for Business Intelligence is very simple, therefore, it is best to take a training course that allows us to correctly apply the collection and analysis of the multiple data that surround the model.

Therefore, knowing the terminology is essential:

OLAP (Online Analytical Processing) systems

A procedure that allows the collection of databases oriented to online analytical processing, which serves to speed up the query of large amounts of data. It can be referred to as multidimensional cubes or, simply, OLAP cubes (online analytical processing) since this system relates the data, giving greater importance to the analysis of the same than to its collection, simulating precisely that, a cube.

OLTP (Online Transaction Processing) systems

A procedure that allows the collection of databases oriented to the processing of transactions. Its use is used to carry out operations of insertion, modification, and deletion of data.

ETL processes

This process of applying ETL tools is developed in three phases: extract, transform, and load. The correct development of them allows organizations to move records from different data storage locations and upload them to other platforms, analyze them and use the information extracted for decision-making.

Data mining

A process that allows detecting errors, patterns, and relationships between large amounts of data in order to predict results and anticipate events that may affect business.

DashBoards

Graphic representation of those indicators that are considered key to obtaining the objectives set. This tool is useful for visualizing information, decisions, and results.

Data Warehouse

Set of company data that are gathered for the same purpose, whatever its source and/or origin. This information repository is often used to organize, understand and make strategic decisions based on business information.

Work in a Business Intelligence department

In recent years, this professional profile has become one of the most demanded in the market and it is that more and more entrepreneurs understand the importance of correctly monitoring the multiple data that their business receives for strategic decision-making. of business decisions.

As we have seen, there are many Business Intelligence tools that facilitate the task, however, certain training and certain skills are necessary to allow them to be used in the correct way.

Also Read: Customer Intelligence: How To Achieve Personalized Experiences

The post Do You Know The Business Intelligence Tools? appeared first on Tech Today Reviews.

In order to properly understand Data Discovery, it is necessary to be clear about the concept of Business Intelligence (BI), or Business Intelligence. Basically, BI can be defined as a process in which the data of a company is analyzed. These data, in turn, are converted into knowledge that will facilitate decision-making.

Today, business users are forced to dive into vast amounts of data. This results in them having to wait for days, sometimes weeks or even months, to obtain from the IT department the reports they have required. Taking into consideration that we are in the era of Big Data, in which the speed of data transmission and analysis plays a critical role, these questions cannot remain unsolved.

Indeed, when analyzing huge amounts of data, information and data must be accessible as quickly as possible, otherwise, it may be too late. This is the challenge that companies have to face when they want to find the information they need efficiently and quickly.

And it is here when Data Discovery has come into play, with the aim of solving these types of situations, which occur frequently. It is a relatively young term, introduced only a few years ago, and it is the solution to this widespread question.

The Data Discovery integrates technologies that are oriented towards the users and is based on the discovery of certain patterns and concrete values. Its objective is, therefore, to find the desired information using the minimum possible time in it. Simply explained, this kind of technology provides users with essential data or statistics for decision making.

All these data, which users will have in front of them transformed into diagrams, graphs, etc., make up the result of analyzing huge amounts of information in real-time, providing true business value.

Also Read: Big Data Marketing Strategy: What It Is, Uses And Challenges

Characteristics

It can be said that Data Discovery is equivalent to its own software analysis service, which has the following characteristics:

• The great speed for the user: It is formed as one of its most relevant features. The process of finding the data must be configured to operate quickly and to be able to find the required information almost instantaneously.
• Ease of use: For this, it is not advisable to involve end-users in the technical details pertaining to the Data Discovery process. Instead, it is advisable to use friendly and simple graphical interfaces that visually show the indications of what to search for, guiding the user through the steps to follow.
• Of a specific nature: The Data Discovery process must be designed for well-defined purposes, that is, it must be focused on obtaining the requested information within a certain scale, which does not involve having to analyze more extensive data than is strictly necessary.
• Flexible: As has already been saying, Data Discovery must be designed with a specific effect, but it can also be applied within the company to any other function, in any other department, as long as said department can access the data you want. are subject to analysis.
• Collaborative: It needs to work seamlessly with other BI processes. which, in this way, will improve the quality of the data and make it easier to access them.

Data Discovery is thus constituted as a tool that enables the end-user all the advantages of integrating Business Intelligence and self-service inefficient coordination.

As an example, a sales manager would have direct access to the data of which are the most profitable products and clients for a period of time, without having to resort to a deep analysis prepared by the Technology department. This, without a doubt, would mean a considerable saving of time, and a greater increase in the productivity of the company.

Transforming Business Intelligence

On many occasions, data discovery falls into the same category as Big Data. since the three elements used to describe this phenomenon come within its scope: variety, speed, and volume. Data Recovery makes it possible for the user claim to be able to handle large amounts of data and obtain results quickly.

The main advantages are:

• The user is able to explore data, whether structured or not, expanding its scope and optimizing and improving the quality of its reports, analysis, work, and decision making.
• The variety of sources becomes limitless.
• Self-service, as mentioned above, is its star motive, which is always enhanced when you have the right tool. Much more when attractive graphic functionalities are available, which offer the possibility of obtaining results extremely quickly.
• IT is no longer necessary. Traditionally, this department was the one that enjoyed power and veto in decisions that had to do with the acquisition by the company of a new computer platform. However, this has changed. In effect, the weight of the company has increased when it comes time to make decisions regarding the purchase of a software solution, even reaching cases in which, as for example when it comes to freelancers, no longer needs approval from TI.

In short, with Data Discovery, Business Intelligence reaches a new dimension, since, although data discovery instruments have existed for a while, the flexibility of its new approach, oriented towards data analysis, makes it possible for to BI reaches the masses more effectively.

“The balance between agility and completeness in business analysis is disappearing as new technologies bring the speed of data discovery to a complete set of BI tools that ordinary business users can easily take advantage of in their everyday lives.” Southard Jones, The battle of Business Intelligence: Data Discovery vs Traditional BI.

Also Read: Reduce Risks And Optimize Costs With The Transformation To Azure

Advantages And Disadvantages

Many businesses have incorporated data discovery into their routine, due to the many pros it offers to businesses:

• Total flexibility in creating dashboards and reports.
• Quick to examine data and reach conclusions. In this way, the business user is able to use these tools and reach their own conclusions, without having to have too extensive training or qualification, and with a very short learning time.
• The user does not depend on the IT, as was the case long ago. “The balance between agility and completeness in business analysis is disappearing as new technologies bring the speed of data discovery to a complete set of BI tools that ordinary business users can easily take advantage of in their daily lives.” Southard Jones, The battle of Business Intelligence: Data Discovery vs Traditional BI. The pros and cons of Data Discovery 7
• Friendly environment. The main characteristics of the interface offered by these tools is its ease of use and its intuitive use, making available to the user a multitude of graphics that can be handled without the obligation to program anything.
• Achieve a broader view of the origin of the data, thus improving its quality and consistency.
• Find key supplemental metadata about core data assets and identify trends.
• Be a support for Business Intelligence and relieve IT of workload, allowing it to optimize its efforts, to focus on governance and data modeling.

Despite its benefits, this tool has not yet reached its maximum degree of excellence, and it continues to evolve. Companies from many different industries are looking to experience the potential benefits of data discovery. However, once they have been implemented, the drawbacks of Data Discovery begin to become apparent:

• The setup time is usually quite long.
• Its applications present limited options.
• It is more difficult to use than you might expect.
• Lack of uniqueness of the data, which, by not being verified, runs the risk of being unreliable or lacking in quality.
• Encourage the creation of silos in the departments, as each one has its data warehouse, with the consequent risk that these do not coincide.
• Absence of the data validation process, so there is no guarantee that the information displayed is valid.

However, four rules of thumb can be considered to overcome these data discovery limitations, which are a drag on data quality, depth of exploration, and reliability:

• Implement fast-cycle iteration mechanisms, increasing the speed of obtaining knowledge with information, as well as value with data.
• Clear search objective.
• Don’t set limits.
• Introduce governance, obtaining independence, flexibility, and speed in the construction of reliable and quality reports.

Also Read: Optimize Task Planning With Microsoft 365

The post What Is Data Discovery? appeared first on Tech Today Reviews.