How Many Business Intelligence Tools Are There

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How Many Business Intelligence Tools Are There – BI tools play a very important role in the democratization or “processing” of data. The purpose of Business Intelligence platforms is no surprise to illuminate.. business decisions. There are two ways to do this:

These two methods are caused by the same problem: processing groups often do not trust the data, which causes constant pings to the data group. Everyone is upset, and nothing is working. The good news is that we are making great progress.

How Many Business Intelligence Tools Are There

While operations teams have gained more autonomy, data teams are becoming increasingly integrated into many organizations. Removing the dependency of operations teams on the data team is important: it allows the data team to focus on high-impact items, and it allows other teams to move faster with work. their daily life. And just like that, everyone is happier.

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So how exactly do we do that? A good first step is to look at the development of Business Intelligence over the past decades. We have identified three generations of Business Intelligence tools: traditional BI, self-service BI, and advanced analytics. The evolution of BI tools is marked by the gradual release of functional groups / domain experts from data and IT groups.

Let’s pause for a second, so we can get on the same page about what Business Intelligence is (and what it isn’t). BI is a term used for the method and technology used to analyze data. BI uses data about your business to give you insights that help you make better decisions, such as how to improve your product line or cut costs. You can use this information to help you make decisions about what products to sell, where to open a new store, or what marketing strategies are effective.

Traditional BI is a database-based approach to analyzing data; it relies on static dashboards made of graphics. Dashboards are well defined in advance, based on common business questions. Answering new questions requires time and technical knowledge. Big tech companies like Microsoft, IBM, Oracle and SAP were the leaders of this BI era. Traditional BI methods are, for the most part, outdated and useless for modern businesses.

Reporting and Dashboards: This feature provides an overview of your business data and how it relates to business performance. It also allows users to manage data, analyze relationships, and create reports that can be shared with others.

Bi Tools: Three Generations

Data Storage: This method involves storing all of your company’s data in one place and making it available for analysis through a database management system (DBMS). It allows you to analyze large amounts of data at a time, as well as perform advanced reporting functions such as drop reports.

The first generation of BI tools is characterized by the complete dependence of the working groups on the data group. Data sets are a complete barrier for every query related to data. This is for a very simple reason: only the data team has the necessary skills to extract information from the data. Early BI tools were complex, code-heavy platforms that could only host technical data; the tables were queried in SQL, a language known at the time mainly for technical profiles.

A typical process for a person who needs a report would be as follows: A business person queries a data group for a report. The information team provides a consistent report that changes over time in the following days, so they ask again and again until they get their hands on something new—or give up entirely!

The first step was to replace static reports with dynamic ones: dashboards or scorecards that could be updated regularly by users themselves without relying on the data group again. Easy-to-use analytics platforms were developed as part of the second generation of BI tools, allowing non-professional users to access all their data sources and generate reports and -dashboard of their choice. These new solutions were easier to use than their predecessors. They allowed users who were not familiar with SQL code to easily get information from their data without waiting weeks or months before getting an answer from a group of data.

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BI tools from this generation have largely eliminated the technology stack and are focused on providing discovery and visualization capabilities to their users. This initiative empowered business users to uncover business data themselves.

Another problem with the first generation of BI tools is that they were difficult to use and difficult to learn. Users had to spend time learning how to use these tools and then spend more time trying to figure out what data they needed to import or export from their data sources in order to get useful information. . The second generation addressed this issue by making the tools easier for users to understand and use.

Although there has been a lot of improvement in that area, the BI tools of the second generation still had business units that relied heavily on data sets for processing and modeling. Not surprisingly, this is what the third generation of Business Intelligence tools have attempted to address.

The third generation of BI kept its promise to reduce the dependency of working groups on the data set, by removing complexity from BI processes. This is achieved mainly through.. augmented analytics. Augmented Analytics is “the use of enabling technologies such as machine learning and AI to aid in data curation, insight generation and insight interpretation to augment the way people analyze and analyze data in analytics and BI platforms” (Gartner glossary.

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I was discussing additional analytics with Anna from Whaly who likened it to the “IKEA effect”. I found this comparison very provocative. The IKEA effect means that we place disproportionately high value on the things we contribute to making. When you make your own IKEA wardrobe, you tend to value it more because you have invested time and effort in building it. And because He built it, it’s a beautiful closet.

The same goes for additional analytics. Augmented analytics empowers teams to have a complete data pipeline. Yes, you heard that right. This new capability enables users to create an automated pipeline to extract data from various sources, convert it to the format required by the reporting tool, and import it into a data warehouse or other location. Automation greatly simplifies every step of the process, completely eliminating the preparation of data for less technical details.

Back to the IKEA effect: when business reports create their own data pipelines for extracting and modeling data themselves, they tend to rely on more data. They put a lot of effort into building reports and tracking analytics. They are involved every step of the way. This cognitive bias makes them trust the data path more than when it is served by another group.

Many people see this as the only way to achieve mass data democratization. Instead of the data team providing pure data and pre-built dashboards in a self-managing manner, we are shifting to a paradigm where functional teams can have complete pipelines: data collection, e.g. of data, visualization, etc. When you create a dashboard from scratch. , from the collection of information to the detailed information, how could you not trust it? You have done all the work above. There are no trust issues to be had, no questions to be asked, no people to ping, or any other waste of time. Of course, this could not be achieved without additional analytics.

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Augmented Analytics also increases the capabilities of technical BI tools. It can be used to find information that cannot be obtained using traditional analytics. For example, if you have an email list with a million email addresses and you want to know which recipients are more likely to open your emails and click on the links in them , additional analytics will look at all aspects of each person’s profile (such as their age, gender, location) and use machine learning techniques to find patterns in 1 million subscribers. Advanced analytics features allow users to access advanced data analysis methods that would otherwise be beyond their capabilities as business analysts or data analysts. For example, many platforms include advanced statistical analysis tools such as stress modeling and time series forecasting. Some also include machine learning algorithms for modeling, which helps experts make better-informed decisions about future performance based on patterns in historical data. (eg, potential for customer frustration).

Business Intelligence tools have gone through three stages of evolution: Traditional BI, Self-Service BI, and Augmented Analytics. With this change, some companies have chosen to focus on removing the complexity of the BI process, giving independence to business teams from data teams (example: Whaly). Other platforms have chosen to address technical issues, using enhanced analytics to push analytics capabilities further. (eg: Method). Regardless of which way BI tools have gone, the goal has been to allow business teams to access and understand data whenever they need it – to be able to make the right business decisions with confidence. Regardless of how this is achieved, business departments’ deep trust in data and data processes is the only way to trust their dashboards.

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