Enterprise Intelligence Parts and How They Relate to Energy BI

Enterprise Intelligence Parts and How They Relate to Energy BI

Business Intelligence Components and How They Relate to Power BI

After I determined to put in writing this weblog put up, I assumed it might be a good suggestion to study a bit concerning the historical past of Enterprise Intelligence. I searched on the web, and I discovered this web page on Wikipedia. The time period Enterprise Intelligence as we all know it at the moment was coined by an IBM pc science researcher, Hans Peter Luhn, in 1958, who wrote a paper within the IBM Techniques journal titled A Enterprise Intelligence System as a selected course of in knowledge science. Within the Aims and ideas part of his paper, Luhn defines the enterprise as “a group of actions carried on for no matter function, be it science, expertise, commerce, trade, legislation, authorities, protection, et cetera.” and an intelligence system as “the communication facility serving the conduct of a enterprise (within the broad sense)”. Then he refers to Webster’s dictionary’s definition of the phrase Intelligence as the flexibility to apprehend the interrelationships of introduced details in such a method as to information motion in direction of a desired aim”.

It’s fascinating to see how a improbable thought prior to now units a concrete future that may assist us have a greater life. Isn’t it exactly what we do in our day by day BI processes as Luhn described of a Enterprise Intelligence System for the primary time? How cool is that?

After we speak concerning the time period BI at the moment, we confer with a selected and scientific set of processes of remodeling the uncooked knowledge into useful and comprehensible info for numerous enterprise sectors (resembling gross sales, stock, legislation, and many others…). These processes will assist companies to make data-driven choices primarily based on the present hidden details within the knowledge.

Like all the pieces else, the BI processes improved quite a bit throughout its life. I’ll attempt to make some smart hyperlinks between at the moment’s BI Parts and Energy BI on this put up.

Generic Parts of Enterprise Intelligence Options

Usually talking, a BI resolution accommodates numerous parts and instruments which will fluctuate in numerous options relying on the enterprise necessities, knowledge tradition and the organisation’s maturity in analytics. However the processes are similar to the next:

  • We normally have a number of supply techniques with completely different applied sciences containing the uncooked knowledge, resembling SQL Server, Excel, JSON, Parquet information and many others…
  • We combine the uncooked knowledge right into a central repository to cut back the danger of constructing any interruptions to the supply techniques by always connecting to them. We normally load the information from the information sources into the central repository.
  • We rework the information to optimise it for reporting and analytical functions, and we load it into one other storage. We goal to maintain the historic knowledge on this storage.
  • We pre-aggregate the information into sure ranges primarily based on the enterprise necessities and cargo the information into one other storage. We normally don’t maintain the entire historic knowledge on this storage; as an alternative, we solely maintain the information required to be analysed or reported.
  • We create stories and dashboards to show the information into helpful info

With the above processes in thoughts, a BI resolution consists of the next parts:

  • Information Sources
  • Staging
  • Information Warehouse/Information Mart(s)
  • Extract, Remodel and Load (ETL)
  • Semantic Layer
  • Information Visualisation

Information Sources

One of many important objectives of operating a BI challenge is to allow organisations to make data-driven choices. An organisation may need a number of departments utilizing numerous instruments to gather the related knowledge every single day, resembling gross sales, stock, advertising, finance, well being and security and many others.

The information generated by the enterprise instruments are saved someplace utilizing completely different applied sciences. A gross sales system would possibly retailer the information in an Oracle database, whereas the finance system shops the information in a SQL Server database within the cloud. The finance workforce additionally generate some knowledge saved in Excel information.

The information generated by completely different techniques are the supply for a BI resolution.


We normally have a number of knowledge sources contributing to the information evaluation in real-world eventualities. To have the ability to analyse all the information sources, we require a mechanism to load the information right into a central repository. The principle motive for that’s the enterprise instruments required to always retailer knowledge within the underlying storage. Subsequently, frequent connections to the supply techniques can put our manufacturing techniques liable to being unresponsive or performing poorly. The central repository the place we retailer the information from numerous knowledge sources is named Staging. We normally retailer the information within the staging with no or minor adjustments in comparison with the information within the knowledge sources. Subsequently, the standard of the information saved within the staging is normally low and requires cleaning within the subsequent phases of the information journey. In lots of BI options, we use Staging as a short lived surroundings, so we delete the Staging knowledge frequently after it’s efficiently transferred to the subsequent stage, the information warehouse or knowledge marts.

If we need to point out the information high quality with colors, it’s honest to say the information high quality in staging is Bronze.

Information Warehouse/Information Mart(s)

As talked about earlier than, the information within the staging is just not in its greatest form and format. A number of knowledge sources disparately generate the information. So, analysing the information and creating stories on high of the information in staging could be difficult, time-consuming and costly. So we require to seek out out the hyperlinks between the information sources, cleanse, reshape and rework the information and make it extra optimised for knowledge evaluation and reporting actions. We retailer the present and historic knowledge in a knowledge warehouse. So it’s fairly regular to have a whole bunch of hundreds of thousands and even billions of rows of information over an extended interval. Relying on the general structure, the information warehouse would possibly include encapsulated business-specific knowledge in a knowledge mart or a group of information marts. In knowledge warehousing, we use completely different modelling approaches resembling Star Schema. As talked about earlier, one of many main functions of getting an information warehouse is to maintain the historical past of the information. This can be a huge profit of getting an information warehouse, however this power comes with a price. As the amount of the information within the knowledge warehouse grows, it makes it dearer to analyse the information. The information high quality within the knowledge warehouse or knowledge marts is Silver.

Extract, Transfrom and Load (ETL)

Within the earlier sections, we talked about that we combine the information from the information sources within the staging space, then we cleanse, reshape and rework the information and cargo it into an information warehouse. To take action, we observe a course of known as Extract, Remodel and Load or, in brief, ETL. As you may think about, the ETL processes are normally fairly complicated and costly, however they’re a necessary a part of each BI resolution.

Semantic Layer

As we now know, one of many strengths of getting an information warehouse is to maintain the historical past of the information. However over time, holding huge quantities of historical past could make knowledge evaluation dearer. As an illustration, we can have an issue if we need to get the sum of gross sales over 500 million rows of information. So, we pre-aggregate the information into sure ranges primarily based on the enterprise necessities right into a Semantic layer to have an much more optimised and performant surroundings for knowledge evaluation and reporting functions. Information aggregation dramatically reduces the information quantity and improves the efficiency of the analytical resolution.

Let’s proceed with a easy instance to raised perceive how aggregating the information will help with the information quantity and knowledge processing efficiency. Think about a state of affairs the place we saved 20 years of information of a series retail retailer with 200 shops throughout the nation, that are open 24 hours and seven days every week. We saved the information on the hour degree within the knowledge warehouse. Every retailer normally serves 500 clients per hour a day. Every buyer normally buys 5 objects on common. So, listed here are some easy calculations to know the quantity of information we’re coping with:

  • Common hourly data of information per retailer: 5 (objects) x 500 (served cusomters per hour) = 2,500
  • Day by day data per retailer: 2,500 x 24 (hours a day) = 60,000
  • Yearly data per retailer: 60,000 x 365 (days a yr) = 21,900,000
  • Yearly data for all shops: 21,900,000 x 200 = 4,380,000,000
  • Twenty years of information: 4,380,000,000 x 20 = 87,600,000,000

A easy summation over greater than 80 billion rows of information would take lengthy to be calculated. Now, think about that the enterprise requires to analyse the information on day degree. So within the semantic layer we combination 80 billion rows into the day degree. In different phrases, 87,600,000,000 ÷ 24 = 3,650,000,000 which is a a lot smaller variety of rows to take care of.

The opposite profit of getting a semantic layer is that we normally don’t require to load the entire historical past of the information from the information warehouse into our semantic layer. Whereas we would maintain 20 years of information within the knowledge warehouse, the enterprise may not require to analyse 20 years of information. Subsequently, we solely load the information for a interval required by the enterprise into the semantic layer, which reinforces the general efficiency of the analytical system.

Let’s proceed with our earlier instance. Let’s say the enterprise requires analysing the previous 5 years of information. Here’s a simplistic calculation of the variety of rows after aggregating the information for the previous 5 years on the day degree: 3,650,000,000 ÷ 4 = 912,500,000.

The information high quality of the semantic layer is Gold.

Information Visualisation

Information visualisation refers to representing the information from the semantic layer with graphical diagrams and charts utilizing numerous reporting or knowledge visualisation instruments. We could create analytical and interactive stories, dashboards, or low-level operational stories. However the stories run on high of the semantic layer, which supplies us high-quality knowledge with distinctive efficiency.

How Completely different BI Parts Relate

The next diagram exhibits how completely different Enterprise Intelligence parts are associated to one another:

Business Intelligence (BI) Components
Enterprise Intelligence (BI) Parts

Within the above diagram:

  • The blue arrows present the extra conventional processes and steps of a BI resolution
  • The dotted line gray(ish) arrows present extra trendy approaches the place we don’t require to create any knowledge warehouses or knowledge marts. As an alternative, we load the information straight right into a Semantic layer, then visualise the information.
  • Relying on the enterprise, we would must undergo the orange arrow with the dotted line when creating stories on high of the information warehouse. Certainly, this method is reputable and nonetheless utilized by many organisations.
  • Whereas visualising the information on high of the Staging surroundings (the dotted pink arrow) is just not very best; certainly, it’s not unusual that we require to create some operational stories on high of the information in staging. A superb instance is creating ad-hoc stories on high of the present knowledge loaded into the staging surroundings.

How Enterprise Intelligence Parts Relate to Energy BI

To grasp how the BI parts relate to Energy BI, we’ve got to have understanding of Energy BI itself. I already defined what Energy BI is in a earlier put up, so I counsel you test it out if you’re new to Energy BI. As a BI platform, we count on Energy BI to cowl all or most BI parts proven within the earlier diagram, which it does certainly. This part appears to be like on the completely different parts of Energy BI and the way they map to the generic BI parts.

Energy BI as a BI platform accommodates the next parts:

  • Energy Question
  • Information Mannequin
  • Information Visualisation

Now let’s see how the BI parts relate to Energy BI parts.

ETL: Energy Question

Energy Question is the ETL engine accessible within the Energy BI platform. It’s accessible in each desktop functions and from the cloud. With Energy Question, we are able to hook up with greater than 250 completely different knowledge sources, cleanse the information, rework the information and cargo the information. Relying on our structure, Energy Question can load the information into:

  • Energy BI knowledge mannequin when used inside Energy BI Desktop
  • The Energy BI Service inner storage, when utilized in Dataflows

With the combination of Dataflows and Azure Information Lake Gen 2, we are able to now retailer the Dataflows’ knowledge right into a Information Lake Retailer Gen 2.

Staging: Dataflows

The Staging element is offered solely when utilizing Dataflows with the Energy BI Service. The Dataflows use the Energy Question On-line engine. We are able to use the Dataflows to combine the information coming from completely different knowledge sources and cargo it into the inner Energy BI Service storage or an Azure Information Lake Gen 2. As talked about earlier than, the information within the Staging surroundings will probably be used within the knowledge warehouse or knowledge marts within the BI options, which interprets to referencing the Dataflows from different Dataflows downstream. Remember that this functionality is a Premium characteristic; due to this fact, we should have one of many following Premium licenses:

Information Marts: Dataflows

As talked about earlier, the Dataflows use the Energy Question On-line engine, which suggests we are able to hook up with the information sources, cleanse, rework the information, and cargo the outcomes into both the Energy BI Service storage or an Azure Information Kale Retailer Gen 2. So, we are able to create knowledge marts utilizing Dataflows. You might ask why knowledge marts and never knowledge warehouses. The elemental motive is predicated on the variations between knowledge marts and knowledge warehouses which is a broader matter to debate and is out of the scope of this blogpost. However in brief, the Dataflows don’t presently assist some basic knowledge warehousing capabilities resembling Slowly Altering Dimensions (SCDs). The opposite level is that the information warehouses normally deal with huge volumes of information, rather more than the amount of information dealt with by the information marts. Bear in mind, the information marts include enterprise particular knowledge and don’t essentially include numerous historic knowledge. So, let’s face it; the Dataflows are usually not designed to deal with billions or hundred hundreds of thousands of rows of information {that a} knowledge warehouse can deal with. So we presently settle for the truth that we are able to design knowledge marts within the Energy BI Service utilizing Dataflows with out spending a whole bunch of hundreds of {dollars}.

Semantic Layer: Information Mannequin or Dataset

In Energy BI, relying on the situation we develop the answer, we load the information from the information sources into the information mannequin or a dataset.

Utilizing Energy BI Desktop (desktop software)

It is strongly recommended that we use Energy BI Desktop to develop a Energy BI resolution. When utilizing Energy BI Desktop, we straight use Energy Question to hook up with the information sources and cleanse and rework the information. We then load the information into the information mannequin. We are able to additionally implement aggregations throughout the knowledge mannequin to enhance the efficiency.

Utilizing Energy BI Service (cloud)

Growing a report straight in Energy BI Service is feasible, however it’s not the beneficial methodology. After we create a report in Energy BI Service, we hook up with the information supply and create a report. Energy BI Service doesn’t presently assist knowledge modelling; due to this fact, we can not create measures or relationships and many others… After we save the report, all the information and the connection to the information supply are saved in a dataset, which is the semantic layer. Whereas knowledge modelling is just not presently accessible within the Energy BI Service, the information within the dataset wouldn’t be in its cleanest state. That is a superb motive to keep away from utilizing this methodology to create stories. However it’s potential, and the choice is yours in any case.

Information Visualisation: Reviews

Now that we’ve got the ready knowledge, we visualise the information utilizing both the default visuals or some customized visuals throughout the Energy BI Desktop (or within the service). The subsequent step after ending the event is publishing the report back to the Energy BI Service.

Information Mannequin vs. Dataset

At this level, chances are you’ll ask concerning the variations between an information mannequin and a dataset. The brief reply is that the information mannequin is the modelling layer present within the Energy BI Desktop, whereas the dataset is an object within the Energy BI Service. Allow us to proceed the dialog with a easy state of affairs to know the variations higher. I develop a Energy BI report on Energy BI Desktop, after which I publish the report into Energy BI Service. Throughout my growth, the next steps occur:

  • From the second I hook up with the information sources, I’m utilizing Energy Question. I cleanse and rework the information within the Energy Question Editor window. Up to now, I’m within the knowledge preparation layer. In different phrases, I solely ready the information, however no knowledge is being loaded but.
  • I shut the Energy Question Editor window and apply the adjustments. That is the place the information begins being loaded into the information mannequin. Then I create the relationships and create some measures and many others. So, the information mannequin layer accommodates the information and the mannequin itself.
  • I create some stories within the Energy BI Desktop
  • I publish the report back to the Energy BI Service

Right here is the purpose that magic occurs. Throughout publishing the report back to the Energy BI Service, the next adjustments apply to my report file:

  • Energy BI Service encapsulates the information preparation (Energy Question), and the information mannequin layers right into a single object known as a dataset. The dataset can be utilized in different stories as a shared dataset or different datasets with composite mannequin structure.
  • The report is saved as a separated object within the dataset. We are able to pin the stories or their visuals to the dashboards later.

There it’s. You’ve it. I hope this weblog put up helps you higher perceive some basic ideas of Enterprise Intelligence, its parts and the way they relate to Energy BI. I’d like to have your suggestions or reply your questions within the feedback part beneath.

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