What Are The Key Components of BI tools?

The world of business has changed dramatically. The direction of current day's business thinking has become completely data-dependent. Even small-to-small business decisions now require analysis of a vast array of data. Because this is the only way to make the business decisions the market completes and profitable. But handling such a bulk amount of data is not a job that can be done by human hand alone. But thanks to the technological advancement that have blessed us with business intelligence (BI) tools. With the help of several BI tools landing with the most competitive and sustainable decision has become quite easy. Consequently, the demand for learning BI has increased a lot. Every single aspirant is enrolling on different BI tools courses. But, have you ever tried to explore the basic concept of BI?  No one. Before jumping to learn the usage of BI tools, know the key components of BI tools. These are the components that make BI so powerful.

Variable Data Resources

Data resources are the heart of every BI tool. Greater is the expansion of data resources better will be the preciseness of analytical output. However, the types of data resources are so vast. They identified data might be in the forms of 

● CSV  files

● JSON field

● Spreadsheet format

● Doc format

● Java script

● Graph

● Charts, etc.

The BI tools that support maximum types of data sources expectations and types seem to be more powerful than others. Thus data resources become the most important components of BI.

Effective implementation of OLAP technology

If Data resources are the heart of BI tools, then OLAP (aka online analytical processing) can be considered as the brain of the same. The collection of a huge amount of data is not our ultimate goal. Rather what you target is the strategic monitoring of sorted and selected parts of data. These selected parts of data are highly accountable to your business process and decision. 

But sorting of such scale data owing high significance to your business becomes a hurdle. Effective application of OLAP technology makes BI tools effectively capable of doing the required data segregation and analysis. Based on such segmented data and analytical output businesses can adjust their strategic decision according to situational needs. 

Live analytics

A BI tool becomes completely worthless until it provides an impressive degree of live analytics. 

Yes. A Human can easily handle conventional analytics if supported by on-time delivery of data. But in the current digitized world, every second brings a new alternation in each of the datasets under consideration. Failing to track such changes in real-time makes your business decision vulnerable to market competencies. 

For example, if you belong to the marketing field then the live analytics component of your chosen BI tool must capable of tracking Customer's  

● Location

● Local trends or occasions

● Product  searching trends

● Product buying behavior

By tracking the above real-time details you can offer your customer a special discount coupon on clothes when a local auspicious occasion is approaching. 

Data Warehousing

For a valuable and precise analysis, you need to identify the interrelationship among different subsets of data. Effective data warehousing helps you to identify the best maximum possible profitable interrelationships. An effective data warehousing makes a BI tool capable of 

● Best-fit shipping route and time identification

● Product and service development

● Geographical or seasonal offers development

● Collaboration between the dataset of different department of an organization

Balanced mixing of CPM and business intelligence

CPM or Corporate Process Management- this BI component is solely decided for product and service betterment. We can say, it’s a set of applications that helps the business owner to identify all possible scopes of product and service development. The CPM component of a business analytics tool helps in

● Regional product modification

● Regional price forecasting

● Launching of product based on market demand forecast.

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Comparing Power BI with other tools

the Business Intelligence (BI) world has been moving towards self-service BI. As expected, several vendors created tools empowering regular users to gain insights from their data. Among the many, there is Power BI. Nowadays, users want to understand the differences between Product X and Power BI.

One of the most common questions in conferences and user group sessions is likely, “can you provide a comparison between this product and Power BI?”.

The answer is almost always, “No, I cannot compare them, because they are too different”. First, one needs to understand the deep difference between Power BI and most other reporting tools on the market. Only later does a comparison make any sense. As a matter of fact, I think Power BI can be compared to only a few products on the market today. I would like to add my point of view to the discussion.

To get in-Depth knowledge on Power BI you can enroll for a live demo on Power BI online training

Indeed, Power BI is a tremendously powerful data modeling tool that happens to come with a pretty face; most other products are beautifully crafted reporting tools with a pretty face. The only thing they have in common is the pretty face. If you stop at what they have in common, you are only comparing a small fraction of the whole product, and that would be unfair.

To go further, a deeper understanding of basic BI concepts is needed.

Beware: this article is biased. I love Power BI and I make my living out of it. Nevertheless, I am a BI professional; I started working with Business Intelligence many years ago and I have gathered experience that I can share. I will try to be as fair as I can in this post, as my goal is not to provide a comparison with any tool. The goal of this post is to help you understand what you really need to evaluate when making (or reading) any comparison between different BI products.

At the top level, any Business Intelligence solution is composed of three layers:

Raw data: these are the data sources that one wants to analyze. Raw data comes as is.
Semantic model: this is where data is re-arranged to optimize it for analysis. Here you also define the calculations required by the reports.
Reports: these are the nice dashboards you can build with the tool.

Power BI manages all three layers: you start from raw data, you can build a semantic layer, and finally you prepare reports. Most other reporting tools are focused on the last layer and are limited in the previous two. In other words, they are missing the capability to build a real semantic layer. It is important to clarify what a semantic layer is, to understand what you would miss by choosing a different product.

In the old ages of BI, there was a clear separation between users and developers. A BI developer would build a project to help users extract insights from their data, and build reports. Users did not need to understand tables, relationships, or calculations. The developer oversaw shaping the tables, providing predefined calculations and giving sensible names to entities. Leveraging the semantic model, users did not have to know DAX, MDX or SQL.

A semantic model lets users interact with business entities like customers, sales, and products. Users would place those entities in reports made with Excel or with other reporting tools. Regular users were happy with just Excel and a Pivot Table. More advanced users wanted more powerful tools, and this led to the creation of several reporting tools with their ad-hoc programming language to create more advanced formulas. Regardless, the important thing is that no matter how powerful those tools are, they were still reporting tools based on the existence of a previously crafted semantic model. No semantic model, no reporting.

Picture this: a BI tool lets a developer build a semantic model. A reporting tool lets a user build a report on top of an existing semantic model. You need both to create a BI solution.Learn more from Power BI online course

Unfortunately, building a BI project takes time. Users were hungry for reports. This led to the start of the Self-Service BI era. Self-service BI is the idea of users building reports themselves, to reduce development time and to build a democratic knowledge about data. Sounds cool and terrifying at the same time.

Anyway, this is where we are today. Obviously, driven by the market several vendors started to build self-service BI tools. A few new products appeared on the market. Rather, existing tools evolved into new ones, targeting self-service BI. Keep in mind: any self-service BI tool requires the functionalities to build both the semantic model and the report in the same tool. Thus, depending on where you start, you have two options to have an existing product evolve into a self-service product:

If you already have a semantic model tool, you need to add reporting capabilities. You need to make it easier to use, because the target is no longer a BI professional but a regular user instead.
If you already have a reporting tool, you need to add the capability to build a semantic model because your users need to massage the data and build calculations on top of the resulting model.

In both cases, in the end you obtain a tool that mixes the capabilities to create a semantic model and to build reports. After this first step, you can add tons of different features like sharing with other users, building wizards to automatically connect to other services, improving the formula language and so on. But the core is always the same: a semantic model and a reporting tool, bound together in a nice package.

Even though we consider Power BI to be a new product, it is actually the evolution of Power Pivot and Analysis Services Tabular (semantic model), Power Query (querying tool), and Power View (the first version of the reporting tool released with Excel and SharePoint). Other vendors took similar steps, with different starting points. It is fair to say that several vendors started from a reporting tool, adding the semantic model to it.

Now, if you need to compare two BI tools, you need to compare at least these two features: the semantic model and the tools to build a report.

Say you want to compare Product X with Power BI; you show me how easy it is to build a gorgeous report on top of an SQL view, much easier and much more powerful than Power BI. Cool, but you are only comparing a fraction of both products. Reporting-wise, sure, Product X is better than Power BI. But there are other considerations: can you load multiple tables in Product X? Can you build relationships between them? Can you use a programming language to author complex calculations that involve scanning different tables? All these operations belong to the semantic model. A fair comparison needs to apply to all the features.

This is what Power BI offers you:

Power Query – a data transformation tool which is easy to use and yet incredibly powerful. It can load virtually anything and join data from different sources.
A modeling environment where you can build different kinds of relationships between tables and build powerful models. It does not hurt that it runs on top of one of the fastest databases I have ever seen.
DAX – a programming language which is not easy, but lets you author nearly any query and calculation. Yes, on this I am biased for sure!
Power BI – a reporting engine which is very good in building dashboards and reports. It can also be extended with custom visuals and third-party products.

Then, there is web-based reporting and sharing, a mobile experience, the ability to load from nearly any data source in the cloud or on premises and many other useful features. Yet, the core is composed of the four features above. If you want to compare apples to apples, you need to compare at least these four parts. Be mindful: you need all of them. A tool that requires you to build a single table because it does not let you relate two tables is nothing but a nice reporting tool. Comparing it to Power BI does not make much sense to me.

Moreover, it does not come by chance that to learn Power BI, one needs to learn new programming languages. Each feature has its own language, and this is just the right thing to have.

Finally, reporting. Reporting is only the last part, even though it is the most visible one. You might find other products are better than Power BI when it comes to reporting. This is fine, if you are aware that you are only comparing a fraction of Power BI with the whole of Product X.

I love Power BI, and I would really love to see a fair comparison between Power BI and any other product. We could learn a lot from the topic. But for it to be fair, it cannot just be based on how easy it is to build a pie chart (just kidding! You are not using a pie chart, are you?). One needs to evaluate everything both products have to offer.

To get in-depth knowledge of this technology and to develop skills to make a great career in this regard one can opt for Power BI online training Hyderabad.

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Tableau vs Power BI: Comparing the Data Visualization Tools

In analytics, Tableau is the leading visualisation tool. Its rich analytical features and attention to data details are the reason behind its popularity. Power BI, on the other hand, is preferred by professionals who are more comfortable with Microsoft Office365. The users can connect Excel queries, data models and are able to report to the dashboard.

While the usage of both these tools might depend on many factors, here is a quick comparison of the two popular tools on various functionalities.

To get in-Depth knowledge on Power BI you can enroll for a live demo on Power BI online training

1. Performance
One of the crucial differences between Tableau and Power BI is that Tableau is an extensible platform which not only provides visualisations but also helps in gaining a better understanding of the data. Both the tools are excellent in visualisation but when it comes to the depth of data, Tableau helps an analyst to dive deeper into the data by performing “what-if” analysis on the data.

2. Flexibility
A user can deploy tableau on-premises, on the public cloud on Microsoft Azure, Amazon Web Services, or Google Cloud Platform, or on Tableau Online. While PowerBI, an upgradation of Microsoft Excel can be said as not so flexible as it serves only as a software-as-a-service model.
Power BI vs Tableau: A Data Analytics Duel - TechnologyAdvice

3. User Interface
Tableau is mainly designed keeping data analysts in mind. The richer analytical capabilities for visualisation helps an analyst gain insight into large datasets. It allows the user to create customised dashboards which can be considered as more of a pro-level. Microsoft PowerBI is simpler than Tableau and offers a better intuitive interface, especially for the beginners. This tool can be used by a coder as well as a non-coder.

4. Visualisation
Power BI focuses on data modelling and offers features of data manipulation and then provides data visualisation while tableau strictly focuses on data visualisation. Learn more from Power BI online course

5. AI-Powered
PowerBI tool with its Microsoft Flow and its AI builder tool can help in building apps with a layer of intelligence. With the advantages of Microsoft AI, the user can prepare data, build machine learning models and gain insights from both structured and unstructured data. On the hand, Tableau is working on natural language capabilities to simplify analytics and help the users who have no prior data analysis experience, known as Ask Data. Recently, Tableau has also announced the beta version of Explain Data, a new AI-powered feature to help users understand the “why” behind unexpected values in their data.

6. Price
PowerBI offers two subscription offerings, Power BI Pro and Power BI Premium. Power BI Pro is priced at .99 per user per month which is a self-service BI where user can collaborate, publish, share and perform ad-hoc analysis. Whereas Power BI Premium is priced at ,995 per month per dedicated cloud compute and storage resource. Here, the user can perform big data analytics, advanced administration and more.

Tableau, on the other hand, offers three subscriptions — Tableau Creator, Explorer, and Viewer. Tableau Creator is priced at user per month and it includes Tableau Desktop, Prep Builder and one creator license of Tableau Server. Tableau Explorer is priced at per user per month where they can explore the trusted data with self-service analytics and the Tableau Viewer is priced at user per month and here the user can view and interact with dashboards and visualisations in a secured way. These pricings are for teams and organisations where multiple users/viewing is required.
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Is Power BI Actually Useful?

The short answer, for most of you, is no. However, the complexity and capability of the products could be beneficial depending on what type of position or organization you work in.

In my effort to answer this common question about Power BI I researched the following:
– Power BI Desktop Gateway
– Syncing on-prem SQL server data
– Syncing SharePoint Online list data
– Syncing data from an Excel workbook
– Building, and sharing a dashboard
– Inserting a Power BI visualization into PowerPoint

To get in-Depth knowledge on Power BI you can enroll for a live demo on Power BI online training

The feature spread above gave me the opportunity to explore the main features of Power BI which break down as:
– Ingesting data, building a data set
– Creating dashboard or reports with visualizations based on that data

In a nutshell Power BI is a simple concept. You take a data set, and build visualizations that answer questions about that data. For example, how many products have we sold in Category A in the last month? Quarter? Year? Power BI is especially powerful when drilling up or down in time scale.
And there are some interesting ways to visualize that data:
However, there are a number of drawbacks to the current product that prevented me from being able to fold these visualizations into our existing business processes.

1. Integration with PowerPoint is not free. This shocked me.

The most inspiring Power BI demo I saw at a Microsoft event showed a beautiful globe visualization within a PowerPoint presentation. It rendered flawlessly within PowerPoint and was a beautiful, interactive way to explore a geographically disparate data set. I was able to derive conclusions about the sales data displayed without having to look at an old, boring chart.

During the demo, nothing was mentioned about the technology required to make this embedded chart a reality. After looking into the PowerPoint integration I learned that not only was the add-in built by a third party, it was not free, and when I signed up for a free trial the add-in could barely render my Power BI visualization. The data drill up/down functionality was non-existent and not all of the visualizations were supported. Learn more from Power bi online course

2. Only Dashboards can be shared with other users, and cannot be embedded in our organization’s community on SharePoint.

Folks in our organization spent 50% of their time in Outlook, and the rest in SharePoint, OneNote, Excel, Word, and the other applications needed for producing documents, and other work. Adding yet another destination to that list to check on how something is doing was impossible for us. Habits are extremely hard to change, and I see that consistently in our client’s organizations as well.

Because I was not able to fold in the visualizations with the PowerPoint decks we use during meetings, I had to stop presentations in the middle, navigate to Internet Explorer (because the visualizations only render well in that browser), and then go back to PowerPoint once we were done looking at the dashboard.

This broke up the flow of our meetings, and led to more distractions. I also followed up with coworkers after meetings to see if they ever visited the dashboard themselves at their desk. None of them had ever navigated to a dashboard outside of a meeting.

3. The visualizations aren’t actually that great.

Creating visualizations that cover such a wide variety of data sets is difficult. But, the Excel team has been working on this problem for over 15 years. When I import my SharePoint or SQL data to Excel I’m able to create extremely customized Pivot Tables and Charts that show precisely the data I need to see.

I was never able to replicate visualizations from Excel in Power BI, to produce the types of visualizations I actually needed. Excel has the ability to do conditional formatting, and other customizations in charts and tables that is simply not possible with Power BI. Because of how generic the charts are, and the limited customization it looks “cool” without being functional.

In conclusion, if you have spare time and want to explore Power BI for your organization you should. However, if you are seriously thinking about how you can fold this product into your work processes, challenge yourself to build a dashboard and look at it once a week. See if you can keep that up for a month, and then think about how that change affected your work habits and whether the data analysis actually contributed value each time. At least half of you will realize that this gimmicky product is fancy, but not actually useful.

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