BI is all about turning data into insightful and actionable information. This includes information that can be used to improve sales, eliminate wasteful processes, and identify opportunities for growing your company. In other words, it is essential for making business decisions that are based on solid facts.
So, what is this data we are analyzing? In today’s era, every company possess tera bytes of data (or even more), data is everywhere – every data centers, every application, every device has exhaust of data available. Every internet sites, the social networks, the buzz words have enormous amount of data.
Now, the question is what do we do with this data? Could we refine these exhaust and convert to leading success of the company? Yes, that’s exactly BI helps us to achieve.
BI tool basically helps us to analyze these exhaust of data (data analytics) and generate reports to the stakeholders at a very minimal time. Mind you, everything is automated and nothing is manual (of course, apart from maintaining the application!). This way the resources are limited and productivity increases.
With the growing competitive market, taking right actions swiftly holds the key to company’s growth and success and to do that, the company rely on data. So BI helps them to extract current and history data and gives them the opportunity to slice and dice data giving them a full 360-degree view of the business and generate the reports in charts and graphs so as to help the decision makers better informed and to embrace strategic planning for business growth, by identifying key trends and patterns that have the potential to unlock new growth opportunities, streamline processes or even reduce the costs.
In short BI tools help businesses to improve the visibility to understand the customer needs better, be more efficient and get insight of what they are looking so as to make successful strategic plans. I would not be wrong if I just say – BI is one of the key spine for company’s growth and success.
If you want to drop a tool in front of the business users that they can jump into, explore, drag and drop, and create good visualizations on the fly, Tableau is likely the better bet. If you plan to have some IT involvement… some level of data modeling … and design/integration control, then QlikView may be the way to go, as I feel QlikView is better for creating a packaged, polished BI product.
First, one rarely hears the term KPI at a predictive analytics conference, but will often hear it at BI conferences.
In predictive analytics, one is more likely to hear these ideas described as metrics or even features or derived variables that can be used as inputs to models are as a target variable.a “use case” is frequently presented in BI conferences to explain a reason for creating a particular KPI or analysis. “Use Cases” are rarely described in PA conferences; in PA we say “case studies”.
Interestingly, DM, PA, and even Prescriptive Analytics are considered a part of BI. There is more cross-branding of BI in conferences that include BI-specific material, like Performance Management and Web Analytics conferences.
The difference lies in the kinds of questions predictive analytics asks compared to business intelligence. The word “likelihood” appears often in case of predictive analytics, meaning we are computing a probability that the pattern exists for a unit of analysis. In customer analytics, this could mean computing a probability that a customer is likely to purchase a product. Implicit in the wording is that the measures require an examination of the groups of records comprising the unit of analysis. If the likelihood an individual customer will purchase a product is one percent, this means that for every 100 customers with the same pattern of measured attributes for this customer.
One customer purchased the product in the historic data used to compute the likelihood. The comparable measure in the business intelligence lists would be described as a rate or a percentage; what is the response rate of customers with a particular purchase pattern. The difference between the business intelligence and predictive analytics measures is that the business intelligence variables identified in the questions were, as already described, user driven. In the predictive analytics approach, the predictive modeling algorithms considered many patterns, sometimes all possible patterns, and determined which ones were most predictive of the measure of interest (likelihood). The discovery of the patterns is data driven. This is also why many of the questions begin with the word “which.” Asking which line items on a tax return are most related to noncompliance requires comparisons of the line items as they relate to noncompliance.
The revelation talent of Tableau is miscellaneous and extremely discerning. Characteristics like “word clouds” and “bubble maps” are grand equipment to augment any command. The hierarchy graphs give the provision to attach framework for graphics. They are mainly utilized to demonstrate comparative magnitudes of various types of data. The potential for putting down a dashboard through “overlaps” is moreover an awfully commanding attribute. This characteristic facilitates well-organized employment of display space. Other additional benefits are high-quality swiftness of R & D, instantaneous correlation to extra 30 figures, possible to include with R, depending on guidance videos, articles and social media it rules the society construction labours, information partaking for complimentary, outstanding in graphs and maintenance of cubes.
How much data that we store, do we use
But storing large quantities of data is still expensive, so be sure you’re getting more value from your data than you’re putting in. Many companies are currently in limbo, paying to store data in the hope that it will provide value. Unfortunately, 43 percent of companies still gain little to no benefits from big data, and realize that value remains a future goal.
Disadvantages Of Tableau
There is a short of a particular selling observation of data. A venture business intelligence key has to supply a reasonable metadata cover for producing a solitary characterization of industry units like buyer and income as soon as the information lives in dissimilar set-ups in altered arrangements.
Customer groups can’t be created for various sizes which may be dangerous to the business.
There is no project coverage, forecast awareness and an announcement of time-based data.
It is slower than QlikView for finding out its memory statistics.
New connections of data cannot be prepared as it does not have the facility to do so.
Tableau has been leading the market for quite some time already as the company was the first to introduce a data visualisation tool and its product is still considered the best. The ease at which Tableau’s tool can be used is hardly comparable to any other product in the Data Viz world.
INTEGRATION AND SHARING
Power BI is integrated into the Microsoft Office 365 Suite, which means that it is highly compatible with all Microsoft (Office) applications such as Excel and others like SharePoint.
DATA SOURCES AND MODELLING
The number of Data Sources included in the Desktop Version of Power BI cover those that Tableau offers. In the online version, we can even find options such as the web query, which creates a dashboard from a web search (although it only works with Bing).
The Data Modelling options that are served after loading the dataset have improved significantly. One can create complex relationships between tables, add columns and rows, import from one table to the other, join, filter and much more.
Big data and business intelligence (BI) used to be only for enterprise companies. Now, however, thanks to the software as a service (SaaS) revolution, even small businesses can afford to track and tap into a wealth of information.
However, becoming a data-driven small business isn’t easy. Because you’re dealing with complex troves of records that have multiple sources and are therefore highly unlikely to be structured uniformly, it can be difficult to process it all and interpret it into insights your business can actually use.
What’s more, many of the leading business intelligence solutions require unreasonably expensive and lengthy onboarding processes and they can only be used by teams of coders. Not exactly small business-friendly.
Finance professionals can feel bombarded with new, expensive reporting and analysis solutions. Every few years, a new BI toolset is in vogue—Business Objects, Microstrategy, Tableau.
Meanwhile, under the radar, Microsoft has been building a powerful and flexible business intelligence toolset. And most of us already own it. In recognition of where data usually ends up being processed and analyzed, Microsoft has built those tools into Microsoft Excel.
PowerPivot, first made available as a free download for Excel 2010, puts the analytic engine developed for SQL Server into the hands of Excel power users. Finance leaders should take note, because these capabilities have the potential to significantly impact the reporting and analysis workstreams—from previously tedious template consolidation processes to data mining on the scale of millions of rows. With PowerPivot, basic BI solutions can be built by finance professionals, instead of waiting for some new system implementation or enhancement.
After being hands-on with PowerPivot and reflecting upon the range of different finance processes, clients, and specific reporting and analysis situations I’ve faced over the last decade, I’ve concluded that these capabilities need to be learned and deployed on a wider scale. The toughest quandaries I’ve seen, the most tedious processes, and the most annoying or complicated workarounds—almost all of them could have been much more easily dealt with if PowerPivot were in my toolbox.