Disruption in Business Intelligence
In January 2015 Gartner had said that there is a power shift happening in business intelligence (BI) space that will fuel disruption. Gartner also mentioned – “by 2017 most business users will have access to self-service BI tools for analysis”. Are you one of them?
Let’s first address what led to this disruption.
Businesses rely heavily on numbers. Whether it is profitability, growth or compliance, there is always a growing need to have numbers and facts in place. This need has fuelled hunger for data that has led to disruption in BI. We have categorized the drivers into categories –
- Big Data – Volume, Velocity, Variety and Veracity
- Ease of use
Let’s look at each of the drivers individually.
1) Big Data – Volume, Velocity, Variety and Veracity
As per the latest research conducted there are 6 billion people with cell phones that are generating the exponential amount of data on day to day basis. It is predicted that by 2020 there will be 40 zettabytes (43 trillion gigabytes) of data created waiting to be analyzed. Not one or two but there are many different forms of the data that are being generated. Billions of hours of videos on YouTube to billions of content on Facebook to millions of tweets generated on Twitter, the variety of data is ever expanding.
Rapidly all the electronic devices are getting connected through the internet. Modern cars have more than 100 censors to monitor items such as fuel level and tire pressure. The velocity at which data is impending is overwhelming. Keeping up with the veracity of data, the trustworthiness of the data is another challenge managers have to face. There are persistent data quality issues in the industry that leads to huge maintenance cost.
Big data technologies have already disrupted how data is stored, processed and consumed, and are extensively used by businesses and governments. Business users are now asking to have the ability to utilize the data being generated. Latest improvements in BI tools have allowed business users to access big data. Tools like Tableau, Power BI and Qlik provide native connectors to connect big data sources.
2 ) Ease of use
A Gartner survey back in 2011 ranked ease of use being the top most criteria. Why is it so important?
Historically speaking poor user adoption has been one of the main reason for BI projects failure. If the BI tool is too difficult to use, then the users will simply abandon it. The usability of a BI platform enables more people from across a business to independently access the benefits of data analysis to help them make better, timely, well-informed decisions. A BI tool that empowers all types of users to easily perform their job function to a higher standard will enjoy sustained user adoption
So what makes a BI platform easy to use for Business users? A few factors –
- Reduction in number of steps or click it takes for the users to achieve desired results
- Speed at which the reports are delivered should be minimal – excessive wait time leads to less or no adoption
- Availability of a metadata layer for users to reduce underlying complexities
- Intuitive and easy drag – drop features
- Ability to easily interact and manipulate data without intervention from IT
- Simple drill through and drill down options
- Ability to share insights and collaborate within the organization
- Mobile presence with simple and appealing UI
- Data visualizations that turn numbers into stories. The ability to derive actionable insights through visualizations
- An intuitive and appealing UI
As ease-of-use is the number one criteria for a BI tool selection, do keep above factors in mind when you go shopping.
BI solutions have evolved over a period of time. They are no longer used for creating reports with summarized numbers with drill-up and drill-through capabilities. BI solutions now have the ability to perform statistical analysis without going to other applications. Tools like Tableau provide modelling capabilities where a business user can use the forecast, trend lines, clustering features to further analyze the data. Modern BI tools allow users to integrate R for advanced analytics. The statistical models created in R can be seamlessly integrated with BI tools with a few configurations.
To conclude let’s have a look at recently published Gartner’s “Magic Quadrant for Business Intelligence and Analytics Platforms” report. More detail here.
Source: Gartner (February 2017)
Tableau, Power BI (Microsoft) and Qlik are clear winners. They have been able to incorporate all three categories – ‘Big Data’, ‘Ease of use’ and ‘Analytics’ as part of their product offering and disrupting the BI space.
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