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Right Tool For The Job

Updated: Aug 6, 2020

The Business Intelligence (BI) market has a long history, dating back to the mainframe days, where business users received their static reports on a regular cadence, sometimes weeks after an event had occurred. The market has evolved and continues to do so at an increasing pace, fuelled by the power of cloud computing and users expectations. Today in many organisations we have dedicated analysts who produce interactive reports using visual tools like Tableau, PowerBI and Qlik. We have seen wait times for reports shrink from weeks to on average just over 4.5 days.

While this has been a significant improvement, there is a growing expectation from business users for self-service and closer to real time reporting. Layered on top of this is the expectation for ease of use and simplicity. Today’s business users expect the simplicity that they have come accustomed to with social media. Nobody has to teach a business user how to use Google, but just think for a second the complexity that is hidden behind that search bar and executed with every search. It's those expectations that they are bringing to the work place and driving the demands for improvements.

When considering which BI tools are required, it is important to consider who is going to be using the application and the problem they are trying to solve. What works for a data scientist or an analyst might not be what works best for the business user. You have to find the right tool for the job.

For example an analyst and data scientist will spend a lot of time and effort in data preparation tasks, where as the business user is unlikely to spend any of their time in this space. The data scientist will want to be able to develop models, and the analyst might want pixel perfect visualisations with fine control, whereas the business user wants a product that is easy to use and helps them discover insights.

What is consistent across all users is the need for performance at scale. The need for transaction level data has become a key requirement as we have moved into the age of personalisation down to the individual level. Our customers and clients are not satisfied being treated as a faceless group, they have an expectation of customised service and this can only be served by a business when we analyse data at the individual level. The time for aggregations and cubes is quickly becoming the way of the past.

So what does this mean for the technology landscape within our organisations?

It is reminiscent of the evolutions of the marketing landscape over the last decade. In 2011 produced the Marketing Technology Landscape which visualised the range of technologies that marketing teams were using. In 2011 there were 150 products on the landscape. With each year forward the number of products roughly doubled to the point where there are 8,000 different technologies in use across marketing teams in 2020.

Some of these products are incredibly niche, but they target specific needs and do it in such an efficient and easy to use way that it justifies the addition of the technology to a business. It is a landscape powered by SaaS that our IT teams have had to adjust to.

While I don’t believe that the BI market will have an explosion to this scale over the next decade, I do believe that the days of being a single “X Tech” shop are coming to an end. What I expect to see are a range of tools in use in an organisation that suit business users, analysts and data scientists. While this will bring some challenges, it will also enable business users to be truly able to self-serve and see the days of hundreds of hours of backlogs of adhoc requests for analysts fade into the distance.

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