Beyond Standard Business Intelligence Tools
No one wants to simply “meet the standard,” and in no field is this truer than in business intelligence, in the true sense of the words. The fields of Big Data, analytics, and business intelligence are constantly being developed, grown, and driven forward by innovators and trailblazers, which means businesses need cutting edge tools in order to stay ahead of the curve.
Take a look at what we can expect from business intelligence tools in the near future as we journey ever further beyond what we believed the standard to be.
A Focus on the Internal
The way in which businesses approach the data they use has shifted, and so business intelligence tools must either shift accordingly, or be flexible enough to accommodate this new normality.
Data can be viewed in two basic categories: internal and external. Once, external data was the be all and end all of customer intelligence. Now, focus has shifted towards internal data. Of course, data from external sources is still important, but ignoring the vast reams of data that can be drawn from within is a huge mistake.
So, what does this mean in real terms? Well, it means that Data as a Service—orDaaS—providers and expensive data consultancy services are less relevant and will become somewhat less prevalent in the business landscape. Company owners now understand that the data resources they need to tap into are already within their reach, and this is inspiring them to look inwards in search of insight.
In order to achieve this, they need self-contained and genuinely intelligent tools which they can apply to their own processes, systems, and interactions. Businesses which make this their primary source of insight, supporting this with data drawn from external sources, are more likely to be successful in the long run.
Machine Learning: Understanding Data
Knowing where the data comes from and resides represents just one step in the ongoing journey towards insight and intelligence. This data then needs to be processed and interpreted; interpreted not only by the end user, but by the tools and systems themselves.
For this to be possible, the next generation of business intelligence tools will need to incorporate powerful artificial intelligence functions. The tools will need to actively learn and understand the data with which they are presented.
This is the next big leap in terms of uncovering insight and processing big data without the usual data wrangling and data analysts to interpret the output. Harness this effectively, and businesses will be able to make accurate projections into the inner workings and future of their market, and thereby dramatically improve the efficiency of data intelligence.
It is a simple equation: automate business intelligence processes to reduce cost, manual input, and working hours, and boost overall return on investment. As tools become increasingly intelligent, the benefits of this process will grow exponentially.
Data of all Shapes and Sizes
Big Data has been a primary focus of business intelligence initiatives for several years now, which means that traditional BI tools are geared towards crunching large quantities of data at a macro level. However, moving into 2017, this is changing.
Universal data is rapidly becoming the norm, as business owners begin to understand that a) you can never have too much data and too much insight, b) although there is a data hierarchy, “lesser” data should still not be ignored, and c) almost everything is a potential source of data. This trend has become ever more persistent with the emergence of the Internet of Things (IoT).
Where data tools are concerned, this means a far greater volume and diversity of data to process than ever before, and in increasingly complex configurations. This means that business intelligence tools with increased scope, enhanced customisability, and greater processing power are now essential.
In the future, it will be the businesses which are able to identify and analyse unique, disparate data sources—both internal and external—who gain the most valuable insights.
Case Study: Amerisure
American insurance companyAmerisure recently found themselves forced to revamp their data extraction processes after they discovered that their reporting backlog was more than two years behind.
Not wanting to turn their back on such a valuable pool of internally-sourced data, Amerisure opted to streamline their processes by creating an analytics portal, applying analytics directly to the vast array of data with which they had been confronted.
The team at Amerisure also discovered that it would be impossible to simply parachute a framework over the top of the whole operation, providing a quick fix. Instead, they developed a roadmap towards their data objectives, adopting an iterative approach. This is something that many organisations will have to adopt and utilise in the coming years, as they discover that they are as yet unprepared for such wholesale changes.
How much will change in the near future, it remains to be seen. What is for certain, however, is that the fields of business intelligence and data management are in a constant state of flux and development. It is only by adopting the right mix of tools—business intelligence tools which go beyond the standard and incorporate semantic processing such as Latize’s Ulysses software—that your business can secure Big Data success for this year and beyond.
