Every user has a different level of skill and their own particular goals when it comes to data. And, in an ideal world, they should be able to answer their own business questions to gain data-driven insights.
In today’s BI environment, user-driven analytics is slowly becoming the norm. But this wasn’t always the case.
The evolution of business intelligence
The concept of business intelligence originated in the 1950s, possibly even in the late 1800s, long before anyone conceived of a computerised system! Either way, the field developed to help organisations improve how they do things, from the business of war, to the continual battle of outmanoeuvring their competitors.
Business intelligence has transformed over the years, driven by the changing needs of users. Initially, a tool to help leaders improve industrial processes, it was adapted to harvest Big Data with the rise of social media and eventually to include solutions directly where users work, enabling them to drill-down into data and steer their daily decision-making.
Vizlib’s Viztips enable users to get a sneak-peak of their data before drilling down into more detail
The first wave of business intelligence
The BI transformation journey began with the first wave, which relied heavily on data science experts using slow, complex stacks to generate reports. These data experts were a select group of individuals trained and experienced in handling technical data science tools. As a result, the first wave was inevitably slow in responding to changing business requirements and limited further by a captive market including players such as IBM, SAP, Microsoft and Oracle.
The main constraint of this approach was the time it took to get your questions answered. Since the data experts were the only ones capable of getting the answers, it caused major bottlenecks in the data pipeline. Those asking the questions had to often wait weeks to get the insights they needed. Not only did follow up questions then slow down the whole analytics process further, but the leadership weren’t even asking the right questions. The result was a limited gain for organisations.
It was time for a change.
Throwing the user ‘net’ wider with the second wave
The second wave widened the user base from only data science experts to include a new group – power users. With this iteration, analytics tools also became easier to use. Power users or business analysts could also analyse and generate reports without relying on a select few data scientists.
But the new wave had its ups and downs too. Yes, it fast-tracked the data pipeline considerably, but with a growing understanding and popularity of data, also swamped the business analysts with individual requests for insights. The average business user did not have the expertise to perform these functions themselves. Again, businesses didn’t realise as much value as they could from their BI, as these end-users frequently didn’t know what to do with the insights they received.
Then, the social media boom in the early 2000s introduced a paradigm – Big Data. A great opportunity for businesses, but only if they had the resources to leverage the growing data deluge. Organisations needed BI solutions, such as computing in the cloud, that empowered more users and brought users and data closer together.
The third wave – a power shift
Now the third wave of BI is forming, and with it, a shift from relying on slow, complex technical systems and reports (and a select group of specialists) to simpler interfaces that broaden the user base considerably. Finally, the power is being transferred to end-users to explore and analyse data themselves. And with fewer vias in the insights journey, business users can acquire vital data in time for agile decision-making and value-generating action.
Yet, for the current wave to truly reach its peak, it requires a two-way flow. Not only does BI success hinge on intuitive solutions and timely access to data, but it also requires something essential from the end-users; data literacy.
Users need to be data literate to work with data effectively; to turn insights into valuable action. And those users that aren’t, can easily become the weakest link in an organisation’s analytics pipeline, limiting the amount of value that can be extracted from data.
One approach to boost data literacy and scale-up BI in an organisation is to provide business users with intuitive, user-friendly solutions. Platforms that build their confidence to slice and dice data themselves, without jamming up business analysts and data scientists with a continual barrage of individual data queries.
Closing the insights gap with self-service analytics
Gartner defines self-service analytics as a form of business intelligence (BI) in which line-of-business professionals are enabled and encouraged to perform queries and generate reports on their own, with nominal IT support. They also reiterate the importance of using simple BI tools with basic analytic capabilities and a simplified data model, so all users have quick access to data and a better understanding of the analytic process.
Self-service analytics should enable all users to perform ad hoc analysis, queries and create reports on their own. And ideally, transform users from consuming reports constructed by data scientists to consuming data. They should feel confident and competent to get to the Why behind the data and, in the process, reveal previously undiscovered insights.
Boosting user adoption and business value with Vizlib
With Vizlib Self-Service, a value-added product for Qlik Sense, users can freely slice and dice data and extract quick insights without the risk of breaking anything or losing vital information. The power of data exploration is truly in their hands.
Vizlib Analytic Search enables quick and easy data exploration
Analytic Search for Vizlib Custom Report lets users query their dataset by typing or speaking—like googling, but within a governed dataset in Qlik Sense. No unexpected results or incorrect calculations that make little sense in their business context. This means that even the least technical users can easily deep-dive into their data confidently, relying on their own aptitude alone. (Along with simple, dynamic analytics solutions, of course!)
To get the best returns from your chosen BI solution, make sure it’s easy-to-use and intuitive. The platform should enable all data users to analyse data easily and confidently, promoting a data-driven culture. With the power in many hands, the business will inevitably generate more insights and ultimately greater business value!
