A product's price, design or advertising are no longer the sole parameters to beat the competition. Rather, understanding customer needs, knowing their ability and willingness to pay are the factors that decide competitive advantage. Data's role in a business has never been more valuable. We have now entered into what can be called as the third generation of business intelligence (BI). But the role of a BI team has always remained the same – to understand and analyze the past and use that knowledge to make better decisions in the future. So, what are the innovations which have helped in shaping up the current BI scenario?

1. Technology Shifts

The early 2000's saw BI platforms building products based on the architecture popular at that time. More users meant more data and that led to buying high-performance desktop computers with single CPU server systems that came with greater memory and directly attached storage. The first two generations had a data-centric stack as the design point whereas the third generation is moving towards network centric stack. When mega-vendors like IBM and Oracle were busy consolidating, critical technology shifts started to appear. The earlier BI solutions were mostly desktop installed and enterprise software products were difficult to deploy globally. But with time, the web became the central design point and thus a fully web-based architecture was developed offering a simple installation process and faster deployment options.

2. Data about Data

Vendors had the insight that analytics was an enterprise-wide function and not just limited to a desktop and hence products with proper governance and security must be designed seeing the involvement of the enterprise. Products were built around a metadata layer. A metadata repository stores and manages metadata. Three types of distinct metadata exist – Descriptive (for discovery & identification of a resource), Structural (giving information of containers of data and describing types, versions and other features of digital material) and Administrative (giving technical information like file type, how was a particular file created and who all can access it).

3. Storytelling with Data

Earlier BI solutions made use of tools focusing on just reporting and dashboards. But with evolving architectures, BI solutions also made functional evolutions. A skilled person might say that a particular set of data has given insights that are quite self-evident. But insights must also be understood by your audience in the same way. Telling stories through data can help bridge that gap using analytical logic. A dashboard will only give graphical insights or tables that a user might not fully understand. To understand the real meaning of the data, solutions were created to bring in tools for storytelling.

4. Smart Data Discovery

Augmented intelligence, also known as smart data discovery, is rapidly setting the scene for the next fundamental shift in the analytics landscape. According to the Gartner's 2017 report on 'Critical Capabilities for BI and Analytics Platforms', "By 2021, the number of users of modern BI and analytics platforms that are differentiated by smart data discovery capabilities will grow at twice the rate of those that are not, and will deliver twice the business value." Early adopters vouch for the lightning speed to insight just by simply asking questions and getting answers. Without any data modeling, machine learning algorithms can analyze billions of data combinations across various data sources. Presently, only a few vendors have been able to create such tools and besides such solutions currently don't allow users to know why something happened.

Modern BI platforms are now expected to allow highly scalable end-users. A modern BI server should provide services like delivering reports and analysis to hundreds or thousands of users simultaneously. With no extra charge, the modern BI platform should be taking full advantage of today's network-centric stack.