The Story So Far
The invention of supercomputers brought down computation speeds considerably. The evolution of such computers in the 50’s and 60’s brought down the costs of acquiring and using one for day to day purposes. More and more organizations wanted these machines for complex computations which led to the birth of Enterprise resource planning (ERP) systems.
Material resource planning (MRP) systems were the first set of proper automated planning systems to be used by manufacturers for production planning.
In the 70’s and 80’s planning and data management spread to other functions such as human resources, finance, accounting.
The 90’s then brought about the commercial use of Business Intelligence (BI) and Analytics.
It was the need of the hour for companies to use the data they were gathering for insight into various aspects of the business and derive decisive actions to increase their competitive advantage. Not to mention, reduce costs across the organization.
Thus, organizations started storing each and every type of data that they could think with the pretext of using it for some form of analysis in the future. Storing every bit of information required lots of infrastructure and associated costs.
The larger companies had the capacity to invest in servers for storing and processing data.
The smaller and medium enterprises (SME’s) could not, however.
In the advent of the Dot-com bubble, Data center’s sprung up all over the world due to high-speed internet connectivity. This allowed the SME’s to collaborate with companies who had setup up data center’s and allow their data to be stored at these data center’s and use the data center infrastructure for data processing. This setup henceforth was commonly referred to as cloud computing.
Business Intelligence as a concept has been existing for a few centuries. With the breakthrough of supercomputers came the technological side of BI which meant completing a task faster than what it would take if it was to be completed by a human. Because the technology was expensive and required a lot of space it was not common for organizations to invest in technology for the purposes of using BI. Only when personal computers were invented and then tools like Lotus notes and Microsoft Excel were accessible, organizations of all sizes ventured into using technology for BI purposes. These tools also gave users a chance to represent data in a graphical format thus allowing a better way of analyzing data and their business.
The Last Decade
The rise of spreadsheet-based tools provided end users the perfect platform for ad-hoc analysis and represent that graphically in the most intrusive form. The dominance of these tools was categorised to its ease-of-use, features, quick report and graph building turn-around-time, ease of sharing between users, the wide variety of formatting options.
All major ERP systems had to get themselves compatible with these tools.
They had to ensure data could not only be an uploaded from the spreadsheet into the ERP but could be extracted from the same ERP in a spreadsheet format.
Thus, spreadsheets became the most common tool of use because the users could extract data, perform the analysis, draw results whether in a tabular form or graphical and present it using only one platform throughout the analysis procedure.
This was all very good for organizations of all sizes. But spreadsheets still had one and only one major drawback. The technological incapacity to process data beyond a certain point. Moreover, organizations irrespective of their size started to realize the power of BI and the positive effect it had on business. Thus, started the evolution of BI tools.
Tools which had one and one only objective; provide means for importing, analyzing and/or presenting information using large volumes of data or Big data which could not be done by any available spreadsheets.
Most tools that existed in the market worked in silos.
Some could only perform ETL operations or provide means of aggregating data or provide a superior formatting and presentation platform.
But there weren’t any known tools which could do everything together and everything efficiently. They probably were prominent and one dimensional and were trying to evolve into a one-stop shop for BI and analytics.
Albeit, a lot of them evolved in the same direction as the requirements of the BI end users i.e. their core was BI and analytics and rest of the features were built upon this core. Tableau, Jaspersoft, Qlikview, SpagoBI were some of them that evolved into excellent tools each one creating its own space in the market. The only thing common between them was a desktop based application where the user would create content and a web-based platform for sharing that content. Thus, all of them were trying to provide the similar output which includes a long list of BI requirements. These include data collaboration, data discovery, master data management, data quality, data integration, data visualisation, data analytics, data science, data storytelling, predictive analytics, cloud BI, Hadoop and Big data, self-service BI.
The next few years…
With BI being a focus for almost all companies the market is now flooded with all kinds of tools. Each competing for some space or the other. However, this cluttered segment shall not remain for too long. Just like how SAP and Oracle created their own niche in their ERP segment, two or three of the mentioned players shall do the same in the comes months or years. This will change once organizations start looking at BI tools with more than a generalistic way i.e. prefer BI tools based on certain set of features. For example, some of them have already started preferring tools which give an excellent could based platform. Thus, from the long list of BI requirements, the following might be a trend in BI and analytics space for the next few years. The top three include:
Master data management
In BI space master data management is the equivalent of data consolidation from various silos into a combined set. A lot of organizations even today have data stored in different module across different erps. This makes BI and analytics extremely difficult since data is spread far and wide and the time consumed on ensuring data quality is not compromised during the consolidation process. In other words, we can say data quality and data management go hand in hand. Organizations always relied on ETL tools but now shall look for an integrated platform. What this feature does is allows flexibility for organizations that do not have data on one platform and therefore spend time on merging data. This will also evolve into tools allowing combination data from all platforms available i.e. if sales data, supplier information, manufacturing data, warehousing are all on different servers the BI tool shall provide the capability of coming this within its own environment as required by the end user. This is irrespective of a size of data existing in each of the data sources being imported into the BI tool. This will majorly reduce the time to insight which actually is the main objective of any BI tool.
Although most BI tools have the objective of end users creating content and using that for analyses, each BI tool has its own learning curve. Moreover, if the learning curve is steep most users tend to give up on a tool thereby adapting or adopting the one they were using earlier. The flipside of this is content creation on these tools is then handed over to the IT team of the organization as they would have a smoother learning curve. This would then lead to a lot of back and forth communication and dependency on the IT team. The efficiency of achieving the desired result would not be high as compared to a self-service BI. This means each user is capable of creating his/her own content without the support of their IT team. This will allow fast turnaround times for insight and is a huge factor for our next trend, Mobile BI.
The evolution of Laptops might have led to the ease of sharing business insights across geographies. But this had created device dependencies. While BI was growing across the world and establishing its requirement for every organization, the smartphone market was born. Before half of the world recognized the impact BI could have on businesses almost all the users creating BI content or at least consuming it had a smartphone. All the BI tools mentioned earlier took the smartphone evolution as another stepping stone and use this platform to evolve their own products. They tried to provide a similar platform if not exactly the same for developing BI content on a smartphone. The features being the same, only an extension to the existing desktop platform. It may not be easy to extrapolate the impact of using BI on a smartphone today. But with more and more businesses adopting smartphones and tablets as their choice of device for means of communication and day-to-day use Mobile BI shall be the next sought after requirement in BI and analytics.
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