By Enrique Falconi, BI solutions specialist at SAS Brasil
A new dynamic is taking over business intelligence and increasingly demanding data analysis tools. Currently, it is no longer enough to be assertive - you need to be agile. In other words, the traditional BI solution model is out of step with the pace required by business.
In fact, the speed of data transformation in the Big Data era can make information irrelevant in a few weeks - or even a few days in the face of market changes. Currently, generating only reports and dashboards by traditional means of developing BI projects has lost its competitiveness due to the evolution in the capture and advanced analysis of data.
Keeping an eye on this and other trends for 2016, Forrester pointed out that traditional companies will be faced with the disruptive need to find more agile analytics solutions and processes in order to work more dynamically. In addition, Gartner has identified that, by 2017, most Business Intelligence (BI) solutions will be self-service Data Discovery models.
For five years, Data Discovery, a new architectural model combining self-service data visualization and exploration techniques, has been overturning the three-decade hegemony of traditional BI. With Data Discovery resources, data analysis was independent of the IT team, which usually allocates a lot of time and resources to corporate projects that are not justified for those who expected quick and punctual responses.
The use of easy-to-view tools and ad-hoc departmental analysis models made it possible for each area of the company to work on specific layers of the database. This means that self-service BI made it possible to work with analytics without having to structure all the data generated by the company, which reduces the time from months to a few weeks.
In this sense, conventional BI, used to obtain deep insights about the company as a whole, lost its preference in the face of the need for quick action by the commercial teams and other departments in the companies. Despite the deepening and data validation architecture of this project development model, the process is still slow and can take from six months to years to deliver results.
This does not mean that the typical BI architecture should be discarded - quite the contrary. A study by Gartner identified that the key to competitiveness for the coming years lies in a bimodal model of conventional analytical intelligence with the flexibility of Data Discovery.
The problem is that, captivated by the agility of self-service, many companies are totally abandoning the structuring BI projects in exchange for data exploration. At first glance, gains in agility seem to meet the most immediate needs of companies. However, many are already showing adverse reactions regarding the superficiality and reduced assertiveness of the reports generated. The level of effective depth brought in at the organizational level and responsible for the most strategic directions of the companies has been lost.
And considering, according to Gartner, that 75% of companies will invest in Big Data by 2017, competitiveness will be based far beyond those who simply use data visualization tools or not, but on those who know how to combine agility and stability, which are the fundamental bases of the bimodal future of intelligence and business.
In fact, the speed of data transformation in the Big Data era can make information irrelevant in a few weeks - or even a few days in the face of market changes. Currently, generating only reports and dashboards by traditional means of developing BI projects has lost its competitiveness due to the evolution in the capture and advanced analysis of data.
Keeping an eye on this and other trends for 2016, Forrester pointed out that traditional companies will be faced with the disruptive need to find more agile analytics solutions and processes in order to work more dynamically. In addition, Gartner has identified that, by 2017, most Business Intelligence (BI) solutions will be self-service Data Discovery models.
For five years, Data Discovery, a new architectural model combining self-service data visualization and exploration techniques, has been overturning the three-decade hegemony of traditional BI. With Data Discovery resources, data analysis was independent of the IT team, which usually allocates a lot of time and resources to corporate projects that are not justified for those who expected quick and punctual responses.
The use of easy-to-view tools and ad-hoc departmental analysis models made it possible for each area of the company to work on specific layers of the database. This means that self-service BI made it possible to work with analytics without having to structure all the data generated by the company, which reduces the time from months to a few weeks.
In this sense, conventional BI, used to obtain deep insights about the company as a whole, lost its preference in the face of the need for quick action by the commercial teams and other departments in the companies. Despite the deepening and data validation architecture of this project development model, the process is still slow and can take from six months to years to deliver results.
This does not mean that the typical BI architecture should be discarded - quite the contrary. A study by Gartner identified that the key to competitiveness for the coming years lies in a bimodal model of conventional analytical intelligence with the flexibility of Data Discovery.
The problem is that, captivated by the agility of self-service, many companies are totally abandoning the structuring BI projects in exchange for data exploration. At first glance, gains in agility seem to meet the most immediate needs of companies. However, many are already showing adverse reactions regarding the superficiality and reduced assertiveness of the reports generated. The level of effective depth brought in at the organizational level and responsible for the most strategic directions of the companies has been lost.
And considering, according to Gartner, that 75% of companies will invest in Big Data by 2017, competitiveness will be based far beyond those who simply use data visualization tools or not, but on those who know how to combine agility and stability, which are the fundamental bases of the bimodal future of intelligence and business.
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