The new technological revolution of Business Intelligence
Once upon a time, Business Intelligence… The first concepts of Business Intelligence appeared at the end of the 1980s through the wish of analyzing data faster and with more structure. Then, Howard Dresner (Gartner) proposed a first definition of the Business Intelligence as “the ability to apprehend the interrelationship of presented facts in such a […]
Once upon a time, Business Intelligence…
The first concepts of Business Intelligence appeared at the end of the 1980s through the wish of analyzing data faster and with more structure. Then, Howard Dresner (Gartner) proposed a first definition of the Business Intelligence as “the ability to apprehend the interrelationship of presented facts in such a way as to guide action towards a desire goal”.
From a technological point of view, it was at the end of the 1990s that sellers started to propose Business Intelligence solutions able to meet this concept. These offers were composed with tools allowing the creation of data warehouse, the development of solution ETL (Extract Transform Load) and OLAP (Online Analytical Processing).
At this same period appeared methodology with “instruction for use” for the implementation of BI (Business Intelligence) solutions. The famous star and snowflakes schema proposed by architects like Ralph Kimball (The Data Warehouse Toolkit) and Bill Inmon.
BI 2.0: Emergence of self-service BI
New Business Intelligence uses arose soon after agile methods (start of the 2000s). Thus, the self-service Self-service BIhas allowed to increase the end users’ autonomy facilitating their access to data source of the company and accelerating the BI dashboard production.
Todays, tools like Tableau Software (Tableau), Qlik Sense (Qlik) or Power BI (Microsoft) are part of leaders within BI agile tools.
These new tools come with new project organizations and new roles like the Data Stewart, responsible for the data quality and its referencing to business users. This new responsibility has also the advantage to make the link between the Business Intelligence service within IT department and the business itself, who is contributor of dashboard.
The Impact of Cloud and Big Data
Today, it is through the generalization of the use of Cloud and Big Data that we can see a real revolution of need regarding the Business Intelligence.
Thus, the progressive deployment of a part of our informatics infrastructure in Cloud (public or private) allows more flexibility in using material resources while respecting their costs. What a huge evolution (and what a huge technological achievement): the ability, in few minutes, to create a new virtual machine from a Web portal comparing with the never-end wait of the delivery, the installation and the configuration of our new BI server.
Simultaneously our client focus on Big Data, a new concept which consists of dealing with large volume of data when classical database management tools are overloaded. The Big Data, with its technical implementation (Hadoop), is not only for Business Intelligence but also for many other applications: log processing, connecteddevices’’ information processing, science research… More specifically in the Business Intelligence, the Big Data opens the way towards new analysis which were not possible before.
Azure Data Lake: the meet of Business Intelligence, Cloud and Big Data
The concept of Data Lake is a good representation of the Big Data use within the Business Intelligence. Far from being a new buzzword in the galaxy of new technologies, the Data Lake corresponds to the idea of breaking down silos which represents all the Business Intelligence applications to maintain within a department of the company.
Around the functional aspect, the Data Lake offers a data catalog for the business. Today, when the data volume that we exploit is exploding, a real referencing of data seems essential in order to take the best of it. This technology also allows to apply a governance layer on hosted data. For this, the Data Lake can include into its architecture some tools like Informatica or tools more specialized like Ab Initio.
One example of Data Lake solution is the Azure Data Lake, a good illustration of what can be the intersection between Business Intelligence, Cloud and Big Data. Azure Data Lake is a solution 100% Cloud within the portal Azure, the Cloud from Microsoft. Based on Big Data technologies (Apache Hadoop, Spark, Storm…), the solution proposed 2 different tools: Data Lake Store for the data storage and Data Lake Analytics for the analysis of the data stored in this store.
More than classical advantages of a data lake, this solution has the clear advantage of limiting hosting costs of dedicated servers and liberating from maintenance and configuration of tools in order to only focus on the data itself.
More information on: http://azure.microsoft.com/en-us/solutions/data-lake/
Alexis Sourceau I BI Practice Manager