Self-service BI has become one of the most used and required tools to extract the most from existing data and bring value to organizations. If you’re thinking about why high-performing data and analytics are required for a company, the reasons themselves can be summarized into a completely different article, but in short, good analytics initiatives are required to have a competitive advantage, improve product performance, increase customer satisfaction, etc. as stated by a recent study by McKinsey & Company.
But this level of competitive advantage can only be created by modernized business intelligence tools and cannot be achieved with legacy BI tools. And some companies are stuck up with legacy systems that think of changing their tools but don’t know how to proceed with it. So, let’s look at what the goals and process of a Business Intelligence Modernization should be:
- Needs Assessment: Assessing the speed of change and what you need would be important to understand the multitude of data sources or new fields that would be required to be added.
- Multiple Data sources Connection: This is one of the reasons for shifting from legacy BI solutions to modern BI solutions as this connection is not possible with legacy systems.
- Superior performance: With the amount of data being generated increasing every year and with organizations moving to online or cloud-based systems, there is a need to have superior performance and decrease data load timings for effective information gain.
- Support Data latency requirements: For Real Time, Operational and Analytical Reporting, can be thought of as the three reporting types for which the data latency should be enough to support the users.
- Reduce overall cost: When compared to legacy BI, the licence models for modern BI software, as well as the costs of computation and storage, have drastically decreased, resulting in a significant cost reduction if applied effectively.
Understanding the Data Latency
Just above we have spoken about the data latency requirements above. But people don’t understand the seriousness of how Data latency requirements are. To understand this let’s talk about Real-Time, Operational and Analytical reporting Data Latecy requirements to get a better understanding.
For real-time data requirements, which can be understood with the call centres or the production planning requirements, the need would be to have data as real-time as possible which needs very low data latency i.e., the time to load the data from a data warehouse or business intelligence dashboard. One of the best ways to do this is used Embedded Analytics or products like Accio Qlik to have real-time reporting. And we don’t have to talk about how much of an advantage it would be in analyzing data for continuous processes.
For Operational data, the data sources would increase but the latency required need not be too fast, the time can range from a few hours to days depending on the requirement by operational managers. This reporting is useful for analytical purposes like knowing the order or call volumes over the last few days, count of the marketing leads generated etc. Some time might be required for this data as the data sources might be available in multiple sources like connecting the PoS data to the CRM or the retailer data etc. Overall, this data is useful for making operational decisions for the immediate future.
For Analytical reporting, the data required would be massive and would be needs for a span of time. This data mostly used by executives is to understand how the profit margins have fared across the years or understanding how the sales data has been distributed across the geographies etc. One of the key requirements of such data is the UI which needs to self service and user friendly such that a lot of data but can be refreshed once a week, month etc. depending on the user requirements.
With this understanding, you might understand the importance of having a modern BI tool when compared with a legacy system as the requirements and the flexibility of the modern systems wouldn’t available with old BI tools. It is why companies like Nvidia is modernizing their BI systems.
And one of the ways to achieve this Business Intelligence Modernization is to create a holistic view to answer important cross-functional questions and use trials to understand which system would be suitable. One of the prime examples of this modernization can be with QlikView to Qlik Sense. Though there are enough capabilities of QlikView as a BI platform and there are companies being benefited from it, Qlik Sense provides better connectivity to data sources, provides better Governed self-service analytics and has the added benefit of using NLP based insights and Active chart suggestions.
QlikView to Qlik Sense migration
Some companies might still be using QlikView and some have transitioned to Qlik Sense but it is important to understand the difference in features and how Qlik Sense is better than QlikView. Don’t get me wrong QlikView is a good tool, but when you can get better why not try for the upgrade with the similar experience. And speaking technically, the main difference between the two is the visualization layer with the ETL and backend layer being the same.
|Free-form associative exploration||Yes||Yes|
|Advanced data preparation||Yes||Yes|
|Broad data connectivity||Yes||Yes|
|Governed self-service analytics||Yes||No|
|Data mining and analytics||Yes||No|
|Visual data preparation||Yes||No|
|Modern platform built on open APIs||Yes||No|
|AI-enabled insights, chart suggestions||Yes||No|
|Natural language processing||Yes||No|
This is one of the key examples of how BI Modernization from legacy systems to Modern BI solutions can be very useful for organizations and improve the way Dashboarding, Analysis, and Reporting can be done with ease. And with Qlik View convertors it can be transitioned from one to another with the help of a Qlik Partner.
When it comes to upgrading your company’s data architecture, it’s a good idea to divide your present BI environment into three categories: real-time, operational, and analytical reporting and understand the significance of each of the models and their requirements. By creating architectures for all the three systems it would be possible to understand the data latency and future data requirements.
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