The COGs issue is a small example of the exponential problem with using reporting tools to do strategic data gathering. And that brings us to introducing the distinctly different business intelligence environment. Business Intelligence Infrastructure: The overall concept of business intelligence platforms is that they take that raw, two-dimensional, live data, parse it down to only the relevant, useable pieces, and then structure it, subjectize it, and finally, dimensionalize it so the data is completely optimized for analysis.
The start of this process is typically accomplished through the use of a data warehouse, which is a separate environment entirely from your production database. The data warehouse is not only a hyper-organized version of your ERP database; it supersedes a production database environment in terms of the data it can contain because it allows you to combine the data from other sources, such as CRM, a legacy ERP, or industry-specific system into one consumable place.
Also, you can consolidate the data pieces and eliminate clutter because the data warehouse is solely built and exists for getting the data out. In contrast, your ERP software database is solely built for—you guessed it—putting the data in. Therefore, the real magic happens when OLAP cubes are built or delivered from the data warehouse. OLAP cubes do all the work by dimensionalizing all combinations of slicing and dicing the data ahead of time.
They also allow you to store rules and pre-calculated KPIs—such as your COGs or Gross Margin—so results are available to delineate and breakdown on any other data point desired on the fly, and they are always consistent across users. For example, a Contribution Margin KPI can be obtained by salesperson, by product, by time, by region, or by reseller, or any combination thereof, instantly.
That instant part is one final important emphasis. As mentioned before, that is because data warehouse environments are not production, they are separate.
Reports, dashboards, and analysis that come from the data warehouse and cubes are not real-time, nor should they be. The data warehouse is synced with your ERP and the other sources you connect it to at defined intervals of time. Every night is extremely common, but in solutions that offer incremental loading and syncing, you can get much more sophisticated. For instance, you can refresh incoming orders every hour if having that more current data is important for your operation, but then save the rest of the refresh for overnight, mitigating any disruption on the system and data your drawing from during business hours.
If reporting is the bread, the must-have nourishment for survival, then business intelligence is the butter, elevating the bread by making it more consumable, relevant, and competitive, leaving you the ability to truly find your bread and butter. Perhaps a more helpful application analogy to summarize the difference between business intelligence and reporting is relating to the task of trying to get somewhere. Reporting is your map with all the data points, and it can be helpful to see the current landscape.
But it can also be interpreted in a slew of different ways especially as it relates to the best route. BI, in contrast, is the GPS that uses the map as a base, then culminates a combination of rules do you want to avoid tolls? How does BI work? What is BI? Data Synthesis A crucial feature of the BI concept is that it joins together data from two or more sources to give a more comprehensive view of the business than is available from separate MI reports from the individual source systems Time-series Many operational IT applications hold only current data or have limited historical reporting capabilities.
Job Description for Reporting Analyst. Reporting analysts design and develop metrics, reports and analyses to drive key business decisions. They provide a link between raw enterprise data and management, so data extraction, analysis and transformation are key job responsibilities. Data analysts translate numbers into plain English Every business collects data , whether it's sales figures, market research, logistics, or transportation costs.
A data analyst's job is to take that data and use it to help companies make better business decisions. For the first five to ten years in this position, pay increases somewhat, but any additional experience does not have a big effect on pay. Most people move on to other jobs if they have more than 10 years' experience in this career.
Data Science and Analyst jobs are among the most challenging to fill, taking five days longer to find qualified candidates than the market average. SAP Crystal Reports. Microsoft Power BI. Qlik Sense. IBM Cognos Analytics. Write Your Answer. Similar Asks What is the difference between potential and potential difference?
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