Solving 3 Common BI Problems
Solving 3 Common BI Problems
Like any statistical practice, analytics needs to be approached correctly. Companies from different verticals run into similar problems when establishing robust practices.
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Adding a business intelligence strategy to practically any sized business can be a great investment — capable of yielding quick returns in revenue, as well as setting the stage for future revenue growth.
But like any statistical practice, analytics needs to be approached correctly. Companies from different verticals run into similar problems when establishing robust practices.
1. Not Enough Data
If you are using relational databases to fuel your BI, your data is most likely scattered across many systems that are incapable of speaking to one another. Ideally, you would access all of your data from a single entry point with a single query language. Additionally, it’s best to leave your everyday databases to do what they do best: operations. Although you may find it possible to run reports at a stated interval in your operational databases (once a day, etc.), it is preferable to have the flexibility to run ad hoc queries whenever you’d like. You should also be able to collect data from a much longer period of time than is practical with operational databases.
2. Data Is Not Usable
Raw data is not useful to a business. Rather, it needs to be processed and clearly presented. Thus, data coming in from your various sources needs to be standardized so that you can easily work with it. Clean and federated data will allow you to properly develop and train your models as well as apply tools like machine learning algorithms.
3. Data Outpaces Hardware
While generally, you would like as much data as possible to perform your analyses, a common problem is that the volume of your data can outgrow your hardware budget. So, your solution needs to be horizontally scalable on commodity machines or your capabilities will quickly run their course. And you don’t really know how your data needs will change, so you must be prepared for growth.
The Data Warehouse
If you are facing the above challenges with your BI and analytics program, a possible solution to them is a data warehouse. A DWH will allow you to collect your data in a standardized format and will establish a one-to-many system for your analysts to access information from across your business.
Through automatic ETL scripts, each of your data sources will be managed as it was previously, while at the same time feeding a central data warehouse to power a variety of tools.
And queries performed in a DWH will be much faster than the same queries performed in a relational database (see the video here for a demonstration of this).
Finally, you will be able to collect as much data you’d like.
But setting up a data warehouse can be a costly undertaking.
An economical solution is TaranHouse, a cloud and on-premise data warehouse that works with popular BI tools such as Qlik, Power BI, and Tableau, and which includes ETL setup in its annual contracts.
TaranHouse can be queried with REST, Python, or SQL. And full support is included for 24-hour deployment in the cloud with fault tolerance, hybrid storage, an advanced query scheduler, and full horizontal scalability.
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