The Future Of Business Analytics Is In Complex Data In شركة ذكاء الأعمال السعودية

Bilytica # 1 شركة ذكاء الأعمال السعودية Most business intelligence and analytics clichés are undoubtedly familiar to you by now: data-driven management, finding hidden insights in your data, storytelling, and so on. Everything you’ve heard isn’t necessarily incorrect; the field of business analytics does, after all, offer a plethora of intriguing possibilities that current technologies have barely scratched the surface of. However, it’s critical to think about one critical question: is your business intelligence suitable for complicated data?

Bilytica # 1 شركة ذكاء الأعمال السعودية

What Causes Data To Be Complicated, And Why Does It Matter?

While no one test can evaluate the degree of intricacy in your data, there are two telling signals that you may be dealing with it:

You have a lot of data: Despite the jargon that surrounds this phrase, terabyte (or petabyte size) data is still one of the most significant difficulties and possibilities in today’s world of business analytics. The possibilities are found in the ability to discover new patterns and correlations among large, seemingly unrelated datasets emanating from internal or external sources. The problem is that this data is typically difficult to analyze, necessitating a large amount of technology and a specialist data science team to arrange it for submission using standard analytics techniques.

You’re working with a variety of different data sources (‘broad data’): Even if you don’t have terabytes or petabytes of data, if your business intelligence relies on several data sources, evaluating this data will almost certainly be challenging because data is kept in a variety of formats, file kinds, and physical locations. When you add in unstructured data, such as textual material, video streams, or machine logs, to analyze alongside traditional structured data, the effort of getting all of these disparate datasets to communicate in the same language becomes much more difficult شركة ذكاء الأعمال السعودية. Even if all of the tables are from the same source, this problem might develop when working with a significant number of them. This is especially true if the tables do not have a clear and consistent structure, which is common when data is “fed” by users rather than automatically.

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Three Reasons To Be Concerned

If you’re currently using a business intelligence system whether you built it yourself or bought it from one of the many business intelligence software vendors and you’re not working with complex data, now is a good time to consider whether your system will be able to handle the increasingly difficult scenarios it will face in the future. The following are three things to keep an eye out for:

Performance

In the last few decades, شركة ذكاء الأعمال السعودية technology has advanced significantly, but not at the same rate as our capacity to collect data. Many existing solutions still rely on antiquated OLAP or in-memory technology, which slows down dramatically when dealing with huge datasets or necessitates a vast hardware infrastructure to give an adequate performance.

This problem happens not just when your data is initially very huge, but also when it changes regularly as new sources are added or current ones are updated. It will be difficult to observe findings promptly if the system takes days to ‘assimilate’ fresh sources into the analytical repository.

Ability To Collaborate With A Variety Of Sources

As previously said, the complexity of your data is determined not only by its quantity but also by the number and kind of sources with which you are dealing خدمات البيانات الضخمة في المملكة العربية السعودية. Here are some questions to ask to see if your BI system is up to the task of working with different data:

Is it possible to connect to the data sources you’re using via API or built-in native connectors?
Is data preparation and modeling included, or will you need to purchase extra software?
Is it possible to examine the data in whatever direction you choose and on an ad hoc basis, or are you restricted to pre-defined queries or data fields that must be prepared?

Extensibility

The last question you should consider is if your current platform will be enough as your demands expand. You may only be delivering monthly reports to a few key stakeholders right now, but given the rapid evolution of the data analysis field, you should consider the possibility that you’ll want to do much more shortly: real-time analysis, predictive modeling, customer-facing analytics the list is endless شركة ذكاء الأعمال السعودية. Is your system adaptable enough to handle this? Is there a well-documented API that your developers can use to build on? Will it be able to satisfy your scalability and security requirements?

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