Data analytics is an integral part of any business. Due to the vast amount of data and a lack of qualified scientists, companies are shifting away from traditional BI reporting tools. Instead, they turn to self-service big data processing. This article covers the most important information about self-service data analysis.
What is data self-service, and what are its challenges?
Data self-service enables all members of the organization to use data and generate insights. An experienced technical expert is not needed here. With data self-service, the employee can perform work efficiently.
Here are some of the challenges of self-service data analytics:
You might also like
- Ensuring data consistency
- Obtaining data from various sources
- Providing sufficient storage for a growing amount of data
- Educating end users in implementing self-service data processing
- Providing appropriate analytical and BI tools
How to implement self-service data
STEP 1: MOVE YOUR DATA TO THE CLOUD
The first step is to transfer data from the data warehouse to the cloud. This is quite an important step because the local data warehouse can have problems with such a huge amount of information. On the other hand, a cloud data warehouse excels at meeting the greatest demands on data self-service. These are primarily the storage of a huge amount of data and the need to quickly share and scale the infrastructure on demand.
STEP 2: DEVELOP A COMPREHENSIVE PLAN
Developing a comprehensive plan is essential before implementing any technology. Self-service data analytics is no exception. You need to ask yourself some crucial questions about end users, management, and your company goals.
STEP 3: CHOOSE A LOCATION FOR YOUR DATA
You should move your data to a centralized location to make data self-service easier. You can transfer your data to the cloud, as we wrote about in step one. Then you should focus primarily on:
- Obtaining data
- Data preparation and transformation
- Data storage
You can consider 3 cloud architectures. These are:
Infrastructure as a Service (IaaS)
The cloud service provider provides the user with a complete IT infrastructure, including:
- Network services
- Virtual servers
- Computing power
- Mass memory
- Operating systems in the subscription model
In the IaaS model, the virtual machines are owned by the service provider, and the user can use shared cloud services and resources. It’s an online infrastructure. There is no need to build your server room and operate physical IT devices.
Platform as a service (PaaS)
In this model, the user receives a prepared and properly configured cloud environment from the service provider. Based on this environment, he can use and manage any application.
In the PaaS model, the service provider is responsible for operating the storage, network, or servers. The user receives access to an environment dedicated to the development and implementation of applications.
Software as a Service (SaaS)
SaaS is about making specific applications available over the Internet. Thanks to this solution, users do not have to install additional software or invest in additional hardware and licenses.
The user also doesn’t have to worry about operating or maintaining the tool. Receives a ready-made solution, fully managed by the supplier.
STEP 4: DECIDE WHAT ANALYTICAL AND BI TOOLS TO USE
The time has come to decide which analytical and BI tools to choose. Of course, you can still use your existing ETL, reporting, and analysis tools. However, the cloud offers newer technologies that are sure to be better suited to your data analytics needs. But you have to know one thing. Analytics infrastructures and tools are structured differently depending on IaaS, PaaS, and SaaS.
STEP 5: IMPLEMENT YOUR SOLUTION
The next step is the implementation of your solution. However, before that, you should embark on POC. Proof of Concept is a basic project, which aims to verify whether you can implement your idea with the use of available technologies and whether the product will work as intended. As part of the POC process, you research new technology, learn how to use them, and build a minimum viable product. This approach gives you a chance to minimize the possible lost time when it turns out that the solution you want to use is not what you want.
STEP 6: BUILD A WELL-DEFINED SUPPORT STRUCTURE
What if there is a problem, and you need help? You need to have a well-defined support structure. We can distinguish two types. The first is the help that comes from the suppliers of tools and products. Most of them have a technical support offer. The second type is supporting business processes.
Conclusion
Today, almost every company is looking for a way to make faster decisions, hit the market faster, gain an advantage over the competition or introduce new products faster. Self-service data analysis is up to the task. And while implementing self-service data analytics has many challenges, it is an investment that may help increase productivity in your business. Find out how to start with big data consulting services.
Also Read: Top Solar Companies 2022.
