Promising statements like “information is the oil of the 21st century” and “data is becoming the new raw material of business” abound in the world of data. However, analytics solutions are what really bring the value of data to life.
Data preparation is necessary prior to the commencement of analytics. The cornerstone for efficient and successful analysis is this step. Data infrastructure now joins the fray in this area.
Data infrastructure, which consists of data assets and methods for managing them, is crucial in converting data into useable information and opening the door for it to become insights.
If the only engineers who can access your information are those who are presently working on the product, it doesn’t matter how neat and well-organized it is. Reports in this situation typically take weeks. Over half of respondents to Broadcom’s survey on the condition of big data infrastructure have already started executing big data initiatives, and another 29% aim to do so soon.
If you don’t have a business intelligence (BI) platform in place, your engineers will extract the necessary data from a data lake, a data warehouse, or some other location, and then transmit the data to analysts. On the other hand, this stage is automated by mature BI systems, making it simpler to obtain the data you want when you require it.
If becoming a data-driven organization is your aim, you must create an architecture that allows all employees to access and evaluate a specific data network.
How can data preparedness be ensured? You must have a well-planned data strategy and be aware of how this data will be used. During the project and implementation phases of the data strategy, security needs and security rules should always come first.
In 2020, a total of 64.2 zettabytes of data were generated. It was just 15.5 zettabytes in 2015. The staggering increase in data flow over the past year has only been increased by the epidemic as more individuals than ever have been working and studying from home. Data engineers had a problem in this case since they had to quickly create new infrastructure to manage the massive volume of data and prepare for exponential expansion in the future.
The design of the serving infrastructure will be increasingly complex the more information you have. Rebuilding the current data architecture and attempting to avoid becoming buried beneath it are two jobs that data engineers need to integrate flawlessly.
Defining Infrastructure Strategy:
Future work will be much reduced if you have a clear data infrastructure plan. First, decide whether you’ll handle your data on-site or in the cloud.
It may appear unprofitable to run your own data center, however this is only true for tiny businesses. Hardware may even prove to be more cost-effective if your company has the means to store it. There is no distinction between the two choices in terms of dependability.
Choose the storage for Collected Data:
The foundation of a technologically advanced BI platform is the appropriate data architecture. A data lake or data warehouse are your two options here as a possible solution. What are their distinctions, and should you choose one over the other or consider a mixed approach?
A data warehouse is sometimes portrayed as a storage for generic data, whereas a data lake is seen as a component of a Big Data infrastructure. However, it’s not that easy, and they vary greatly.
Data warehouses used to be the only option for data storage when the amount of data wasn’t as vast as it is now. This was due to the fact that creating a repository that suited a certain business’ needs didn’t require data engineers to spend a lot of time on it. The days of the data warehouse monopoly, however, were over when Big Data entered the scene, bringing with it information whose quantity was rapidly increasing but its quality was declining.
Presently, creating a data warehouse system takes more time and effort due to the daily volume of data created. Fortunately, you have an option between using a data lake with simpler architecture and raw data or investing money in creating a data warehouse that contains organized and undoubtedly easier to analyze data.
Maintain Data Quality:
Inaccurate data can cause a variety of issues, and it can also have an impact on several organisational departments. Data cleansing must thus be given top importance. The actions listed below must be taken in order to build an acceptable data cleaning process:
1. Find and remove duplicate and unnecessary datasets.
2. Correct the data structure’s flaws.
3. Create guidelines for the organization-wide cleansing of incoming data.
4. Spend money on data tools that let you instantly clean data.
Last but not least, be mindful of the calibre of your information. Six requirements must always be met by the data:
1. Completeness. Recordings of all data sets and data items are required.
2. Uniqueness. If data has only ever been registered once, this parameter is retained.
3. Timelessness. This has to do with how usable or pertinent your info is based on how old it is.
4. Validity. The data you’ve gathered must correspond to the kind of information you intended to document.
5. Accuracy. The accuracy of the information you possess is assessed using this measure.
6. Consistency. If all of the data is recorded in the same manner, you may compare it across media and data sets.
Extract, Transform & Load:
It is impossible to exaggerate the significance of an ETL process to a company’s data warehousing and analysis in general. Your information gains structure via a well-engineered ETL pipeline, which also improves its clarity, completeness, quality, and velocity. Nevertheless, you may face several difficulties when you work on your ETL project. Only a few of the most typical are included here:
Data formats that change over time, broken data connections, inconsistencies across systems, dealing with the problems of many ETL components using the same technology, disregarding data scalability, and failing to foresee future data requirements.
Without sound data governance, none of the aforementioned measures make any sense. By providing your company with a reliable database to work from and saving time on updating the current data, it improves efficiency. Additionally, it aids in avoiding hazards related to dirty and unstructured data as well as legal and compliance problems.
When you are prepared to adopt data governance, make sure that all parties participating in the process, including data owners, are involved and that the objectives you hope to accomplish are precise, quantifiable, and explicit.
Actually, there is one more point to bear in mind while implementing data governance: it’s not a project, but rather a practice that should continuously advance.