Building data warehouse for better analytics

Is it time to build a data warehouse for your business? You are probably here because you need a data warehouse that is scalable, efficient, and reliable.

Now, if your business is serious about implementing data reporting as a key strategic asset, building a data warehouse will become a priority eventually. However, building a data warehouse is not an easy task, as a lot of warehouse projects end up with limited acceptance or fail right away.

To avoid failure, take a look below and learn what you need to do to design a data warehouse that will work for you. This guide will be especially useful if you are looking to build a Saleforce data warehouse. Salesforce is the industry standard for managing core sales relationships with prospects and customers, and that is why a great number of companies rely on it.

What is a data warehouse exactly?

Companies nowadays usually have data stored in different places coming from different data sources. This data can come from applications databases, web applications, or spreadsheets.

What a data warehouse does is syncing data from multiple sources into a single place for all data reporting needs. Basically, it collects and stores data from disparate sources.

In addition, a data warehouse provides data that is considered to be reliable. It can also handle the querying workload from all team members that make up a certain company.

Now that you are aware of what a data warehouse is and why many businesses have one, you should learn the steps you need to take to successfully implement your own.

Determine the reason why you need a data warehouse

Many companies fail with data warehousing because they fail to identify a clear business case for it. On the other hand, companies that start the process by identifying a business problem for the data and remain focused on finding a solution actually increase their chances of success.

To make this step easier, take a look at some of the main reasons why your business might need a data warehouse.

1. Standardizing the data. Data warehouses store data in a standard format, which makes it easier for businesses to analyze it and gain actionable insights. Standardizing data reduces the risk of errors and improves general accuracy.
2. Better decision-making process. Successful companies make informed decisions and this is not possible without good analysis that comes from their data. Data warehousing boosts the speed and efficiency of data access, which allows businesses to make data-driven strategies and gain an edge over their rivals.
3. Saving money. Data warehouses allow employees to delve deeper into historical data and evaluate the success of previous initiatives. This allows people to determine what they need to change and how in order to reduce costs, increase operational efficiencies, and drive growth. Ultimately, this has a positive impact on the bottom line.

Devise a schema for each data source

Devise a database schema for each data source that you would like to sync to your business’s database. An action like this one should work in your favor in many ways, including the following:

1. A schema helps users identify the data source each table comes from and this helps as the number of data sources grows. Future data analysts and business team members that join the company can also learn fast what each data source includes.
2. Creating a schema for each data source allows users to assign specific permissions for them. For instance, a data engineer may want to allow a junior analyst to only read but not to write a specific schema.

Keep in mind that this action is very helpful as the number of data sources grows over time.

Agility and why it matters

In the world of modern businesses, being agile can make a difference between a winner and a loser. But, how does this relate to data warehousing?

It can take a few months to a few years to build a modern data warehouse, depending on the complexity. During the implementation period, the company can’t materialize any value from the investment. The business requirements also change over time and sometimes differ considerably from the initial requirements.

Now, a big bang approach to data warehousing has a high risk of failure because businesses put the project on hold as they don’t see immediate results. The problem with the big bang approach is that it cannot be tailored to a specific company, vertical, or industry.

And that is why an agile approach might just be the better option. Choosing an agile approach allows the data warehouse to evolve with the business requirements and focus on current business problems.

The agile approach is an iterative process where modern data warehouses are developed in multiple sprints. This approach involves the business user throughout the process for continuous feedback.

In turn, the company enjoys quick results instead of waiting for many months or years. In addition, agile data warehouse development usually has a lower TCO compared to the traditional (big bang) approach.

Understanding the analyzed data

As it was mentioned, a data warehouse is a repository where information is collected from multiple data sources. To get the maximum value from a data warehouse, the data stored there has to be clean, accurate, and consistent.

That’s why it is essential to identify all the data sources and understand the characteristics of all possible ones as well as the dependencies between them.

That’s why you should go for the ‘ideal’ scenario where all the information comes from an integrated data model. An approach like this one reduces the time needed to build and maintain a data warehouse and improves the data quality in it.

Give ELT tools a chance

Data warehouses usually use either the extract, transform, load (ETL) or the extract, load, transform (ELT) data integration method.

ETL and ELT are two of the most common methods of collecting data from multiple sources and storing it in a data warehouse. The main advantage of ELT over ETL is the flexibility and ease of storing new, unstructured data.

Moreover, with ELT, you can store all types of information, including unstructured information providing immediate access to all of your information and saves business intelligence analysts time when working with new information.

Final thoughts

This mini-guide should provide you with enough tips to help you build a solid data warehouse for better analytics.

When you have access to meaningful analytics, you get a better chance to make informed business decisions that impact the bottom line positively.

Just remember to determine your business needs and then start devising your data warehouse. Once you have that sorted out, feel free to start the process.

 

10/03/2021