Eight Best Practices for Datawarehouse Solutions Implementations

 In Analytics, Business Intelligence, Datawarehouse

Data is very integral for an organization to execute its day-to-day functions. It is data that drives the decision-making process in corporations all around the world.

“In God we trust, all others must bring data”.
W. Edwards Deming

So it only makes sense to have a structured architecture where all the data accumulated by an organization can be stored. This is where “datawarehouse” comes into play.

A datawarehouse (DW), also known as enterprise datawarehouse (EDW), is a well-organized repository where all the raw data that is collected from different sources within an organization gets stored. This collected raw data is then used by corporations to make well-informed decisions which in turn leads them to the road of success in a corporate world.

datawarehouse provides excellent management and data analysis facilities. And this is why corporations all around the globe are now implementing datawarehouse solutions. Everyone wants to hop on the bandwagon but you would be surprised to know that most organizations fail in implementing the datawarehouse solutions in an efficient manner.

And with all kinds of existing advises on “how to implement datawarehouse solutions the right way”, it is quite daunting to pick something that works for you. So to give your Grey cells a break, here is a list comprised of only the best practices to implement datawarehouse solutions.

1. Selecting the executive sponsor

It is very central for the overall benefit of an organization that the executive sponsorship of the data warehousing project be deputed to the senior leader or the CEO of the organization as opposed to assigning this duty to one specific department within an organization.
The CEO of an organization has the right resources and means to spread the awareness regarding this project all across the enterprise which in turn provides strong foundation for datawarehouse implementation.

2. Avoid focusing on multiple business areas at one time

The key point here is to take “one step at a time”. Different business areas yield different results. Development of business areas is very crucial during the implementation stage of datawarehouses. The best way to make the most out of all the existing business domains is to divert all the attention and focus to a single business area at a time.
Putting your entire focus on a single business realm that provides faster return first, helps in better development of that specific area ,which in turn leads to quick success.
Hence, taking one business area at a time will help your organization climb up those stairs of successful datawarehouse implementation, one step a time.

3. Use KPI to analyze crucial aspects for datawarehouse investments

KPI or Key performance indicator is a measure of how effectively an organization is able to achieve its business goals. Identifying the KPIs helps analyze different aspects that are important for the better growth of an organization.
Aspects like revenue increase and ease of operation are quite important to examine as they help transform the current state of an organization for the better.
Also, they play a really important role during datawarehouse development.

4. Understand the needs of the end-users

Everything and anything that gets produced as the final output by the datawarehouse teams will eventually be used by the end-users. So it is highly beneficial if the datawarehouse teams have proper comprehension of the needs and aims of the end-users. If the final output produced is not acknowledged by the end-users then it not considered a very productive and efficient output.
Being aware of what the user wants during the implementation step itself is a great way to kick-start the implementation process.

5. Aim for flexibility and extendibility

Flexibility and extendibility go hand in hand. It is important that a datawarehouse model be flexible enough to accommodate new changes and to extract data from different sources.
Keeping not only the present needs in account but also the future growing needs without reconstructing an entirely new datawarehouse helps increase the operational efficiency of a corporation.
Extendibility,as the name suggests,refers to extending or expanding the datawarehouse to make space for excess data to be stored effectively.

6. Get the right funding

Implementation, development and maintenance of datawarehouses are the three primary phases that needs the right amount of funds to work at its fullest capacity. Add to that the need of adapting to the ever-growing demands of the end-users, which also needs resources and funds.
Keeping all the factors in mind, a well-organized plan is necessary to provide effective resources and right funds for the proper functioning of a datawarehouse.

7. Ensure the datawarehouse quality

Anything, if left untamed for a while, can go haywire.
Same is the scenario with datawarehouses. Regular maintenance of datawarehouse is very important as it helps ensure the data quality. Integrity of data is very essential when it comes to proper datawarehouse implementation.
Examining the data on a daily basis for any flaws and errors helps maintain the quality of data. Also, in case of errors, reports should be generated to alert the organization regarding any mishaps in the data quality.

8. Business intelligence (BI) knowledge of datawarehouse teams

datawarehouse is a fundamental component of Business Intelligence solutions. Needless to say, datawarehouse team members should be well-versed with business intelligence concepts.
Having knowledge about business intelligence concepts acts as an indispensable base with the help of which datawarehouse projects can be implemented with great efficiency.

So keep these critical points in mind the next time you decide to implement datawarehouse solutions and you are sure to have an edge over the others in this highly competitive corporate world.
Not to forget, the added bonus of overall progress of your organization that comes along with the right implementation of datawarehouse solutions.


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