Over the past few years, we have continuously witnessed the data warehousing failure and tried to hunt down the reasons for it. Although, it seems as interesting yet an arcane subject to delve into – especially in our age and times – because we do own the utilities, skills and strategies to deliver them successfully and quickly. However, we still fall short of achieving this goal. As a matter of fact, the name “Data Warehousing” is in itself intimidating.
As consumers question big data and its collection processes, it is clear that data warehousing does carry a risk and could ultimately make or break your business. Let’s evaluate a few common data warehousing mistakes, beginning with the foundation.
User-Base
One of the most pre-imminent and important factors to consider in the success of any business intelligence (BI) project is whether or not there are any active users. If the answer is no, then the easiest sub-answer is that it has been failed. The whole point of collecting data is to use it to attract users and target current ones. Data is valuable, but worthless if it doesn’t generate activity.
BI solutions Necessitate Contrast
Whether the difference is coming from exterior or interior, there should be contrast. By evaluating, comparing and contrasting your technique and collection methods, you’ll be able to chart a clear direction for your business, not simply market to new and existing customers. If your skills, method or toolset is not willing to accept change, your project will ultimately fail.
Users are unaware and will remain unaware
It seems futile largely that we hire and pay a Business Analyst only to fritter several weeks asking users what they need from a BI project. Users are unaware – and they will remain unaware unless they experience something for supreme rationale. In simple context this means that if you are utilizing Gantt chart or waterfall/SDLC methodology for the administration of your BI Project you will most likely disappoint. The more acceptable methodologies currently used are Scrum and Agile or Iterative and Incremental steps to proceed.
Data Security
If the Target fiasco has taught us anything, it should be that data security is a must. Not only do you need to protect your data in the event of a natural disaster or equipment malfunction, but also from outside cyber security threats. Data collection is common, but when you lose consumer trust you lose long-term sales. Invest in security and perform regular backups.
Overall, if you are aiming for success in your data-warehousing scheme, go with an approach that embraces a renaissance at every turn, while filing your environment with strategies, utilities and peoples that are cooperative, dedicated and willing to support. As far as Big data is concerned, by undertaking some basic steps and several others for the improvement and mastery, we can only get success with it when we first become highly successful in data warehousing practices. If we, however, collect information just to hoard it, we waste valuable time and resources and leave customers at risk.