Fast answers to business questions with data warehousing
A data warehouse stores all your business data. This data can be used for integral analysis, reporting, justification, data mining, and making dashboards. Where necessary, internal data can be supplemented with external (open) data, because it’s important to pay attention to your (business) environment.
Organizations that don’t use data warehousing effectively are missing out on integrated customer profiles, and with it crucial insights needed to continually improve, innovate, and seize market opportunities. Managers, controllers, or business analysts should consider the following points when it comes to using a data warehouse:
- What are the advantages to using a data warehouse? When do you really need one?
- Are there alternatives to using a data warehouse, and if so which ones are affordable?
- Which issues are easier to address using a data warehouse than without one?
- How can you convince senior management of the necessity and usefulness of a data warehouse?
- Does a data lake make a data warehouse obsolete?
- Which data warehouse and ETL tools can you use?
- Which data models best fit your situation and use cases?
- How do you successfully approach data warehouse integration? What are the do’s and don’ts?
What is a data warehouse, and why use one?
A data warehouse provides a flexible information infrastructure. It can be used to retrieve data from various sources (business processes) and clean it up, integrate it, match it, and sort it by theme. This allows employees to connect data easily, so they can more easily analyze it, report on it, make dashboards, data mine and manage performance. Data can then more easily be turned into financial gains and quality improvements.
Answer complex questions at the speed of light
A data warehouse mixes company data with relevant external data on an equal level. Data analysts and decision makers can answer countless questions extremely quickly, for example:
- Which customers generate the most revenue, why is that, and what is the customer journey?
- Where are the largest workloads and the longest processes?
- In which zip codes do potential customers for our new product live?
- Which financial statements are suspicious, and how can you filter them?
- Which department has the highest absence rate and what is the cause?
- Which customers have an above-average average payment deficit?
The graphic below illustrates how data warehousing can help answer these questions. Users can cross-reference data between departments and systems with one press of a button.
Accelerate learning processes tenfold
Data warehouses don’t just answer complex questions quickly, but also straightforward ones. Because of this, learning processes of teams and employees can be at least ten times faster, given that the data warehouse performance is up to speed. This allows you to open up process improvements and innovation to the right people, and make your organization more agile. All this contributes to making your organization more intelligent. Our experienced data architects are eager to help you in this process.
Excel Dorado: not a city of gold
It’s a best practice commonplace that investing in data warehousing makes an organization more intelligent, and can even generate a lot of revenue or cut costs. Yet, the use of Excel as a makeshift data warehouse is still commonplace.
The risks are self-evident. An “Excel Dorado” enables errors. It’s labor-intensive and everyone can have their own version of the truth. You can lose entire meetings to interpretations and checking data accuracy. This gets in the way of translating the data into insights and actual improvements.
That is to say nothing of the precious time that highly-educated employees lose to Excel. They have to gather and correct data, time and time again. Excel really gets in the way of effective use of information, thorough data analysis and good management.
Alternatives to a data warehouse
There are certainly sensible alternatives to setting up and designing a data warehouse. Excel, obviously, isn’t one of them. But so-called appliances and data virtualization can be.
- A data warehouse appliance is a combination of hardware, software, and storage capable of processing data and making it available to users very quickly. It’s an all-in-one solution. Given the minimal maintenance and the solid performance, an appliance can be very useful in a data warehouse environment. Almost all the premiere BI tools/platforms can work with appliances. One of the components of an appliance is an in-memory database, which loads all data into a server’s internal memory. The result is lightning-fast response times. This comes at a cost, however, as a solution like this limits an organization’s agility, as it’s largely pre-programmed.
- Data virtualization software decouples data sources from applications and reports, so you can access them in real time. The software will transform, integrate, and deliver the data. This allows you to see a heterogeneous collection of data sources for all reports holistically. Traditional integration solutions integrate the data physically into a data warehouse. Data virtualization delivers the files, unified, on demand. This increases the speed and flexibility of data delivery. However, a virtual layer has to be clearly defined beforehand, which typically costs more time than a “normal” data warehouse layer. Also, you have to consider whether your IT infrastructure is suited to this. Here, too, this isn’t the ideal approach in all instances.
We know the pros and cons of data warehouses and the various alternatives like no other. Our years of experiences enables us to give you excellent advice about all the different options.
The 9 biggest reasons to build a data warehouse
More and more organizations are wondering what the use is of a data warehouse and whether or not it’s worth the investment. And they wonder what alternatives are available. A growing number of IT vendors and some ‘experts’ are proclaiming that the data warehouse’s time is nigh. But we strongly believe that the data warehouse is still the beating heart of any intelligent organization, and will remain so for the foreseeable future. Read more…
Choose the best data warehouse solution
Our ETL Tools & Data Integration Survey 2018 is a 100% vendor-independent comparison of tools and a market analysis of the 22 most important data integration tools. Many companies around the world use our survey to choose the most suitable ETL tools or data integration solution for their company quickly, saving precious time and money and gaining true insights into the strengths and weaknesses of the various vendors and platforms. Get our Survey now.
Data modeling: normal, dimensional, or data vault
Data warehouses can be modeled normally or dimensionally, according to the schools of Inmon and Kimball, respectively. The Data Vault method of modeling by Daniel Linstedt has been gaining traction in recent years. This method is especially suited to very large, company-wide data warehouses, the so-called Enterprise Data Warehouses (EDW). For example, banks and other financial institutions with heavy compliance requirements.
In our experience, applying a Data Vault is more likely to lead to a data-driven, instead of a question-drive, data warehouse. A direction that you’d rather not see in your development team. After all, the data warehouse is “only” a method of obtaining high-quality, accurate, and timely business intelligence.
“Excellent. Passionned Group provided insight & a set of best practices plus an approach that put a BI strategy and a data warehouse within reach.”
The importance of Master Data Management & metadata
- Master Data Management (MDM): This discipline spotlights essential master data and provides one-time input. The end result is storage in one central location. In a few specific cases, the master data also stays available in the appointed source systems. Centrally managing a universal set of master data is of crucial importance in correct and efficient management.
- Metadata: Metadata turns underlying data into useful information. Metadata says what the data is, where it comes from, how it was produced, the quality of the data, etc. Combined with MDM it can do wonders for the correct, timely, and reliable use of data in your organization. Metadata is especially important when the data is stored in fragments, which happens more and more thanks to the rise of Big Data.
Do you want to invest in a data warehouse?
Do you want to invest in an effective data infrastructure and reap the benefits? Feel free to contact us for an appointment to discuss the possibilities of data warehouses for your organization with one of our data warehouse specialists.