Contributing to order through Data Warehouse

To assume total control over project execution or recurring operations, decision makers need a reliable system of data aggregation. Data Warehouse is designed to contain information about all the company’s departments and products, allowing to respond quickly to any extraordinary situations.

The ever-growing trend

Data Warehouse (DW) is the memory of a company. Its contents are unified to provide secure data storage and quick navigation in order to support decision-making. Warehoused data helps to quickly locate the needed information for usage in ongoing processes, reporting and forecasting. DW in paving the way to data visualization.


Let’s assume a large company finding itself facing a choice of Data Warehouse implementation. Every month there is a need to spend a lot of time and human resources producing hundreds of reports to clients, regulatory bodies, and for management. The reports are being taken from various systems. Risk management, trading, compliance, fixed income, finance and other systems. The data is copied from these systems into Excel for calculations, then serving as the data source for reports in PowerPoint and PDF. Let’s find out how much this operation cost. Let’s say there are 8 systems, costing $200 per user per month, and there are 10 people in the team. The licence cost is 8 x $200 x 12 x 10 = $16k/month x 12 months = $192k/year, make it $200k. The salary is $60k x 10 = $600k/year. Total cost = $800k/year.

If the company decides to develop a data warehouse, taking 1 year and costing $500k, it becomes possible to put together the data from the respective 8 systems into 1 database, and there will be no need access to those system anymore. That saves at least $200k/year. Besides, DW allows to automate the production of those reports and eliminates the so-called human factor, e.g. mistakes, by accident or on purpose, in reporting during manual data input. That will reduce the amount of time required to make those reports, thus reduce the number of people required, freeing them to do other activities. Assuming that half of the reports can be automated, that would save us around $300k per year.

So total saving per year is over $500k. The approximate project cost is $500k. Surely, a project with this level of “cost vs benefit” is well worth pursuing.


Data Warehouse architectures continue to evolve to support new technology and business requirements, as well as to prove their continued relevance in the age of big data and business analytics. This process has become known as Data Warehouse Optimization. Every organization and its DW (if implemented) is a unique scenario, so every optimization program is, too.

Optimization scenarios may range from software and hardware upgrades to the periodic addition of new data subjects, sources, and dimensions. However, data types and data velocities are diversifying, so data modernization involves users diversifying their software portfolios to include tools and data platforms built for big data from new sources. As portfolios swell, most DWs are evolving into complex and hybrid multi-platform Data Warehouse environments (DWEs) optimized for today’s requirements in big data, analytics, real-time operation, high-performance, and cost control

Data Warehouse keeps years of company history and business intelligence within a query reach.

Every DW architecture holds many opportunities to initiate or expand the use of recent technology advancements, such as in-memory processing, in-database analytics, massively parallel processing, multi-platform federated queries, Hadoop, etc. Best practices can likewise be modernized by adapting Lean, Logical, and Virtual methods.

Business intelligence (BI) is experiencing its own modernization right now, and BI needs the DW to provision data for modern BI practices, such as data visualization, data exploration, and self service. Likewise, many organizations are complementing their investments in online analytic processing (OLAP) with a great variety of techniques for advanced analytics.

Complex yet simple

While popular in-memory solutions may be fast and simple, they lack benefits of a traditional Data Warehouse approach such as reliability, governance and data quality. DW supports data governance, creates a data standard, and helps to increase query execution speed for up to 1-3 sec compared to 20-30 sec. for Data Lake systems. DW facilitates building and maintaining your own enterprise data storage by eliminating manual efforts and automating routine daily tasks.

One of the most compelling reasons for creating a Data Warehouse is to bring transparency, and eventually reduce expenses.

Wrapping up

Data Warehouse is not a static system and can evolve along with your enterprise. Obviously, every company benefits from order in its data and Data Warehouse architecture is the solution that grants this very possibility, presenting business-valuable benefits:

  • Post-facto analysis
    Data Warehouse contains information about all the company’s aspects of activity. In this regard, decision makers can investigate incidents’ circumstances to mitigate future risks and prevent repeating the mistakes.
  • Foresight
    Based on the large amounts of warehoused data one can perform a Predictive statistical analysis, allowing to predict events with specified accuracy.
  • All data at one place
    Business analytics allow to aggregate information from separate data sources both external and internal. This feature is crucial for every company, as it enables decision-makers to grasp the big picture of their business.
  • Safe legacy data migration
    Some companies are hesitant to embark on software modernization fearing to lose valuable business data stored in legacy systems. Data Warehouse allows to perform safe migration of data from client’s legacy systems to separate operations from analytics. At the same time, the data from legacy systems saved in Data Warehouse, can always be loaded into a new operational system.
  • Security
    Implementing Data Warehouse solutions enables companies to better protect important business applications, speed disaster recovery times, and reduce data protection costs.