Pete built a data warehouse with the help of a specialized vendor. After the data from the various heterogeneous systems had been extracted, cleaned, merged, and uploaded in a data warehouse, he felt exhilarated to prepare reports for his sales department.
When Pete inferred the results from the data warehouse, his reports failed miserably to meet his expectations. Although the advanced features like pivoting and charts could create a nice sales report, the graphs were not showing data he wanted to display. Apart from the plain graphs depicting region-wise sales performance, the reports did not produce insights on why the sales dipped in the northern territory. His achievement was limited to descriptive analytics.
A data analyst explained to Pete that he had missed Business Intelligence—a crucial layer to analyze data—over the data warehouse. To evaluate Pete’s scenario, let us understand the concepts of a data warehouse and BI and how are they different.
What is a Data Warehouse?
When an organization has data coming from various source systems, most likely such bundles of data do not match in many ways. Data differ in standardization like the size of each field, nomenclature, decimal positions, etc. Currency representation in one system could be dollars while another system can denominate a local currency. Additionally, one system can have missing values while the same is represented as NULL or 0 in another system. To bring all data coming from diverse platforms in one repository and make them compatible throughout is called Data Warehousing and the repository is called a Data Warehouse.
Data from the source systems are cleansed and transformed to get rid of anomalies, filled in with placeholders for the missing fields, and converted into desired currency and time format. Thus, the resultant uniform data are loaded into a comprehensive central repository ready to be queried to breed meaningful insights and inferences.
A data warehouse can be an Enterprise Data Warehouse (EDW) when the whole organization’s data are stored in one large database and offer a unified way to find the results from data. When data are segregated based on a business area like sales, human resources, etc. or according to departments, such repository—called Data Mart—makes it easy for maintenance and quick retrieval, but lacks unanimity. ODS (Operational Data Store) holds the real-time and recent data coming from the sources and exists before the actual data warehouse as an interim layer. It is still good to use ODS for recent data reporting as data are consistent although lack history.
What is Business Intelligence?
Although data warehouse hubs the complete data, anything that has not been purged, analytics architecture still needs a standalone process called Business Intelligence. Various BI tools available in the market are designed to present data in the most appealing way, both aesthetically and with intellect.
BI is a package of tools, methods, and strategies to highlight the current and past data of an organization palatably and deduce its performance. These results aid in leveraging better and informed choices to harness opportunities and mitigate vulnerabilities.
BI tools make it possible to present the business information with an analysis of ‘what has happened and why’ from the past data. Not just historical knowledge, advanced BI tools like Power BI can produce awareness of how similar incidents can be handled in the future. Foresights, when amalgamated with machine learning and artificial intelligence can be generated to take preventive and corrective measures in advance.
A BI tool sits over the data warehouse, pulls the content from it, and enables the users to see business outcomes. For data warehouse, the same content is just a constellation of illegible numbers and strings without meaning and hence, unfruitful to the users.
Why do you Need BI Tools Over and Above a Data Warehouse?
Data Warehouse and BI are Two Different Components in the Architecture
The common perception about data warehouse and BI being identical is not true. A data warehouse is important because it collects different data sets and aggregates them in a unified system. On the other hand, BI techniques generate the corollaries from these cleaned and consistent yet raw datasets and present them as knowledge to the crucial decision-makers.
Although both the terms look synonymous, the data warehouse comes first in the journey of data and then comes the BI system. A data warehouse acts as a data repository, and some organizations use the prepackaged reports bundled with the data warehouse having limited functionality. These are static reports and demand time, effort, and money when amended according to the need.
A Data Warehouse Cannot Cater to Business Needs as BI
Designing and maintaining a data warehouse without the right analytics tool is like an e-commerce site that has products and consumers in plenty, but the consumers have no means to find and make a choice to buy the right product and consume the same.
BI is important to an organization for the thrust it provides in decision-making, operational planning, and devising new business strategies. The easy-to-use KPI driven dashboards show the threshold breaches and benchmark achievements, and by considering past data, they define these limits for the coming days. The contemporary analytics tools not only offer insights—what happened and why—but also produce foresight such as what will happen and how to make it happen.
Market trends hidden in the raw data can be explored through analysis. With innovative tools, you can also ask voice supported queries with quicker results. When combined with social media inputs, BI helps you to delve deeper into customer sentiments and market needs making itself inevitable for the organizations.
BI’s Potential to Replace the Data Warehouse
With all the benefits of advanced BI implementations, a data warehouse that has been the epicenter in the legacy decision-making system has taken a backseat. Now, the data warehouse can be (not always) bypassed, thanks to the highly sophisticated BI tools, which can take care of messaging and cleansing of data. In the absence of well-thought-out BI techniques, the insights can nosedive and fail just as Pete’s initiative did.
Functionally, a data warehouse cleans data, fills the void with appropriate placeholders, removes the redundancy, aggregates various source systems, and maintains history. Nowadays, a BI tool can perform many of these functions. So, skipping a data warehouse (of course, only after checking data quality) would not be a hurdle to implement BI directly over data.
If you want to harness the power of data analysis and see business-related helpful insights, don’t settle for only a data warehouse. Invest in a good BI tool, which encompasses the maturity and features of a new era analytics software because the foundation of analyzing business results lies in Business Intelligence.
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