Enterprise data warehouse is the core of business intelligence operation and can make a huge difference to the success of big data initiatives of the organizations. According to Gartner reports, the worldwide business intelligence and the analytics market is set to grow at $22.8 billion by the end of 2020.

With evolving business processes, growing digitization, and increased customer interactions through multiple channels, there are huge volumes of data generated. This makes it vital for enterprises to invest in enterprise data warehousing solutions for organizing, securing and storing their valuable data for quick and easy analysis and decision-making.

Building an enterprise data warehouse may be time-consuming and challenging, but when done right, it can empower your business by enabling in making smarter decisions and boosting the customer experiences.

Let us take a look at some insights on how organizations can reap the best benefits of having enterprise data warehouse and the common mistakes that they must avoid while implementing such systems.

The Many Benefits of Enterprise Data Warehouse

Enterprise data warehouse acts as a central repository which stores all the essential data and information related to an organization and provides access to authorized users. It offers support with in-depth analysis for tracking, managing, and analyzing information with detailed reporting capabilities.

• Data warehousing provides better and immediate access to the right information at the right time to the relevant stakeholders allowing them to make more informed business decisions.
• Data integration is one of the most crucial aspects of data warehousing – it helps in gathering and combining data from various sources and converting it into a standard useful format. With this, accessing data from multiple sources becomes easier and the users don’t need to look around multiple places to access relevant information – leading to faster and more accurate decision-making.
• Data warehousing converts data from multiple sources into a common format. This single window of truth helps multiple departments in accessing the latest and same data pointers for generating reports. With this, all the departments across the organization function more efficiently and get results in accordance with the needs of an organization.

Top mistakes to avoid while building enterprise data warehouse

Data warehouse implementation can be expensive and may take months to build with high chances of failure, if not done properly. Gartner reports have indicated that more than 50% of the data warehouses would be unsuccessful in reaching the user acceptance stage.

Some of the common issues leading to failure are poor data quality, extended development cycles, and lack of proper understanding of business requirements. Due to the size of the investment and time involved in such projects, it’s critical to ensure success by knowing about some of the common mistakes during data warehouse implementation and avoiding those:

Lack of clear vision and business objectives for the team

Most of the data warehousing projects are undertaken without even knowing why they may be needed for the organization in the first place. Besides, those companies where data warehousing projects fail, the technical staff is unaware of how it fits into the big picture and why and how are they expected to complete certain tasks. There is no understanding of the high-level business goals and objectives of the project by the team members right from the architect, business analyst to the Project Manager and the QA.

No focus on creating smart KPIs

Organizations do not realize the value of filtering the relevant and actionable data that can be turned into smart KPIs. If the right information is not filtered, it can lead to a huge deluge of data with loss of valuable time and money in trying to measure everything. Companies that are forward-looking and want to make informed decisions need to work out Key Performance Indicators (KPI) with their department heads and identify the appropriate filters needed to accomplish different business goals.

Trying to accomplish everything at once

The most common mistake that enterprises often make is trying to build a data warehouse to address all the business priorities. This results in the development of one huge component which is deployed in one go. Since dissimilar data comes through disparate sources at a different pace and the number of metrics and measurements need to be aligned appropriately, it makes sense to build the data warehouse incrementally. Break the project into smaller pieces so that it can be developed, deployed, and tested to achieve greater success.

Lack of communication between stakeholders and users

Lack of constant communication between different groups involved in various phases of the life cycle of the data warehouse project can create issues with the implementation. The technical staff, stakeholders, business analysts, and the leadership teams have a different understanding of the analytical and functional terms. Communication is the key – right from requirements gathering, setting up goals and expectations from team members to deployment and training, all the teams need to work together cohesively.

Underestimating the need and value of historical data

Real-time data is essential for all the organizations for making informed business decisions in a timely manner. However, the importance of historical data cannot be overlooked as well. This is especially true as more sophisticated processes require information regarding the current situation with perspectives from historical data. For example, to obtain the best results by combining information about customers from the current context with relevance to the past behavior of customers, companies can significantly improve their customer management. Most of the tactical decision support applications such as supply chain management and logistics require a mix of both real-time and historical data today.

Not asking for assistance

Building an enterprise data warehouse is not an easy task and requires core competency and a major issue lies in deciding whether companies should build it on their own or seek external assistance. Companies that may not have the required technical expertise must not try to build data warehouse internally as they may fail in their efforts. Instead, it is better to get help from companies which have specialized knowledge and can provide better analysis on business issues.

Apart from taking care of all these aspects, organizations need to employ strong data governance and agile approach to data warehouse development. A sharp focus on usability is also essential to ensure a wider acceptance.