From facilitating faster analytics to driving data-driven business decisions and ensuring organizational efficiency to enhancing customer experience, business intelligence (BI) endeavors are critical to the success and growth of any enterprise.
Today, businesses have access to vast data environments but leveraging the data is only possible with business intelligence. It helps them combine the power of business expertise and technology to make informed decisions and stay ahead of the competition.
But while businesses know that they need high-quality data to make informed decisions, obtaining accurate data in a user-friendly and timely manner and managing the data efficiently is a major challenge.
So, how do you get over this data challenge? The best solution is to learn from the data management mistakes that can derail business intelligence projects.
4 Data Management Mistakes in Business Intelligence Projects
1. Poor Data Warehousing
Most databases are optimized for storage but not extraction. This is understandable because it is necessary to store the data somewhere before taking any action on it. However, when the embedded BI solutions are launched, they directly launch against the raw data to meet the deadlines, which results in poor report execution and inefficient queries.
These setbacks can be easily avoided with nominal structural preparation. In other words, the denormalization process can speed up the retrieval of data by decreasing the number of tables within the database. Denormalizing the raw data can eliminate the unnecessary data burden before launching the BI solution and avoid further issues of redesigning all the reports.
2. Lack of Accessibility
At times, not much thought is lent to the actual utilization of data when designing the databases. To that end, it can be challenging to imagine how the schema names will be interpreted and anticipate the type of decisions to be made. The main problem is the missing name of the columns. Failing to add an alias in the columns can lead to unutilized data by the users.
Another issue is data overwhelming which means getting bombarded with a number of field options that are not even necessary for the users. To that end, providing the data definitions is very useful. Simple explanations of every field can help users identify the required field. This can add value to the end-user experience and make analytics and reporting painless. Learn how master data management is driving data transformation in enterprises.
3. Poor Management of Dimension Information
Most businesses work with recorded transactions and associated data. In this case, the new records are only stored when the event is triggered. For example, the sales data will only be available when the orders are placed by customers.
On the other hand, dimension information is a complete set of data that provides additional context. It is recommended to add dimension information such as a calendar table. It will display all dates, whether an event occurred on that date or not. The dimension information is critical to deriving value out of BI projects.
4. Lack of Data Stewardship
One of the most common data management mistakes is that a single person does not know the complete story of data, from storage to extraction. For example, the person who set up the database might not be working in the organization anymore, and the provided documentation might not enough to onboard a successor.
Another example is when the data management job is distributed across different employees. One group of employees has an in-depth understanding of data semantics, while another has insights into its maintenance. And the third group is responsible for data analysis and is familiar with the utility of data to the end users. But unfortunately, none of these groups have complete knowledge of the data. This can result in a confusing data lifecycle. Here, tribal knowledge can be consolidated in a single document towards good stewardship protocols.
Way Forward for Successful Business Intelligence Projects
Having a successful business intelligence system has ample benefits for every business, irrespective of the industry. It can help leverage the data to improve internal processes and external health in today’s fast-paced environment.
But the above-mentioned data management mistakes can hamper the business intelligence game and entail a lot of money, effort, and time. The analytics initiatives can be crippled with mistakes and never be able to reach their full potential. And all this can blur the business focus – prevent them from determining what metrics are valuable, how is the internal team supposed to interact with the data, and how the business can harness data and respond to every challenge.
To that end, the best solution is to prevent any mishaps before they transpire. And that’s where we can help. Get ready to overcome these data management mistakes and make your business intelligence project successful with us. Talk to a specialist today!