Data Science for business
Data science is an interdisciplinary industry, which is aimed at deep data (structured and unstructured) study and understanding using scientific meth...
The huge data amount that modern companies own provides new opportunities for their development. But it is also a source of additional problems. The main purpose of Business Intelligence is to provide processes for collecting, processing and analyzing data. However, there are some issues that make it difficult for BI to be efficient, effective, and useful. Let’s take a look at the main Business Intelligence challenges.
The number of data sources is increasing. For a complete and correct analysis, companies need to collect data from various sources: databases, big data platforms, internal and web applications. A fairly common option is to use a data warehouse as a central repository for business intelligence data. Using data virtualization or BI tools to integrate data without putting it in a database system is a more flexible option, but too difficult process.
The accuracy of BI applications is directly dependent on the data they are based on. The start of any BI initiatives assumes that the user has access to high quality data. However, many companies are in the pursuit of collecting as much data as possible. That’s why they don’t pay attention to the quality of the data. Moreover, they believe that problems can be solved after the data is received. The reason for this position may be users’ knowledge lack about the data management importance and necessity. It makes sense to develop a strategy for collecting the right data and a data management plan before BI technologies deployment.
A common BI problem is siloed systems. Successful business intelligence requires data completeness. However, different levels of access and security settings complicate the process of necessary analytical data obtention. To achieve the desired result, it is necessary to disintegrate silos and harmonize the data. Contradictory disparate data can lead to different versions of the truth. Then business users get different results for KPIs and other indicators that are the same in different systems. To avoid this situation, it makes sense to start with some level of data modeling and precise definitions for each KPI.
It is important for the end-user to understand the purpose of introducing and using the innovation. Company executives and managers should also be actively involved in effective learning and change management initiatives related to BI projects. For a wider and faster changes implementation, it is worth developing a short and understandable user training program.
An uncontrolled deployment of self-service BI can lead to a chaotic data environment with disparate repositories and conflicting insights. BI tools are regularly updated. Business analysts should interact with end-users to better understand their needs and develop strategies for delivering relevant data and dashboards using off-the-shelf functionality.
End-users quite often choose the simplest option and use Excel or SaaS. It is important to develop a good user scenario that will demonstrate immediate business benefits and encourage employees to use the new system before starting to deploy BI technologies.
Difficulties in understanding information can occur due to failures in data visualization. Dashboard and analysis can be useful when end-users can easily explore the information provided. However, the focus on data acquisition and the analytics process takes the design issue into the background. It is worth involving a UX designer to create a simple and understandable visual interface.