Business intelligence (BI) projects are a high priority for companies seeking to empower better, faster, data-driven decisions and actions based on high-quality, high-value reports.
However, new BI implementations are complex and inherently risky, and BI team leads must identify all associated data quality risks upfront while engaging all stakeholders from the top down.
He or she has to ensure that executive-level buy-in filters through all departments involved in the project, that there is sufficient infrastructure to support the multiplicity of data sources available to an enterprise in modern times, and that there is sufficient support in place to incorporate real-time analytics.
Taking the time to plan a BI project and create a clear roadmap carefully will lead to a better, more functional BI deployment.
Here, we address three common mistakes that companies should avoid making so that the implementation process is smooth, and the project is ultimately successful.
Many companies try to modernise their BI solutions while holding onto core solutions that are dated and may no longer be fit for the task. The challenge is that companies often have different tolerance levels for retiring ageing solutions. Deciding on when to phase out or replace solutions can depend on critical infrastructure, data sources, how integrated they are with legacy systems, and so on.
Also, certain solutions stay viable for longer than others. BI implementations designed to be more reporting-centric or built around batch-oriented extract, transform, and load (ETL) processes built around data warehouses tend to age fast, as they do not support many modern data types.
ETL based data integration is also resource and time-intensive, limiting data ingestion and delivery to scheduled batches. This approach will not be able to support many modern use cases like mobile dashboards or live web applications.
Traditional BI solutions that are embedded in ERP systems, as well as some simpler, disparate reporting tools that support limited uses, also have shorter lifespans.
Some companies may simply use a spreadsheet tool to perform fundamental data analysis and manual cutting and pasting between files stored on different computers or emailed back and forth to perform some rudimentary data integration. Companies should look at supporting or replacing these tools with integrated modern analytics technologies if this is the case.
Modern data architecture enables companies to streamline interoperability on data models and data integration. This speeds up business processes and reporting, which increases efficiency.
For instance, in data preparation, data that was created or prepared in one specific product can be further extended to support various other functions using data virtualisation, enabling the organisation to share a virtual view of the data without actually moving the source data. Once this virtual data source is created, it can be shared with other parts of the analytics workflow, including emerging augmented analytics tools.
Such tools leverage machine learning (ML) and natural language processing (NLP) technologies to generate business-friendly intuitive insights. Advanced data architecture provides the foundation for business users to leverage real-time information for timely decision making.
Businesses today are ingesting data from many varied sources to gain deeper insights into customer behaviour, market opportunities, and competition. BI infrastructures can incorporate a wide variety of data sources and data ingestion points.