Three mistakes to avoid when planning and building a business intelligence solution

Business intelligence projects are a high priority for companies seeking to empower better, faster, data-driven decisions and actions based on high-quality, high-value reports.

The implementations are complex and inherently risky, and the team leads must identify all associated data quality risks while engaging all stakeholders from the top down. To ensure that executive-level buy-in filters through all departments involved in the project and that there is sufficient infrastructure to support the multiplicity of data sources available to an enterprise in modern times. 

1. Holding onto Inadequate Technology

Many companies try to modernise their BI solutions while holding onto outdated core solutions and may no longer be fit for the task.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 keys can depend on critical infrastructure, data sources, how integrated they are with legacy systems, etc.

BI implementations that are 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 must be re-coded, re-tested, and re-deployed to accommodate most standard infrastructure changes.

Modern data architecture enables companies to streamline interoperability on data models and integration. 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 visualisation, which enables the organisation to share a virtual view of the data without actually moving the source data. 

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.

2. Attempting to Collect All Possible Data in a Single Place

Today, businesses are ingesting data from many varied sources to gain deeper insights into customer behaviour, market opportunities, and competition. As a result, BI infrastructures can incorporate various data sources and data ingestion points. These can include databases, social media sources, streaming video sources, and other sources that can come in both structured and unstructured formats. In today's time, companies must account for all these data sources and plan for other various possible sources in their BI strategy. However, they must not attempt to collect all of this data into a single repository. 

For a modern BI platform to work, companies need to have a modern data infrastructure. In addition, companies need to ensure connectivity with a diverse range of data sources, including structured or unstructured data, relational or non-relational, or on-premises or in the cloud.

One of the ways that companies can achieve this is, once again, through data visualisation, which provides a virtual data layer that facilitates access to data assets regardless of the data's format or where it is present. Rather than attempting to collect data, data visualisation enables companies to connect to information. 

3. Not Preparing for Real-Time Analytics 

The rapid growth of data generates challenges beyond data volume. Before companies can harness the data, they need to deal with the variety of the data, the time it takes the data to move around the organisation, and the speed of data generation.

The current data growth is mainly in unstructured data. This data is often known as "human-generated information," such as high-definition images and videos, social media posts, and phone and chat logs. 

This increase in data ingestion highlights the need for a more agile way to integrate data in real-time to perform analysis at the speed of origination. Companies will need modern data architectures that allow for agility and real-time access to a broad base of data sources to make this possible.

The best solution enables each BI reporting solution to interface directly with each relevant data source, so there is no latency in data access, analysis, and reporting. With data virtualisation, companies can establish an intermediate data access layer that can source a wide variety of data while abstracting away all of the data access complexities from the consuming applications, enabling real-time analytics. Data virtualisation provides a holistic, real-time view of the integrated data without replicating any source data.

Conclusion

Business intelligence solutions empower decision-makers with data-driven insights. In today's highly competitive landscapes, making informed decisions at the earliest can make a critical difference between success and failure. Therefore, organisations must get it right with BI early in the process. By considering data virtualisation at the beginning of a BI project, companies will be well to get it right.

The article has been written by Ravi Shankar, Senior vice president and chief marketing officer, Denodo