Data is critical for competitive advantage in today’s digital age. Businesses depend on data insights to improve efficiency, understand customers, and identify opportunities.

But data in its raw form is useless for these purposes. Data offers value only when it is easily accessible and meets specific quality standards. And unless managed properly, data does more harm than good.

Enter data warehousing.

A data warehouse consolidates data from various sources into a centralised repository. Business Intelligence (BI) applications access data from these warehouses for analysis. It pre-empts silos and offers a single source of truth for BI analysis.  

Here is how to use data warehouses in business intelligence in the best way.

Get Data Consolidation Right

A methodological and structured approach to data consolidation improves the efficacy of the data warehouse.

As the first step towards data consolidation, identify the different data sources.

Any enterprise has multiple data sources. Examples include the ERP, CRM, accounting suites, sale terminals, social media channels, web analytics, and more.

Identifying the relevant sources depends on the objectives of the enterprise data strategy. For instance, if an objective is to understand customer behaviour, the data warehouse should co-opt data from social feeds and the CRM.  

Ensure Data Quality and Consistency

The effectiveness of BI analytics depends on reliable data that meets the stipulated quality standards. A typical enterprise believes 29% of their data is inaccurate. Such a high proportion of inaccurate data creates trust issues and flawed analysis.

The basic step is to establish data cleansing and standardisation processes. Some of the common processes include:

• Removing duplicates
• Parsing, to transform data into a consistent format.
• Adding missing values using predictive modelling
• Adding additional information to existing data from external sources, to add context.

There are many ways to apply these processes.

As a first step, validate data at source. Catching inconsistencies at source is much easier than identifying and correcting them at a later stage.

Use data profiling tools to identify inconsistencies and anomalies. These tools could, for instance, unearth unexpected column values, missing data in fields, or inconsistent formats. 

A data quality firewall prevents low-quality data from entering the warehouse. It allows the enterprise to enforce data quality rules, and block data that does not meet the stipulated criteria.

Data quality firewalls, however, are not always viable. For instance, it introduces latency in high-performance situations, where speed is critical. As such, it is also necessary to co-opt data quality checks throughout the data warehouse pipeline. One way to do this is by establishing data quality KPIs and monitoring these metrics. Some of the possible metrics include:

• Percentage of null values, to validate the completeness of the data.
• Duplicate record count.
• Data latency, or time to move data from source systems to the warehouse.
• Range checks, to ensure data is within the stipulated range.

Maintaining high data quality standards builds trust in the data warehouse and improves accuracy.

Fix the Architecture

Many enterprises set up data lakes to centralise data into a single repository. But data lakes are mostly data dumps that store any data as it is.

Data warehouses go a step beyond and organise the centralised data. 

Many enterprises set up a data warehouse as a layer on top of the data lake.

The data warehouse itself may be on-premises, cloud-based, or hybrid. Cloud-based data warehouses are the most scalable. On-premises warehouses offer complete control, but lack scalability, and require sizable upfront capital. A hybrid approach balances control and scalability.

But there is no best architecture, though. Choose the best approach depending on the enterprise need, budget, and technical expertise. Also, consider the data volume, security concerns, and integration with existing systems.

Optimise ELT or ETL

It is important to optimise the process of loading data into the warehouse, to ensure it contains current information.

Getting data into the warehouse is through ETL or ELT software. 

The ETL (extract-transform-load) process consolidates data from disparate sources. Next, it transforms such data through cleansing and other methods. Finally, the transformed data loads into the warehouse. 

Companies may also extract data from source, load it into a data lake, and use data warehouses to transform data (ELT).  ELT works better to leverage the scalability offered by cloud-based data warehouses.

Regardless of the approach, Change Data Capture (CDC) makes the process faster and more efficient. CDC captures the incremental changes since the last extraction, instead of extracting the entire data set every time.

Likewise, parallel data loading processes improve performance. Incremental loading techniques minimise processing time.

Setting up monitoring systems for the ETL or ELT process allows for resolving any issues in the bud.

Implement a Robust Data Governance Strategy

Data warehouses need to ensure robust data governance, to ensure data quality, security and compliance. Make sure the data warehouse:

• Allow only authorised users to access the data.
• Data storage and access meet all regulatory and compliance requirements. For instance, the storage may need to comply with GDPR, NIS2, or other regulatory requirements depending on the region.
• Deploys appropriate security policies associated with the data. For instance, data warehouses may encrypt sensitive data to prevent it from falling into the wrong hands.

Metadata enhances data governance. It also improves data discoverability and enables more efficient data analysis. Effective metadata management requires documenting the history of the data and creating a data dictionary that defines key data terms. Data cataloguing tools deliver both these goals.

A strong data governance strategy makes the data warehouse a trusted source of information.

As a best practice, a data governance committee could assume a data stewardship role.

Ensure Efficient Querying and Reporting

The primary function of the data warehouse is to provide a structured environment for BI tools to query and analyse data.

Effective BI relies on complex queries that compare multiple data sets over time. The best data warehouses host granular historical data to facilitate such complex analysis. The continuous record of data over time enables multi-dimensional analysis.

BI applications use data mining techniques to extract and analyse data from warehouses. They apply statistical analysis or machine learning algorithms to identify patterns, trends and correlations. SQL queries retrieve specific data subsets from the warehouse. Techniques such as location intelligence, what-if analysis or predictive analytics extract meaningful insights from the data.

But there is no best or recommended method for querying, analysing or visualising data. The best approach depends on the specific business needs and data characteristics.

Monitor and Fine-Tune Performance Regularly

Speed is critical in today’s fast-paced business environment. A slow or unresponsive data warehouse hinders user adoption. The most effective data warehouses co-opt performance optimisation techniques such as:

• Indexing strategies
• Partitioning to improve query performance on large tables
• Materialised views for frequently accessed data
• Optimising query designs and execution plans

Performance monitoring solutions track query execution times and resource utilisation.

Many businesses underestimate what it takes to manage data for analytics. Without a data warehouse to centralise data, extracting meaningful insights becomes challenging. The right use of data warehousing and BI tools allows businesses to integrate and manage their data well to enhance the customer experience and make informed decisions.