Top 6 Business Intelligence Challenges IT Teams will Face in 2023

Big Data is exploding. Enterprises get swamped with data of all types and sizes. By 2025, enterprises will create 463 million terabytes of data every day. Attempting to make sense of all such data is practically impossible. A robust business intelligence (BI) strategy helps enterprises gain relevant insights from data.

BI tools ensure the correct data reaches the analytical engines. Post-analytics, these tools offer the right visualisations to enable users to make informed decisions. But these tasks are easier said than done.

Here are the common BI challenges businesses will face in 2023 and how to address these challenges.

1. Low user adoption rates

The success of BI tools depends on wide-scale user adoption. Low user adoption means less relevant data for the analytics engine.

Many business users remain hesitant to use BI tools for various reasons.

The biggest obstacle is resistance to change. Business users, harried with their day-to-day tasks, often do not invest in new BI tools due to lack of time or expertise. The new tools may need changes in the way of work, bringing the usual resistance to change.

Most people, especially the sales team, get accustomed to doing things a certain way. For instance, many sales teams are still accustomed to providing daily updates over the telephone instead of emails. In today’s environment, where most employees remain overworked, resistance to change becomes stronger.

As solutions,

Secure buy-in from stakeholders upfront. Initial reluctance, even by a few, stalls widespread adoption and results in low adoption rates.
Convince the workforce that BI enables making data-driven decisions and how it benefits them. One way to convince the workforce is through proofs-of-concept. BI dashboards that clarify how to connect to and interact with data and visualise the same in meaningful ways to make a big impact help. Consider a stakeholder who thinks one product line is the most profitable. A BI-powered dashboard may provide otherwise.
Have training sessions to familiarise the workforce with the technology.
Support end users by providing resource persons and appointing internal champions.

2. Challenges related to self-service

Self-service BI is the rage. It empowers lay users to get real-time insights without relying on a data scientist intermediary. As BI insights become popular, the number of requests increases, overwhelming the IT team. IT becomes a bottleneck to getting insights, and decision-making gets held up, waiting for BI insights from IT.

Self-service BI also enables deep customisation. Traditional BI offers a well-structured workflow, complete with ready-made dashboards and reports. Self-service BI tools come with intuitive dashboards and UIs. Self-service allows non-technical staff to wield custom reports and derive better value from the data.

But self-service BI comes with the unintended consequences of too many costs, security risks, and the risk of a lack of focus.

When lay users have too much access across many departments, it drives up costs and creates data security problems. With everyone raking up insights, people may come to meetings with different numbers and argue about whose number makes sense.

There is the risk of insights based on flawed input data as well. For instance, the sales team makes decisions based on whatever data it gets. They have the autonomy to mix and match to see what works best. Such a situation becomes the perfect recipe for disaster.

As solutions,

Enforce central, standardised control over tool rollout
Establish strong data governance protocols. Robust governance improves data quality by ensuring the ingested data’s consistency and reliability. It also ensures proper integration, mapping, and alignment of data from various sources. Proper access controls and encryption improve security as well.
Install both IT-managed delivery and self-service, business-managed approaches. Business-managed delivery allows business users the flexibility to go on their own.
Have a centralised governed set of KPIs and metrics certified by the organisation to ensure consistency.

3. Challenges over the model to adopt

An IT-managed BI delivery model is process-intensive and takes a lot of effort. BI’s issue at the enterprise level is often not a lack of tools but too many. Running too many tools risks analytical insights that are wide but need to go deeper to give meaningful, actionable insights.

BI tools are anyway expensive. The average cost of a single BI tool is around $3000 a year, with advanced tools costing more. The cost of purchasing, implementing and maintaining BI tools also starts to pinch the enterprise’s bottom line.

As solutions,

Standardise one toolset and create enterprise skills around it.
Direct the AI analytics towards revenue-focused tasks or fulfilling the core implementation objectives. Make sure the installed tool delivers the needed insights. Opt for additional tools only to fill any gaps.
Ensure the analytics tool aligns with the enterprise IT architecture to save costs.
Prioritise BI tools that integrate with other solutions. Business intelligence gets better when it can access data from multiple sources. Or work in tandem with other third-party tools.

4. Ensuring scalability

Ensuring scalability is essential in today’s unpredictable demand. BI tools will need more memory and processing power to scale up. Also, when data complexity increases, the tool needs advanced algorithms to process and analyse data.

Another dimension of scalability growing in popularity is analytic scalability. Analytics scalability is using data to understand and solve many problems. Solving problems in many forms need flexible tools that address issues differently.

Only BI tools designed for scalability deliver the goods when the situation calls for a sudden scale-up.

Make sure the BI tool can

Leverage distributed computing resources quickly.
Use efficient algorithms and data structures.
Monitor and test the tool’s performance as data, and user volumes increase and make adjustments as needed.
Use statistical tools and forecasting to create and integrate different data views.

5. Challenges to data integration

Business intelligence tools are only as good as the data fed into them. Incomplete data leads to flawed analysis and counterproductive insights.

Feeding the complete data required for data analysis is a perennial challenge for companies. The challenge has become rife in 2023 with the growing digitisation leading to a quantum increase in data volumes. Most enterprises transitioning to work from home during the pandemic set up ad-hoc systems. Different departments and teams introduced multiple data sources. To compound matters, most such sources have silos. Integration of such data sources is a big headache for enterprises. Most IT teams have their plates full of managing day-to-day activities and cannot pay attention to analysis.

Many Business Intelligence tools need data to integrate data from multiple on-premises and cloud sources. Such processes remain complicated, time-consuming, and costly.

As solutions,

Have a single data source to circumvent the challenge of integrating data from different sources.
Establish a data warehouse at the back end of different software and load data tables there.
Several Business Intelligence tools also double up as a data warehouse. Connecting to the software will pull the data into a table. All tables in one place make accessing and getting the needed information accessible.
But it is only possible to dump some data into warehouses. Some silos exist by choice and strategy to protect sensitive information. It is optional to take all siloed data into a data lake. Instead, rethink ways to access and manage data.
Establish a data fabric which enables frictionless access to data and data sharing in a distributed data environment. A good data fabric framework allows easy access and management of data regardless of the data storage location. The tool uses semantic knowledge graphs, active metadata management, and embedded machine learning.
Establish strong metadata management to enable data fabric to fetch data that resides in the network. Sound data fabric systems find relevant data and connect it through a knowledge graph.
Select the BI approach best suited for the business application at hand. For instance, in a regulated industry such as pharmaceuticals, are restrictions on the disclosure of data. This makes some critical data unavailable for business analytics. Also, several companies may have silos by choice as part of their data protection strategy. In such instances, an all-encompassing BI strategy may become unfeasible.

6. The quandary of seeking perfection

Conventional wisdom places virtue on high-quality data. Many enterprises spend considerable time and resources to validate and clean data and improve data quality. But In 2023, as the quantum of data goes out of control and the velocity of business decisions increase, perfection is becoming unviable. Any delay in improving the data quality may lead to the opportunity going away.

It is not necessary that all data needs to be of the highest quality for many actionable insights.

Consider Project Zebra, the open-sourced think tank working to improve supply chains. The project made good business decisions and improved supply chain operations using correlation.

BI implementation takes time to mature and deliver value. But identifying the challenges enables enterprises to leverage powerful insights without hassles.

Also, check “Six Ways the Cloud Enhances the Capabilities of Business Intelligence Tools“

Andre Rodrigues

As a software and IT solutions advisor, Andre leads a team of technology consultants for implementing Account-based Marketing strategies to IT customers. In his 30 years of working experience, across the region, Andre has helped numerous clients improve existing business systems and IT infrastructure. This experience has helped Andre secure a unique knowledge and understanding of the challenges faced by these sectors.