Business intelligence (BI) ensures organizations and enterprises make measured decisions. And teams are aware of its importance. However, many analytics teams in businesses struggle with slow, fragmented, or downright counterproductive BI systems.
What’s interesting is that these challenges do not emerge due to a lack of sophisticated technologies or adequately trained employees. The main culprit is data accessibility.
Data should be immediately accessible to the senior team members in a digestible format to empower them to make informed decisions. They often need to contact different departments or pass multiple permission layers to access the latest information.
A key reason why this fundamental challenge is overlooked is due to a subtle lack of awareness. Business management teams need to realize the impact of data accessibility on their BI processes to tackle the relevant issues readily.
In this blog post, let’s look at how data accessibility shapes business intelligence to elevate informed decision-making in modern enterprises.
Enhancing Prediction Accuracy
Teams need clean real-time data to make reliable forecasts. Accurate predictions of essential performance metrics help with planning, reducing risks, and improving workflow resilience.
Consider the real estate industry, for example. Property costs can vary frequently, as they depend on various factors such as the economic conditions and buyers’ preferences. Real estate agencies can leverage advanced predictive machine learning (ML) models to estimate variations of the properties in their listings, allowing them to make attractive offers and close deals, especially if prices are about to shoot up.
These forecasts must be as accurate as possible, which necessitates access to dynamic information signals from many corners of the open web. Bright Data’s Haim Treistman recommends blending data sourced at scale from social media posts, review platforms, and property transaction feeds.
“Many companies are leveraging this type of data to build predictive analyses and algorithmic models indicating future buying, living, and investment trends,” says Treistman. “When traditional variables like household income, vacancy rate, [and] the year it was built are used, they can predict real-estate values with a 40% predictive power.” The improved accuracy in making estimations or providing quotes is vital for elevating the quality of BI-driven strategic decision-making.
Moreover, the forecasts from readily available real-time market data in a single source of truth also give confidence to real estate professionals when communicating with stakeholders.
Promoting a Data-Driven Culture
The phrase “We’re promoting a data-driven culture” has become an overused piece of jargon – though it’s not entirely these companies’ fault.
Ensuring that daily operations are truly driven by analytics is difficult to measure in hard numbers, which makes implementation more challenging. The root cause is that establishing data-driven procedures requires professionals in various roles to shift their approach to work at a fundamental level. Even then, there’s a lot to do in terms of processes, tools, and personnel.
Ganes Kesari, CEO at TensorPlanet, explains in a column, “Organizations need to empower all individuals by giving them not just access to data but also the ability to use it effectively. To get an organization ready, this empowerment must occur at three levels: data readiness, analytical readiness, and infrastructure readiness.”
Of the three levels outlined by Kesari, infrastructure readiness is, comparatively, the simplest, as it requires teams to pick the right BI tool. Analytical readiness depends on how effective the employee training program is.
This leaves us with data readiness – the foundation of a data-driven culture.
Companies must empower business users with clean, accessible, and well-organized data to facilitate data-backed decision-making at the task level. Understanding what data can be collected and who needs what data is pivotal here, and providing line-of-business team members with basic “data literacy” education goes a long way towards empowering everyone to become self-service citizen data analysts.
To ensure this readiness, data accessibility also involves standardizing data sources, removing silos, and establishing compliant data governance workflows. Consequently, teams can collaborate better and trust each other’s decisions more, as they can instantly validate strategies through readily available data, establishing BI as a strategic enabler rather than just a reporting function.
Streamlining Daily Operations
One of the core objectives of BI is to optimize processes to save resources and improve their efficiency. BI achieves this by tracking workflow metrics such as time spent on a task and the number of team members allocated to a project.
And a key enabler in this effort is data accessibility.
For instance, consider a food delivery service. The company must monitor delivery times, fuel usage, and routes to optimize logistics. This data will help the delivery executives batch orders efficiently and design the fastest routes, improving customer satisfaction.
Note that it is pivotal to identify and keep an eye on the right metrics. In the case of speeding up food delivery, the relevant metrics, as mentioned above, are delivery times, fuel usage, and route distances. Teams must work collaboratively to determine what needs to be monitored and at what scope.
Tiffany Perkins-Munn, the head of data analytics at JPMorganChase, reinforces the importance of this collective effort, which often hinges on processing customer-focused information signals, which need to be anonymized.
“When it comes to client data, it’s most valuable when it’s accurate and captured consistently across all touchpoints,” she asserts. “This is why it’s essential to implement clear rules for gathering and managing data and conducting regular audits that help standardize the data gathering and analyzing processes while maintaining data integrity and ethics.”
The last component, “maintaining data integrity and ethics” is as crucial as keeping operational data accessible. Apart from adhering to regulations, ensuring customers don’t feel exposed is equally essential.
When beginning to access real-time data for streamlining operations, it can be beneficial to start with a pilot project. Businesses can demonstrate impact, test frameworks, and build organizational confidence in BI.
Elevating Customer Experience
BI provides actionable insights to enterprises to improve their products and services for their customers, clients, or end users.
Traditional BI is quite inefficient in this aspect, due to the prolonged time to insights. By the time customer success teams identify a potential friction point, it’s usually too late. The buyer may already have left a scathing review in a public forum and taken their business elsewhere.
Consider an abandoned shopping cart in an e-commerce store. This can occur due to multiple reasons, such as lack of offers, absence of desired payment methods, or high shipping charges. It is pivotal for online stores to get to the bottom of the hold-up and address it before the buyer loses interest.
Keeping real-time customer data accessible to analytics teams can help combat this. Various data points – identity data, attribute data, and behavioral data – reveal unique insights, enabling teams to tackle the issue before it escalates.
Fortunately, now companies can establish customized data pipeline visualizations to monitor these metrics in real time. Additionally, even non-technical users can set automated alerts to maintain agility.
However, business teams must be careful with the amount of data they collect. While modern browser APIs make it affordable, they can quickly lead to analysis paralysis. As CMSWire’s Michelle Hawley elaborates, “Just because you can collect certain data points doesn’t mean you should. If you do, you might encounter a data deluge — so much information that you don’t know what to do with it all.”
An effective workaround to elevate the customer experience with BI is by focusing on the data points that matter most and then looking at additional metrics on an as-needed basis. At the same time, it is crucial to get permission from customers or buyers for data collection.
Wrapping Up
Business intelligence is essential for organizational growth, efficiency, and maintaining a competitive advantage. BI achieves all these goals by helping analytics teams extract actionable insights from various data points.
One of the fundamental ingredients that drives accurate BI is data accessibility. If business decision makers get their hands on clean, real-world data quickly, they can expedite their analytics to plan their next course of action.
Data accessibility improves BI by enhancing prediction accuracy, promoting a data-driven culture in the company, streamlining daily operations by tracking resource allocation, and elevating customer experience. Simply put, when teams have access to the right data points, BI becomes an engine for smarter and faster business growth.