For many, the worlds of BI consulting and data analytics are the same. Both help inform vital business decisions, and both use tools and techniques to help the flow of reliable and high-quality data throughout an enterprise. But how correct is this?
While users would be right in assuming that the worlds of Business Intelligence and data analytics are closely related, it can be difficult to pinpoint exactly where one discipline ends, and the other begins.
The relationship between analytics and consulting – defined
Before we move into exploring the dynamic between these two worlds, let’s begin by defining how BI consulting and data analytics work together.
Business Intelligence consulting involves liaising with key stakeholders, optimising architecture, and implementing the infrastructure needed to make effective data analytics possible.
Data analytics, on the other hand, takes advantage of these previous improvements to present datasets effectively, allowing users to gain insights for a range of real-world use cases.
While data analytics is the process of gaining enhanced insights that can make a real difference to a business, these processes can only be made possible, and trusted, through the work of BI consultants, and optimised infrastructure. Without this optimised architecture in place, how data is collected, stored, and maintained may be inconsistent and unreliable – leading to inconsistent and unreliable results.
Before effective analytics can be deployed, consultants need to:
Examine current architecture
For enterprises to take advantage of machine learning, predictive analytics, and data visualisation, organisations must make sure their underlying architecture is fully organised and regularly maintained.
Examining the current architecture is usually the first step for any Business Intelligence consultancy. It helps create reliable foundations on which future projects and analytics techniques can take advantage of.
Establish a single source of truth for trusted and consistent results
The quality of any results gained from data analytics is dependent on the quality of the data itself. With unreliable, incomplete, and unstructured data feeding into reports, users can only expect unreliable and incomplete intelligence. A sophisticated and commonplace solution to this involves building a single source of truth, either on-premises or in the cloud and will be unique in scope for each business.
Establishing more intuitive data sources may take on many forms, such as data lakes and data warehouses, but the overarching concept remains the same. Consultants should replace separate versions of datasets with one universally accessible form, and improve consistency and transparency for all teams involved.
Make improvements that facilitate long-term scaling
As a business grows, so too does the amount of data it collects and stores. Over time, this can lead to bloated analytics – demanding significantly more time and computing resources to achieve the same insights that were previously possible.
To facilitate long-term analytics, BI consultants may improve the scalability of analytics processes and data storage. As a result, users can continue to make use of data analytics processes in the long term, without experiencing the disruption and inconvenience that can traditionally accompany bloated datasets.
Once these steps, and any others prescribed, have been completed by BI consultants, enterprises can experience the suite of accompanying advantages. Once this is done, users can perform reliable data analytics designed to produce trusted and secure results.
The benefits of data analytics
Data analytics brings a wide range of advantages to businesses of any size – from those beginning to take their first steps into the world of Power BI, to veteran organisations considering the most effective use of ML analytics possible.
Seek out previously invisible trends
With the rise of Big Data and more streamlined approaches to data analytics, users can investigate underlying insights between thousands of data points in the blink of an eye.
As a result, they may find intelligence on trends and correlations that were previously inaccessible – providing the entire enterprise with significant insights to take forward into other business practices.
Identify unforeseen risks
While ML analytics were traditionally only available to businesses with the most sophisticated infrastructure and the highest budget, this is now changing for the better. By making use of predictive and prescriptive analytics, users can explore potential future outcomes to make more informed decisions, and identify unforeseen risks.
Change mindsets for more secure strategic decision-making
How can stakeholders adapt their ad-hoc and instinctual processes for strategic decision-making?
The inclusion of data-driven intelligence for strategic decision-making can help change the mindsets of key stakeholders to one embedded in reliable and trusted information over instinct.
For enterprises, this means reliable and unwavering results that emphasise the value of data-driven intelligence.
Empowering enterprises with actionable intelligence
At DataShapa, we provide market-leading BI consultancy services throughout a range of industries. Our team of specialists are passionate about empowering enterprises with the actionable intelligence needed to form strategies that keep businesses ahead of the competition. Click here to visit our case studies hub and find out more.