A Formula 1 car racing event is exciting not just for its ability to keep you on edge or with the concrete melting speed of the cars but also because of what it represents, a precision-driven ecosystem that runs behind the scenes to account for and prepare for every single possibility. Be it measuring performance through sensors, analyzing lap times, anticipating tire burns, or predicting driver fatigue, F1 races are the pinnacle of what modern data analytics can do to unlock value and improve outcomes - both on and off the track.
Operating a business in today’s world is akin to racing on an F1 track. There are unanticipated twists and turns on the road, the competitors are going to whizz past, and just as things seem stable, an unanticipated event can throw a spanner in the works. Businesses are often defined by their differentiation, but having a fast car is no longer enough. To navigate the unexpected, it becomes key to complement the fast car (competitive differentiation) with a sapient driver in the form of robust intelligence and advanced data analytics.
Business intelligence is the value-churning flywheel that can unlock exponential value. The secret sauce to business competitiveness in the current paradigm stems from a company’s ability to consistently improve results by deriving more value from the data it generates.
A business intelligence tool plays the role of the captain of a ship. It should be able to navigate the employees at any skill level to make data-driven insights through simpler insight discovery. The insights shouldn’t be just diagnostic in nature but also predictive and prescriptive. These BI insights can enable agile and high-accuracy decision-making in the volatile, uncertain, complex, and ambiguous (VUCA) global business environment.
However, building these capabilities in today’s data-overloaded world can be a challenging proposition. The massive amount of data being generated from organizations’ operations, businesses, products, and even customers can be overwhelming and full of noise. However, investing in the right business intelligence tool can assist an organization in solving such conflicts by bridging the gap between data and decisions.
In many instances, the best data analytics teams tend to spend most of their time framing the business problem before analyzing the data. Framing the business problem could be an extensive exercise in itself, spanning from identifying the strategic gaps in a process/platform to identifying the right sets of data that must be collected and analyzed to arrive at the insight that will then guide the strategic course of action.
Once identified, the problem will require varying amounts of data from different sources. A marketing team will require a survey, customer behavior data, and advertising effectiveness data before changing the outreach strategy. On the other hand, operations and customer satisfaction teams will likely depend on grievance redressal statistics, customer churn, and internal records of customer service to analyze potential bottlenecks in the post-purchase consumer experience.
The entire process of collecting, cleaning, sorting, and analyzing this data could potentially encounter a host of problems. These could range from data obsoletion, data silos, skills deficiency, misaligned teams, and noisy data entering the systems. The key to a successful BI insights strategy is not to limit the size or complexity of data but to build a unified Data Universe. By creating a unified infrastructure that allows for the consistent evolution of data systems, businesses can create a heightened consumer experience.
However, the business world is already traveling afoot ahead in finding the right solution for it. Industry numbers prove that the next generation of business intelligence tools is coming of age. A Forrester Consulting survey reported that 61% of organizations are increasing their range of data and analytics initiatives. Meanwhile, 96% of business leaders were planning or executing data-driven decisions across all levels and verticals of the company.
However, finding the right set of tools, leaders, and data professionals to build high-quality business intelligence operations is proving to be a challenge due to accelerated demand in the market and crowding out by the deep wallets of leading firms. A McKinsey survey reported that 60% of respondents said that it’s more difficult to source talent for data-and-analytics roles compared to other positions, a rise from 48% reported in a previous survey.
While successful data and analytics programs are culture-driven and work best by altering the DNA of the organization, most companies face challenges in implementing this strategy due to a limited supply of technical talent and limited resources to build these programs from the ground up.
This is where the role of a powerful BI platform like Lumenore comes in. Lumenore provides the right data infrastructure by building Data Universe, a one-stop solution for all data-related problems, starting from consolidating data and moving ahead to the processes of ingestion, processing, and scheduling. Data infrastructure quality and reliability are ensured by Lumenore.
Additionally, encapsulating powerful modules including predictive and conversational analytics, Lumenore ensures providing rapidly generated data-driven insights in a user-friendly, intuitive, scalable, and cost-effective way to empower human intelligence with BI insights. Such AI-powered solutions ensure every individual in an organization has access to in-depth analysis and actionable business recommendations which intuitive in nature.
To gain a transparent view of the operations and drive successful business outcomes, it has become a necessity for organizations to invest in a unified business intelligence tool that transforms data into decisions. The question of moving to a 360-degree business intelligence framework is not of ‘if’ anymore but of ‘when’ and ‘how’. The business outcome and competitive advantage will belong to the first movers - akin to driving a car that can upgrade its components while zooming through the track.