In today’s fast-paced business world, using data for key company decisions is critical in steering the organisation in the right direction. While this practice may be second nature for successful companies, we will delve into the evolution of Data-Driven 2.0, providing a competitive edge. We’ll explore the ways companies are getting smart with data: by looking inside their own office walls and checking what’s happening beyond.
What it means to be Data-Driven
Being “Data-Driven” means taking strategic decisions based on empirical data analysis and interpretation in contrast to a human intuition or instinct. Decisions backed by actual data consistently outperform those based on intuition alone. According to research conducted by McKinsey Global Institute in 2020, organisations that prioritise data-driven approaches are significantly more successful in various aspects. These data-centric companies are 23 times more likely to attract new customers, exhibit a sixfold increase in customer retention rates, and are 19 times more likely to achieve profitability compared to those not leveraging data-driven strategies.
How organisations are Data-Driven today
In the automotive industry, an approach to data-driven optimization can be implemented with a focus on vehicle maintenance and customer satisfaction. Imagine an auto repair shop that meticulously records data on every repair job it undertakes. This data includes the types of vehicles serviced, the nature of the repairs, the time taken for each job, customer feedback, and the frequency of repeat visits for similar issues.
By analysing this data, the shop can identify patterns and trends. For instance, they might discover that certain models of cars frequently require specific types of repairs after a certain mileage. With this insight, the shop can proactively reach out to owners of these vehicles for preemptive maintenance, thereby reducing the likelihood of major breakdowns and enhancing customer satisfaction.
Moreover, by tracking the time efficiency of different repair jobs, the shop can optimise its scheduling and staffing, ensuring that it has the right number of technicians with the appropriate skills available at peak times. This leads to quicker service times and increased customer satisfaction.
Additionally, the shop can use this data to maintain an optimal inventory of parts. By knowing which parts are most commonly used for the types of vehicles they service, the shop can keep a well-stocked inventory, reducing wait times for repairs and avoiding the cost of holding unnecessary inventory.
In essence, the auto repair shop uses its internal data to optimise operational efficiency, improve customer service, and enhance overall profitability.
5-step process for implementing Data Driven Decisions
1. Clarify Digital Business Goals:
Review existing documentation regarding company vision, strategy and existing goals. Gather insights on digital business objectives, including their priority.
2. Identify Key Metrics
Collaborate with departments to identify existing or desired key performance indicators (KPI’s). Prioritise the metrics in collaboration with stakeholders.
3. Access Key Metric Measurability
Evaluate data availability for the prioritised metrics. Assess measurability in terms of dimensionality and historization and map it to user needs.
4. Map Key Metrics to Data Sources:
Identify what data sources that are involved in the calculation of the prioritised metrics. Engage IT/data team to assess data source reliability and accessibility. Prioritise data sources based on relevance to key metrics.
5. List Data Source Gaps (Data Driven 2.0):
Identify gaps in data coverage. List potential new data sources to explore from a technical standpoint.
Become Data-Driven 2.0 with External Data Sources
Data-Driven 2.0 signifies the next phase in the evolution of data-driven decision-making. It involves not just understanding internal metrics but also harnessing data from diverse external sources to gain a comprehensive view.
By tapping into a variety of external data sources, companies can paint a vivid picture of the market landscape, consumer behaviours, and industry trends. This expanded perspective allows for a more nuanced understanding, enabling businesses to make informed decisions that go beyond the confines of internal data.
The synergy of internal and external data sources is the cornerstone of Data-Driven 2.0, where the richness of insights from the broader world becomes an integral part of strategic decision-making.
External data is available within industry specific data platforms, such as leads databases containing contact information on relevant clients to approach, or financial analysis platforms to better benchmark your performance against peers. Also, external datasets can be found available for sale on public data marketplaces.
However, grasping how to best use and apply external datasets to get the most relevant insights and analysis input to your own business needs might not always be crystal clear. This is where the UnionAll platform enters the playing field - it helps you both identify what external metrics, KPI’s and trends are essential to incorporate and deliver these external data sources all within one platform.
On this, we will delve deeper into in our upcoming article - applying external data to your business.