When decision-making is real-time and forward-looking, instead of mere ad hoc reactions, the perspective of business shifts from the rearview mirror to the crystal ball.
For example, rather than looking at a map of where maintenance workers are currently, advanced location analytics could be used to optimize maintenance targets automatically.
So, knowledge management delivers relevant information to the right processes at the right time, resulting in better decisions for business development.
The decision-maker can be a human or a machine. Data-driven management can provide business with, for example, additional sales opportunities, streamline processes, create entirely new business possibilities, and generate significant cost savings.
If a customer service representative isn't even aware that a customer is dissatisfied, how can customer retention be prevented?
When decision-making in practical work is enhanced with advanced analytics, it becomes easy to choose the right actions and words, leading to increased customer satisfaction—and sales as a result.
For instance, if the only analytics on sales situations come from monthly performance reports, quick responses to changes become impossible, and predicting the future relies largely on guesswork.
If dashboards are created solely for the purpose of creating a visually appealing representation of data, the utilization of data in data-driven management remains rather thin.
In surprisingly many situations, a machine's ability to make decisions surpasses human skills—and yet in many situations, a human is still the one pressing the OK button.
For some reason, mistakes made by humans, which happen constantly, are accepted, but a machine is not allowed to make a mistake ever.
Utilizing open data effectively enriches the information obtained from a company's own systems.
For example, if traffic congestion is predicted solely based on the traffic volume data collected by the company, neglecting the use of open weather data, the estimate may not be very accurate.
A map view of the destruction caused by a forest fire only tells what has already been destroyed.
It is more useful to generate a predictive model of the fire's spread and provide pre-digested information to firefighting units.
The adaptability of culture is the essence of data-driven management.
If business decision-making is based on gut feelings, sophisticated guesses, and a childlike belief that we truly know our customers, a complete shift in mindset is required.
To ensure that data-driven management doesn't appear as an oversized monster to the staff, its benefits should be highlighted through examples.
A key factor in creating a data-driven management culture is for the staff to integrate analytics into their own work and genuinely see the benefits from it.
Data-driven management, of course, requires a lot of technical expertise for building integrations, transforming data from one platform to another, cleaning up the right information, implementing analytical models, and so on.
However, often the most challenging aspect to manage is the ability to understand the business: why data is collected and what is the goal of analytics?
Especially in many large organizations, data tends to rest in silos.
Each business unit has its own budget, and when it comes time to build a common data platform, the critical question becomes whose budget the funds will come from.
For this reason, it is important for the organization to have a Chief Data Officer (CDO) or a person responsible for analytics who can work across organizational silos.
Technology is an important component but not an end in itself. The technologies used should be chosen based on the situation and business needs, taking into account existing solutions.
Sometimes this requires investing in a completely new solution and abandoning the old, while other times existing technology should simply be utilized more efficiently.
