This growth and advancement in technology has led to data-driven decision making (DDDM) being used more often in different industries.
This approach guarantees that company decisions are not based on personal feelings or instincts, but are supported by factual data from various sources.
This article discusses how data-driven decision making is used in various fields and the benefits it provides.
Data-Driven Decision Making
The practice of making decisions based on data that has been collected and analyzed.
Predictions about the future are more likely to be accurate if they are based on data from the past. Without data, it is easy to make false assumptions or be influenced by biases.
Data from multiple sources must be collected and processed to provide decision-makers with analytics so they can understand what the next step in their business should be.
When making decisions based on data, it is important to set up Key Performance Indicators (KPIs) and other benchmarks that will help track the progress of the KPIs.
KPIs are measures of performance that can be quantified and that help businesses see how close they are to achieving their goals.
There are a number of different KPIs and value metrics that help measure the performance of systems and their business impact.
The success of data-based decision making is contingent on many factors, like the techniques used for data collection and the data gathered’s quality.
This type of decision making relies heavily on data that can be quantified, which often means that businesses need access to powerful machines that can compute and analyze large data sets quickly and effectively.
Building a System for Data-Driven Decision Making
A lot of what goes into becoming a data-driven organization is figuring out what data you need to collect, how you need to collect it, and how often The company’s mindset heavily impacts its ability to make data-driven decisions. Every individual affiliated with the company needs to understand the data-driven decision-making process and how to apply it on a daily basis. A large part of becoming a data-driven organization is figuring out what data you need to collect, how you need to collect it, and how often.
The first step to understanding a problem is identifying it. The next step is to understand everything surrounding the problem and to find data that validates assumptions.
To do a deep analysis, you need to collect and process data. You can do this by using data pipelines, data lakes, or data warehouses. This way, you can capture data without bias.
Data that has been collected is usually processed by programming languages like Python or R.
Visualizing data in an easily understandable way is important for decision-makers to be able to use complex data.
After all the data has been collected, it is presented to the stakeholders in the form of charts, graphs, and dashboards.
Making decisions is easier when there is a decision model set in place, as it provides a framework for making programmed and unprogrammed decisions. Without a decision model, making decisions often takes more time as it requires more data to support certain conclusions.
KPIs are essential for measuring the outcomes of any decision. Developing good KPIs, targets, and goals takes time, but once they have been created, every decision’s outcomes become measurable. This makes it possible to assess the success of each decision.
Linked Data
hypertext links
And linked data could help customers move between different data sets and websites, or link data sets together in the background, for example connecting transactional data to complaint data to customer service data.
This is all about connecting different pieces of information together to get a clearer picture of where your customers are going, what they are doing, and what the main obstacles are that you are trying to fix, which can then give you concrete benefits.
Delivering Value
The value of data is not just commercial, it includes the value to customers and regulators as well as the importance of data security. Linked data is the key to achieving this.
Process of Linked Data
If you want people to be able to look up information about the things you’re talking about, use URIs and make sure they’re HTTP URIs. That way, people can use the same standardized methods to find information that they’ve used in the past.
This text is discussing things that may be second nature to those who use this type of thing regularly, but for those who don’t, it’s important to get these basics right from the start.
Make sure to include links to other websites in your text to encourage further discovery. All of these key things are very important when trying to create a robust, comprehensive journey for your customer.
Semantic Technologies
Semantic technologies involve extracting meaning and identifying relationships between concepts in order to automatically categorize information. This process of categorization makes it easier to find similar data sets.
Labeling and badging things in a way that is meaningful to your customers can help them navigate more easily.
- Auto-recognition of topics and concepts: You might be a content-led business. Think of the likes of Facebook, or of LinkedIn, for example. They’ve all categorized things through tags. It enables people to get to your data quicker.
- Information meaning and extraction: This in turn enables people to pick up relevant things that are interesting for them in a meaningful way and quicker as well. Information and meaning extraction become quite pertinent.
- Categorization: Categorization involves tagging this up into different core elements that people can relate to. This becomes very important and an exciting way to go about doing things.
The Internet of Things
It is a system consisting of interconnected computing devices, both digital and mechanical, that can identify individuals and transfer data over a network.
Applications
- Think about ambulance services going down the main street and being able to talk to traffic lights to be able to get you know, when to change at the right time at the right pace as they’re going through.
- Think about the detection of healthcare problems before they happen which alerts the GP to then fixing or to then automatically send out a prescription to solve that problem for a particular patient.
- Think about home automation where you know, the system predicts that you’re about to walk in the door, turns the lights on and turns the heating on for you.
- Think about when you’re running short of milk and a computer needs to just re-order it from the website and it does that. It’s re-ordered and then basically to your door the next day.
Considerations
- Embedding objects: You need to be able to put intelligence into certain objects for them to make it meaningful.
- Location of living things: Electronics is a great example, whether it’s chipping of human beings, whether it’s GPS locations on your mobile, whether it’s patient input, or whether it’s Fitbits in your health tracking devices. It involves building and inputting key technologies to be able to monitor and then do something about that monitoring all those insights in real-time from one object to another.
- Software: It also needs a lot of software in the backend to make that decision process for you. Updating that software is actually critical. Consider Fitbits and health evaluations. When that software links your Fitbit data or hardware data to your GP’s data, that becomes quite a meaningful thing when you use it to make predictions about who is more likely to do certain things or fall ill or whatever that may be. You can use software as a means to get an additional richness within your data set.
- Sensors: The sensor could be in your home or in your car. Think about driverless cars and the number of sensors that it would have, or when you’re walking in the door and it tracks your movement through your mobile. Using sensors enables the intelligence to be activated at certain points within that journey, so you need to have those sensors at appropriate locations. RFID is a great sensor that’s likely to take place across the retail environment. And a good example of this is that of Argos. Argos is basically predicting an onwards vision to get the customers out within three minutes of walking into the store. And one of the key ways they’re thinking about doing that is by having sensors at the door which predicts when customers are about 50 meters away, so the store can get the items that they’ve ordered online ready for delivery.
- Network connectivity: It’s no use having these networks in silos. You need to basically have the entire network connected together to be able to understand how the linkages work. And so that’s why it’s important to create this connectivity.
Advantages of Using a Data-Driven Decision Making
1. Greater Transparency and Accountability
When companies make decisions based on data and have clear goals, it improves transparency and helps avoid bias. To get the most accurate results, it is important to consider all data without bias.
An emphasis on data throughout the company allows employees to better understand the importance of data backups and encourages them to use data to guide their decision making in their own work.
This provides the organization with risk-management benefits and increased performance, as well as higher morale for employees.
If an organization collects and manages data properly, it will be seen as more accountable. This data can be used for record keeping and compliance.
2. Continuous Improvement and Innovation
Making decisions based on data allows organizations to improve and innovate indirectly. By implementing small changes and monitoring vital statistics, organizations can make changes based on the data they capture.
Client feedback can help a business improve.
3. Faster Decision Making Process
The ability to accurately analyze data can help solve many different types of problems that businesses face. In many cases, data-driven decision making requires business leaders to analyze data in order to gain predictive and prescriptive insights.
Data-driven decision making leads to quicker decisions.
The ability to analyze data in real-time and identify patterns allows businesses to make decisions more quickly and confidently.
4. Clear Feedback for Market Research
The data-driven decision making approach helps organizations to formulate new products, services, workplace initiatives, and even identify trends by looking at data.
Looking at historical data helps companies understand what is likely to happen in the future and what they need to do to improve their performance and be more competitive.
Companies can use customer feedback to understand how to keep their customers happy and to find out how best to introduce new products and services to keep their business moving forward.
Challenges of Data-Driven Decision Making
1. Low Quality of Collected Data
The goal of collecting data is always to collect as much as possible. However, if the data collected is of low quality or does not contribute to a better understanding of any obstacle or problem, it may be obsolete to use.
Data that is high-quality is simpler to work with and gets you to the right answer more quickly.
2. Different Data Formats
You will need to create scripts to convert data into a single format if it is saved in JSON, CSV, or XML formats.
A simpler approach is to use data management tools to collect and format everything using one universal standard.
3. Learning Curve for Interpreting Data
It is important to have a clear understanding of both data gathering and data interpretation processes to ensure that insights are accurate.
Every employee should know the best practices of a data-driven culture since they are all in some way involved in DDDM.
Data-Based Decision Making in Banking
Brick-and-mortar banks realize that they are losing customers to their FinTech competitors because the latter offer faster and easier-to-use products and procedures.
Fintech companies are far ahead of traditional banks when it comes to using DDDM to create new customer experiences. Fintech companies are using DDDM to create new experiences and specialized customer journeys on their platforms, and traditional banks are falling behind.
Many organizations are still struggling to understand what their customers want and how to deliver it effectively. They also face challenges in developing innovative solutions.
Banking is moving from real-time and in-person communication to online platforms and apps. Although this platform-based model comes with many challenges, it can also create a chance for huge gains if implemented correctly.
Option for generating new revenue exist, but they are contingent on data-driven decision making processes rather than a traditional mindset.
Fintechs are rising in popularity due to their innovative use of technology and their unique approach to the market as compared to banks.
Customer expectations have been increasing, especially when it comes to convenience and simplicity. In order to compete in this platform-based sphere, a different mindset is required, as well as a willingness to change and new set of skills to unlock potential rewards.
Data-Based Decision Making in Banking and Finance
Fintechs have been successful in attracting customers away from traditional banks because they are able to make better decisions using data. Traditional banks have been struggling to keep up with the demands of their customers for digital offerings, so they have been placed on the back-burner.
Brick and mortar banks are increasingly basing their decisions on data, adapting their business models to the changing landscape.
Banks that use digital tools to collect, analyze, and use data are able to resolve customer pain points faster and more easily.
The demographics, income, spending habits, and more of customers can offer valuable insights into how they manage their money.
Banks can provide their customers with offers made specifically for them by using insights generated from data. This data can be used to understand what products they need to develop to compete in the digital market.
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