Data-Driven Decision Making: What, Why & How

data driven decision making what why how

Data-driven decision-making is a data-driven approach to making decisions. This data can come from data analysis, data visualization, or other data resources. Data-driven decision-makers use data in their decision process and they make decisions based on the data that they have collected. The goal of this type of decision-maker is to make informed decisions rather than quick ones. In this blog post, we will discuss what data-driven decision-making is, how it differs from other types of decision-making, and why you should consider going for this method in your business!

What are the different types of decisions?

The following represents different types of decisions made in an organization on a day-to-day basis which can leverage data-driven decision making.

  • Programmed decisions: These are long-term decisions that can span over years and require data to be collected from various sources. Programmed decisions can leverage descriptive analytics including KPIs tracking against benchmarks with the help of dashbooards. Actions can be tracked using KPIs and the same can be instituionalized later into organization appropriately. Programmed decisions can also be termed as policy decisions at times as this results in long-term policy definition and implementation. For example, the following are different types of programmed decisions:
    • Marketing decisions – Whether a company should spend more money on online marketing or resorting to the traditional methods
    • Manufacturing decisions – How much capacity needs to be added in order for a company to meet increased demand?
  • Unprogrammed decisions: These are short-term decisions that need data to be analyzed in order to make a decision. Unprogrammed decisions, at times, can also be termed as operational decisions. Unprogrammed decision can leverage predictive and prescriptive analytics. Data-driven decision making helps in reaching better conclusions than human intuition For example, the following are different types of unprogrammed decisions:
    • Sales manager’s daily sales report – What customers should I focus on? Which customer can give maximum revenue for me this month?
    • Marketing manager’s daily data report – What are the best strategies to increase my company’s Facebook followers?
  • Organizational decisions: These are data-driven decisions that need data to arrive at the correct decision. These data can be used by multiple people in an organization and they will make similar types of unprogrammed or programmed decisions based on them. For example, the following are different types of organizational decisions:
    • Industry benchmarking data – How is my company comparing to others in the same industry?
    • Market data – How much demand is there for our products/services in the marketplace? Are they increasing or decreasing over time?
  • Strategic decisions: These data-driven decisions are used to make long-term level planning of the organization. For example, the following are different types of strategic data-driven decisions:
    • Strategic plan – Where do we want to be in five years? What should our company’s mission statement be?
    • Long term data collection for programmed decisions – Whether a new data-collection system is required to record data over a long period

What is data-driven decision-making?

Data-driven decision-making is the data-driven approach to making decisions. In data-driven decision making, one uses data in their decision process and the decisions are made based on the data collected from one or more data sources. The goal of this type of decider is to make informed decisions rather than quick ones. Data-driven decision-making requires different systems to be put in place, such as data collection, data processing, data visualization, etc.

Data-driven decision-making requires defining KPIs, benchmarks, tracking the KPIs and driving actions appropriately. KPIs can be of the following different types:

  • Business KPIs – These help in measuring the performance of data-driven decision-making. For example, a revenue growth rate is an indicator of data-driven decision-making because it shows whether decisions are being made to increase sales or not. Business KPIs can also be termed as lagging indicators or lagging KPIs. These KPIs can also be called projects outcome KPIs.
  • Customer KPIs – These help in understanding data-driven decision-making from a customer perspective. For example, if the data shows that customers are complaining about an issue and it is not being addressed quickly, then one would need to use this insight and drive actions to address the customer complaints. Customer KPIs can also be termed as leading indicators or leading KPIs.
  • Project output KPIs: Project output KPIs are data-driven decision-making KPIs that are used to measure the success of projects. Examples of project output KPIs include the number of user stories completed on time, system availability, system performance, etc.

How to go about creating the system that facilitates data-driven decision-making?

The following are some of the key systems that are required to be deployed in order to facilitate data-driven decision making:

  • Data collection or ingestion: The data collection systems enable the data analysts/scientists to capture data points that are required for analysis. The data should be captured in a way that it is not biased towards any particular outcome, and can be used by anyone who has an access to data. The example of data ingestion systems includes data ingestion using data pipelines, data warehouses, etc.
  • Tagging and annotation: Tagging allows the analysts/scientists to annotate each data point with a set of tags that provide enough details about the data point for use by other analytics professionals. The tagging system should be easy to use so as not to interrupt workflow processes across teams/organizations. At the same time, data points should be tagged with the most appropriate tags.
  • Data processing: The data collected would need to be processed so that it can be used for decision-making. This is where data processing systems come into the picture. Example of data processing systems includes big data processing engines such as Hadoop, data processing tools like R and Python for data analysis.
  • Data visualization: The data analysts/scientists would need to figure out the data insights required for decision-making, and how they can be used by business leaders. This is where data visualization comes in place which enables visualization experts to share the insights with the stakeholders through visually appealing graphs, charts, plots, etc. This is where tools such as the Qliksense dashboard comes into the picture.
  • Decision model: Data scientists would come up with the decision models which can be used by business stakeholders to make data-driven decisions. An example of a decision model includes the deployment of predictive analytics systems for data-driven decisions.

Why adopt and adapt to data-driven decision-making?

The following are different benefits or advantages of data-driven decision-making.

  • Faster, better & informed decisions: Better business decisions are made by considering both data and human intuition, which leads to better results for companies. It reduces the bias introduced due to gut feeling.
  • Increased data visibility: Data-driven decision-making helps in increasing data transparency as the data is available to multiple people and it can be easily shared.
  • Increased data accessibility: Data-driven decision-making helps in creating data access for everyone, not just a single person or department. This leads to increased data sharing and reduces the chances of mistakes being made due to a lack of information.
  • Increased data quality: Data-driven decision making ensures data quality is increased by removing data redundancy and errors. This leads to more accurate data collection and higher precision in the analysis of the data.
  • Efficient use of time: With data-driven decision-making, companies can save on time and resources as human input and associated risks are reduced significantly.
  • Increased data usage: Data-driven decision-making ensures data is used in a better way, for example, data can be easily shared and reused. This leads to increased data value as more people use the same data which increases its worth over time.
  • Enhanced data-literacy: Data-driven decision-making helps data professionals to improve their data literacy skills, which in turn will lead them towards better career opportunities.
  • Increased customer satisfaction: When multiple data points are collected about customers, companies can come up with various ways to increase their overall satisfaction levels which will lead to increased retention rates and ultimately better future revenue for the company.

What are the risks associated with data-driven decision-making?

The following are risks or disadvantages of data-driven decision making:

  • Confirmation bias: The business stakeholders might tend to use data points which support their decision-making and ignore the ones that may go against it. This leads to decisions driven by confirmation bias, which can lead to poor business outcomes in future due to wrong data analysis.
  • Data leakage: The data collected for decision-making needs to be safeguarded so that other organizations cannot get access to it and use it against business interests. This is where anonymization methods come into play.
  • Data bias: Different data sources may have data points with different levels of biases that need to be taken into account while deriving any data insights from them.
  • Unforeseen consequences: The data analysts/scientists fail to take into account other factors such as data quality, data collection errors, etc. while coming up with data insights which can lead to unforeseen consequences in the future because of wrong data analysis.
Ajitesh Kumar
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Ajitesh Kumar

I have been recently working in the area of Data Science and Machine Learning / Deep Learning. In addition, I am also passionate about various different technologies including programming languages such as Java/JEE, Javascript, Python, R, Julia etc and technologies such as Blockchain, mobile computing, cloud-native technologies, application security, cloud computing platforms, big data etc. I would love to connect with you on Linkedin.
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