Data-informed decision making can be challenging

Data-informed decision making can be challenging for a variety of reasons. Let’s dig a little deeper in the context of WHY it is challenging:

?1.????? Data Quality Issues

The first reason is Data Quality Issues, the accuracy and reliability of the data used for decision making is crucial. Whenever the data is incomplete, outdated, or bad of quality, it can lead to misguided decisions. Ensuring data quality requires continuous monitoring, cleaning, and validation processes. So therefore tools like #talend, reporting about your data quality elements is very important.

2.????? The complexity of Data

Many decisions involve complex and interconnected data from various sources. Analyzing and interpreting this data can be difficult, especially when dealing with large datasets or data from diverse domains. Not fully understanding that organizations and processes are systems, that are interconnected to each other what makes it even harder.

3.????? Lack of (underdeveloped) Data Literacy skills

Decision makers and employees might lack the necessary skills to understand and interpret data ?and the insights created from it effectively. This can lead to misinterpretation, miscommunication, and poor decision making or just doing nothing at all and take the outcomes as they are and hope for the better next week, month, quarter or year.

4.????? Bias and Misinterpretation

Human bias can unintentionally influence how data is interpreted and used, there are many forms of biases like; confirmation bias, ?hindsight bias, survivorship bias, groupthink and so on. Next to that, misinterpreting data or drawing incorrect conclusions can lead to poor decisions.

5.????? Uncertainty

Data doesn't always provide clear-cut answers. There might be uncertainty in the data itself or in the outcomes of different decisions based on the data. Decision makers need to navigate this uncertainty and make informed choices.

6.????? Resource Constraints

Organizations might lack the necessary resources, including time, money, and expertise, to collect, analyze, and act upon data effectively. Therefore they have a lack of data and information to make actual Data Informed Decisions.

7.????? Resistance to Change

Implementing data-informed decision making and defining your organizations readiness and plans for a Data Literacy Organization often requires changes in processes, culture, and even job roles. Resistance to change from employees or stakeholders can hinder the adoption of data-driven approaches, therefore we need to think of “change management” and guide our organizations and its people.

?8.????? Lack of Data Integration

Data might be stored in different systems or departments and organizations have a siloed structure. Those components are making it difficult to access and analyze data in a unified manner. Integrating data from various sources can be a technical challenge but there are tools amongst us that truly can help to bring al that data together in “one single environment”.

?9.????? Short-Term Focus

Short-Term-Focus is not a good thing, sometimes, organizations prioritize short-term gains over long-term benefits that data-informed decisions might bring. This can lead to decisions that ignore valuable insights provided by data and doesn’t help to safeguard your organizations future. Therefore it is a necessity to think strategically and always envision your organizations future data strategy. ?

10.?? Overemphasis on Data

While data is crucial, relying solely on data and the created insights can neglect important qualitative aspects and contextual understanding. Some decisions might require a balance between data and human intuition. Therefore our human intelligence and our data literacy skillset is mega important.

11.?? Data Security and Privacy Concerns

Sharing and using certain types of data and insights might raise privacy and security concerns, especially with regulations like GDPR ?and other coming into play. This can limit the availability and usability of data for decision making.

12.?? Complex Decision Environments

Some decisions involve complicated factors, such as market dynamics, human behavior, siloed structures of organizations and external events. Data might not fully capture all these complexities, therefore it could be wise to think of centrally focused data & insights teams that is filled with employees from various departments with their specific overall knowledge of data and their specific domain knowledge.

?Overcoming those challenges

To overcome these challenges, organizations need to invest in data literacy training, establish robust data governance practices, develop effective communication strategies to translate data insights, and foster a culture that values evidence-based decision making. It's important to recognize that while data can significantly enhance decision making, it's not a some wonder glue but all of it needs to be ?used in conjunction with other forms of information and expertise.

If you have any questions about our Data Literacy Training offering, please contact me via [email protected] or +31 6219 44524

Wishing you a happy weekend!

Angelika Klidas, Challenger, Educator & Advisor.

Business Data Challengers