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Head Office Singapore: 31 Rochester Drive,Level 24,Singapore,138637
+65 6808 8760
India Office: 880,Adarsh Nagar,   Jogesheri West, Mumbai,400053
Understanding Data Driven Decision Making

Understanding Data Driven Decision Making

Data-driven decision-making is just what it sounds like a system by which participants use information to guide their choices. The whole industry of insights is focused on this feeling, as we are the miners of insightful golden nuggets of data on which companies lead their whole brand.

I know it sounded lofty, but it didn't go wrong. We are in the business of collecting data, converting it into insight, and handing it on a golden platter to stakeholders to ensure that their organization makes the right choices at the right time. Now, if these organizations really take the data and observations into account in those decisions, it is a completely different matter, and I plan to spell out how data-driven decision-making works in this blog.

Choosing the Right Data

Now you need to make sure you have the right data to make a fully educated choice in order to make the best decisions. Many stakeholders, and individuals in general, make decisions based on minimal situational data making use of what they can see at the moment, but that only works with short-term, not long-term, decisions.

The fact of the matter is, data that is reliable and important to your decision at hand is the only data you need. It's up to you how you get this information, but make sure that the approach gives you the in-depth, rich insights you need to make the right decision.

There are a range of factors that go into providing accurate, appropriate, and reliable insights, such as the sample used the participation of the researcher, the methodology(s) used the analytical methods used at least the amount of time it took to collect and analyze the data. For example, if you combine quantitative and qualitative methodologies, instead of only using either a quantitative or qualitative approach, you are more likely to get an in-depth answer from participants. But then again it might be easier to get in-the-moment reviews from one of the world's largest online audiences using social media analytics, but you don't know exactly how committed they are to your brand.

As long as the analysis you do is carefully prepared in advance, you have consulted your options with insight experts and decided on the best way to collect the data to make sure it is the most reliable and meaningful it can be so you can comfortably use it in your decision-making processes.           

Making the Decisions with Data

Now that you have the detailed and necessary details, it's time to choose the best way to do it. In the process used to analyze the data and proceed with a result, there are a few commonalities in any decision-making model, which include:

·         Understanding the purpose of the decision, both in its individual state and how it fits into the larger goals/goals of the company.

·         Identify the best insights from others you've managed to collect to take into consideration.

·         Make sure that professional advice is taken into account and that you know how your decision in the predicted future will affect the company.

·         Make the decision and analyze the result so that you can learn next time.

There are a few Knowledge Management techniques that enable data and insights to be compiled, processed, and distributed so that they are directly accessible at the time they are most needed. Creating this central data and insight repository will help immensely when data is required to make a decision right at the very moment, but this central repository is built up over time, and it is important to periodically check the data that might be stored within the repository to ensure that it is still valid.

There are a range of data-based decision-making frameworks for us to choose from but there are two specifically that I believe will help launch the right start on your data-driven decision-making journey.

Cognitive Mapping, which is a structured collection of ideas to direct your thinking, define areas of confusion, and describe assumptions made in the process, is the first one I want to discuss. This is a great place to start having a proper grasp on any situation, and when new knowledge or data is introduced to the mix, it lends itself to an engaging approach. In all sectors, this is used a lot by practitioners, whether they know it or not, and is the most simple logical method to begin with.

The second one I want to note, though, is not a model focused on business or market analysis at all but rather a police model that works to help determine the right steps to take on the basis of the evidence presented. In both operational and non-operational situations, the National Decision Model can be extended to spontaneous incidents or scheduled activities, used by a person or a team of officers, and has a great deal of applicability to this situation. To analyze circumstances, facts, and acceptable results, the model has six main elements based on the code of ethics.

Getting Data to Stakeholders

It's all well and good to have the right resources to actually make the decision, but all decisions depend on insight professionals having the right data at the right time to the right people, and delivering it in a way that catches their interest so that our customers have no choice but to listen to the data and insights we present to them. Fortunately, that is what we do best.

Don't be afraid to play with data distribution formats to find the best presentation strategy to optimize the potential of your knowledge for and stakeholder you come across. In order to make it more detailed, coherent, and exciting, experimentation in this form can come in a variety of ways, including blending the data formats.

This may have all made data-driven decision-making sound a little more complex than first thought, and while the idea is fairly clear, once you look under the surface, you can see that there are different stages in making a data-driven decision: producing the right data, presenting it in an entertaining way, using it to optimize capacity in decision models, and analyzing it at the end to make a decision.