Data Driven, Data Informed And Data Inspired

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Analyzing Survey Data2
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Data driven is an adjective that refers to a process or action that is fueled by data rather than intuition or personal experience. In other words, the decision is based on actual empirical data rather than guesswork or gut instinct. The word is used in a variety of contexts, although it is most usually associated with technology and business.

A data-informed strategy involves making judgments based on data as well as user research, experience, and personal insights. There is still a human aspect to decision-making, rather than allowing statistics to rule everything. Using the same scenario, we wouldn’t necessarily turn off one ad because of the lower CAC. Instead, we’d think about other factors and maybe change the ad accordingly.

Data-inspired firms typically appreciate data exploration. For example, they may bring together disparate sources and try to uncover common ground amongst them. Finally, data-inspired organizations search for patterns in data but do not rely primarily on such trends.

What Is Data Driven?

Data-driven means that the data drives all choices and activities. If evidence shows that sales are down due to negative brand perception, certain steps can be done to reverse the trend. If data analysis indicates that consumers of a current generation of mobile device like a certain feature, the next-generation device can take use of that information.

Data-driven indicates that data determines the behavior of those who carry out an event or activity. This is particularly visible in the realm of big data, where data and information are the foundation of all actions and data collection and analysis is the primary motivation. Because data is increasingly simpler to collect and store, big data analytics is gaining traction as the finest tool for corporate decision making. Having so much data offers individuals enormous insight into the world and allows them to affect outcomes as a result.

When To Be Data Driven?

Answering business questions and guaranteeing any modifications to a product will not have a negative impact on the business are the two finest use cases for data-driven outcomes.

These are some data-driven questions:

  • When is the greatest moment to launch X or promote Y?
  • Which variant of the design performs better, A, B, or N?
  • Which customizing approach outperforms the others?
  • How much money are we going to make next month or year so that I can manage my budget?
  • How will the addition of additional features/user types affect the product?

Exploratory Research Guide

Conducting exploratory research seems tricky but an effective guide can help.

Benefits Of Data Driven

Many organizations like the fact that they don’t have to make judgments since data does it for them. Gut impulses are disregarded, and there is no emotional component. It is also feasible to work around the agendas of specific stakeholders. No stakeholder can argue otherwise if the business’s attitude is to listen to the data.

This method is also deemed proactive by numerous authorities, including Harvard Business School. Rather than responding to market changes, we may monitor the data and discover anything that may be troublesome in the future.

Limitations Of Data Driven

The data in this sort of request is intended to address a very particular query. It indicates that we can’t get other insights from the same data; the data has a one-dimensional application. If we witness people utilizing data from a data-driven request to answer a question that it was not meant to address, the work of being data-driven will be undermined.

This use case should not be used to inspire a new strategy or the design thinking process, but rather to validate the solution.

What Is Data Informed?

Data-informed implies that the team understands the performance of key performance indicators (KPIs), drop off rates, and user pathing inside any particular product. They can identify broad upticks or downticks in performance and ascribe the reasons for such changes. To be a data-informed team, we must understand both what and why. The team must also be prepared to use this data to fine-tune and inform future tactics. Using this data to inform a future action will get our team data-informed.

When To Be Data Informed?

The following are the main reasons to implement a data-driven process:

  • When deciding what adjustments and improvements to make to a product,
  • Organizing a feature backlog

The team must operationalize two procedures in order to produce data-driven work:

  • Explain why we believe a plan will work using a hypothesis-driven method; this informs the why.
  • To ensure that everyone is aware, communicate plans and release dates to all teams.

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Benefits Of Data Informed

A data-informed strategy, combined with extra creativity and expertise, assists a firm in discovering innovative solutions. We are nearly limited by the apparent when we rely just on statistics. With a more personal touch, solutions are unique, and data is solely used as input.

Data-driven techniques can assist you in identifying patterns. In the past, we’ve seen businesses get so focused on data that they lose sight of their rivals or sector. Consumers suddenly don’t see the value of our service, and we’ve lost. This issue does not occur with a data-informed approach. 

Limitations Of Data Informed

A data-informed analysis, unlike a data-driven mindset, should not tell us what to do. It should be used to explain previous mistakes and accomplishments in order to drive future tactics.

Teams must be able to properly convey assumptions about why the methods they are developing will be effective. If they do not do so, the previous plan cannot be examined. If the previous approach cannot be reviewed, the team will never understand why certain events occurred and, as a result, will never actualize a data-informed team.

What Is Data Inspired?

Data-inspired design has no criteria or results expectations; it is experimental in nature. The results of these sorts of studies mix together data from several sources, revealing surprising similarities across data sources. The major takeaway from this research is that the individual doing it is relying on intuition and inference rather than solid, statistically valid procedures.

The problem with data is that it can only explain what has occurred in the past and estimate what may occur in the future based on historical trends. It struggles to generate new, inventive ideas since there is no precedence for such new ideas. Data-inspired can assist in resolving the data dilemma by uncovering seemingly unrelated data sources to inspire new ideas.

When To Be Data Inspired?

The following are the greatest ways to use a data-inspired mindset:

  • During the strategy phase of determining a product’s creative direction
  • During Design Thinking exercises, the data will never tell you how to proceed with your design or strategy, but it will tell you that there are some fascinating concurrent trends, providing more context to the larger picture than other techniques.

Limitations Of Data Driven

This information should never be referred to as solid information. Everyone should be aware that the data patterns that appear might be the result of erroneous interactions (seemingly related interactions but are actually not related). As long as everyone understands that there is a danger in deriving definite conclusions from this data, it is a risk worth taking because this data brings light into previously dark places.

Data Driven, Data Informed Or Data Inspired?

Being data-informed enables us to evaluate data while also admitting and comprehending its limits. To be data-driven, we must use the data as it is. Being data-inspired enables you to conceive possibilities based on data interpretation.

There is no single correct answer, and the best technique is ultimately determined by the goals we want to achieve and the sort of data we have available. And, of course, as our business changes, so will our data strategy.

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