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Translating data into action, keeping up with all the data movement

Meet Maya AI translating data into action, keeping with all the data movement

Business data is increasing at incredible rates, but what’s more important is turning this data into actionable insights. Data tells a story and can be used to improve various aspects of a business, from marketing strategies to product development. The challenge for many businesses is knowing how to extract these stories and turn them into tangible improvements. In this blog post, we’ll explore some tips on how you can turn data into actionable insights for your business.

The Data > Insights > Knowledge pyramid is a map for how companies can progress from data collection at the bottom all way up to acting based on what they find in their research. However, many organizations only move past insights and stop there instead of moving on to another stage. It would then be converting that information into actions such as acting upon customer needs or improving processes through automation. 

What was once an easy decision has become more difficult than ever. The assumptions you made at the start of your initiative are likely not going to hold up as added information and data sources appear, requiring substantial pivots along the way.


It’s time to stop guessing and start adapting! The world is changing faster than ever before. Systems that relied on predictability need an upgrade because nothing can be certain anymore – not even death or taxes (both happen regularly). linear problem-solving methods aren’t cutting it anymore; we need agile strategies that allow us more frequent reassessment of our plans to keep up with market changes & new opportunities alike.

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The agile approach to problem-solving is a way for companies with muddled problems and unknown factors, like those in the business world today. By applying this discovery-driven planning method; you can explore new avenues of exploration while learning as much about your current state or situation that will help move things forward towards a solution–allowing us not only to see what’s working but also to hear from different perspectives on how we might improve certain aspects if needed!
The key principles of successful discovery-driven problem solving are not only important in today’s agile landscape, but they were also essential when teams had less information at their disposal. In those days it became clear that individuals must be given the opportunity and freedom to explore innovative approaches on behalf of an organization if there is any chance for success because every person has different skillsets which can lead them down paths never thought about or imagined:
  • Choosing the right problems to solve using this approach
  • Defining clearly what success looks like
  • Benchmarking on common market information
  • Setting the limit where failure would be declared to prevent unprecedented losses
  • Communicating the operational requirements (tools/techniques) to solve the problem

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Tips for Turning Data into Action

  1. Define your goals – Before you start sifting through all your data, it’s important to have a clear understanding of what you’re looking for. What are your business goals? What do you hope to achieve by analyzing your data? Once you have a good understanding of your goals, you can start looking for the data that will help you achieve them. 
  2. Know your audience – It’s also important to understand who your audience is and what they need from you. Different teams in your organization will have different requirements for the data you supply them. For example, the sales team will be interested in data that helps them close deals, while the marketing team will be interested in data that helps them better understand their target audience. By understanding the needs of your different audiences, you can make sure that you’re providing them with the right information. 
  3. Clean and organize your data – Once you’ve defined your goals and understood your audience, it’s time to start cleaning up your data. This step is important because it will make it much easier to analyze later. There are a few things you can do to clean up your data, such as removing duplicates, filling in missing values, and converting all the values to a consistent format. 
  4. Use the right tools – There are a variety of different tools available that can help you analyze your data more effectively. If you’re not sure which tool to use, it’s worth consulting with a data analyst or someone with experience in working with data. They should be able to recommend a tool that will suit your needs based on the type of analysis you want to perform. 
  5. Analyze and visualize your data – Now that your data is clean and organized, it’s time to start analyzing it! Depending on the type of analysis you want to perform, this step might involve using a statistical software package or creating visualizations (or both). Once again, if you’re not sure how to go about this step, consulting with a data analyst or someone with experience in working with data can be immensely helpful. 
  6. Act! – After analyzing your data and extracting insights from it, it’s finally time to act! This might involve changing some aspects of your business based on what you’ve learned or sharing your findings with other members of your team so they can make decisions accordingly. Whatever course of action you take, make sure it aligns with your original goals and takes advantage of the unique insights provided by your data storytelling!  
A) Look for relationships
One of the best ways to turn data into actionable insights is to look for relationships between different pieces of data. For example, let’s say you’re a sales manager and you’re trying to increase sales in your department. You might look at your sales numbers and compare them to the number of leads your team is generating. If you see a correlation between the two—i.e., more leads generally result in more sales—you can act accordingly. You might start by increasing the number of leads your team generates or by providing them with added resources so they can close more deals.
B) Identify trends
Another way to turn data into insights is to find trends. This can be helpful whether you’re trying to predict future behavior or understand past behavior. For example, let’s say you run a website and you want to increase traffic to your site. You might look at your traffic patterns and try to find any trends. Are there certain times of day when traffic is higher than others? Are there certain days of the week when traffic is higher than others? Are there certain weeks when traffic is higher or lower than usual? Once you’ve found any trends, you can act accordingly. If you see that traffic is higher on weekends, for example, you might start publishing updated content on Fridays or Saturdays.
C) Find anomalies
Finally, another way to turn actionable insights from data is to find anomalies. Anomalies are unusual or unexpected patterns in data. For example, let’s say you run a website and notice that there’s a sudden spike in traffic from one region. This could be an anomaly worthy of further investigation because it doesn’t fit with the rest of the traffic patterns on your website. Once you’ve found an anomaly, you can investigate it further to try to decide its cause. In our example, if we figured out that the sudden spike in traffic was coming from users in China, we might investigate why that is and whether there’s anything we can do to capitalize on it (e.g., start publishing content in Chinese).

Data is becoming increasingly important in today’s business world but simply having lots of data isn’t enough; businesses need to know how to turn this data into actionable insights if they want to stay ahead of the competition. Thankfully, there are some simple steps that any business can follow to start turning their data into valuable insights: defining goals, knowing their audience, cleaning and organizing their data, using the right tools, analyzing and visualizing their data, and acting! By following these steps, businesses can use their data storytelling abilities to improve various aspects of their operations and stay ahead of the curve. 

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