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How Do Data Driven Insights Inform Data Optimization?

How Do Data Driven Insights Inform Data Optimization?

How Do Data Driven Insights Inform Data Optimization?



Data can be an organization’s most valuable asset if properly strategized. No matter the industry, organizations everywhere have become dependent on data every day. The world has become extremely data driven over the past decade and increases by the year, so frequently collecting data at a high volume is a necessity. In addition, understanding where these datasets live and the level of insights each of them provides allows for proper analytics to take place. The valuable context added from analytics ultimately informs actionable data optimization efforts for those datasets, allowing you to define what you are trying to accomplish as an organization.

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The value of data driven insights—collected by connected devices, such as IoT—gained from a well-constructed data analytics solution allows organizations to analyze, improve, and predict products from a concept level through completion.There are additional considerations—production and workflow, for example—where optimization is addressed at a micro-level, and each specific step of certain processes can be affected. When looking at different systems and machinery, monitoring productivity and functionality can guarantee consistently completed work while reducing the chance of waste and production halts caused by unnecessary errors. Data optimization often equates to process optimization because the insights uncovered in analytics provide real value—it is a moment of clarity and a breath of fresh air that promotes organizational growth and success.

What is Data Optimization and How it Works

Data optimization can be considered the continual practice of prioritizing data strategy and management. If anything, it should be viewed as the decision to actively change how you leverage the value in your data. Again, organizations have large amounts of data that has to be accessed and managed, and the correct data has to be provided to the right people at the right time. Since data can change sporadically, organizations may find themselves revisiting this decision often, ensuring that it aligns with their overall goals.

There are different levels of what this process can look like, depending on the organization, so a handful of workers can be involved in the process. Operations groups usually pull the data, understanding what it is and how it is being visualized. There is useful data that can affect those working in areas like finance, accounting, and human resources, making them stakeholders as well. If there is equipment, working environments, or processes that put workers’ safety at risk, an organization can consider that during their data optimization process. Organizations can’t accomplish data optimization until they pull their data, and the method they choose for this process can be endless. Though sometimes, if the data is hard to access and/or analyze, organizations may reach out to other companies who offer data analytics services and solutions.


The Value of Data Driven Insights

Data driven insights reflect any valuable information gained from exposing, visualizing, integrating, and analyzing data; they are just that—insightful pieces of evidence. In other words, there can be a lesson (or even multiple) discovered during an organization’s data optimization process which adds context and reason behind the data analytics. Data driven insights may not be considered a deliberate process, instead a moment of clarity resulting from analyzing information in your data. In some cases, this is not that easy to accomplish because organizations need help defining what their data means. That is a critical reason why optimized data analytics is so essential overall.

There are a few steps before attaining data driven insights that mutually have their own significance. Data visualization, data integration, and data analytics are processes that maximize data driven insights, which inform data optimization altogether. They may all be slightly different, but each process influences another because they have a similar purpose: unlocking the power of your datasets. One step can lead to another, so it is no surprise that individuals within these disciplines often work together. The collective goals are synonymous with each other, despite the fact each process looks slightly different.

It can be somewhat difficult to understand where each piece of this data puzzle fits, so it is best to dissect and illustrate what each process looks like.

  1. Data Visualization, which usually ends up being a pivotal first step in making sense of your data, involves identifying data. A helpful way to define this term is by highlighting the second word. This process involves making data visible to discover patterns and trends—exploration and representation through visualization. Analysts who specialize in this area often start this process by introducing tons of data in pictorial or graphical form. This way, organizations can examine their data and analyze what it all means. Visualization doesn’t always depict a complete picture, but the data presented will often directly influence the level of data driven insights an organization can develop; with those insights, you can extract valuable information within large amounts of data more broadly. It can become strenuous work having to dive deep inside your datasets, and data visualization helps simplify everything.
  2. Data Integration can be described as the process of taking an organization’s data from multiple sources and centralizing it. Within this process, there can be other smaller, procedural steps. There is no one way to integrate data, so organizations will find themselves extracting data and dumping it all into a data lake or warehouse no matter the method they choose. The main goal of data integration is to develop a unified display of an organization’s data transparently so that they can analyze and facilitate impactful insights. Again, once you dispose of any irrelevant data in the visualization process, valuable data is revealed. That information allows you to make important organizational decisions that can lead to durable success.
  3. Data Analytics is a process of discovering trends in the data you have siloed in one location and developing valuable data driven insights from it. Without data, you are just someone with an opinion; even with that data, there is no real, significant value unless you ask questions and analyze. One way to think is: What do we want our data to tell us? Which software do we need? Will this analytics process help achieve our overall goals? Data analytics should be a process that is approachable and actionable. Implementing connected devices (IoT) helps communicate and analyze large chunks of data that affect how organizations function. Several industries rely on this technology to perform efficiently in their work.

Again, these processes may look different, but they help achieve data optimization. Imagine a padlock latched onto a door with one of those number-dial combination locks and you are trying to open the door. Data visualization, data integration, and data analytics are each number of the combination—individual components needed to accomplish the overall task. Once each number is dialed, the combination lock is opened and can be removed from the padlock. Data insights come from recognizing any lessons in the process. In this case, that may be acknowledging that the combination lock is very secure or that the padlock is faulty, and you need to replace it. With all this data, organizations can learn a lot from that information. The overall process of data optimization emulates the process of removing that padlock. Again, unlocking your datasets provides valuable information within your data.

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What Are Some Benefits of Data Optimization?

Each aspect of data optimization offers several benefits for an organization. Visualization allows you to display data clearly, which forces you to locate what’s valuable and eliminate what’s useless. The integration provides easier accessibility because of how centralized data becomes. Then, with efficient data analytics and IoT strategy, organizations can improve several aspects of their organization. They add value to their internal processes, sales and marketing, and risk management—it all goes back to data.

The way that data analytics and IoT impact healthcare is one example of the benefits of data optimization. Healthcare managers and providers collect data through IoT devices that allow patients to be better connected, affecting how their health is examined. There is also a plethora of technology—connected monitoring devices, disease management devices, ingestible sensors, surgical robots—that is used to help maximize the efficiency of patient care workflow. The data that IoT devices constantly tracks allows organizations to see how their technology functions, and whether it needs updates in real-time.You not only lower any potential extra expenses, but you also help minimize risk and reduce errors—both internally and externally. Will it prevent every single hazard lurking in the shadows? Not entirely. But implementing a data optimization strategy that aligns with an organization’s overall goals and plans drastically reduces any chance of disaster.

Some issues can derive from not choosing to optimize your data. Frankly, data optimization helps minimize exposure to challenges and dilemmas. In addition, the level of quality in an organization’s products and/or services is often what shows the difference between those who have properly optimized their data and those who haven’t. Different industries may look at quality differently, but the ultimate hope is that products function consistently within their intention. Another aspect of maintaining quality would be looking at quality control. Within quality control, having an efficiently connected feedback loop allows organizations to have visibility of their products, making sure they are functioning within their established standards and requirements.

Using another example, industries like transportation use proper data analytics to help prevent product recalls. If consumers who have bought certain vehicles need to express complaints about a product, IoT devices allow that to happen. Even if organizations can’t avoid this issue, data analytics can still help minimize the overall damage involved. This process of data optimization gives sufficient context to an organization’s data; if that context is not there, its internal processes and management can be at risk. Mitigating any potential risk lowers the occurrence of setbacks and failures, allowing consistent organizational growth.

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One key note to reiterate is that data optimization is a continuous process, one that ultimately determines the level of quality of an organization’s products or services. It can also provide growth in revenue and success if organizations can accommodate their needs by capitalizing on their resources to achieve their goals. An organization needs to be able to access its data, know where it is coming from, and understand why it is being pulled so that they have proper visibility and traceability. You should also clearly understand how your data will be exposed and visualized to the right people at the right time so they can make the right decisions. Data visualization, data integration, and data analytics are several aspects of data driven insights that help create more proficient data optimization. Organizations would need to invest time and money in this process, though the resulting benefits outweigh any costs. By investing data driven insights, organizations are increasing the opportunity for better outcomes, whether related to their internal growth or their external impact on others. One of the greatest achievements that data optimization can provide an organization is a continuous guarantee of value for the customers they work with and the consumers they strive to satisfy and service.


Interested in learning more about Data Optimization for your organization? Contact our team today!

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