Save Time, Money, and Lives with Data Automation

Automation as a concept is a lot older than some may think… automatic control has been around for a long time. We can date it back to the 17th century with the creation of temperature control, steam engines and more! 

Obviously, living in the 21st century we have advanced a lot more. With great new technologies, we are not only able to create practical living situations, but also save lives within the health care sector. Today, we’ll be looking at how automation is changing the lives of many all over the world. 

The Healthcare Industry Summed Up

Before jumping into the tasks automation does, it’s important to understand how the healthcare industry has been functioning. Innovation in technology has helped to establish new and effective medicine, technological solutions for non-life threatening surgeries, and improved diagnostic aids. Of course, this is just a few great achievements within the masses of technological healthcare equipment. 

With every success though comes a challenge. One of the most prominent ones being quality and affordable healthcare, even in developed countries. The reasons for this are: 

Rising expenditures 

One of the largest portions of investments within the healthcare sector goes towards drug research and facilities, which results in massive hospital bills that make their way down to the patients. 

A study was conducted by Research Gate which showed that supply expense per patient in the United States was approximately a little over $4,000! This would be fine if each person had a health insurance plan or even a proper insurance plan for that matter, but unfortunately, this is not the case. 

With more complex procedures, the demand for medical devices increases while also increasing the bills. As mentioned, identifying research and development opportunities for medicinal drugs requires lots of cash. This is mainly due to the fact that large quantities of data need to be assessed and in turn, this requires lots of time and money. 

To be able to establish a balance between cost and health results, a consideration of multiple data variables must come into play. 

Appointment Scheduling 

Nearly 500 healthcare workers lost their income earnings due to canceled appointments. 

Other problems that occur are: 

• Lack of time to call patients

• Keeping patient contact information up to date

• No efficient ways to contact patients via text messaging applications 

• Lack of resources for reminders 

Security 

Patients willingly give up their private information to health care providers, including financial statuses and social security numbers. However, hackers are able to steal this information, causing a terrible security breach. 

Some of the highest data breach solutions cost around $400 for each patient record. In order to identify and restrict a breach from occurring, the time needed is a significant loss for the sector. Not to mention, the loss of reputation and trust from patients which can ultimately result in a loss of revenue. 

So, what is data automation? 

Data automation essentially replaces manual tasks with the use of information technology. Tasks that are repetitive in nature and require the least amount of human intervention ultimately benefit the most from automation. 

Data automation is a form of programming which handles storage, assimilation, and analysis of data. This process most often involves:

• Extraction: open data sources are evaluated and analyzed for relevant information. 

• Transformation: the data is converted into a program which is usable by machines.

• Loading: the data is put into the system to take place of raw material for the automation.

But wait...there’s more!

Big data automation (BDA) uses innovation and IT tools to create value. It assists with the reduction of manual labour during the process of collection and data analysis. Also, it reduces administrative work and improves patient healthcare.

Let’s Solve Your Problems with Data Automation 

So we’ve discussed data automation, BDA, and now we’re going to look at the robotic process automation (RPA), which is the next level within the automation realm. RPA uses machine learning and AI to complete tasks which would otherwise be done manually. It has immensely helped the healthcare industry. 

We mentioned above the 3 main challenges, now we’re going to provide you with solutions for all three: 

Rising Costs Solved 

A large sum of financial burdens within the health sector are due to growing demands for medical equipment and innovation costs for drug research. Scott Gottlieb (FDA commissioner) revealed a new budget plan which focused on modernizing drug and device production and using innovative practices to lower costs while advancing health needs. 

Data automation helps with this as it provides assistance in fast-tracking the assessment of safety and efficacy of medical drugs and devices. The data collection and analysis also plays a crucial role in drug discovery and new product functions. 

Appointment Scheduling Solved 

Do you know how much money and time would be saved if tasks like scheduling appointments and reminders were to be part of an automated process? Lots and lots of savings! There are multiple applications available which automate these tasks and have in turn given more time towards staff and resources allowing them to focus on the patients. 

The greatest features available on these apps: 

• Scheduling, booking, and cancelling appointments

• Feedback 

• Email or text message appointment notifications 

• Reporting to decrease cancellations and refine processes 

Security Issues Solved 

Most times, a manual error is the root cause for security breaches. However, when you replace manual work with data automation, the healthcare industry can ensure that these errors are minimal. Healthcare providers are now able to enable alerts, prioritize abnormal behaviour, and focus on monitoring all with the help of AI and automation. 

What else?

By using automation for diagnosis, predictive analysis, and recommendations, you’re able to ensure that the system will continuously operate in a way to detect and eliminate security issues. 

Although these three challenges are prominent within the healthcare world, they are not the only ones that arise. With that said, there are various forms of resolutions using data automation. Get in touch with us today to learn more about how you can save lives with data! 

Four Types of Data Analytics to Improve Decision-Making

Due to the sheer amount of data now accessible to companies, it is easier than ever to leverage information accumulated in order to push real business value. Nevertheless, it can be tricky to find the best way to examine the data.

Hence, why you need to understand the types of data analytics

 There are four different types of data analytics.

Descriptive analytics

Descriptive analytics helps to better comprehend the changes that have ensued in a business. It organizes raw data from several data sources to give significant insights into the past. With a scope of data, decision-makers get a full view of performance and trends from which they can base their business strategy off of.

The statistical technique used within this type of analysis usually focuses on the patterns in data which help to filter out less meaningful data. Descriptive analytics provides significant information in an easy-to-understand structure.

Diagnostic analytics

Diagnostic analytics measures data against other data to answer the question of why something happened. This occurs by taking a deeper look at data in a bid to grasp the causes of events and behaviours. It lets you understand your data faster to solve vital questions. Diagnostic analytics reveal the rationale behind specific results.

With this type of analytics, you get in-depth insights into a specific problem by interpreting your complicated data into visualizations and insights that everyone can understand. And you should have detailed information at your disposal, else, data collection may turn out to be time-consuming.

Predictive analytics

Predictive analytics predicts future trends. Using the findings of descriptive and diagnostic analytics, predictive analytics can detect clusters and exceptions, and identify risks and opportunities for the future. It is a valuable tool for forecasting.

Predictive analytics permit organizations to become proactive, forward-looking, foresee outcomes and behaviours based upon the data and not on assumptions. Keep in mind that the accuracy of the results highly depends on data quality and stability of the situation since forecasting is just an estimate.

Prescriptive analytics

The objective of prescriptive analytics is to assist your business in identifying data-driven strategic decisions and eliminate a future problem. Prescriptive analytics uses data to comprehensively understand and predict what could happen, then advises the best steps forward based on informed models.

Advanced tools and technologies, like machine learning, business rules, and algorithms are utilized to stimulate various approaches to numerous outcomes. Prescriptive analytics also helps to reduce errors because it involves data aggregation, both internal data and external information.

So what’s next?

Now that you understand the different types of data analytics, let’s talk about how to identify the one(s) your business needs. First, you need to provide answers to the following questions:

• Firstly, what is the present state of data analytics in your business?

• Secondly, what is the depth of the data needed?

• Thirdly, how far are your present data insights from the insights you need?

• Finally, are there obvious answers to the issue?

The answers will guide you on the next steps and strategy. You will be able to work with the best data analytics option with the most favorable technology stack, and then commence and execute it effectively. Keep in mind that the more complex an analysis is, the more value it brings.

The goal of any analytics program should be more relevant information, which will lead to more valuable decisions and a more complete understanding of your business landscape. Additionally, if you want to read about our Custom Software Solutions and Consulting Services, Get In Touch and we will get back to you shortly.

Data Visualization vs. Data Analytics

As data is becoming more of the central focus point for competitive advantage, many enterprises are seeking new ways to identify and analyze the data being generated. These enterprises use pie-charts, intuitive graphs, and various forms of visualizations to form a deeper analysis for their sales, revenue and other factors of company operations. 

Although, a balanced approach with both data analytics and data visualization is necessary when formulating an effective data strategy. The reason being, the use of the data visualizations listed above, completely depends on how effective the data is or how the data is used to form conclusive decisions. 

If you thought data visualization and analytics was one in the same- don’t worry, that’s a common mistake many enterprises make. The confusion stems from both aspects allowing users to understand the data and acquire the metrics, which assist in decision making. 

As each year passes, more and more data is generated; causing information overload. The data being generated multiplies EVERY 3 years! So, you can understand why it is crucial to have the necessary resources to interpret all of that information.

On the other hand, this information overload isn’t so bad…

There’s quite a few projections showing an almost certain exponential growth in revenue for big data within the software market. 

Are you still unclear on the difference between data visualization and data analytics? 

Don’t worry, this confusion is common as we mentioned because both forms represent data in visual interfaces. 

However, regardless of the similarities between the two, data analytics dives in deeper with data comprehension than data visualization. The pretty picture at the end is significant, but is definitely not the backbone - the tools and algorithms used to produce the final product is just as important (if not more)! 

Confusion No More: Difference Between Data Visualization and Data Analytics 

Let’s start with data visualization: 

This is the representation of the data in visual form - making the trends and patterns essential in the data the central factor. If you’re using text-based data, such visualizations may not be possible or explicit to the data. As the traditional forms of visualizations are falling off the grid, such as line graphs, charts, and so on, 3D visualizations are taking their place. With 3D visualizations, users are able to manipulate the data with tools available through the application of filters. 

Now let’s dive further into the world of big data. What does data analytics look like? 

This aspect identifies and discovers new trends and patterns throughout the data. Although data visualizations allows users to understand the data, it doesn’t show everything. Visual representations can only be effective if the data being used to create the visualization is effective. So, what does that really mean?

If you’re inputting incomplete data into your visualization machine, then you can only expect a half complete representation of your data. What makes this EVEN more complex is the fact that enterprises are receiving data from multiple sources and storing this data into varying archives. This makes it more difficult to gather comprehensive data for data visualizations. 

Visualization tools handle the fresh, raw, unformatted data, while analytics tools use data mining algorithms to properly clean and evaluate the data by using different evaluations and software resources. With the completion of this, you’re able to subject the data to algorithms and proceed with your decision on how to display your results. 

First Step: Data Integration 

In order to produce an effective analysis, it is required that you consolidate all the data into one space. There are of course analytical engines that collect data from multiple sources, however, by consolidating the data into one space; it provides you with one single version of “the truth”. This prevents the risk of duplication and contradicting information from distorting the visualizations. 

With the continuous increase in data production, manual aggregation has become nearly impossible. Which is why, there are more and more releases of software tools and platforms available on the market - to provide you with an effective automated solution. These automated solutions clean your messy data, which would otherwise be inevitable with disparate sources and users. 

Second Step: Data Analysis 

After the cleaning process, the data is subjected to analysis and/or performance calculations on the data. With a growing business environment, data analysis is becoming more complex. With speed being the #1 necessity, multi-stage formulas have been integrated into the process which allow for multiple calculations to be done all at once. Data visualization involves reporting data rather than analyzing it and because of that, most tools are restricted when it comes to aggregations per formula. 

This is why we have data analysis! It allows for users to create complex formulas, even while working in separate sources. The software proves to be useful as it takes the required pre-calculations automatically - saving you time. 

Are you a business seeking success in today’s speedy world? 

Consider analytics tools that update your data and facilitate collaboration. An analytical tool such as IBM Cognos takes your data and uses a plug-and-play structure to create colourful interfaces. 

Many businesses within the retail sector are using data analytics to advance their processes and in turn, maximize their revenue. Data visualizations and analytics have assisted them in not only discovering new trends, but also have shown insights into customer behaviour, which help companies develop initiatives to achieve success. 

Moreover, advanced analytics such as comprehensive business intelligence analytics suites, offer a predictive projection which is based on complex algorithms using languages like R and Python. Some of the key technologies used by business intelligence platforms are: dashboards, data warehousing, and advanced data visualization. 

Always make sure that the solution provides you as the user, flexibility and ability to combine data in whichever way you need. 

It’s also important you’re staying up to date and keeping up with the trends. The latest analytical platforms are using natural language processing along with chatbots to ensure users are easily able to perform calculations and input their inquiries without trouble. Some of the current advancements in the technology include location-based intelligence, which increases your chances of revenue through the use of analytics and customer insights. 

The Last Step

Keep in mind that although the most effective visualization is based on analytics, the representation doesn’t always need to be the end of the process. It is common to take data analytics and visualization and throw them into a cycle. 

If we look at machine learning and predictive modeling applications for example, the success of targeted emails depend on the cyclical process. Data visualization can start us off, followed by analysts putting specific variables into a graph in order to identify patterns or metrics, such as median averages, standard deviation metrics, and data spread. This helps you gain an understanding of your data. 

Thus, it’s obvious that both analytics and visualization handle data. Data visualization creates a user-friendly guide to understand the report, but without cleaning the messy data and applying it to advanced algorithms, you will end up with more confusion than comprehension. This is where data analytics comes into play, while data visualization provides a summary of the data, the analytics provides the necessary tools for the correct portrayal of the data. 

Incorporate both and you will receive the best possible software solution!

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