In Part 1 of this blog we learned how data analytics assists companies in making impactful business decisions with an example I shared from one of our clients, Square Root. Here I'll share another instance wherein a client of ours benefited from the use of big data by increasing their bottom line.
A global IT services firm, whom we partnered with previously, focuses on providing proprietary data and insights across the healthcare industry - particularly with insurance providers. Using the elements of data analytics, we assisted in building the frontend and APIs for a pharmaceutical platform that quickly compares pricing structures for various insurance providers, including Medicare, and Medicaid.
Organizations across the healthcare industry now rely on this platform to know where and how to invest their resources, and identify opportunities in developing new drugs and technologies.
3. Elements of data analytics
Organizations often rush to the product of data analytics: an application or web-based tool offering important company insights. But these are only the end result of many components of data analytics.
At GAP, we use the following key elements of data analytics to build the exact data system that your organization needs to thrive:
1. Data transformation
Data transformation is the process of locating and organizing your data so you can begin to answer what happened, why it happened, and what will happen. Data often resides in varying locations and formats across the operation, so the transformation of this data is a necessary step in applications integration to ensure data from one application or database is intelligible to other applications and databases.
2. Data science
Data science is a critical part of turning data into knowledge. Data science is useful for educating the business to uncover its challenges, search for problems to solve, research new techniques, present insights, and more. Data science is typically comprised of these key elements:
Algorithms - Applying formulas to the data in your data warehouse to find patterns.
Predictive Analytics Using different predictive techniques, we decipher the patterns that the algorithms pinpointed. Predictive analytics uses historical data to determine why something happened in order to predict the likelihood of a repeated outcome.
Predictive Modeling - Contrary to Predictive Analytics, Predictive Modeling utilizes data and probability models to forecast outcomes and the determine the variables that may influence future results.
3. Data visualization
Use Data visualization to tell the history and future of the data and offer clear, actionable insights that can help guide better business decisions. Data visualization can include tools such as dashboards, scorecards, and operational and financial reports. Here are some examples for how organizations can utilize these tools in order to enhance their mission:
Design the visualization to be interactive so users may manipulate the complex datasets and analyze them efficiently.
Use dynamic graphics to emphasize the story and draw attention to key ideas and areas of opportunity.
Consider a time-series visualization to analyze a sequence of data points, measure them at successive points in time, and space at uniform time intervals. This method is ideal for forecasting, anomaly detection, and more.
Once a systematic way of researching and applying the data to make positive changes that improve overall business development is established, we examine the other company systems that can benefit from the integration of this data. API integration provides users with a "cooperative" experience wherein data is seamlessly shared, allowing vital insights to be presented.
Data quality & quality assurance
A common term in data analytics is "veracity", which assures that the data output from the data transformation process is accurate. The way to ensure accuracy is through quality assurance (QA). When it comes to data analytics, QA isn't as simple as ensuring features perform as intended. While that is built into each stage of the process, data analytics QA must also ensure that the data resulting from the entire process is reliable and complete.
Regardless of your industry, data must be securely stored both physically (servers), and digitally (encryption). If the data is stored in an unsecured manner, that data could be subject to breaches, i.e. loss of data or hacking. In highly regulated industries such as finance and healthcare, security mandates are a necessary part of each process.
Data analytics is a complicated but worthwhile business component. While these elements comprise a complete data analytics solution, each company must apply them in ways tailored for their company's individual needs. By understanding the entirety of these elements, you can begin to identify areas of opportunity within your data analytics system.
The next article in this series will focus on exactly how to get started putting data analytics to work for your organization.
About the Author
Joyce Durst, co-founder of Austin-based Growth Acceleration Partners (GAP), is driven to help software companies achieve rapid growth through business-focused applications. Joyce has launched startups and led teams at enterprise companies by applying her passion and business knowledge to efficiently create software that solves business problems. Active in the community with Special Olympics and the Women Presidents Organization, she enjoys helping other women in technology to achieve their dreams.