Many modern businesses are essentially drowning in a sea of data. The explosive growth in the sheer volume of information which an organization must assimilate can be overwhelming for traditional data mining techniques to negotiate successfully. New data streams from Internet of Things (IoT) devices and big data sources are rich with raw information that if used correctly, can give a company a huge competitive advantage or lead to ground-breaking innovation. Data analytics is a method that can help an enterprise make sense of its data and unlock the value hidden in the daily flood of numbers and images.
What is Data Analytics?
In simple terms, data analytics is the practice of analyzing raw data to provide insights and conclusions about the collected information. Its primary goal is to uncover metrics or hidden trends that are not easily obtainable due to the quantity of data under consideration. Organizations make use of sophisticated algorithms and processes to perform data analytics in efforts to improve their internal and external processes and develop innovative products and services.
A viable data analytics initiative is only as good as the raw materials it processes. To that end, there are several steps required to prepare the data for analysis. They are:
- Determining data requirements such as how it will be grouped or categorized is the place to start.
- Collecting the required data through physical or digital means.
- Organizing the data, which is usually done in a database, to be used for further analysis.
- Cleaning and scrubbing the data to remove inaccuracies or duplication that can skew the results of the analysis.
Once the data has undergone this preparatory process it is ready to be used for data analytics by the appropriate software tools.
How Businesses Use Data Analytics
Data analytics is not a singular process. There are four main categories of data analytics that a business can use for different reasons.
- Predictive analytics is used to identify trends and connections and the factors that cause them. Predictive and statistical modeling are subdivisions of this type of analytics. This is the most commonly used form of data analytics and is responsible for discovering avenues that an innovative business can exploit for their benefit.
- Prescriptive analytics is used to predict outcomes and develop action plans. It makes use of artificial intelligence (AI) and machine learning (ML) to perform optimization and random testing. Variables are tested and refined to find the optimal solution to a problem or situation.
- Descriptive analytics provides content for reporting and business intelligence (BI) dashboards. This type of analytics is concerned with furnishing insight regarding basic questions about the quantity, location, or status of resources. It can be deployed with standardized reporting or used in an ad hoc manner to take a deeper look into specific issues.
- Diagnostic analytics is used to understand the cause and effects of past events. By better understanding the fluctuations in processes or performance, plans to address similar future scenarios can be developed. Drilling down into reports is a technique used in diagnostic analytics as is data discovery and alerting.
Data Analytics in Action
There are many examples of businesses benefiting from insight into their products, processes or customers through data analytics. Here are two notable instances of data analytics in action.
- Microsoft achieved substantial productivity gains through the use of analytics when considering ways to foster more collaboration by their internal teams. By shrinking the campus from five to four buildings, the company was able to save 100 hours per week in meeting travel time and over $500,000 yearly in gained employee productivity. Analytics led to an innovative solution that improved the company’s ability to remain a market leader.
- Uber improved their customer support through the use of A/B testing and prescriptive analysis to fine-tune the process by which agents respond to customer-generated problem tickets. This led to a reduction in handling time and an improvement in customer satisfaction. Streamlining the ticketing process saved the company millions of dollars.
Data analytics are behind many of the decisions made by business leaders every day. To remain competitive, an organization needs to use analytics to make the best use of their data.
Tools for Data Analytics
Aqua Data Studio (ADS) is a flexible and versatile database tool that offers a platform for performing visual analytics on the data contained in a wide variety of databases. Microsoft SQL Server, MySQL, Oracle, Sybase, and Amazon Redshift are just some of the platforms supported by Aqua Data Studio. It can be run on Windows, Linux or macOS computers.
One of the strengths of ADS is its ability to perform visual analytics to make it easier to understand what your data has to tell you. Easily create custom dashboards that present your data in compelling ways. You can also use the data management features of ADS to scrub and sanitize your data prior to analysis. If you use databases to store information that can be used for analytics, Aqua Data Studio is a tool that deserves your serious consideration as a way to unleash the real value of your company’s data resources.