Why Automate Data Analytics?
It’s no secret that the world of data is growing at a breathtaking pace. Analytics play a critical role in putting this data in context and ultimately transforming it into useful information. Automation goes a long way in enabling enterprises to achieve and maintain data management scalability. We at Onyx Government Services specialize in enterprise analytics automation, and we can help you derive predictive and prescriptive insights into your data more effectively and more efficiently by leveraging the best practices and lessons learned from our experience implementing analytics automation at Federal Civilian Agencies across the government.
But, what is data analytics automation? What are the benefits of automating data analytics? When should you automate data analytics? And how do you automate data analytics? If these questions have been boggling your mind, we’ve got your back. In this article, we will explore everything you need to know.
What is automated data analytics?
Automated data analytics is when you remove the human factor from analytical tasks, and instead use computer systems and processes. Data analytics automation is particularly useful when organizations deal with big data; and can be used for functions such as data collection, data preparation, data warehousing, and data replication.
Mechanisms of automated data analytics vary in complexity. Some are simple scripts that work easily with pre-established data models. Others are more complex full-service tools that do feature discovery, data analysis, model selection, and statistical analysis.
What are the specific benefits of automating data analytics?
Automation of analytics comes with several perks, such as improved operational efficiency, reduced operational costs, reduced task hours, enhanced self-service modules, and an increase in the scalability of operations supported by big data.
We explore the specific benefits of automating data analytics below:
- Helps in analysis of time-varying big data
Automated analytics usually uses a framework for analyzing any volume of data over a specific period. This categorization of data into different time segments using a pragmatic approach helps analyze data from a specified time frame. Data automatically analyzed this way makes it easier during retrieval and decision making.
- Role in making predictions
Automation of data analytics reduces the time taken for predictive analysis. Data Scientists doing predictive analysis say one of the most challenging parts of their work is that it is tedious, expensive, and time-consuming.
A robust automated data analysis language or tool will simplify identifying prediction problems and making the analysis process streamlined. Automated predictive analysis can also automatically work with varied analog categories and labels of data being processed.
- Faster decision-making
Automated data analytics can contribute to faster decision-making in enterprises. Based on preset models, automated data analytics can make decisions without human intervention, as well as offer a useful feedback mechanism.
For example, an automated analytics system that regularly carries out a particular study, then creates improved business processes based on the results, while adjusting study variables in real-time.
- Providing complex business insights
Automated data analytics can be instrumental in providing business insights that would otherwise be unavailable by manual human analysis.
For example, a cybersecurity firm might use a classification automated tool to make categories of large data from various web activities, then relay analyzed information about these categories to an interactive dashboard for their clientele. Client input and feedback to this interactive dashboard can then be reverted automatically into a preset model, without needing any input from a human being.
- Faster analysis
Automation can increase the speed of analytics. A data scientist can perform analytics faster if analysis requires little or no human input. Computers can quickly complete tasks that are difficult and time-consuming for humans.
For example, automated data analytics can be used to flag defined variables in a dataset. It suggests the statistical model to be used, saving a data scientist time and effort that they would otherwise spend trying out different models manually.
- Saving enterprise costs
Automated data analytics saves a huge chunk of the enterprise budget, as employee time is more expensive when compared to purchasing computing resources. Sometimes, these automated computing resources are a one-off purchase and they perform analytics efficiently for a long time. Not so for employees; they will need to be compensated every time they do an analysis.
- More business value
Automation has been accused of “taking jobs away”. In actuality, automation mechanizes basic and tedious business and reporting activities, translating into saved time for data scientists.
By automating some of the analytics lift, Data scientists will reap the benefits of additional time for considering additional questions to ask of the data, or identifying new data sources to expand business knowledge. This, in turn, sets the stage for creating more business value.
When to automate data analytics
As seen above, automation of data analytics comes with a ton of benefits to an enterprise, but how does an organization know the best time and the right way to automate? A general rule to automation is that it is best done for tasks and processes that are performed often, are rule-based, and which follow a stable business process.
Automating a one-off task doesn’t make much business sense. However, automating processes in an enterprise with many data scientists, each working with different sources of data would be more effective.
Analytical tasks that are good for automation include:
- Reporting
This includes creating dashboards. Automated data analytics can process, stream and aggregate analyzed data for publishing on live data summaries and interactive plots.
- Data maintenance
Data maintenance tasks - for instance modifying data or even tuning a data warehouse - are simplified by automation. Many tools integrate new data sources or import data from legacy systems into existing warehousing.
- Data preparation
Automated tools such as KNIME -a visual programming tool- automatically labels, validates, and trains data models. It also iterates test runs to optimize parameters.
- Data validation
Automated data validation can help detect typos, identify a mismatch in content and formats, flag, and correct missing values in a dynamic data model. Not only does this kind of data analytics automation streamline data modeling, but it also transforms data to adhere to defined models.
- Data ingestion and replication
An automated system with data ingestion and replication capabilities can intelligently monitor available bandwidth and delivery calendars in a system. It can do batch processing at the right times, and stream systems in real-time without the need for manpower.
While many processes in data analysis can benefit from automation, nothing replaces human intelligence. After getting automated insights, it is prudent that there are humans asking questions, translating analyzed data and graphs to actionable insights, and validating statistical models. These tasks should not be left to the machines.
How to automate data analytics
If you are ready to start automating your data analytics, the process outlined below will help you with effective implementation, minimize inconvenience to your data science team, and prevent interruptions to current analyses and processes.
- Delineate your objectives
In enterprises, a set of data tends to be cross-functional, serving various roles in different departments such as marketing, sales, technical, human resources, and operations. This calls for the involvement of the various departments when you want to automate data analytics.
Make sure you all set clear goals and expectations for the automated process. This will foster understanding and cooperation in the whole enterprise as the automated processes take off.
- Determine metrics
Metrics are important for measuring the performance and utility of the automated processes. This ensures that the defined objectives are met. Metrics are also important as a reference point for future projects, or comparison when improving the initial automated system.
- Select reliable, well-supported automation tools
Some popular automation tools are SAS, Informatica, Apache Nifi, Apache Airflow, Databricks, Power BI, Selenium. These tools are developed with a focus on extract, transform, load (ETL) processes, automation, analytics, and scalable machine learning.
Cloud platforms that host many enterprises big data might also provide automated data analysis tools. For example, Google through Google Analytics has a built-in intelligence tool that uses machine learning to quickly detect anomalies in data.
Some tools like Hadoop on the other end are difficult to automate. Hadoop is great for an array of data analysis tasks, but it requires extensive human involvement for the execution of processes.
- Develop the tool, test it, then iterate
After prototyping an automated data analysis process, make sure you have thoroughly tested it. Make sure the automation reduces repetitive tasks. Testing is very important as a faulty automated data analytics system is prone to repetitive erroneous results, which can cost an enterprise a lot of time and money (even more than a manual process) to undo an avoidable mess.
- Implementation and monitoring
After satisfactorily testing an automated data analysis tool, implement it, and monitor how it performs. Most automated tools come with built-in systems logging and reporting, which is helpful as they function with very little oversight unless an adjustment is needed or a failure occurs.
Final word
Automated data analytics is a big step towards improving data science's efficiency and usefulness in the world we are living in. Automating data processes comes with a ton of benefits to an organization, which ultimately leads to easier work for everyone.
If you are ready to automate, Onyx is ready to advise you on the best automated data analytics tools for your business, and the process to follow.