Is repetitive and tedious work impeding your data scientists, analysts, and engineers from delivering their best work? Regardless of whether you realize it or not, you are likely sitting on mountains of valuable data. What can you do about it? Data analytics and automation can be your solution.
Data is the lifeblood of every business. Customer information, products, services, processes, hardware performance, finances, operations, staff, etc. As explained by an article from McKinsey and Company: “Rapid technological advances in digitization and data and analytics have been reshaping the business landscape, supercharging performance, and enabling the emergence of new business innovations and new forms of competition.”
Companies can benefit from automating their data analytics processes. Historically, we had limited feedback on a business process and often had to wait until this analysis was completed. As a result of automation, however, analysis occurs in near-real-time, providing feedback even as the business process is being carried out. This enables course correction, resulting in a better final outcome, and ultimately a more profitable business.
For example, integrating your sales and marketing systems means everyone is working together with the same data. Complete data. Teams need data that gives them direction and confidence. So, instead of working in silos, where each team has a partial view of the customer and duplicate effort, they can work together and boost each other’s campaigns.
What is Data Analytics and Automation?
It may seem obvious, but let’s break down what customer data analytics and automation are. In automated data analytics, computer systems and processes automate the process of analyzing data with little or no human intervention. Some data tables can be altered to fit pre-defined data models by running simple scripts. As a result, you will have useful reports.
The no-code solutions use intelligent data automation to create marketing and sales alignment across every application and stage of the buying process. With these platforms, you can sync your teams and data to create a connected customer environment without the hassle of coding or using technical resources.
Automation can help analyze data faster and more effectively than the typical exploratory and iterative process. Irrespective of the use case or outcome, automation removes the person from the task and allows a computer to handle it, usually faster and more accurately.
When you’re dealing with big data, data analytics and automation are especially helpful, and they can be used for a variety of tasks, such as data discovery, data preparation, data replication, and data warehouse maintenance. Automation can provide an organization with insights that might otherwise be unavailable.
There is a wealth of information that sits in your possession if you remember it. What can you do about it? Automate the processing and analysis of your valuable data.
Benefits of Data Analytics Automation
By 2025, IDC’s “Data Age 2025” whitepaper, says worldwide data will grow 61% to 175 zettabytes, with as much of the data residing in the cloud as in data centers. Three locations make up the datasphere.
The core consists of traditional and cloud data centers, the edge consists of things like cell towers and branch offices, and the endpoints consist of PCs, smartphones, and Internet of Things (IoT) devices.
The barriers to access and process data would be enormous without data analytics automation. Automation has never offered more advantages than now. Here are a few of them:
- Analytics can be made faster with automation. An analyst can analyze more quickly when the process requires little or no intervention, allowing computers to rapidly perform typically manual or complicated tasks. For big data to be effectively analyzed, automation is essential.
- Automated data analytics saves a company time and money. When it comes to data analysis, employee time has a cost, often more than what you pay for technology to do the same job. By automating some tasks where it makes the most sense, data scientists can focus their efforts elsewhere, such as discovering opportunities to find new data sources that can bring new insights.
- Data analytics and automation has far-reaching benefits. Getting updated, complete, high-quality data without all the effort helps data scientists concentrate on higher-value work instead of gathering data and verifying its quality. Computers can perform this work faster and more efficiently than humans, allowing employees to focus on more important tasks, like interpreting the automated data and developing new courses of action based on it. Now, these employees are utilizing their time more effectively for the company.
When to Automate Data Analytics
You can enhance data analytics with automation, but how do you know when and where to use automation versus human efforts? In general, data analytics and automation are best suited to tasks that are rules-based and performed frequently.
It makes little sense to automate a one-time study. Automation of data discovery processes in an organization with many data scientists, each dealing with different data sources, would be more efficient. Automation is a good choice for many analytical tasks, such as:
- Generating reports can be a highly manual task that takes hours to weeks. Automating this process can not only speed the process but ensure the data is accurate, comprehensive and even predictive.
- Automating tasks like cleansing a data warehouse can save significant time and ensure that only the data that’s supposed to be there is.
- Typos, missing values, mismatched formats and other inconsistencies are easily missed. By automating their discovery and fixes, you ensure data quality and reduce the risk of issues impacting compatibility.
It’s important to understand that not all tasks are ideal candidates for automation. Humans are still needed to leverage the data appropriately, build models, tell the system what to do and when, and make decisions.
How to Automate Data Analytics
Interested in data analytics in automation? Approach implementation with a phased approach to minimize disruption of existing operations or over-utilizing your resources.
- Define your objectives. As with any initiative, the first step of a plan is to clearly define and communicate the objectives. Involve stakeholders from sales, marketing, operations, and human resources. Everyone must understand why data analytics automation is necessary and the steps needed to implement it successfully.
- Define KPIs. You can’t know if you met your objectives unless you know how to measure performance. Define the metrics each stakeholder needs and determine how you plan to obtain those measurements.
- Choose your automation tool. A platform beyond BI Tools will let you use machine learning to find problems, set up and automated configuration and automated predictive and prescriptive analytics. Work with a partner that will provide onboarding tools to get you started quickly.
- Test and repeat. Test your automated process thoroughly after you have prototyped it, ensuring it actually reduces manual efforts versus creating more work. You will also avoid the tendency to fail by choosing a good system, as manual systems are more likely to take up more resources and take longer.
- Monitor and measure. Build those KPIs you already defined into automated reports you can share with stakeholders. By looking at real-time data, everyone can determine whether the data analytics automation is bringing anticipated or unexpected value and whether the model can be expanded to other areas or use cases.
Data can support business decisions, suggest them, and even uncover new product needs to become a product itself. All of this is made possible by the advances in data analytics and automation tools and platforms. Request a demo from Vertify now and uncover the benefits for your company.
Author: Matt Klepac | CEO | Vertify