Learn why automation is crucial for the sustained use of data analytics in modern enterprises.
Data is an essential part of business operations. From improving business visibility to engaging customers more effectively, data has transformed practically all aspects of business operations. This has led to an extremely high demand for talent and increasingly stretched data teams. Here is how automation can help data teams improve the way they conduct data analysis and produce better results for the business.
Why Data Teams Are Increasingly Overworked and Underutilized
Unstructured Data Needs a Lot of Cleanup Before it is Useful
While most businesses gather data, not all of them know how to use it to achieve specific goals. This leads to a vast amount of unstructured data sitting in databases spread across the organization. Unfortunately, unstructured data cannot deliver any insight into business operations or customer needs to executives.
Even before this data can be organized, data teams must spend significant time cleaning the information, removing duplicates, and fixing errors. When executives demand data-based insights, data teams are forced to wade through large data repositories to look for specific data points and organize them quickly and accurately.
Complex Analytical Processes are Prone to Human Error that is then Fixed Manually
Data processes can be very complex, depending on how much data is collected, how it’s managed, and what it’s used for. As a result, any error made early in the analytical process can create negative effects. These errors can be amplified as they go through additional processing and can give business leaders inaccurate insights.
These mistakes are usually managed manually. Errors that are detected further in the analytics process need to be tracked to their point of origin before they can be resolved. These processes can be extremely time-consuming and tedious. This makes them more prone to human error and increases the likelihood of mistakes going undetected until they are enlarged.
A Lack of Understanding of Data’s Role in Meeting Business Objectives Leads to Unrealistic Expectations from Business Partners
Successful business teams are always built on clear goals and objectives that every employee can work towards. A failure to define how each team helps the organization meet those goals can lead to employees working tirelessly while having zero to minimal effect.
Business teams can often make requests that data teams find unreasonable or unrealistic. However, they often do those tasks anyway, especially when the request comes from members of the C-suite. As a result, 97% of data engineers report experiencing burnout in their day-to-day jobs.
The Role of Data Analytics in Meeting Business Goals and Objectives
Make Data-Driven Decisions Instead of Relying on Gut Feel
Ultimately, data is meant to serve as an indicator of performance, preference, or behavior. This insight cannot be used until it reaches the desk of a decision-maker. Even if insights are delivered to other team members, their ability to use the information to enable change is limited. The role of data analytics is to generate insights that are actionable at different levels and deliver those insights to the most relevant stakeholders as quickly as possible.
Identify Areas for Improvement and Optimization Across the Organization
Every business has processes that are unoptimized. Information silos can prevent employees from learning about assets that they might have access to or processes that are ripe for automation. Managers and business leaders are often in the perfect position to receive and disseminate this information where appropriate. However, they are unable to extract the right information from unstructured data. Data analytics helps business teams make sense of seemingly endless strings of unorganized information.
3 Reasons Automating Key Data Processes is Crucial for the Sustainable Use of Data Analytics
- Free up Data Resources to Engage in High Value, Profit Generating Tasks
While data initiatives are designed to find ways in which businesses can generate more profit, undirected efforts can actually increase costs. Data engineers can spend more time cleaning data instead of analyzing it. Only a little over a quarter of business executives say that their company’s data initiatives are profitable. Automating time and resource-intensive tasks allows team leaders to direct data resources to activities that can more directly and effectively create value for the business.
- Deliver Critical Business Insights to Leaders with Effective Reporting
Even when data is effectively analyzed and presented, the resulting insights can be extremely time sensitive. Businesses that operate in rapidly changing and evolving business environments can rely on consistent insight to make data-driven decisions and gain a competitive advantage using data analysis. Automating time-consuming processes and insight delivery can ensure that business leaders receive information that is accurate and timely. Automating low-value tasks can also free up data engineers to analyze business information more deeply than before.
- Improve Operational Efficiency with Less Rework and Grunt Work Assigned to Data Teams
Some business leaders can hesitate to automate processes due to the perceived costs associated with automation and modernization. However, inefficient data processes can often be more expensive. Data teams must allocate more hours, software, and manpower to prepare for analysis and resolve data issues that are found after the analysis is complete. Automation resolves these issues early by removing the possibility of human error in early analytics processes. Additionally, these issues need to be addressed only once and the effects of automation or optimization are accumulative.
Data is a crucial asset for most modern businesses but not every business knows how to use it well. The success of data-driven businesses depends on the extent to which business leaders can empower their data teams and give them the resources and support they need to consistently deliver actionable insights. As data teams are put under pressure to deliver more with less, automation is likely to give them the lifeline they need to meet their goals and objectives more effectively.
Guest Author: Loretta Jones
Loretta Jones is VP growth at Acceldata.io with extensive experience marketing to SMBs, mid-market companies, and enterprise organizations. She is a self-proclaimed 'startup junkie’ and enjoys growing early-stage startups. She studied Psychology at Brown University and credits this major to successful marketing as well as navigating a career in Silicon Valley. She’s a nature lover and typically schedules her vacations around the migratory patterns of whales and large ocean creatures.
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