When you employ data-driven approaches, you make strategic decisions based on how you analyze and interpret data. A data-driven approach enables you to examine and organize data to serve your customers better.
While data-driven approaches make decision-making faster, better, and more accurate, they also come with several pitfalls when you do not handle data correctly.
When data is misinterpreted, it no longer delivers its intended benefits, and here are ways to avoid the pitfalls of relying on data-driven approaches.
Data Dependency
Data is a valuable business resource. Whether you need it for marketing or any other purpose, data can only be useful if it’s valid. Incorrect, missing, or irrelevant data can create issues for your company leading to bad choices.
Businesses depend on data to determine how to allocate resources and conduct marketing campaigns. Invalid data leads to wrong business strategies as leaders will be making decisions blindly.
Matching feedback with data can help you paint a better picture than relying on data alone. To improve data quality, you need to conduct data governance to ensure that data is well managed and that you can rely on the information presented.
Before making decisions:
- Validate the source of data.
- Refine your data collection technique.
- Manage your data regularly.
- Consider adopting unified communications as a service (UCAAS) technology to boost workplace collaboration and communication.
Data Overload – Clean Data Not Needed Anymore
In 2014, it’s projected that the digital universe would be 44 trillion gigabytes by 2020. As the internet penetration continues growing, the numbers can only go up. With the growing need to collect as much data as possible, you can easily get sucked into the plethora of information that can be collected through digital platforms such as customer feedback, wireless sensors, and employee engagement data, among others.
No business can market products and services, follow trends, and ensure that the business thrives without data. Data accuracy is critical to business success, hence the need for data optimization and cleaning. If you fail to maintain data accuracy, your business can lose credibility, profit, and customers.
Clean data improves customer acquisition, leads to informed decision making, enhanced business strategies, and increased revenue. Optimizing collected data and cleaning data that is not in use can help reduce the annual cloud storage costs.
Data Results Might Be Biased – Revisit Elements
Data bias is one of the most overlooked dangers to businesses across industries. Data in itself is neutral because it does not hold opinions. However, the person handling data can easily translate it to mean something different. Every step of handling data, from data collection to interpretation, is in the hands of human bias.
Data bias is an error where certain elements of a set of data are more heavily weighted than others. Data bias doesn’t accurately represent a model’s use case; therefore, it could lead to skewed outcomes, analytic errors, and a low accuracy level. Ensure that parameters and elements for the dataset are accurately positioned to avoid data bias.
Low-Quality Data – Cross-Check Data Entry
Business information and decisions are as good as the data behind them. Poor quality data can harm your business, leading to inaccurate analysis, poor business decisions, and poor customer relations.
Poor business decisions can have adverse effects on how your business performs. Poor quality data can be caused by inconsistent data capture protocols, poor data integration and migration, and data decay.
To prevent low-quality data, keep your data up to date to avoid data decay. If your data is not uniform, you need to implement rules to help solve inconsistency problems. Actions like asking a software developer to help integrate systems can prove to be a worthwhile investment as they can help you avoid basic errors, save time, and improve productivity.
Data-Driven Decisions
When you adopt a data-informed attitude rather than a data-driven approach, you make data part of the decision criteria and not the only one. Set strong data governance and be tough on data quality, select KPI to help you measure high-priority problems, and empower your team through methodologies and cognitive bias to analyze information.
