The Biggest Big Data Mistakes Every Business Should Avoid

The success of big data and data analytics in different areas of business has completely changed the way businesses think about and use data. Both of them have become indispensable for modern business operations and processes. Businesses are using big data and data analytics in various business areas including marketing, customer analysis, sales and price optimization, and security intelligence. The proper implementation of big data projects can be subject to some pitfalls that we are going to look at below. We will also look at some common solutions to these pitfalls.

Not Identifying the Right Use Cases

A common challenge for businesses is knowing which data to collect and for which purpose this data is for. Many businesses then end up collecting too much data hoping that they can use all or most of it. This is the wrong approach as it puts a lot of strain on every part of a big data project. More data means businesses have to hire more analysts or have to wait for a long time before the data can be processed. Not understanding the difference between ELT and ETL is a huge mistake during data processing and analysis.

To avoid this, businesses should have clear and established data use cases, success criteria and key performance metrics laid down before they start collecting data. Brainstorming data uses with clear key performance metrics, analyzing the use cases to see if they are viable for a project, and determining whether the complexity of the required data is fine for the use cases laid down can go a long way in mitigating this problem. Identifying the right use cases to direct how much and which types of data collected can also lead to a viable ROI for a big data project.

Ignoring Technology and Business Readiness

Before collecting any data, businesses have to decide whether the data will be right for their use case, whether they have the tools to handle this data or they need to re-tool, if they have the infrastructure to handle the data, whether all staff requirements are met and whether business processes can support the project.

Businesses have to also think about the quality of the data they will be collecting. For the best results, businesses data integration tools require cleaned and structured data. If the data is of poor quality, these systems are ineffective, which leads to a lot of time, effort, and money wasted on cleaning the data first.

Partnering with a big data consulting company can help businesses assess whether they have the right processes, people, and technology in place to embark on a big data project.

Compromising Data Security

Mitigating risks is critical in big data and data analytics applications. The data businesses use for their big data solutions and implementations is becoming more valuable by the day and businesses should do everything they can to ensure it is safe. Because of all the security risks that now exist, data security is both imperative and non-negotiable.

Organizations that have skilled professionals with a deep understanding of their data and systems can regularly audit both of them to ensure the integrity and consistency of the data organizations hold. Additionally, measures such as providing access on an as-needed basis and having a unified system of controls are all important in ensuring data is not compromised.

Doing all of this can be overwhelming for business owners and managers who would rather focus on running their businesses or enduring primal results from their teams. Managed IT services that provide businesses with tools and services to help keep their IT infrastructure and data safe from the numerous security threats floating around are a great solution for these business owners and managers.

Ineffective Data Use

This is another product of collecting too much data. This problem is mainly because it is now easier than ever for businesses to collect all the data they need and, if unrestricted, collect and hoard a lot of unused data. Having clear use cases as mentioned above or removing all bottlenecks that make it harder to make use of all the data collected should be prioritized. Having data in a silo is wasteful, in terms of both time and money.

Not Focusing on Business Requirements

Infrastructure and technology stacks are highly important for any big data project. The only problem is that businesses will often focus on both of these and forget that they should be driven by business requirements. Having all the infrastructure and technology in the world is not helpful if it does not help advance or help fulfill business requirements.

Businesses should focus on achieving business outcomes and fulfilling business requirements instead of accumulating the latest technology or having an advanced infrastructure.

The benefits of big data and data analysis are numerous, but many businesses are currently unable to get all these benefits. Businesses must think about implementing robust strategies that help reduce the pitfalls associated with big data and data analysis.