How to Avoid Big Data Marketing Mistakes

Make note of big data marketing mistakes that even data scientists face and address them promptly to avoid the pitfalls many big data projects see.

Data-based marketing agencies have helped numerous clients revamp their approach to marketing data storage, processing and analysis in significant ways. During these projects, the agencies often see the same pain points repeated time and time again. What they have come to realize is that many such hurdles stem from common misconceptions about a big data marketing program and how to structure one.

Therefore, to help businesses avoid the challenges that plague other big data programs, clients should note the following most frequent big data marketing mistakes, along with recommendations on how to fix them.

Implementing Big Data Projects Without a Business Case

Many big data projects are implemented without a clear idea of what the primary purpose or goal is. Whether the objectives are left vague or too many ideas went into the pot, organizations can end up dissatisfied with even the most capable systems and software because the marketing agencies did not first consider their needs.

Solution: Before examining big data options like servers, software or data sources, consider the problems you want them to help solve. Then examine the problems from a 360-degree perspective and consider the context as well as interoperability to make sure that data continues to flow. By focusing on objectives, C-level executives will realize that data-backed insights are only one piece of a complex puzzle

Forgetting to Prioritize Data Quality

“Garbage in, garbage out” is a popular maxim, and this applies to data quality. Without proper data maintenance, even getting to the “insight” phase of analytics can be difficult. Data must be quality-controlled, sorted and meta-tagged before processing; otherwise data points may not be accessible in relevant case studies. The worst-case scenario is bad data infiltrates the study pool and yields false insights.

Solution: Some of the most important data-quality tips include developing a taxonomy governance, applying descriptive meta-tags for rapid organization; maintaining version control among data sets to preserve the latest information; and appointing a data-quality project manager to identify and correct problems in the analytics pipeline.

Disregarding a Data Architecture Plan

Just like poor-quality data, poorly structured data architectures can make retrieving the data for a case study extremely difficult. Many businesses end up with data silos, where information from CRMs is not co-mingled with POS reports, leading to constraints in the ability to obtain insights.

Solution: Where your data lives has a huge effect on how you are able to use it. Your organization must consider its storage options such as on-site or in the cloud and invest in low-latency data management tools to harmonize the data and unlock the full potential of analytics.

Falling Victim to Misleading Trends

Dashboards and visualizations are great tools for rapid insights, but decision-makers must be able to understand the science behind those charts to fully benefit from them. Many times, what appears to be a trend or a connection may actually be the result of other phenomena.

Solution: You should follow the rules for establishing causation beyond correlation, which include testing for strength, gradient and specificity. Also, test findings with other variables to see if an alleged connection can be more accurately attributed to another relationship.

These common problems are something that even data scientists and statistics experts face, so it’s best to maintain awareness of them and attempt to address them promptly to avoid the pitfalls many big data projects face.