Artificial intelligence is a powerful tool for making the most of big data. It gives the advantage of speed, efficiency, and accuracy. When properly applied I can improve the quality of data analytics, thereby making collection efforts worth the while.
Effective use of big data for marketing efforts is not fixable using manual data analysis. The users need to take data from various sources and segment the data into different portions before they are able to extract actionable intelligence from the data. Because of the complexity and diversity of data, traditional tools for this often do not extract useful information successfully.
Why Big Data?
Big data is a high volume and high variety of information assets that demand innovative forms of processing in order to enable enhanced insight decision making and process automation. This means that big data is a tool and process that could help companies make use of large sets of data. They analyze the data and extract information relating to customer preferences customer motivations and things of that nature. This insight gives them ideas for the creation of new products that are accepted by the consumers.
Improvement of data quality
Generally, people assume that more is better. With respect to data, more data are not always better. On the contrary, quality is more important. Although lack of sufficient data is an issue, the bigger problem is the lack of quality data. The term ‘garbage in, garbage out holds true. A survey of global executives conducted showed that many organizations that collect data do not get sufficient value due to poor data quality.
To improve the quality of data, we collected we can apply algorithms to data preprocessing, so as to help with the higher quality of input data that we are going to use for analysis. We can train models to prioritize data based on quality and recognize when there are issues with the data.
Handling a Wide Variety of Data Sources
Since marketing data comes from various sources and takes different forms, getting coherent analysis using traditional methods could be a challenge. AI and machine learning can help coordinate varying data sets which supports data monetization. Going by the prevailing trends, the number of devices and data sources is likely to increase in the future. Therefore, it is important to develop the ability to handle data from different sources.
Targeted Marketing for Efficient Results
Artificial intelligence has been very useful in targeted advertising. When you search for a term on Facebook, you get to see different groups and content related to the search term. Only for you to see ads displayed on your feed related to the pages you interacted with. This is possible because Facebook and Google use data and activity to serve advertisements.
The potential of handling data with artificial intelligence is limitless. Through efficient cleaning of input data, you can potentially improve your decision-making. Fortunately, there are tools that can handle the sophistication required due to the variety of data sources available. Despite the limitations, you would benefit from embracing AI sooner rather than later.