In the age of advanced artificial intelligence (AI) and complex machine learning models, there's a tendency to overlook the power and utility of foundational data analytics. Yet, when leveraged correctly, these basic analytics can help businesses identify 'low-hanging fruits' - opportunities that are easy to seize without the need for sophisticated technology.
What is Foundational Data Analytics?
Foundational data analytics refers to basic analytical techniques and tools used to extract insights from raw data. This can include descriptive statistics, trend analyses, and straightforward data visualization techniques. These methods aim to help businesses understand their current state and identify patterns and anomalies.
How to Use Foundational Data Analytics to Identify Low-Hanging Fruits:
Start with Descriptive Statistics: Begin by summarizing your data. Calculating measures like mean, median, mode, standard deviation, and range can offer insights into the distribution and variability of your data. For example, a retailer might find that certain products have higher than average returns, signaling an issue with product quality or customer satisfaction.
Visualize Your Data: Charts and graphs can help identify patterns, trends, and anomalies in data. Bar charts, line graphs, scatter plots, and heatmaps are all useful tools. For instance, a sales heatmap could quickly show which regions are underperforming, highlighting an area for targeted marketing efforts.
Segment Your Data: Divide your data into categories or groups to understand variations between different segments. This could be by customer demographic, product type, region, or any other relevant classification. By segmenting, a business might discover that a particular age group is not engaging with their services, pointing towards a potential market segment.
Conduct Trend Analysis: Examining data over a period can help spot upward or downward trends. Recognizing a declining trend in a particular product's sales can prompt a review of its market positioning or quality.
Look for Anomalies: Outliers can sometimes signify problems or opportunities. An unexpected spike in website traffic, for example, could be due to a successful marketing campaign or an issue causing multiple site visits.
Leverage Basic Correlation Analysis: Understanding how two variables relate can offer insights into potential causative relationships. For example, finding a strong correlation between customer reviews and sales can emphasize the importance of maintaining product quality.