Statistics only show one side of the story. For example, statistics will highlight the peek time of users on your website, and from this data you can make conclusions such as when to send emails or special promotions. However, without in-detail analysis your efforts will be wasted, as there are multiple details that you need to factor in to achieve optimal results. Analytics provides more information such as sales and website users ratios and when users check their emails.
Now is the time to reach for analysis
With data analysis gaining popularity, demand and supply for data generation and collection raise and its cost is dropping.
Data aggregation – cross-referencing data from various sources – is relatively cost-steady.
Because of the increase in demand and shortage of analytical talent, analysis prices are on the rise.
What makes processing data real analysis?
Processing data without applying applicable science is not analysis. It is simple statistics. Sifting through expected and steady results to discover the meaningful associations between variables that could lead to significant results can only be done via strong data science.
Apply the right rules to study the data
Statistics are general. Analysis does not just aim at raising awareness of the current state of transactions. It introduces focus. Collecting and cross-tracking compatible variables without registering false positives or biases gives you real, ready-to-use information and not just a vague, temporary picture of Now.
Do not oversimplify the results
Statistics are plain numbers. They become ineffective and unreliable when all relevant factors that can affect data are dismissed. Stubbornly chasing numbers is not only a bad strategy for success. It can seriously harm, and has harmed, many organizations.
Quality control of data
Statistics are a partial image. Real data science reaches outside the frame, aggregating compatible data from various sources. Data is incomplete without its contemporary and relative context. You will recognize skillful analysts by their insistence to provide the complete picture, and the interpretation behind it.
Data aggregation done right
- Create and store your own quality data
- Proper documentation
- Control for biases
- Free to obtain
- Control for compatibility
- Control for integrality
Third party data
- Purchase data
- Control for reliability