ISO 17025 as per clause 7.11 requires a functional validation of the laboratory information management system use for data collection, processing, recording, storage and retrieval. This means to verify the data integrity from all inputs to the final result. Typically the depth is dependent on the sector that the laboratory is supporting. For example, clinical trials and other regulated sectors will have more stringent requirements than ISO 17025. I cannot tell from your question what Standard or regulatory requirement you are being assessed against and what the level IV data validation is requires specifically. This should be provided to you.
Basically the validation level of effort depends on the intended use of the data and the data quality objectives. On a risk basis it involves an understanding of how every quality control component affects the validity of the final result. For example, the risk of a false-positive or false-negative result.
Regarding control charts, I am not clear if you are referring to analytical, environmental or data related. Charting is a one of the quality process control activities /tools. Note that as per clause 7.7.1 Control charts are not mandatory, as long as monitoring the validity of results is planned (have a procedure) and the data is recorded in such a way that trends are detectable. This could be a simple table with the use of flags (e.g excel conditional formatting). ISO 17025 requires that where practical, a laboratory should use statistical techniques to review the results. Either way a control chart can typically be set up in Excel or software packages. From your analytical validation data you need to determine the permitted range of results upfront, you set QC rules and then you plot your ongoing data points to monitor any trends. The purpose is to proactively see if results are shifting from the expected measurements. Then you need to act on a correction.
There are three steps 1) Obtain QC data - say 30 analysis results obtained over time during your method validation. 2) Set the QC pass, fail and QC trend rules. These must be fit for purpose based on your required method performance. That is do you take action on a result of mean plus or minus one standard deviation or two or measurement uncertainty (MU) A simple approach uses mean and standard deviation. Depending on your measurement uncertainty (MU) decision rules, it may be more appropriate to use you MU as the limits.
3) Build the chart to indicate upper and lower limit within each result should fall within. For example, for a specific quality control check with a mean of 10 mg/ml and a Std deviation of 1 mg/ml you could set a warning at 1 SD (i.e. less than 9 and greater than 11 mg/ml and fail at over 2SD (i.e. below 8 and above 12 mg/ml. ) The set or running mean , upper and lower limits are charted on the graph.
Then monitor and review upward, downward trends and act according to your preset rules.