What makes data management important?
The fact that data is everywhere and must be carefully analysed to be of any use makes data management important. Collecting the required data for a business report, analysis or ground breaking research is a herculean task in its own for which big technology giants especially those dealing with raw data, data mining, data extraction and data management are working hard to make it easier. The very first step for any study or research starts with data management which can provide a hidden leap over the competitor. Knowing the data management best practices will enable you to gain advantage which can yield optimal and great results. Data management will save you time, physical labour, field and subject experts, data analyst without compromising on the quality of data analysis.
Related: Data Processing Cycle
6 data management best practices
Following the data management best practices will boost your profits with an added reduction in operational cost for the project. Follow the industry proven data management practices and start realising the effectiveness of these practices.
- Increased useful data collection – Collecting lots and lots of raw data might seem to fulfil the need of collecting large data sets but their usefulness for the project might be zero. Collecting project or problem specific data is the most effective practice which helps in saving time, labour and money at the very first step. Having hold of information rather than data will prove to be more beneficial as compared to gigabytes of useless data. It will further help in reduction in processing time and storage space which accounts for significant cost of required IT infrastructure.
- Distinguishing between “desirable” and “essential” – This is the very basic and easy step with far-reaching consequences. Dividing data into two categories, desirable – data which is required but in not much important, having this data will be an added advantage but its lack will not have any adverse affect on the analysis, essential – data on which the analysis depends and must be obtained without which the work will not be done. Essential data should be obtained first and desirable data should to added as and when available.
- Quality and Reliability of data – This is the back bone of any study in any study based on collected data. Questions over the accuracy of the used data might result in a complete failure. The collected data should come from a reliable source over which its quality depends. Having less data with higher quality is more sensible than depending on tons of data which lacks reliable and authentic source. The quality of over all analysis will depend on the data used. Keep in mind one simple rule while dealing with computers “Garbage in, Garbage out” – if you feed the wrong data than the outcomes are bound to be wrong and useless.
- Excluding outliers and establishing correlation – Some analysis will provide illogical results simply because of the inclusion of outliers, in such cases removal of outliers from the data sets is the key for a sensible results. Using the data sets and parameters with high degree of correlation is much more beneficial as compared to suing scattered information. Doing some study of best practices for obtaining correct parameters will further prove to be beneficial.
- Data cost and data volume – Volume of data comes at a cost and if it is being collected for the first time than the expenses incurred are huge. Try to look for existing surveys and collected data from secondary sources with minimal collection from primary survey. It is more logical to spend on collection of essential data than to hire data analyst which will be provided with incomplete data making the cost incurred on both data collection and data analyst a waste. Always remember, a good analyst will not be able to provide any result without good data, after all data analyst deals with data. Providing the required data an analyst require will give you results which you desire.
- Choose best available technology – You will never regret spending huge sum of money on good softwares and robust hardware. Harnessing the power of existing and powerful softwares will provide you with exceptional and astonishing results. This will be a long-term investment with multiple benefits. Using a specific combination of softwares will further catalyze the process and provide results in lesser time.
Other considerations for data management – Hidden value of the data should never be underestimated, data once collected must be preserved as it desirable data for one study might become essential data for other project. Having a repository of data will provide access to rich and diverse data sets which will increase with the expertise and completed projects of the company. prioritization of the data management best practices mentioned above should be done according to budget and available time.
You can read more about data management at SAS