The constant technological advances, as well as the process of digital transformation, are allowing companies to take more and more business decisions based on data at all levels. But, what happens when this information is inaccurate? Or is it wrongly interpreted? Gartner Research talks about losses of $15 million on average in organisations annually because of poor quality data. Are you sure it is not hurting your business too?
The dirty data causes headaches to any analyst but, above all, in Big Data projects where its detection is very complex. According to this article by The New York Times, it is estimated that a Data Scientist devotes 50-80% of his or her time to the collection and preparation of digital data before being able to explore them. The cost of data cleaning is high but, probably, it does not approach the possible consequences of working with inaccurate data.
We share some ideas to facilitate the preparation of data and, also, to activate it and get business insights more quickly. Are you analysing the correct figures?
How to streamline the data preparation process?
When we speak of dirty data, we refer to duplicated, incorrect, incomplete, or outdated data… information that can alter the results of our analysis due to its lack of accuracy. Unfortunately, it is very easy for your company to accumulate this type of data since it is usually generated by human error, incompatibilities between formats used by different solutions, invalid registrations, changes in workflows… The list can be very long.
If you want to improve the quality of your data and streamline the analysis process, here are a couple of ideas that may work well to prevent inconsistencies:
1. Definition of a standard for your company
An important step to avoid quality problems is to know the whole process of data processing, from how it is being collected, through what systems and with what structure, through to analysis and business objectives. You need to establish a standard that defines all these issues and applies throughout the organisation. Thus, if any doubt arises, the analyst may correct it.
It is advisable to involve the different teams so that a common agreement can be reached to avoid silos. The day-to-day demands encourage each professional to work only with what they need, thus duplicating data and processes that are not reusable. The standard should be revised periodically to adapt it to the business requirements and contemplate the different points of view of the different teams.
2. Appropriate solutions and flexibility
The process of preparing data for analysis, in general, is not fast and is usually the responsibility of the technology team. Consequently, when marketers working with tighter timings manage to access, the data may no longer answer all the questions with the desired accuracy (or is outdated).
For this reason, it is important to try to simplify workflows as much as possible by incorporating tools that facilitate preparation. In some companies, for example, specific solutions have been developed that automate or distribute the work between different teams. The objective, in the end, is to integrate all your data sets in the same repository to facilitate their subsequent activation.
And with the data ready… we can only see them
If you have already passed the preparation phase and you are sure of the quality of your data, it is time to move on to analysis and get insights that justify your business decisions. And here your ally will be the data visualisation tools that can help you visually identify patterns, trends or areas for improvement. They connect with your sources to allow you to picture the information.
In fact, some of these solutions incorporate features to streamline the data preparation process but that is not their main objective. Have you ever tried to make sense of an Excel with thousands of numbers? Detecting anomalies in your sales, understanding what factors influence the customer journey or making predictions about campaign results is only possible if you represent that data.
We have already repeated it several times in our blog but, if you still do not use them, we recommend that you start experimenting with these type sof tools. Data Studio, the Google solution, for example, is free and is characterised by being very intuitive, although it does not allow analysis as complex or advanced as in Tableau or Qlickview.
Do you have problems in the data preparation process? Are you taking advantage of the visualisation tools available in the market? Leave us a comment below.