The Work.ru service chose Tableau for multivariate data analysis. We talked with Alena Artemyeva, director of analytics of the Rabota.ru service and learned how analytics changed after the solution implemented by the BI GlowByte team.
Q: How did the need for a BI solution arise?
Alena Artemieva: At the end of last year, the Rabota.ru service team began to grow rapidly. It was then that the need for high-quality and understandable analytics from various departments and management of the company increased. We realized the need to create a single and convenient space for analytical materials (ad hoc research and regular reports) and began to actively move in this direction.
Q: What criteria were used to search for a BI solution and who took part in the assessment?
AA: The most important criteria for us were the following criteria:
- availability of an autonomous server for data storage;
- cost of licenses;
- availability of a Windows / iOS desktop client;
- availability of a mobile client Android / iOS;
- availability of a web client;
- the ability to integrate into the application / portal;
- the ability to use scripts;
- simplicity / complexity of infrastructure support and the need / absence of the need to search for specialists for this;
- prevalence of BI solutions among users;
- feedback from users of BI solutions.
Q: Who took part in the assessment:
AA: It was a joint work of the teams of analysts and ML Rabota.ru.
Q: What functional area does the solution belong to?
AA: Since our task was to build a simple and understandable analytical reporting system for the entire company, the set of functional areas to which the solution belongs is quite wide. These are sales, finance, marketing, product and service.
Q: What problem (s) did you solve?
AA: Tableau helped us solve several key problems:
- Increase data processing speed.
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Q: What came before Tableau? What technologies were used?
AA: Previously, like many companies, we actively used Google Sheets and Excel, as well as our own developments, to visualize key indicators. But gradually we realized that this format did not suit us. Primarily due to the low processing speed of data, as well as due to limited visualization capabilities, security problems, the need to constantly process large amounts of data manually and inefficient use of employee time, a high probability of errors and problems with providing shared access to reports (the latter most relevant for reports in Excel). They also cannot handle large amounts of data.
Q: How was the solution implemented?
AA: We started by rolling out the server side on our own and started making reports, combining data from storefronts with prepared data on PostgreSQL. A few months later, the server was transferred to the infrastructure for support.
Q: Which departments were the first to join the project, was it difficult?
AA: The vast majority of reports are prepared from the very beginning by employees of the analytics department, and subsequently the finance department joined Tableau.
There were no critical difficulties, since when preparing dashboards, the task is decomposed into three main stages: researching the database and creating a methodology for calculating indicators, preparing a report layout and agreeing it with the customer, creating and automating data marts and creating a dashboard visualization based on showcases. We use Tableau in the third step.
Q: Who participated in the implementation team?
AA: It was mainly ML team.
Q: Was staff training required?
AA: No, our team had enough public material, including marathon data from Tableau and information in the Tableau user communities. There was no additional training for any of the employees - due to the simplicity of the platform and the previous experience of the employees. Now the team of analysts has made significant progress in mastering Tableau, which is facilitated by both interesting tasks from the business and active communication within the team on the features and capabilities of Tableau found in the process of solving problems.
Q: How difficult is it to master?
AA: Everything went relatively easy for us, and the platform turned out to be intuitive for everyone.
Q: How quickly did you get the first result?
AA: Within a few days after implementation, taking into account the fact that it took a certain amount of time to "polish" the visualization in accordance with the wishes of the customers.
Q: What indicators are already available on the basis of the project?
AA: We have already implemented more than 130 reports in various areas and increased the speed of data preparation several times. This turned out to be important for the specialists of our PR department, since now we can quickly respond to most urgent requests from the media, publish voluminous research on the labor market as a whole and in individual industries, and prepare situational analytics.
Q: How do you plan to develop the system? Which departments will be involved in the project?
AA: We are planning further development of the reporting system in all key areas. Reports will continue to be implemented by analytics and finance specialists, but we are ready to connect colleagues from other departments if they want to use Tableau for their own purposes.