Los Angeles County had an inefficient recruiting process that often resulted in delays of a year or more before positions were filled. So in 2021, its human resources department modernized its analytics system with an integration between Microsoft Azure and Databricks to improve recruitment efficiency.
The Azure cloud platform includes tools such as Azure Data Lake, which customers can use to store and obtain insights from large datasets, and Azure Synapse Analytics, a service that joins data integration, data warehousing and big data analysis.
Databricks’ Data Lakehouse platform enables customers to query structured data with SQL as they would in a data warehouse, as well as query unstructured data as they would in a data lake.
Beginning July 2021, with guidance from Accenture Consulting, LA County – largest county in the United States.with over 10 million residents, redesigned its analytics stack with Azure Databricks Lakehouse in an effort to improve its hiring practices.
According to Majida Adnan, acting assistant general manager of information technology services for Los Angeles County.
“It worked really well for us,” she said recently at the Data+AI Summit, a user conference hosted by Databricks.
Too much time
Before LA County overhauled the technology that powers its hiring process, the average time to hire was 384 days in the last fiscal year, according to Roozan Zarifian, chief information officer for the human resources department. of LA County.
In fiscal year 2019-20 – the last year before the COVID-19 pandemic – LA County received more than 350,000 applications and filled more than 13,000 positions, with each hire taking an average of 327 days.
Over the next two fiscal years, although the number of hires and applicants fell due to the pandemic, the average time to hire jumped by more than 50 days.
“We strive to attract and retain a talented workforce,” Zarifian said. “We want to be innovative, and we want to be flexible and transparent.”
But before redesigning its HR analytics stack with Azure and Databricks, LA County’s hiring process was rigid. And one of the main reasons was that its existing analytics technology was hindering the speed at which it could monitor its hiring practices, according to Zarifian.
Among the challenges the county faced were a cumbersome data acquisition process, data managed in isolation repositoriesdifficulty in cleaning and analyzing data, and difficulty in sharing information once the data has been analyzed.
Additionally, the county relied on its IT department for analysis, resulting in long delays between requests for information. And much of what needed to be done throughout the data management and analysis processes was manual, which also added time to reviewing processes and procedures.
Roozan ZarifianChief Information Officer, LA County Human Resources Department
LA County’s problems are compounded by the complexity of hiring within such a large organization. The county requires exams for certain positions, has more than 1,200 job classifications, and is subject to a myriad of civil service regulations related to hiring.
“Without the right tools, our resources spent most of their time cleaning data, identifying anomalies, and then handling exceptions,” Zarifian said. “This semi-manual process of collecting, analyzing and reporting data was laborious, inefficient, inconsistent, costly and error-prone.”
In its effort to become more efficient, the human resources department set clear guidelines for what it wanted to accomplish – the steps enabled by better technology that would cumulatively result in better efficiency – which ultimately led to the integration between Azure and Databricks.
More importantly, he wanted to identify the bottlenecks that caused such long delays between when a position opened up and when it was finally filled.
Additionally, the department wanted to improve the hiring experience for candidates, improve its access to talent so it can compete with other organizations for the best available talent, improve recruitment so it can proactively identify potential candidates, and increase the diversity of talents available to him. jobs attract.
“We want to have candidate information so we can continue to attract and retain a diverse and talented workforce,” Zarifian said.
To achieve these hiring goals, the department implemented a series of technology goals, Zarifian continued.
The goal was to build an information loop that would feed on itself and improve over time and as more data was added.
First, the county needed to automate the extraction, transformation, and loading process to ease the manual burden on data engineers. Additionally, it needed to create real-time interactive dashboards that could be continuously updated with the latest data, automate anomaly detection, improve sharing and collaboration capabilities, and automatically capture data from multiple sources. .
Five months after teaming up with Accenture to develop a new analytics stack, a set of interactive dashboards was ready for implementation in late 2021, and since then LA County has been transforming its analytics practices. ‘hiring.
“We now have access to a wealth of information through these dashboards,” Zarifian said. “We can see how long it takes to hire employees and how long it takes to fill vacancies. We are also able to see how efficient we are at every step of the process. candidates and which source gives the best candidates. We can also tell where in the process we are losing candidates.”
Underlying the dashboards is an analytics stack including Azure Databricks Lakehouse.
According to Adnan, LA County had four technology challenges to address when developing its new analytics system and ultimately solving those challenges with a data lakehouse architecture.
They needed to develop an easy-to-use stack that adhered to modern industry standards and could handle data sets of unlimited size. Additionally, data acquisition needed to be simple, requiring little manual ETL work, and it required strong data governance capabilities and automated alerting capabilities.
Now, the LA County HR analytics stack starts with data ingestion into Azure Data Factory, which pushes raw data to Azure Data Lake. There, Azure Databricks Lakehouse takes over when the data is prepared for analysis. After the data is cleansed and integrated, it is transferred to Azure Synapse and Power BI for data-driven analysis, insights, and decision making.
“Data is the new oil, and as it grows and multiplies every day, we wanted to make sure we designed a data system with a big data approach in mind,” Adnan said.
And because it’s built for the cloud — in addition to automating labor-intensive tasks that previously had to be done manually — LA County’s revamped HR analytics system is cheaper than it wasn’t until 2021, she continued.
“By moving these processes to the cloud, from an infrastructure perspective and from an automation perspective, a lot of costs were saved and the total cost of operations went down,” Adnan said.