Data-driven decision-making is a pivot an enterprise can make to create impactful changes to performance and efficiency. Predictive maintenance is a case in point.
Predictive maintenance is performing maintenance only when necessary. You can accomplish this through data analytics or historical and real-time data analysis to reveal patterns that can predict outcomes. According to Deloitte, predictive maintenance can lead to 75% fewer breakdowns, 25% lesser maintenance costs, and 25% greater productivity.
If you want to shift to predictive maintenance (and enjoy the many other benefits of data analytics), get serious with your data analytics recruitment.
Hire Through an IT Staffing Agency
Unless you have someone in-house who is well-versed in data analytics recruitment, finding, hiring and onboarding data analysts, architects, engineers, developers, scientists, managers, administrators, and the like can be highly challenging. This is true even if you’re a big organisation with a large human resources department and a sizeable recruitment division.
Hiring data analytics specialists (or any tech staff in general) can be difficult if you don’t have the expertise to evaluate your job candidates. This is why it makes sense to outsource tech recruitment — especially when it comes to your first few hires in a particular niche (business intelligence, digital transformation, cybersecurity, etc.) — to an information technology or IT staffing agency.
Even if you decide to outsource your data analytics recruitment to tech staffing specialists, you may still want to read through the following list of attributes to look for in a data analytics hire.
What You Want in a Data Analytics Job Candidate
When looking to fill a data analytics role in your company, look for the following skills and qualifications in your job applicants.
1. Demonstrable Tech Fluency
A data analyst or a data scientist cleans, analyses and manipulates data to answer business questions and come up with solutions to business challenges. Thus, they must be fluent in relevant programming languages and tools.
Must-know programming languages for data analytics include the following:
- Java
- Julia
- Matlab
- Python
- R
- Scala
- SQL
Data analytics candidates must know how to use business intelligence (BI) tools like the following:
- Cloudera
- Dundas BI
- Hadoop
- IBM Cognos Analytics
- MicroStrategy
- Oracle Analytics
- Power BI
- Qlik
- SAP BusinessObjects (BO)
- Sisense
- Tableau
If you’re working on a big data project, you want candidates to have experience and expertise in the following big data tools:
- Apache Hadoop
- Apache Spark
- Apache Storm
- Cassandra
- MongoDB
- RapidMiner
In sum, you want someone who knows how to work with databases, extract insights from datasets, analyse data using statistical analysis tools, and create data visualisation charts.
Note: A candidate doesn’t need to know all programming languages, business intelligence, and big data tools. However, he must be able to demonstrate fluency in the languages and programs that he does know. Naturally, however, you should prioritise candidates proficient in the language and tools your company or project requires.
2. Superior Data Sense and Data Analysis Proficiency
Someone with data sense can look at a chart or dataset and tell that something (a data point, trend or pattern) is off, say, due to peculiar spikes or anomalously low figures. During your interview, you can ask a candidate to tell you about a time he exhibited such data sense.
Note that data sense is something someone hones through years of data analytics work. It is also more acute when someone has extensive domain knowledge. However, some are simply gifted when it comes to statistical analysis.
3. Relevant Education and Experience
A good candidate has training in data analytics, machine learning and related fields. Ask your candidates to submit their certificates on data analytics, machine learning, specific programming languages, BI software, and big data tools and platforms.
Ideally, moreover, a data analytics candidate has actual hands-on experience. Ask your job applicants for their portfolio or a link to their capstone projects and other projects they have worked on.
A good shortcut is to check for work experience in machine learning and data analytics. You may want someone who has held relevant job titles, like those enumerated below. Incidentally, these are some of the most common data analytics job titles.
- Big Data: Visualizer, Architect, Engineer, Researcher, Consultant, Administrator
- Business Intelligence: Manager, Architect, Analyst, Consultant, Developer, Administrator
- Chief Data Analyst
- Chief Data Officer
- Data Warehouse: Manager, Architect, Analyst
- Data: Governance Expert, Analytics Manager, Modeler, Architect, Engineer, Analyst, Scientist
- Database: Administrator, Developer, Manager, Architect
4. Well-Developed Business Context Intelligence
Someone in data analytics shouldn’t only be good at data mining, cleaning, handling, and processing. He must also be able to put data in its proper business context.
To illustrate, if a data analyst sees a list of percentages, he must be able to think beyond what the numbers say and tell what they mean for the business. Only in this way can the data be utilised to enhance business performance.
Additionally, you want a candidate who can convert business goals into machine learning problems he can solve using the data analytics tools in his toolbox. You want someone you can give a general business objective (e.g., achieve a 10% increase in revenue using artificial intelligence) and turn it into a solvable machine learning problem.
5. Extensive Domain Knowledge
Ideally, you want a data analyst who knows your particular industry and domain. Such knowledge will guide him when he works and ensure he knows the business context of the data he’s processing and analysing.
6. Strong Analytical Skills
You don’t want someone who merely knows how to perform statistical calculations. Instead, you need someone who can analyse data and draw valuable business insights from them.
7. Excellent Communication Skills
The importance of excellent communication skills can never be understated. The insights drawn from analysing your data can only be helpful to your organisation if your data analytics team can tell you clearly about these insights and their implications.
Therefore, you want someone good at creating data visualisation aids and communicating findings and insights effectively to everyone, including technical and non-technical stakeholders and decision-makers.
Grow Your Business With Data Analytics
Data analytics can boost business performance and efficiency. Create your own data analytics team and wait for valuable insights that can change how you operate your business.
