Big data experts must have more than technical know-how--they must also demonstrate soft skills and business acumen.
In 2019, big data and analytics skills is the number one area of need in companies. According to AWS recruiting agency Jefferson Frank, technical skill areas in demand include programming languages such as Python, C++, and Java, machine learning and AI experience, competence in quantitative analysis, data mining, and SQL/NoSQL databases and algorithm development.
These are just the technical skill sets that are needed. In order to be a big data digital star and influencer,
you must also have soft skills and business acumen.
SEE: 60 ways to get the most value from your big data initiatives (free PDF) (TechRepublic)
These are the six must-have skills for big data stars.
- Know the ins and outs of your company
Do you understand your company's product lines, revenue sources, financial and sales reports, and strategic goals? Having your finger on the pulse of the business and what makes it tick is as important as being able to cut code and/or execute technically if you want to bridge the gap between IT and data science and the end user.
2. Be knowledgeable about business process engineering
Big data technologies like analytics, machine learning, IoT, robotic process automation, and AI are disruptive to businesses. These technologies disrupt because they impact established business processes that have to be redesigned, and this means users must be retrained.
Too often, IT and even the end business inserts new technologies into business processes without evaluating how existing processes and workers will be affected.
This can lead to the rejection of a project that could have been successful if it had been properly inserted and tested in a new business process before the process was put in place. You need to be able to work with technologists and end users so technology that adds to a business process improves the process and makes work easier.
3. Collaborate and command collaboration
Big data technology insertion and business process reengineering depend on a healthy collaboration between end users who are familiar with the business process flow and technologists who are providing the new technology to be used in the business process.
A rising digital star must lead by example, so you need to be seen as a selfless collaborator who does everything possible to make the project a success. You must also be able to inspire others to enthusiastically collaborate so the team can create great business processes that leverage some of the outstanding big data technologies that are available.
4. Follow-up on big data projects
One of the best ways to gain experience with big data projects is to follow up on implemented big data projects; this enables you to see what's going well and what can be improved. You can apply this knowledge in future projects.
Plus, following up on projects after implementation tells customers that you care about their systems and work environment, and it paves the way for great user cooperation and collaboration in your next project together.
5. Adhere to compliance and governance
Big data champions always pencil in project time for compliance and governance conformance and QA checkout. It is never an option to skip this step.
6. Maintain data quality
One of the reasons digital big data projects fail is because of poor data quality. Most IT and business users know this, but they also know that cleaning the data--especially if some of the cleanup must be manual--is tedious work that gets in the way of other projects.
The result is the data cleansing step is not done as thoroughly as it should be, and this leads to major risks. A poor business decision might be made because the data it was based on was poor. A project can be cancelled because the data was poor, even if the algorithms are right. Digital big data champions always insist on quality data.
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