Another good practice is learning to ask the right questions and solve the right business problems.
“Every data science project should start off as a consulting exercise — understanding the ‘what’ and ‘why,’” Miglani says. “Also, the objective on any analytics exercise cannot be to implement a tool or platform. The objective should always be designed towards the right business outcomes, and you can get there by asking the right questions.”
Data: The foundation for success
The data analytics team is more likely to succeed if the organization creates a “data foundation,” Clark says. “Technical experts in the field of data will want to see real commitment from the organization to a data foundation,” she says. “In our treasury division we ran a coordinated program to streamline and improve data quality and accessibility over a two-year period. We have seen improved employee productivity, lower technology costs, and a broader community of digitally savvy employees.”
Ensuring high-quality data should be a cornerstone of any data foundation.
“Knowing and managing your data is critical to success,” Wilson says. “Your analysis will only be as accurate as your data. When we see success with our own analysis, we are often asked to leverage this analysis into reports or dashboards, so business users can leverage these findings from day to day. If your data process is unreliable or your data incomplete, your results will be flawed, and any actions taken on them will be erroneous.”
To keep on top of fast-changing developments in analytics, ongoing education and personal development are important to maintaining a vibrant and successful team, Nimeroff says. “Data analytics is amongst the set of fastest-growing fields out there, and even though the leading-edge techniques aren't applicable in every situation or organization, it doesn't mean that being able to stay current isn't important,” he says.
Paytronix emphasizes ongoing training of the analytics staff, as well as the ability to communicate results.
“Your team needs to understand the finer points of your data, know how analysis may go wrong with statistical biases, and understand how to effectively distill and then communicate actionable results,” Wilson says.
“I often tell my team that the best analysis in the world will be a wasted effort if it is not clearly understood and acted upon,” Wilson says. “To this end, keep the end in mind when working through a problem: How will business users change behaviors based on this information? This should help you tailor your approach and curate the conclusions you provide.”
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