Managers today are under incredible pressure to deliver revenue and cost reduction business transformation. Between meeting revenue goals, profit targets and helping to meet corporate objectives, the need to utilize data analytics to improve results is critical to a business’s success – but we are falling short of analyzing data at the speed of business?
While managers understand the importance of data analytics, most are frustrated and feel powerless that they can’t just pull the data and insights themselves. Instead they often must either rely upon analytical experts with advanced knowledge to utilize complex tools in order to pull reports or wait in line to get IT and analytics experts assigned to work on their projects. By the time IT is done, critical business decisions may have been made with obsolete or no data.
The problem is that these teams need this data quickly in order to make business judgements based on the analytical insights that are hidden in the data. To help optimize the business, they need proof that their business hypothesis is backed up by data. However, not being able to pull their own data using business friendly self-service tools is slowing down the decision-making process. Additionally, if new ideas or insights are conjured, there’s no expeditious way to test ideas if they cannot pull the data on their own. Finally, things can get out of hand fast forcing teams to fix a crisis with insufficient data analysis to make a business decision that can’t wait.
And while these teams believe success is inhibited by their inability to pull the data needed to be successful, the rest of the organization seems confused because a variety of tools have been purchased throughout the years at great cost. It’s often not that existing tools can’t solve the problem, but that they are too complex and require specialized understanding to use, resulting in delays or, worse yet, reverting business users to rely on gut instincts. While progress has been made, some basic measurements coming from different sources provide different results and create confusion, wasting time needed to reconcile these results.
So, what’s a manager to do?
While becoming a citizen data scientist sounds like a win-win solution for everyone, there are several essential considerations to ensure the role is right for you and your organization.
Have clearly defined analytical goals: Depending on whether you want to explore, measure or prove a point in a business case, you need to formulate measurable goals. If your goals are too vague, you’ll likely not advance your goals. If your definition is too precise, you may limit yourself with extracting value from the data in the future.
Conduct a data quality assessment: Once you’ve clearly defined your goals, there are a multitude of questions to answer that will help you determine if becoming a citizen data scientist is possible in your organization. At the end of this assessment, you want to be able to know the extent of your ability to proceed based on the availability and quality of the data you can access. Questions include:
While IT or the analytics team will likely support you at the beginning, it’s important to find out to what extent and for how long.
Conduct a data prep assessment: Once you know that the data is reliable, secure and available, it is time to assess your ability to access the information and transform it into insights that can help achieve your goals. Questions during this phase may include:
At the end of this assessment, you should have a plan that identifies how you are going to reign in the data to meet your goals. You may want to enlist a trusted adviser to guide you through this phase.
Analytical assessment: For this assessment, you want to figure out what you should do with the data and how can you process it in a way that tells you what you need to know.
At the end of this phase, you’ll have a clearer picture of what you can do with the data, as well as what potential insights can be gained from your effort.
There is more than one way for managers to arrive at achieving the analytical data and insights they need. However, to do it on your own requires some important questions and internal assessments before proceeding. But the opportunity it opens up is endless and can help propel the organization if you’re able to flexibly pull the data necessary to make better, smarter and faster business decisions.
To learn more about how an all-inclusive enterprise data intelligence platform can help create citizen data scientists, check out the data sheet below.
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