What is Data Analysis? Data analysis is the process of turning raw data into actionable insights in order to aid decision-making and help solve problems. Do you need to know how large your marketing budget should be for your new product? A data analyst can use past performance of similar products and other market data to help determine that. Did your last mass communication not get as much engagement as you hoped? A data […]
Data analysis is the process of turning raw data into actionable insights in order to aid decision-making and help solve problems. Do you need to know how large your marketing budget should be for your new product? A data analyst can use past performance of similar products and other market data to help determine that. Did your last mass communication not get as much engagement as you hoped? A data analyst can conduct a retrospective analysis and discover why the communication was unsuccessful. Do you need to uncover instances of waste, fraud and abuse? Data analysis can help discover where to look.
The world is fast-paced and the rate of change is accelerating. If your organization is not already using data-driven evidence to make decisions, then a focused, timeboxed data analysis effort can help kickstart that change. Whether you need to overhaul your current analytics capabilities or create a framework to make analytics possible, a data analyst can help you harness the value of your data quickly; 30 days is all they’ll need to make a big difference.
Above all else, the most important factors for success in data analysis are buy-in from the organization’s leadership and a willingness to invest in the strategic potential of data. Let’s face it, data cleanliness and governance are not usually high priorities for most organizations until they want insights from their data.
The challenge is that a data analyst can only analyze what currently exists. It’s a misconception to think that data can answer every question and provide solutions to every problem; data capture and governance has to form part of an ongoing strategy. Otherwise, you may be able to glean some information from your data, but important questions will impossible to answer. Data analysis is not a one-size-fits-all solution; it has to be integrated into your organization’s plan and vision.
Working with disparate data sources and non-standard data points requires a high level of data literacy – this is the comfort zone of a data analyst. In as little as 30 days, a data analyst can help provide the following for your organization:
You may have many different data sources from established databases to manually tracked spreadsheets. Gathering useful observations and intelligence from these sources is difficult. A data analyst can determine the “lay of the land” for the current state of your data and advise a way forward.
A data analyst will quickly acquire the necessary domain knowledge and start to turn your data into evidence for strategic decisions. Whether you have an established analytics program or have never delved into the world of data analysis, a data analyst will be able to provide best practices in data collection, governance and reporting that help drive better decision making.
Sometimes what you want to accomplish with your data isn’t feasible in 30 days. If this is the case, a data analyst can recommend how to meet your goals in the future and lay out a path to get there.
It is important to be judicious and efficient when exploring the various datasets in a short time frame. In a typical environment, a data analyst would engage with both business and IT teams continually in order to provide the best solutions. However, with a limited time frame compromises have to be made. The keys to success when operating under a limited scope are flexibility, adaptability and the ability to work multiple avenues in parallel.
Here’s an example itinerary for doing an analysis in 30 days that integrates those keys to success:
Wait, there’s a week 0? Yes, ideally a set of preliminary work is performed to ensure the data analysis goes as smoothly as possible. This includes getting the necessary access to IT systems, identifying work to be done with stakeholders, setting expectations and gathering requirements. This step is not absolutely necessary however, if possible, this step will make the data analysis more rapid and more successful.
The first week focuses on the groundwork that will enable the delivery of valuable insights throughout the engagement. In this week the data analyst becomes familiar with the business and the IT infrastructure simultaneously. During this time the data analyst determines exactly what questions need to be answered, initiates a dialog with internal leadership, identifies their audience and gains access to all necessary data sources.
While this is occurring the data analyst will start exploratory data analysis. This means gathering valuable insights such as frequencies of different data points, correlations between data points and determining the best way to socialize outputs to the desired audience.
By this time in the engagement, the data analyst should have a good grasp on the baseline for the organization; this applies to both business processes and data sources. The focus will start to shift from the theoretical — “What needs to get done?”— to the practical — “How will this get done?”
Say, for example, the organization wants the data analyst to work in a new CRM tool. After taking time the first week to learn the tool and become familiar with stakeholder needs, the data analyst recommends a solution where data is regularly extracted from the tool to allow more flexible analysis using tools such as R, Python, and SQL. These can rapidly gather insights and create desired reports.
By the start of the third week, the data analyst knows their audience, their tool and has developed a relationship with stakeholders to ensure that their specific questions and problems are being addressed. As solutions to their biggest problems start to emerge, it is important to socialize the findings with the main business stakeholders and subject matter experts. Feedback from these groups will allow the data analyst’s work to be iterative so that initial approaches can be refined and adapted to specific needs.
Presenting, presenting, presenting. The last week is crucial for knowledge transfer, creating a shared understanding of the outcome and getting leadership buy-in to the solutions. The data analyst should regularly hold workshops and communicate to all relevant parties the work that has been done and what the next steps ought to be. The data analyst will now have an expert-level understanding of your organization, your data and how to lead you to successfully make data-driven decisions.
Data analysis is an ever-changing effort to innovate and stay ahead of the market using your data. In 30 days, a data analyst can unlock the door to this capability. Whether your organization needs someone to make sense of unused data or your reporting capabilities need a boost to the next level, consider leveraging a data analyst to pilot that effort.
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