Data Here, Data There, Data Everywhere
Think about where and how you use data to make decisions.
You will create a list of at least five questions that you might use data to answer.
- What’s the best time to go to the gym?
- How does the length of your commute to work vary by day of the week?
- How many cups of coffee do you drink each day?
Few examples from everyday life.
You might not realize it, but people analyze data all the time.
For instance,
I’m a morning person. A long time ago, I realized that I’m happier and more productive if I get to bed early and wake up early.
I came to this conclusion after noticing a pattern in my day-to-day experiences.
So I thought about the relationship between this pattern and my daily life, and I predicted that early to bed early to rise would be the right choice for me.
Let’s put this process into a business setting.
There’s a ton of data out there. And every minute of every hour of every day, more data is being created.
Businesses need a way to control all that data so they can use it to improve processes, identify opportunities and trends, launch new products, serve customers, and make thoughtful decisions.
This is the process of turning data into insights, and it’s how analysts help businesses put all their data to good use.
This is actually a good way to think about analysis.
Data analysis is the collection, transformation, and organization of data in order to draw conclusions, make predictions, and drive informed decision-making.
Data analytics can help organizations completely rethink something they do or point them in a totally new direction.
Data leads them to a new product or unique service, or maybe it helps them find a new way to deliver an incredible customer experience.
Any time you observe and evaluate something in the world, you’re collecting and analyzing data. Your analysis helps you find easier ways of doing things, identify patterns to save you time, and discover surprising new perspectives that can completely change the way you experience things.
Data goes through several phases as it gets created, consumed, tested, processed, and reused. With a life cycle model, all key team members can drive success by planning work both up front and at the end of the data analysis process.
According to Google, The Data analysis Life cycle steps,
- Ask: Business Challenge/Objective/Question
- Prepare: Data generation, collection, storage, and data management
- Process: Data cleaning/data integrity
- Analyze: Data exploration, visualization, and analysis
- Share: Communicating and interpreting results
- Act: Putting your insights to work to solve the problem
Furthermore, In a descriptive manner,
First up, the analysts needed to define what the project would look like and what would qualify as a successful result.
So, to determine these things, they asked effective questions and collaborated with leaders and managers who were interested in the outcome of their people analysis.
These were the kinds of questions they asked:
- What do you think new employees need to learn to be successful in their first year on the job?
- Have you gathered data from new employees before? If so, may we have access to the historical data?
- Do you believe managers with higher retention rates offer new employees something extra or unique?
- What do you suspect is a leading cause of dissatisfaction among new employees?
During this step, the analysts identified what data they needed to achieve the successful result they identified in the previous step — in this case, the analysts chose to gather the data from an online survey of new employees. Rules were established for who would have access to the data collected — in this case, anyone outside the group wouldn’t have access to the raw data but could view summarized or aggregated data. They finalized what specific information would be gathered, and how best to present the data visually.
Employees understood how their data would be collected, stored, managed, and protected. The raw data was uploaded to an internal data warehouse for an additional layer of security. Since employees provided the data, it was important to make sure all employees gave their consent to participate. Collecting and using data ethically is one of the responsibilities of a data analyst. In order to maintain confidentiality and protect and store the data effectively, access was restricted to a limited number of analysts.
The analysts found that employees who experienced a long and complicated hiring process were most likely to leave the company.
From the completed surveys, the data analysts would discover that a new employee’s experience with certain processes was a key indicator of overall job satisfaction. Employees who experienced an efficient and transparent evaluation and feedback process were most likely to remain with the company.
Only the managers who met or exceeded the minimum number of direct reports with submitted responses to the survey were eligible to receive the report. The group first presented the results to eligible managers to make sure they had the full picture. This gave the managers an opportunity to communicate the results with the right context.
The last stage of the process for the team of analysts was to work with leaders within their company and decide how best to implement changes and take actions based on the findings. The analysts recommended standardizing the hiring and evaluation process for all new hires based on the most efficient and transparent practices. Turns out, the changes improved the retention rate for new employees and the actions taken by leaders were successful.
One of the many things that make data analytics so exciting is that the problems are always different, the solutions need creativity, and the impact on others can be great — even life-changing or life-saving.
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