Data is Talking to You. Are you Listening?

They say, data will talk to you if you’re willing to listen. And why wouldn’t it! With the humongous amount of data being generated by organizations about employees, customers, competition, market trends, industry regulations, and more, the need to glean this data to unearth insights is grave.

Data science is becoming an integral part of data-driven organizations today. It is enabling them to gather, inspect, clean, transform, and model data – to make better, real-time, and evidence-based decisions. It is predicted that the demand for data scientists will soar 28% by 2020.

However, the process of data analysis is not as easy as it seems. Given the variety, volume and velocity at which data is being generated, and the dearth of data scientists the world is reeling under, driving value is extremely difficult. But there’s a way out – there always is!

Here’s what you can do to prepare the data your organization generates, and have your data scientists and citizen data scientists leverage it to improve decision-making – to become a truly data-driven enterprise.

1.     Define your questions: There is a ton of data that gets produced every single day. But not all of it is useful or relevant. If you try to analyze all of the data that you have, you might just hit a dead end – despite all the money and effort you put in to uncover important information. Therefore, the first step in any data analysis program is to define your questions. Always begin with the right questions – questions that are clear, concise and measurable. Make sure the questions allow you to either include or exclude potential solutions to your specific problem or opportunity. For example, if you are suddenly witnessing rising costs of operating your business, the question you need to ask is: Can the company cut down on staff to save costs? Can it automate some of the processes to drive better cost efficiency? Can it abolish products that no longer add value?

2.     Decide what to measure and how: The next step requires you to decide what you want to measure, and how you will go about measuring it. To overcome the challenge of rising costs, consider what kind of data you would need to get a solution to your problem. In this case, you would need to know things like the cost of operating the current staff, the time they spend on business functions, the cost of embracing automation, and the savings you would achieve if you discarded some of your products. Decide how you will measure these aspects: consider the data you will use for the analysis, the time frame you have for the analysis, your unit of measure, and other factors that might be dependent on your analysis.

3.     Collect the data: Once your questions are clearly defined and your measurement priorities set, it’s time to move on to collecting your data. Here are a few things to keep in mind: determine what kind of data will be collected, from where, and how frequently, decide how you will be storing that data, and how you will ensure the same data isn’t collected or stored twice. For example, to understand the challenges your staff is facing, or the processes that need improvement, you might need to interview your workforce. In that case, you need to collect data through feedback or interviews, so have the templates ready to save time. Also, make sure the data you’ve collected is stored in chronological order, so it’s easy to get back to it, if, and when required.

4.     It’s time for deeper analysis: Once you have all the data in place, it’s time to carry out a deeper analysis. Leverage the capabilities of data scientists, and even citizen data scientists (or power users). These people will have data science-like skills to delve into data, improve model efficiency, and create and deploy new models to carry out advanced analysis. It will help you gain additional insights. Make sure they take data from across different timelines, tweak the size of the data sets, manipulate the data in different ways. Use algorithms or models to spot trends, find correlations, or discover variations. You might get the exact analysis results or need, or you might need to revise/refine your questions to drive more efficient analysis. Keep making changes to your data so that you can conclude from it better.

5.     Draw conclusions: After you’ve run all your data through statistical and exploratory analysis tools, you need to interpret the results and draw conclusions. When in the process of interpreting the results, remember to ask these questions: does the analysis result answer the questions you defined in Step 1? For example, do you find a co-relation between automating processes and staff productivity? Does discarding products lead to reasonable savings in operational costs? As you draw these conclusions, also ask the following questions: Have you considered a relevant, and large set of data to come to a conclusion that’s fair and unbiased? Have you made any assumptions? Are there any aspects you haven’t considered? Once you have answers, then you have most likely come to a reasonable productive conclusion.

The only step then is to use the results of your data to decide the best course of action and set out to become a successful data-driven enterprise.

Of course, to make sense of the data and enable its processing and analysis, you need to empower your team of data scientists and non data scientists with tools and technologies that are easy to use, don’t require strong programming knowledge and are powerful. 

Have you looked at Rubics.io?