How Data Science can power your business
It is becoming clear by the day that there is enormous value in data processing and analysis. Data science principles can be applied to the data of any industry, type, form, and format can be turned into actionable insights.
Most of the businesses have the basic technology to manage the data and the infrastructure for visualization, data mining, and data warehousing. Data science is a step forward in that direction, where simple algorithms can be developed to extract the hidden patterns to predict or prescribe the solution to the problem that may occur in the future. Here are some advantages of the application of data science in business:
- Empowering management to make better decisions
- Directing actions based on trends—which in turn help to define goals
- Challenging the staff to adopt best practices and focus on issues that matter
- Identifying opportunities
- Decision making with quantifiable, data-driven evidence.
- Testing these decisions
- Identification and refining of target audiences
- Recruiting the right talent for the organization
Our Step-by-Step Approach
Reliable Inc. will access your end-to-end data infrastructure, virtually integrate multi-source data sets, clean and synchronize. Our techniques will address both structured and unstructured data assets. Once we have a clean dataset, we will build data infrastructure to support data science activities.
Step 1: Frame the problem
The first thing you have to do before you solve a problem is to define exactly what it is. You need to be able to translate data questions into something actionable. You’ll often get ambiguous inputs from the people who have problems. You’ll have to develop the intuition to turn scarce inputs into actionable outputs–and to ask the questions that nobody else is asking. It’s important that at the end of this stage, you have all of the information and context you need to solve this problem.
Step 2: Collect the raw data needed for your problem
Once you’ve defined the problem, you’ll need data to give you the insights needed to turn the problem around with a solution. This part of the process involves thinking through what data you’ll need and finding ways to get that data, whether it’s querying internal databases, or purchasing external datasets.
Step 3: Process the data for analysis
Now that you have all of the raw data, you’ll need to process it before you can do any analysis. Oftentimes, data can be quite messy, especially if it hasn’t been well-maintained. You’ll see errors that will corrupt your analysis: values set to null though they really are zero, duplicate values, and missing values. It’s up to you to go through and check your data to make sure you’ll get accurate insights. You’ll want to check for the following common errors:
- Missing values, perhaps well without a well name
- Corrupted values, such as invalid entries
- Timezone differences, perhaps your database doesn’t take into account the different timezones of your users
- Date range errors, perhaps you’ll have dates that make no sense, such as data registered from before sales started
Once you’re done working with those questions and cleaning your data, you’ll be ready for exploratory data analysis (EDA).
Step 4: Explore the data
When your data is clean, you’ll start playing with it! The difficulty here isn’t coming up with ideas to test, it’s coming up with ideas that are likely to turn into insights. You’ll have a fixed deadline for your data science project. You’ll have to look at some of the most interesting patterns that can help explain why well production is decreasing in this area.
Step 5: Perform in-depth analysis
This step of the process is where you’re going to have to apply your statistical, mathematical and technological knowledge and leverage all of the data science tools at your disposal to crunch the data and find every insight you can. In this case, you might have to create a predictive model that compares your underperforming oilfield with your average oilfield. You can now combine all of those qualitative insights with data from your quantitative analysis to craft a story that moves people to action.
Step 6: Communicate results of the analysis
It’s important that the stakeholders understand why the insights you’ve uncovered are important. Ultimately, you’ve been called upon to create a solution throughout the data science process. Proper communication will mean the difference between action and inaction on your proposals. You need to craft a compelling story here that ties your data with their knowledge. You start by explaining the reasons behind the underperformance. You tie that in with the answers your stakeholder gave you and the insights you’ve uncovered from the data.