Seven Key Steps to Data Analysis

Working alone, applying esoteric formulas to large amounts of data searches leads to useful insights. But that's still just one step in a process. Data analysis is not a goal in itself; the goal is to enable the business to make better decisions. Data scientists must build products that enable everyone in the organization to make better use of data, enabling data-driven decision-making in every department and at every level.

The data value chain is the capture of automated collection products, cleansing and analyzing data to provide information and forecasts through dashboards or reports. Automation performs the analysis, and data scientists can work on improving work with business models to increase predictive accuracy.

While each company creates data products tailored to its own needs and goals, the overall steps and objectives are the same:

1. Decide on the goals: The first step in the data value chain must begin with the data, and then the business unit has decided on the goals of the data science team. These goals usually require significant data collection and analysis. Because we are looking at data-driven decision making, we need a measurable way to know that the business is moving toward its goals. Key metrics or performance indicators must be identified early.

2. Identify business benchmarks: The business should make changes to improve the key metrics in order to reach their goals. If there is nothing to change, there can be no progress, not with the amount of data being collected and analyzed. Identifying goals and targets early in the project provides direction and avoids meaningless data analysis. For example, if the goal is to increase customer retention, one of the metrics could be for customers to update their subscription rates, and the business could set up reminder emails and do special promotions by updating the design, timing and content of the page.

3. Data collection: cast a wide net of data, more data, especially data from different channels to find better correlations, build better models and find more actionable insights. The big data economy means that personal records are often useless, and it is in each record that can be analyzed that real value can be provided. Companies closely monitor their Web sites to track user clicks and mouse movements, track the way they move through radio frequency identification (RFID) technology, and so on.

4. Data cleansing: The first step in data analysis is to improve data quality. Data scientists deal with correct spelling errors, deal with missing data and remove meaningless information. This is the most critical step in the data value chain, even the best data value analysis if there is junk data this will produce false results and mislead. More than one company has been surprised to discover that a large percentage of its customers live in Schenectady (US city), New York, and the town has a population of less than 70,000 people, etc. However, the Schenectady zip code 12345 so disproportionately appears in almost every customer profile database due to the fact that consumers are often reluctant to authentically fill out their online forms. Analyzing this data will lead to false conclusions unless data analysts take steps to validate and thus get clean data. This usually means automating the process, but that doesn't mean humans can't be involved.

5. Data modeling: Data scientists build models, correlate data with business outcomes and make recommendations and identify changes in business value as the first step in the process. This is where data scientists become uniquely specialized in becoming business critical, working with data, building models and predicting business outcomes. Data scientists must have a strong background in statistics and machine learning to build scientifically accurate models and avoid the pitfalls of meaningless correlations and models that rely on existing data and their future predictions are useless. But a statistical background is not enough, data scientists need to understand the business better and they will be able to recognize if the results of a mathematical model are meaningful and valuable.

6. Data science team: data scientists are notoriously difficult to hire, and it's a good idea to build a data science team through those who have an advanced degree in statistics focusing on data modeling and forecasting, while the rest of the team, qualified infrastructure engineers, software developers, and ETL specialists, build the data collection infrastructure, data pipelines, and data products necessary to make the data to display results and business models through reports and dashboards. These teams typically use the large-scale data analytics platform Hadoop to automate data collection and analysis and run the entire process as a product.

7. Optimize and repeat: The data value chain is a repeatable process that improves the business and the data itself through successive improvements to the value chain. Based on the results of the model, the business will be driven by the results measured by the data science team. Based on the results, the business can decide on further actions to improve its data collection, data cleansing and data modeling through the data science team. If the business is quicker about repeating this process, the sooner it can move in the right direction to get value out of its data. Ideally, after many iterations, the model will generate accurate predictions, the business will reach predefined goals, the results of the data value chain will be used for monitoring and reporting, and everyone moves to solve the next business challenge.

The above is what I have shared with you about the seven key steps of data analytics, for more information you can follow the Global Green Ivy to share more dry goods