A Definition of Data Analysis
Data analysis is a primary component of data mining and Business Intelligence (BI) and is key to gaining the insight that drives business decisions. Organizations and enterprises analyze data from a multitude of sources using Big Data management solutions and customer experience management solutions that utilize data analysis to transform data into actionable insights.
Dennis Junk, a HubSpot certified inbound marketer with Aptera, aptly explains data analysis in his blog post: data analysis is “all the ways you can break down the data, assess trends over time, and compare one sector or measurement to another. It can also include the various ways the data is visualized to make the trends and relationships intuitive at a glance.” Data analysis involves asking questions about what happened, what is happening, and what will happen (predictive analytics). As Junk puts it, “analytics is generally the data crunching, question-answering phase leading up to the decision-making phase in the overall Business Intelligence process.”
Data Analysis Model
Gwen Shapira, a solutions architect at Cloudera and an Oracle ACE Director, outlines seven key steps of data analysis for Oracle’s Profit magazine. Shapira explains that while each company has its own data requirements and goals, there are seven steps that remain consistent across organizations and their data analysis processes:
- Decide on the objectives – Determine objectives for data science teams to develop a quantifiable way to determine whether the business is progressing toward its goals; identify metrics or performance indicators early
- Identify business levers – Identify goals, metrics, and levers early in data analysis projects to give scope and focus to data analysis; this means the business should be willing to make changes to improve its key metrics and reach its goals as well
- Data collection – Gather as much data from diverse sources as possible in order to build better models and gain more actionable insights
- Data cleaning – Improve data quality to generate the right results and avoid making incorrect conclusions; automate the process but involve employees to oversee the data cleaning and ensure accuracy
- Grow a data science team – Include on your science team individuals with advanced degrees in statistics who will focus on data modeling and predictions, as well as infrastructure engineers, software developers, and ETL experts; then, give the team the large-scale data analysis platforms they need to automate data collection and analysis
- Optimize and repeat – Perfect your data analysis model so you can repeat the process to generate accurate predictions, reach goals, and monitor and report consistently
Benefits and Challenges of Data Analysis
Data analysis is a proven way for organizations and enterprises to gain the information they need to make better decisions, serve their customers, and increase productivity and revenue. The benefits of data analysis are almost too numerous to count, and some of the most rewarding benefits include getting the right information for your business, getting more value out of IT departments, creating more effective marketing campaigns, gaining a better understanding of customers, and so on.
But, there is so much data available today that data analysis is a challenge. Namely, handling and presenting all of the data are two of the most challenging aspects of data analysis. Traditional architectures and infrastructures are not able to handle the sheer amount of data that is being generated today, and decision makers find it takes longer than anticipated to get actionable insight from the data.
Fortunately, data management solutions and customer experience management solutions give enterprises the ability to listen to customer interactions, learn from behavior and contextual information, create more effective actionable insights, and execute more intelligently on insights in order to optimize and engage targets and improve business practices.
For more information on data analysis, check out these posts: