Data has been the catchphrase for years now. Working with data is always exciting. The data analytics industry is projected to account for about USD 78 billion by 2023. Also, the data analytics market predicted that there would be approximately 2.7 million jobs open for data scientists and data analysts in the coming years.
Data can be produced from an individual or large-scale enterprises. And to get benefits from data, one needs to analyze data. Their question arises, how do we do it? This is where data analytics comes into the picture.
Today, we are going to concentrate more on what exactly does data analytics mean? And how it varies from other buzzwords out there? What are the benefits, challenges, and use cases?
About data analytics – never vanishing buzzword
Data analytics (DA) is the process of evaluating data sets using specialized software and systems to get inference regarding the information they contain. Data analytics techniques and technologies are exclusively used in commercial businesses to allow companies to make more-informed business judgments. Also, researchers and scientists use DA to confirm or refute the scientific hypothesis, models, and theories.
The term mostly implies a range of applications, from business intelligence (BI) to several forms of enhanced analytics.
In short, one can say that data analytics is quantitative and qualitative techniques and processes exclusively used to increase business productivity and overall revenue. Data is categorized and extracted to find and evaluate behavioral data forms; thus, the use of techniques differs from organization to organization.
Many times, people get confused with data science and data analytics. Anyways, these two techniques are almost similar in nature; data science is more into the development of novel models and algorithms via programming and coding, whereas DA is more in solving issues using defined data sets. Analytics can foresee the future as well.
When analytics extend beyond BI and covers areas such as text/data mining, pattern matching, machine learning (ML), neural networks, forecasting, sentiment analysis, and semantic analysis, thus can be defined as “advanced analytics.” Advanced analytics is semi-autonomous or autonomous that requires less individual involvement for data interpretation.
To understand the term and procedure of how data analysis is performed, take a look:
Inside the DA process –
- Data mining and setting up systems help to analyze, process, and manage data at a developed infrastructure. This process involves the use of ETL (extract, transform, and load) data function. The use of this function makes the job easy.
- Construct databases/storage systems using data warehousing for easy data recovery and access.
- Solving of particular issues and extract valuable insights.
- Cleansing of data to enhance quality.
- The last step is the creation of a detailed report to present to business shareholders.
- Data analytics plays an essential role by increasing the revenue of the business, responding immediately to developing industry trends, enhancing the operational efficacy, gaining a competitive edge over rivals, and optimizing consumer service efforts and marketing campaigns. This is all done to drive business performance.
- It is used to address the most common issues faced by enterprises, including operational efficiency; sales forecasting; marketing segmentation, targeting, and positioning; price optimization; and fraud prevention.
- It will help one to save time by analyzing various responsibilities and tasks.
- DA will help to unlock novel insights. Thus, predictive modeling and enhanced analytics will allow producers to augment earning by about 55%, as per the McKinsey report.
- It not only allows reactive and real-time interactions by enabling enterprises to predict outcomes and performance but also deals with events in-the-moment. Taylor Barstow writes in his editorial that real-time analytics is “shifting the paradigm to a place where businesses can proactively drive their own destiny, rather than just reflect on it.”
- The most important benefit is it will help one to save money, as small enterprises may access insights using inexpensive solutions designed especially for them, using free software, and by using open-source tools. For instance, small enterprises can use Blendo to execute data analytics on their storefronts. Free tools such as RapidMiner, Wolfram Alpha, and OpenRefine can be used to analyze data quickly.
Data analytics is recognized to support a broad array of business uses. For instance, mobile network operators use DA to analyze consumer data to boost customer relationship management (CRM) efforts. E-commerce organizations and marketing services providers use DA to identify website visitors. Also, DA is exclusively used in healthcare organizations to mine patient data to evaluate the effectiveness of treatments for cancer and other diseases.
A real-life example will help you to understand the benefits of DA:
Let’s imagine you are the owner of a site, which is into publishing sports videos. As people regularly visit the site, you can collect information regarding various visitors that watch videos as well as how frequently they rate the videos, which videos are getting more comments, and more. You can also gather data related to the demographics of each user. Using data analytics tools, you can determine the segment of video watchers, thus suggesting videos based on their likes. For instance, mature men are more fascinated by golf, while younger men are most interested in football or basketball.
Challenges associated with DA
- The critical challenge is the collection or handling of data in less time as there is vast data production in enterprises on a daily basis, and it is difficult to determine what to prioritize. Thus, DA tools play an essential role by collecting information from website visits and ad clicks, thus delivering it to a usable format.
- Low quality of data will negatively affect the growth and revenue of the business.
- Consumers may feel anxious or confused about swapping from traditional data analysis methods to automation.
- Information collected occasionally can be inaccurate.
The most common data analytics use cases one need to know –
- Advanced analytics
- Self-service analytics
- Customer relationship analytics
- Cloud analytics
- Embedded analytics
- Augmented analytics
The final words…
DA has come a long way, and there are many more innovations on the horizon.
“Data analytics is the science of analyzing data so as to decide on the daily production of huge information.” Nearly all methods of data analytics are mechanical processes that operate via algorithms that are working over necessary information for human consumption.
Thanks to data analytics techniques for making the complicated information world more straightforward!
“If I knew what you were going to use the information for, I would have done a better job of collecting it.”- A famous quote from a Migrant and Seasonal Head Start (MSHS) staff person.