7 Key Differences Between Data Analytics and Data Mining

As we stay online more and more, we leave a digital mark! For each of our Google searches, social media posts, and link clicks, we leave unique digital trails which contain our activity data. Experts in the field of Big Data Mining can use this to generate useful data for firms. Businesses can leverage this data to expand their customer base. This enables them to embrace newer technologies & innovations. For instance, they may close sales from social media quicker with the help of Big Data prescriptive analytics or utilize artificial intelligence to minimize cart abandonment.

Both data analytics & data mining are the subsets of Business Intelligence. Data mining is actually a step of data analytics, the latter being a process. Data science performs extensive research of data trails to precisely forecast the steps that a business should take to close sales with their target audience and enhance customer retention. Both data mining and analytics have their importance in Business Intelligence. But, they differ on many counts. Let’s have a look at the key differences between them!

Data Analytics vs Data Mining

Team Size

Data mining is an easier process vis-a-vis Data Analytics and thus a single specialist having proficiency in the field can accomplish it. Data can be collected easily with the right software. Simply put, there is no requirement of a big team.

On the other hand, data analytics generally requires a team of specialists. The team has to assess the data and detect patterns to draw conclusions. They can utilize Machine Learning(ML) to process the data but it still requires a human touch. The Data Analytics experts should be aware of the right questions.

Hypothesis Testing

A hypothesis is essentially a beginning point that has to be investigated further. The hypothesis is based on a small amount of evidence, and it is then researched further.

Data mining differs from data analytics as it does not need any preconceived assumptions prior to operations on data. It simply collects data into usable formats. Data analysis, on the other hand, requires a hypothesis to be tested because it looks for answers to specific questions.

In short, data mining is all about detecting and recognizing patterns.

Data analytics, however, examines a hypothesis and derives meaningful insights. It aids in approving/disapproving the hypothesis and it can rely on the data mining discoveries during the process.

Data Quality

Data must be presented in a certain way for data analytics and it varies from data mining. Data mining gathers data & looks for patterns. On the other hand, data analytics examines a hypothesis and then the findings are translated into convenient information. This implies that the quality of the involved data differs from each other.

A data mining expert’s job is to utilize big data sets to derive the most valuable data. Since they often utilize huge and free data sets, the data quality is not always that great. The objective is to simply mine the most valuable data and subsequently report the findings in easy terms to businesses.

Data analytics, on the other hand, entails gathering data and ensuring that it is of good quality. In most cases, a member of the data analytics team will be working with high-quality raw data. Poor data quality might have a detrimental impact on the results.

Because this is such an important step in data analytics, the team must ensure that the data quality is satisfactory, to initiate the analysis.

Data Structure

In data mining, studies are mostly carried out on structured data. A specialist utilizes a select set of data analysis programs to investigate & mine data. The findings are recorded in spreadsheets & graphs. They are mostly intended for a visual explanation because of the data complexity. Algorithms to find specific data patterns are built on scientific & mathematical concepts. They gather precise data for further analysis.

When it comes to data analytics, it can be performed on unstructured/semi-structured/structured data. A Data Analytics team is generally tasked with recognizing valuable patterns in the data and utilizing them to strategize the next steps for a business.

The marketing department would love to see the visual representation of the consumer and industry data. If a business can comprehend the consumer behavior of its competitor(Big Data Descriptive Analytics), it can leverage it in many ways.

Forecasting

Forecasting is one of the tasks that a data mining expert has to do after interpreting the data. He discovers data patterns and perceives what they could lead to. Knowing how the market will respond to imminent products is important for businesses across multiple sectors.

A data mining expert does the following with the data:

• Clustering data
• Detecting deviations(anomalies)
• Finding correlations
• Classification of data based on new patterns in the data.

In comparison, data analytics focuses more on concluding the data. The conclusions drawn can sometimes be coupled with data mining forecasts as new techniques can be applied from the findings.

Roles & Responsibilities

Data mining experts generally work with 3 types of data:

1. Metadata,
2. Transactional, and
3. Non-operational.

This is in line with their responsibilities in the data analysis process.

When it comes to data analytics, the team deals less with algorithms and more with interpretation. For continuous data, they anticipate yields and analyze the underlying frequency distribution.

This is so that after they finish their tasks, they can report on important facts. Data analytics teams are usually hired by businesses to aid them in implementing critical strategic decisions using Big Data descriptive analytics/Big Data prescriptive analytics.

Area of Expertise

Data mining is a mixture of statistics, Machine Learning(ML), and databases. The data mining experts should have the following skills:

1. Fluency with operating systems like LINUX,
2. Adept in programming languages like Python/Javascript
3. Adept in data analysis tools (NoSQL & SAS)
4. Excellent public-speaking skills,
5. Machine Learning basics
6. Knowledge of industry trends

Data analytics demands a unique mix of skills- knowledge in computer science, machine learning, arithmetic, and statistics.

To be a data analytics expert, one must have:

1. Solid industry knowledge,
2. Proficiency in data analysis tools like NoSQL and SAS along with machine learning,
3. Mathematical skill set for numerical data processing
4. Great communication skills, and
5. Critical thinking ability.

Wrapping up:

As the world becomes more interested in data science, it’s common to have confusion over terminologies. With that in mind, we examined the differences between data science and data mining in further depth. Both of them have their fair share of benefits and use cases. Big Data mining and analysis can completely overhaul the potential of any business.

On the other hand, we have an easy way out to clear the air around Big Data processes with highly-celebrated Big Data Services. Put up a query that is bothering you or hire the best Big Data experts for your business by calling Ksolves India Limited today.

Email: sales@ksolves.com

Call : +91 8130704295

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