Big Data and Business Analytics: Similarities & Differences 101
Organizations are in a never-ending quest to derive value out of their data. As part of this endeavor, they form large data teams with expertise in different areas of a data-driven organization. Most organizations accumulate data first and then strategize how best to use the data. This approach results in a large amount of data being scattered across the organization.
Table of Contents
Deriving value from such data requires two kinds of skill sets: Technical knowledge to quickly process large amounts of data and business knowledge to think from the customer’s perspective to define what creates value. Big Data and Business Analytics are two common terms that get thrown around while talking about these scattered data and how to derive insights.
In this article, you will gain information about Big Data and Business Analytics. You will also gain a holistic understanding of the similarities and differences between Big Data and Business Analytics.
Table of Contents
- What is Big Data?
- What is Business Analytics?
- Big Data and Business Analytics: Similarities
- Big Data and Business Analytics: Differences
- Conclusion
What is Big Data?
Big Data is an umbrella term that represents capturing, processing, and analyzing large amounts of data to improve business outcomes. Big Data was made famous by the advent of parallel processing systems that could process virtually any amount of data using commodity hardware. Big Data is generally summarized in terms of 4Vs:
- Volume
- Velocity
- Variety
- Veracity
The incoming data comes in high volume with a wide variety and velocity. The variety in the case of Big Data refers to the different kinds of data that are coming in. The data could be structured or unstructured, audio, images, log files, or even natural language sentences.
Veracity refers to the authenticity of the data. With such a large amount of data coming in, ensuring data quality is a big challenge. Hence, processing big data requires one to have checks and balances to ensure data governance and data quality.
Big Data generally includes the engineering side of handling the data and the business side of deriving insights from the data. The analysts recommend definitions of how to derive insights from the data. Data scientists then make recommendations and analyze data to come up with insights. The data engineering division defines the batch and real-time data pipelines to make these insights available to the decision-makers.
Of course, this delineation is not often maintained, and responsibilities can overlap at times. Data scientists and Business analysts often work on overlapping areas. Ideas for insights can also come from the bright minds in the engineering division.
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What is Business Analytics?
Business analytics enables businesses to make better decisions by thinking from the customers’ perspective. It includes everything from defining features set for applications to metrics that define the organization’s performance or even customer satisfaction. Business analytics also focuses on measuring and reporting data to enable better decisions.
Business analytics interacts with the Big Data paradigm and works on defining the metrics and projections that finally translate to customer value. Business analytics exists outside the big data paradigm since it can work based on smaller data. All It needs is a relational database or an excel file that one can use to do a meaningful analysis of an event.
Business analytics relies on knowledge about the domain and the customers. It deals on a much larger scale at a very close point to customers and the business. Big Data processing can be a part of business analytics if the data that forms the foundation is present in the extensive data ecosystem – A massively parallel data warehouse engine or a flat file in the data lake.
Visualization and reporting are critical aspects of business analytics. The outcome of a business analytics task is often a dashboard, a report, or a set of metrics that tells the story of what is happening in a company.
Big Data and Business Analytics: Similarities
The similarities between Big Data and Business Analytics are as follows:
- Big Data and Business Analytics: Data at its core
- Big Data and Business Analytics: Deriving Insights
- Big Data and Business Analytics: SQL Knowledge
1) Big Data and Business Analytics: Data at its core
In both Big Data and Business Analytics, data is the primary foundation. While Big Data deals exclusively with vast volumes of high variety data, business analytics do not have this hard constraint. It looks at more from the perspective of business value, and anything that helps it derive value is welcome.
Source data for business analytics can be big data or traditional data stored in relational databases and excels. In the case of Big Data, business analysts may use a querying engine like Presto or a business intelligence tool like Tableau that can integrate with big data sources. Business analytics also has the responsibility of finding ways to acquire data if they fall short. This could be initiating a customer satisfaction survey or defining performance tests.
2) Big Data and Business Analytics: Deriving Insights
The Big Data paradigm and traditional business analytics focus on deriving insights from data. For a Big Data platform, deriving insights can be the last step after a series of complicated data pipelines. For business analytics, this can either be straightforward, like defining reports on top of excel or a database; Or it could be specifying the requirements for a big data team to build the data pipelines to get to this.
3) Big Data and Business Analytics: SQL Knowledge
A critical skill that connects both Big Data and business analytics is the mastery of SQL. With most big data platforms now offering querying engines and processing capability using SQL, even data engineers spend most of their days writing SQL queries. For a business analyst, knowledge of SQL is a must. He will be using SQL to explore data in traditional databases. SQL can even with used with Excel files to build reports. It is also vital since business analytics interact with big data platforms, and SQL is the preferred way there.
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Big Data and Business Analytics: Differences
The differences between Big Data and Business Analytics are as follows:
- Big Data and Business Analytics: Focus on Handling Data vs focus on Business
- Big Data and Business Analytics: Focus on Automation vs Creativity
- Big Data and Business Analytics: The difference in Data Sources
- Big Data and Business Analytics: Key Skills
1) Focus on Handling Data vs focus on Business
Big Data is mainly focused on the technologies and processes involved in handling huge amounts of data. People working on Big Data mostly excel on the engineering side of things and focus on how to achieve the specifications defined by businesses most efficiently.
Whereas, business analysts focus on the operational and financial metrics that directly correlate with customer success or business profitability. They explore data to define such metrics, form hypotheses based on them, and validate them.
2) Big Data and Business Analytics: Focus on Automation vs Creativity
Despite the fact that Big Data requires a significant amount of programming and creativity, the ideal outcome is typically a set of jobs that run automatically to generate insights and metrics. Big Data professionals strive to arrive at this ideal scenario as quickly and efficiently as possible.
Business Analytics requires one to think from the perspective of the customer and form ideas based on how to evolve the business in the best possible way. It deals with finding ways to improve the operational and financial performance of the organization. Exploring data, digging deep, and finding creative ways to represent data in a form that directly translates to business outcomes are keys in business analytics.
3) Big Data and Business Analytics: The difference in Data Sources
Big Data generally deals with massively parallel processing database engines. The focus is on using architectures like Data Lakes, Warehouses, and Data Lakehouses to streamline the data pipelines, data governance, and data quality. The data source can be structured or unstructured and can come from virtually any source.
A Busines Analyst is mainly tasked with working on structured data. In rare cases, there may be unstructured data, but the data does not reach an analyst without some aggregation already performed.
4) Big Data and Business Analytics: Key Skills
A person working in Big Data is expected to have core engineering skills in implementing and maintaining Data Lakes, Warehouses, etc. They will have skills in processing frameworks like Apache Spark and querying engines like Presto, Athena, etc. He is also expected to have good knowledge of data frameworks provided by Hyperscalers like AWS, GCP, and Azure.
In business analytics, the core skill is the knowledge of the business and the domain itself. It also requires deep knowledge of statistics and predictive modeling since data exploration and validating hypotheses are important aspects of the job. Knowledge of SQL is handy for exploring data located in different sources.
Conclusion
Business Analytics and Big Data are very different streams often thrown together when talking about analytics. The reliance on data and skillset in SQL is mainly the only similarity between these two streams. Big Data focuses more on data pipelines’ operational aspects and automates them to derive insights. Business analytics takes a holistic view and defines the insights that can be valuable to the business. Since both rely on data a lot, there are places where there is overlap, and both teams can recommend ideas to each other.
In this article, you have learned about Big Data and Business Analytics. This article also provided information on the similarities and differences between Big Data and Business Analytics.
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