In today’s data-driven world where information is sacrosanct, decisions are increasingly being made through the use of data analytics. While both business analysts and data analysts are involved in data analysis to help make better-informed business decisions, there are a few fundamental differences between the two.
This article takes a look at the difference between a business analyst and a data analyst and underscores the growing importance of data analytics in an organisation.
According to Wikipedia, a business analyst (BA) is “someone who analyses an organisation or business domain (real or hypothetical) and documents its business or processes or systems, assessing the business model or its integration with technology.” On the other hand, a Data Analyst is someone who makes use of specialised data analysis tools and methodologies to enable informed decision making.
There is also the view that a data analyst works at an underlying intrinsic plane of an organisation, at the nuts and bolts level, while the business analyst works at a comparatively higher level of the business’ ladder.
The Business Analyst Profile
A Business Analyst helps in streamlining and improving the business metrics of an organisation. These metrics may include revenue generation, marketing operations, forecasting, budgeting, regulatory and compliance issues, profitability, hiring, resource allocation, and the like.
The end goal of the business analyst is to help drive the business into intelligent decision making. A B.A. should be able to work seamlessly with executive management, have advanced excel skills, have a cognitive and analytical bent along with strong interpersonal and communication skills. Domain expertise is strongly desired for a functional Business Analyst. BA typically come from the following backgrounds:
- Masters in Business Administration
- Bachelors in Business Administration
- General Management
- Information Technology
- Business Studies
In simple terms, the business analyst gathers information, creates documentation, does analysis, and creates reports. These reports are consumed by senior management to help them make informed and intelligent decisions. A Business Analyst fundamentally works across the types of business analysis:
- Tactical Analysis: Applying the right business techniques in a project at the appropriate time.
- Operational Analysis: Delivering business improvements through analysis of the holistic supply chain.
- Strategic Analysis: Devising improved strategies, goals and objectives through domain and business knowledge.
What Does a Data Analyst Do?
Data capturing exploded after the integration of multiple touch points when data collected from sensors, call centre interactions, production environment, web and systems and platforms became standardised and interoperable. Coupled with the steep decline in storage hardware and the advent of cloud computing, the amount of data available to an organisation increased exponentially.
Even small enterprises could now afford the technology of storing billions and billions of bytes of data. Suddenly businesses had this vast trove of data but did not know how to convert this raw data into information and knowledge.
Enter the Data Analyst. Throughout the 2000s, many organisations and academic institutions began to recognise data science as a mainstream discipline. Data was now a ‘commodity’ which had to be harvested for business intelligence. Companies began to understand that the wealth of information contained in this data could help them to gain a competitive advantage. Traditional companies started to use predictive analytics and statistical and quantitative analysis to beat their competition.
The primary job of the data analyst is to access and understand this data and finally extract intelligence from a bunch of numbers. A data analyst brings structure to large swathes of disjointed data. A lot of this structured data is then passed on to the business analyst to help them analyse business requirements and help make decisions that are clear and compelling.
The following table breaks down the key differences between the Business Analyst and Data Analyst:
|Parameter||Business Analyst||Data Analyst|
|Contribution||Collaborates across various departments/functions within an organization. Rarely an individual contributor, Requires direct interaction with Customers, Developers, Sales, Strategy, Management team.||Typically an individual contributor, often working initially with Subject Matter Experts (SME’s), not much direct contact with external or internal stakeholders.|
|Must Haves Skills||Domain Knowledge, Business Knowledge, Communication Skills, Negotiation, Basic Data Science Knowledge, Management, Analytical Thinking, Reporting, Data Visualization||Analytics, Data Modeling, Database Administration, Statistics, Technical Data Skills, Systems Programming, Quantitative Analysis, Numeracy Skills, Scripting Languages|
|Typical Tools Used||Modeling, MS Office, SmartDraw, Trello, SWOT Analysis, Rational Requisite Pro, Balsamiq, Version One, Project Management software||R Programming, Python, Tableau, SAS, Excel, RapidMiner, Apache Spark, Qlikview, KNIME, Splunk, All types of Database Management Systems (DBMS), Hadoop|
An important point to note is that many organizations use the term Business Analyst and Data Analyst interchangeably, since both the roles involve analyzing data. A Data Analyst could be a broader term for a Business Analyst because a data analyst would typically cover systems analysis. However, in a large organization, there could be no differentiation between the two as the majority of the work would be similar. In some cases a data analyst and a business analyst could coexist, covering the same type of analytical work.
An Astronomical Growth in Data
The graphic below from IDC clearly shows the rise in data from an estimated 33 Zetta Bytes (ZB) in 2018 to a staggering 175 ZB in 2025. While we are happy with an extra 64GB of data on our Smartphone, one can only imagine what the addition of another billion Internet of Things (IoT) connected devices and driverless cars will bring to the amount of data already being captured.
Treading a Path in Data Analytics
One can think of a data analyst as a mix of a data hacker, analyst, communicator, and trusted recommendation expert. This combination is potent and hard to find. There is already a shortage of skilled data analysts worldwide. Progressing in the field of data analytics could lead you to become a Data Analyst, Senior Data Analyst, Big Data Engineer, Big Data Architect, Hadoop Architect, and Data Scientist.
A Data Analyst is more of an umbrella term. It could also include titles like Data Warehouse Analyst, Business Systems Analyst, and Business Intelligence Analyst.
The initial requirements are as follows:
- A graduate degree in mathematics, statistics, finance, information management, economics or computer science.
- For an advanced level data analyst job, the following tools are very essential.
- R Programming
- Database Administration
- Computer Programming and Scripting
- Knowledge of Hadoop / Big Data
- Data Visualization and Reporting Techniques
- Data Cleansing
- Data Mining
Additional Training and Certifications:
There are many courses which provide training and certifications for Data Analysts
Data protection and privacy protection are two areas that come under increasing scrutiny. The recently introduced European General Data Protection Regulations (GDPR), the most significant change in privacy laws, opens up a whole new avenue in the area of data rights management which can be an additional area of expertise for Data Analysts.
The Future of Data Analyst
If you are thinking of transitioning to the role of a Data Analyst, be assured, there is a tremendous scope of growth. Creativity and curiosity are two key attributes required to make a foray into data analysis. Having a sound mathematical or statistical foundation is also essential. A data analyst should be driven by finding the explanation of key events in an organisation by analysing raw data and providing the same either at a departmental level like marketing, sales, HR & operations or at an organisational level.
Data Analysts are set to revolutionise the global economy. Creating algorithms to put the data to work for you is already opening up new avenues across all industries. Real world examples include Governments, the Healthcare industry, Education, Retail, Supply Chain and Logistics and Banking and Finance (BFSI).
The BFSI industry alone is estimated to have saved billions of dollars in fraud detection and cybersecurity through the use of Big Data. Governments are using data analytics to find tax defaulters and for planning in Smart City initiatives. Facebook, Google, Netflix and Amazon are the early adopters and big-time users of data analytics. Data analysis is interwoven in the very fabric of these companies and is responsible for the enormous success and the meteoric rise in profits.
Spotify, the world’s most extensive music streaming services become so popular because it used Big Data and analytics to provide informed music suggestions to every one of their users individually. All this is thanks to data analytics as social media engulfs society, data collection for every interaction a person has, increases. Each swipe on a smart screen is collected and stored by various companies for further analysis. Thus there is no dearth of data to analyze, but a vacuum in terms of skilled data analysts, who could decipher this data.
Finally, a word about AI and Machine Learning-
The job of a data analyst is considered to be the cornerstone of a career in AI and ML, the two hottest jobs in the market today. As of December 2018, five of the fifteen fastest growing jobs in the US involved Machine Learning and Big Data, according to LinkedIn’s 2018 US Emerging Jobs Report. Thus it is very apparent that the role of the data analyst is critical in today’s business environment. They will be responsible for the underlying success of an organisation in this data-driven world.
To sum it all up, in today’s data-driven environment, a data analysts position is far more vital than a business analyst’s role. The data analyst can unlock many more nuggets of information, which can be pivotal to any organizations. The data analyst has the tools to analyze and predict future business with a degree of accuracy that a business analyst cannot. So for someone looking at the option of becoming a data analyst versus a business analyst, the sky’s the limit for a data analyst.