Introduction
Data Industry is on boom today and it seems no shortage of intelligent opinions about the job responsibilities and roles accelerating the data industry. Most of the people are usually confused between the role of a Data Scientist and the Data Analyst. Even if both of them deal with Data only still there are plenty of significant differences that make them suitable for different job positions.
Here, we will discuss how to differentiate Data Scientist from Data Analyst, and their job roles too. Before we switch on the actual topic, let us have a quick look at the differences. Later on, we will try to find out the reasons for the diminishing gap between data scientists and business analysts today. We will try to analyse if there is actually any gap between the two roles and look further into it.
Difference Between a Data Scientist and Business Analyst
A company relies on its business analysts to gain business insights by interpreting and analyzing data and predicting trends-related aspects which help in making critical business decisions. Business analysts also focus on end-to-end automation to eliminate manual intervention and optimizing business process flows which can increase the productivity and turnaround time for an efficient and successful end result. They also recommend systems changes needed to optimize an organization’s overall execution.
Data scientists, on the other hand, specialize and purely rely on data which is further broken down to simpler facts and figures by using tools such as statistical calculations, big data technology, and subject matter expertise. They use data comparison algorithms and methodologies to identify and determine potential competitors or resolve day-to-day business issues.
Business analysts often work on preconceived notions or judgments related to the factors that help drive the businesses. Data scientists, whereas; have had an edge over business analysts, as they leverage data related algorithms which provide accuracy and also use mathematical, statistical, and fact-based predictions.
As organizations are proactively defining new initiatives and campaigns to evaluate the existing strategy on how big data can help to transform their businesses, the role of business analyst is slowly but certainly widening into a major role.
Upgradation in Duties of Business Analysts and Data Scientists
In recent times, there have been a lot of advancements in the data science industry. With these advancements, different businesses are in better shape to extract much more value out of their data. With increased expectation, there is a shift in the roles of both data scientists and business analysts now. The data scientists have moved from statistical focus phase to more of a research phase. But the business analysts are now filling in the gap left by data scientists and are taking their roles up.
We can see it as an upgrade in both the job roles. Business analysts now hold the business angle firm but are also handling the statistical and technical part of the things too. Business analysts are now more into predictive analytics. They have reached a stage now where they can use off-the-shelf algorithms for predictions in their business domains. BA’s are not limited to just reporting and business but now are more into the prescriptive analytics too. They are handling the role of model building, data warehousing and statistical analysing.
Keep a note here that Business analysts are in no way replacing Data scientists. Data scientists are now researching new methods and algorithms which can be used by Business analysts combined with their business acumen in specific business domains.
Recent Advancements in Data Analytics
Data analytics is a field which witnesses a continuous revolution. Since data is becoming increasingly valuable with each passing time, it has been now treated with great care and concern. To cope up with the constant changes in the industries and societies as a whole, new tools, techniques, theories and trends and always introduced in the data analytics sector. In this article, we will go through some of the latest data analytics opportunities which have come up in the industry.
1. Self-service BI
With self-service BI tools, such as Tableau, Qlik Sense, Power BI, and Domo, managers can obtain current business information in graphical form on demand. While a certain amount of setup by IT may be needed at the outset and when adding a data source, most of the work in cleaning data and creating analyses can be done by business analysts, and the analyses can update automatically from the latest data any time they are opened.
Managers can then interact with the analyses graphically to identify issues that need to be addressed. In a BI-generated dashboard or “story” about sales numbers, that might mean drilling down to find underperforming stores, salespeople, and products, or discovering trends in year-over-year same-store comparisons. These discoveries might in turn guide decisions about future stocking levels, product sales and promotions, and even the building of additional stores in under-served areas.
2. Artificial Intelligence and Machine Learning
Artificial intelligence is one such data analytics opportunity which is finding widespread adoption in all businesses and decision-making applications. As per Gartner 2018, as much as 41 per cent of organizations have already adopted AI into some aspect of their functioning already while the rest 59 per cent are striving hard to do the same. There is considerable research going on at present to incorporate artificial intelligence into the field of data science too. With data becoming larger and more complex with each passing minute, management of such data is getting out of manual capacities very soon. Scholars have therefore now turned to the use of AI for storing, handling, manipulating and managing larger chunks of data in a safe environment.
3. R language
Data scientists have a number of option to analyze data using statistical methods. One of the most convenient and powerful methods is to use the free R programming language. R is one of the best ways to create reproducible, high-quality analysis since unlike a spreadsheet, R scripts can be audited and re-run easily. The R language and its package repositories provide a wide range of statistical techniques, data manipulation and plotting, to the point that if a technique exists, it is probably implemented in an R package. R is almost as strong in its support for machine learning, although it may not be the first choice for deep neural networks, which require higher-performance computing than R currently delivers.
R is available as free open source and is embedded into dozens of commercial products, including Microsoft Azure Machine Learning Studio and SQL Server 2016.
4. Big Data
the applications of the Big Data world. Well, most of us are now more than familiar with terms like Hadoop, Spark, NO-SQL, Hive, Cloud etc. We know there are at least 20 NO-SQL databases and a number of other Big Data solutions emerging every month. But which of these technologies see prospects going forward? Which technologies are going to fetch you big benefits?
Why the Role Update?
1. Advancement in technology
There have been a lot of technological advancements in data science. Machine learning, deep learning, automatic data processing are just to name few. With all these new technologies, organisations are expecting more out of their business analysts. Organisations are looking to leverage all these technologies into their decision-making process. To fulfil this, business analysts need to upgrade their role and take the role of data scientists too. Also, data scientists are more towards researching new methods and algorithms. They are the ones now bringing innovation in data science one after another.
2. Identification of more areas of application
Organisations are now able to explore more areas where they can leverage the power of data science. With more applications, organisations are aiming to automate their decision-making process. Business analysts need to step up for more diversified applications. Hence, they have to expand their skillset and takes upgraded roles. Decision scientists are more towards finding newer methods which can help the BA’s in solving complex business problems.
3. Increase in complexity of the business problem
Applications of data science in business are getting both complicated and complex day by day. With an increase in complexity. business analysts have now more prominent and complex roles. This can be one reason where the new BA’s may need to expand their skillset. This is due to the fact that organisations are expecting more out.
4. Growth of data
There has been a tremendous increase in data generation, practices like BIG data are coming as a prominent player in the picture. Business analysts today may need to be handy with Big data technologies rather than just having a business mindset towards the problem.
5. Lack of qualified talent
Today, there is also lack of qualified professionals in data science. This results in one individual taking multiple roles like BA, data engineer, data scientist etc. There are no clear boundaries between these roles in most of the organisations today. So a business analyst today, should also have knowledge of maths and technology. This is one reason too about business analysts acting as data scientists in many organisations.
The Tools of the Trade
The world of a business analyst is business-model centric. Either they are reporting, discussing, or modifying the business model. Not only must they be proficient with Microsoft Office, but they also must be excellent researchers and problem-solvers. Elite communication skills are also a must, as business analysts interact with every facet of the business. They must also be “team players” and able to interact and work with all departments within a company.
Data scientist’s job descriptions are much different than business analysts. They are mathematicians and understand the programming language, as opposed to reporting writers and company communicators. They, therefore, have a different set of tools they use. Utilizing programming languages, understanding the principles of machine learning, and being able to generate and apply mathematical models are critical skills for a data scientist.
The commonality between business analysts and data scientists is that both of them require generating and communicating figure-rich reports. The software used to generate such reports may be the same between the two different positions, but the content of the reports will be substantially different.
Which is Right for You?
If deciding between a future career between a business analyst and a data scientist, envisioning the type of position you want should steer you in the right direction. Do you like interacting with people? Do you like summarizing information to make reports? If so, you are more likely to be happy with a business analyst position than a data scientist. This is because data scientists work more independently. Data scientists are also more technical in nature. So if you have a more technical background, a career as a data scientist might before you.
Summary
In any case, organisations are now on the lookout for new age business analysts. They need to be a combo of the intelligence of knowing the right analytic tools, big data technology, and machine learning. Companies should rather not simply rely on business analysts to predict the future of a business. So if you are a business analyst then you have a lot to learn to stay relevant. But the good news is, there are various data science programs which can help you retool to stay competitive.
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