Data science is an expansive field that incorporates multiple sub-domains that all work with data. Various scientific and statistical methods are used for processing, classifying, and modelling data with the help of data science. Data-driven companies work with all kinds of data, including unstructured and semi-structured, also requiring many other essential processes, such as data cleaning and data mining.
Similarly, business analytics also deals with data but rather than running general data science procedures, the domain deals with using the data to optimize business performance and various processes. Business analytics helps companies analyze raw information and gain crucial insights from the data in order to help them make good business decisions.
Organizations of all sizes have started incorporating business analytics into their processes. This helps them improve their process performance and increase their revenue. How exactly does business analytics help companies save money?
By utilizing and deriving insights from data, companies can cut costs and use manpower in a more efficient manner. Analytics even helps achieve more sales and better marketing results. All of this eventually leads to a significant increase in revenue.
Profit is very important for companies to survive. And, with the help of data and analytics, companies can keep striving for more gains. Data science is crucial for these kinds of companies as well, if not directly, then indirectly.
Let us take the example where a production factory is incorporating an analytics system into their process for all their manufacturing units. Now, in order to effectively collect data from these machines and then proceed to process it, an infrastructure must be put in place for these data transactions. Companies in modern times heavily rely on automation and AI as well, thus requiring complex data architecture.
If the factory does not decide to have its own data scientist on board for building databases and making the data accessible for the process, the management can simply take the help of a third-party information technology or data science firm. These firms employ data scientists who will design and implement the infrastructure in a ready-made format that will be put into place in the factory.
The management can then decide to hire a business analyst for connecting the data to a business intelligence system, such as Microsoft Power BI and then carry out analytics for the manufacturing unit.
Fundamentals of Data Science
Data science involves various tools, technologies, and methods that help companies use data to its full extent. Whether it is building an application or preparing data for analytics, data science is crucial for almost any IT-centric process.
Today’s world is data-driven and we rely on data for everything we do. Our Google searches are powered by data science and machine learning (ML) while the advertisements we see are displayed to us after carefully identifying that we are the right target audience. Data can be used for a multitude of things and this is why data science is so popular nowadays.
Foundationally, data science is all about data structures with different attributes and algorithms. The range of data structures and their complexities have changed with time and in today’s world, we are able to collect and store complex data in an automated fashion with ease. Data science also features a variety of computation methods and techniques that allow you to model data according to your needs or completely transform it.
Converting or transforming data is one of the essential parts of data science that many organizations cannot do without. Let us take the example of a legacy system that does not allow us to extract data from it. In this case, the manual data would need to be recorded by another system in a different format, which would further relay it for the desired business requirement.
This is especially useful when the generated or sourced data is completely unstructured and you need to structure it in order to use it for any business purpose. Similarly, data cleaning is important for removing the noise from valuable information. Noise in the data can lead to inaccurate analysis or can compromise development processes.
Fundamentals of Business Analytics
Business analytics is similar to data analytics but is only focused on business requirements and the optimization of any business process. This kind of analytics helps companies understand how they are performing in the market and how they can improve their performance. Business analytics contributes heavily to business intelligence, allowing stakeholders, leaders, and managers to make data-driven business decisions.
With the help of business analytics, you can also predict various business, process, and market outcomes. This is why, companies use analytical procedures on data to forecast anomalies, events, and even results.
For instance, let us assume that a company wishes to initiate a set of actions based on a business decision. In order to understand and simulate the outcome of these actions, business analysts can use historic or real-time data and blend the new variables or changes. These changes would then allow us to forecast the kind of results these changes would lead to.
In today’s world, business analysts are highly in demand as companies know that they can significantly increase their revenue with the help of skilled professionals. By improving the efficiency of a manufacturing plant, companies can save on energy costs as well as manpower. Like data science, business analytics contributes greatly to helping companies increase their revenue.
Marketing analytics, market analytics, customer behaviour analytics, and other forms of business-oriented analytics are also associated domains that greatly help companies optimize their sales and market performance. Business intelligence that is derived from analytics helps companies make game-changing decisions, such as mergers, acquisitions, large investments, and even liquidation of assets.
How Data Science and Business Analytics Help Businesses?
First, let us understand how data science can help businesses.
- Data science is essential for application and software development processes.
- Data can be used to power artificial intelligence (AI) and machine learning implementations.
- Storing company data and protecting it are important for businesses.
- Data can be used for further analysis and analytical processes, such as business or financial analytics.
Now, let us understand how business analytics can help businesses.
- Optimizing business processes, such as manufacturing and transportation
- Increasing revenue and cutting costs
- Helping companies increase their goodwill in the market
- Increasing the effectiveness of marketing, thus leading to more sales or brand value
Opportunities Provided by Data Science and Business Analytics
By getting a certification in data science or business analytics, you are automatically qualified for extremely lucrative job roles in these valuable domains.
For instance, if you are qualified in data science, then you can become:
- Data scientist
- Data analyst
- Data Engineer
- Data Architect
- Cloud computing specialist
- Data visualizer
- ML or AI engineer
And, if you are qualified in business analytics, then you can take up roles like:
- Business analyst
- Market analyst
- Performance analyst
- Operations Manager
- Unit manager
- Marketing analyst
- Financial analyst
Business analysts and data scientists are extremely valued by companies of all sizes and these organizations are willing to pay great remuneration for skilled personnel. MNCs and conglomerates have all incorporated data science into their processes while all kinds of businesses (both large and small) use analytics too. Thus, you would never have a hard time finding a good job after mastering either of the two domains.
5 Reasons Why Data Science and Business Analytics are Used Across Sectors
We understood why data science and business analytics are important for businesses, but why are they essential across all kinds of sectors? In today’s world, we can notice that the two domains are essential for any sector, whether technical or non-technical. Let us understand why.
1. Automation: Data science helps in preparing AI systems and training machines to become autonomous. Without solid training data, AI would never be as effective as it is. AI is now being used across all kinds of sectors, whether it is for customer service or manufacturing. Thus, this is one-way data science is essential for all sectors.
2. Profitability: Business analytics and data help improve the revenues of companies. This is done by analyzing data and understanding where a process or a unit can cut costs or make more profit. Also, data can help companies identify patterns or trends that lead to better sales or marketing strategies.
3. Security, compliance, and surveillance: Security and monitoring are essential for safeguarding any company or office. Data science is also essential for setting up advanced security systems that are used across all kinds of companies, offices, and sites (like factories or farms). Governance-centric data also help companies be compliant and adhere to regulatory frameworks.
4. Forecasting, reporting, and visualizing: Knowing the future can give any company an edge over the competition. Forecasting is essential for companies from any domain in order to make successful business decisions. Reporting and visualizing are also essential for any kind of company, thus always fuelling the need for skilled analysts who can use data for building reports and visualizations.
5. Storage and monitoring: While servers, databases, distributed file systems, and the cloud are essential for companies to store and use their data, analytics helps in monitoring the performance of their company processes. This is the same across companies from all sectors and domains as these are primary requirements to sustain themselves.
Data science and business analytics are here to stay and these domains will only grow more valuable with time. Learning essential data science and business analytics skills can lead to extremely rewarding careers in them. There are many data science courses for working professionals that help them learn while they continue to work in their current jobs. A data science or business analytics certification goes a long way in helping you transition and pivots your career to a data-oriented one.