Advancements in Artificial Intelligence (AI), machine learning, and automation are changing the business landscape at meteoric speeds. In 2021 alone, businesses spent a staggering USD 215 billion on big data and business analytics, as noted by IDC analysts. However, investing in data-driven solutions is not enough if the data is not efficiently scaled to derive value. This is where business analytics comes into the picture.
Often used in association with big data analytics and business intelligence (BI), business analytics comprises a set of automated data analysis practices and tools that provide clarity on the operations of a business. It helps professionals make informed decisions that help mitigate potential risks. The ultimate result is a workforce that, on recognizing its benefits, values, and practices, encourages the use of data, or a definitive data culture.
The rewards of business analytics are, evidently, enormous. So let’s dive deeper into the workings of business analytics and understand what makes it so indispensable to organizations in the present day.
How does business analytics work?
To decipher business analytics, we need to understand its evolution which is intricately connected to AI. In the 1990s, computer programming helped organizations in the collection, conversion, and analysis of data to generate usable information. Fast forward to the next decade, when programming languages like Python and R began to offer deeper insights into workflows and determine the underlying causes of the generated outcomes. However, something was missing.
Despite considerable developments in the field of programming, there was no available tool that provided a more holistic view – a way of understanding past performance, predicting matters of the future, and planning for the same. It is only in the last decade that cloud management software and AI have made it possible to visualize real-time trends, spotlight business problems, and make informed choices about the future. The growth of business analytics has been so exponential that it is now possible to put its relevance down to a single sentence — if you seek insights from your data, you need business analytics.
The Lifecycle of Business Analytics
In the world of business, analytics amount to a living, breathing organism. And like all other living beings, business analytics also goes through a lifecycle. Put simply, there is a definitive order to the practices that make up business analytics. It is what helps organizations deploy the same to derive value from data.
1: Planning Analytics
It all starts with a plan, be it a financial plan, a corporate plan, or a departmental plan. Planning analytics uses historical data to substantiate present-day plans and prepare for the future. Collaboration and quick responses are key to modern planning analytics and greatly enhance business performance.
2: Descriptive Analytics
Data visualization is integral to descriptive analytics and prevents decision-makers from relying on false data insights. Today, anybody, from individual contributors to executives, can employ self-service business intelligence tools to get an overview of a business’s performance, track Key Performance Indicators (KPIs), and share trends. In case of large volumes of data, automated tools help clean and combine multiple data sets, and solve Structured Query Language (SQL)-related queries.
3: Diagnostic Analytics
Diagnostic analytics are immensely useful in determining the true drivers of a business and unearthing hidden relationships within the data. Such exploratory analysis techniques, coupled with AI-infused solutions, enable the use of data science algorithms to quickly find objective insights and streamline workflows.
4: Predictive Analytics
While reading about business analytics, it is easy to identify one of its core strengths — it allows organizations to optimize for the future. To make this possible, business analytics use AI and machine learning to recognize existing patterns. Other advanced tools include Python, data mining, and statistical analysis, all of which allow decision-makers to predict future outcomes.
5: Prescriptive Analytics
Prescriptive analytics help in the efficient management and allocation of resources. Harnessing the strength of optimization engines and statistics, allows business analytics professionals to filter out alternatives and select the most appropriate method of operation.
Benefits of Business Analytics
Put simply, business analytics help companies make informed business decisions. With the skyrocketing growth in data volumes, it is becoming increasingly difficult to process it, identify trends and recognize the real drivers of a business. According to IBM, the lifecycle of modern business analytics is designed to make the processing time 80% faster.
In addition to improving speed, business analytics also allow businesses to be more cost-efficient. Automating the processes of planning and analytics saves labor costs, as well as time, and helps professionals take quicker action and streamline budgets.
The ability to forecast future trends and optimize outcomes accordingly is easily the greatest advantage of business analytics. It helps in the more efficient implementation of plans and allows stakeholders to test the potential results of their decisions. If required, businesses can then alter existing plans and respond to predicted situations with ease.
The influence of analytics on business decisions is undeniable. Along with the aforementioned benefits, business analytics has far-reaching repercussions on the workforce as a whole. A data-driven work culture that values business analytics – fosters collaboration, trust, and a knack for exploration and curiosity. It empowers users to deepen their engagement with the business and imbues each stakeholder with a sense of accountability.
Core business analytics skills
Business analytics professionals are an inquisitive lot. They like identifying and solving problems and possess exemplary communication skills. However, in a tech-driven world, certain hard skills go a long way in the pursuit of success, no matter what the role. Let’s look at five such skills that form the core of business analytics.
1: Data Literacy
Data literacy refers to familiarity with data language, its types, sources, and the analytical tools and techniques involved. Data literacy is fundamental to business analytics and plays a significant role in tackling everyday business challenges. It’s especially useful while working in a team as it helps one communicate in the language of data and work towards a common objective.
2: Data Collection
The first step in data analysis, data collection is the process of collecting data sets from which inferences can be drawn to gather insights. In cases where no dataset exists, professionals generate data samples with the help of surveys, research, and focus groups, among others. An understanding of data collection is important to avoid dealing with biased or faulty data.
3: Statistical Analysis
The ability to analyze and interpret data is the backbone of business analytics and calls for strong statistical analysis skills. There are several methods that aid this process, including hypothesis testing, linear regression analysis for two variables, and multiple regression analysis – used to determine the relationship among three or more variables. Using them, professionals can make well-informed decisions about pursuing fruitful opportunities and mitigating potential risks.
4: Data Visualization
An organization is run by professionals from various backgrounds, some of whom may not be as data literate as others. Therefore, to communicate the findings from a dataset, they must be presented in a comprehensible format that conveys only the relevant information to stakeholders. Data visualization also allows professionals to distill the key takeaways from the findings, which is especially useful while communicating with the higher management.
Business Analytics vs Data Science
Being so thoroughly intertwined, it is a common mistake to use business analytics and data science interchangeably. However, there are several distinguishing factors that set the two apart. Business analytics refers to the statistical study of business data to gain insights and deals primarily with structured data. Data science involves both structured and unstructured data and studies the same with the help of algorithms, statistics, and technology.
Another important determiner is the use of coding. Data science relies heavily on coding, combining conventional analytical techniques with a sophisticated knowledge of computer science. Business analytics is more statistics oriented and does not involve much coding.
A few last words on business analytics
Digital transformation has revolutionized the space of business. From retail to healthcare, the role of business analytics is hard to ignore. In such unprecedented times of political crises, natural calamities, and human disasters, analytics help save businesses and, in turn, human lives. The focus of the field on risk management is a crucial driver of its growth. It makes information accessible and usable in real-time. In its nascent stages, business analytics held the promise of changing the world, a commitment it has lived up to.
If you’re interested to learn more about business analytics or want to upskill yourself in this field, take a look at some of our online business analytics courses.
By Deyasini Chatterjee
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