Introduction:
In today’s rapidly changing business landscape, businesses must contend with wide range of challenges including changing market or industry dynamics, technological advancements, shifting consumer behaviour and so on. These constraints have significantly reduced the margin of error for any business looking for a long-term growth and intends to create value for its stakeholders. Hence, to thrive in such a competitive business environment, along with sound business principles and models, businesses need to embrace the concept of data analysis.
So, what is data analytics?
Analytics is the process of finding, interpreting, and disseminating significant patterns in data sets. In short, analytics helps us to see relevant information and insights that we might not have otherwise noticed from raw data. Businesses use analytics to make better decisions that can help to enhance customer understanding, improve product/ services and in turn boost sales or revenue, cut expenses, to enhance business operations or to gain competitive advantage. This entails gathering valuable and usable information in a strategic manner and using it to improve decision-making processes.
But why to automate business analytics?
Consider this for an instance. You must ascertain sales growth by obtaining the region-specific sales value for the preceding five years. The data can be worked on manually or with the aid of computer programs. Both approaches will provide you with the necessary details, but computer programs will produce results more quickly and accurately.
Since past decade, due to ascent of social media, high internet penetration, widespread use of mobile devices, digitisation of business processes, the volume of data being captured by businesses have exponentially increased. Working with these kinds of massive data sets necessitates the use of computer systems and automated procedures to carry out analytical activities with the least amount of human involvement.
Analytics may be performed more quickly using automation. Businesses can benefit from automating their data analytics. Businesses can save time and money by automating analytics to receive insightful information more rapidly. Data analysts can focus on uncovering new insights to inform data-driven decision-making by automating tasks that do not require human creativity.
But is automating data analysis for all businesses?
Considering the many advantages of automating data analysis, complex data analytics setup and solutions are not necessary for every business decision. Simple tools for data analysis and visualization can frequently bring new insights and perspectives on profitability, KPIs, and business operations.
However, when a business expands and its data becomes more complex to manage using simple analytical tools and methods, it must think about automation technologies to process, aggregate, and visualize the data faster. Data analysts can utilize this automation to generate graphs, dashboards, and other visual elements that help them find new patterns or insights in the complicated data sets.
Organizations are shifting more and more toward data analytics, whether it is to support fact-based decision-making, to adopt data-driven business models, or to expand data-backed product offerings. Like every other field, technological innovations have caused multiple advancements in analytics. Big data and AI are the key trends shaping the industry. Edge computing, augmented analytics, and predictive analytics are a few of the innovations that are changing the way businesses use and analyse their data.
In a nutshell, prior to applying various approaches of data analytics and solutions, it is advisable to have a strong data strategy in place and a methodical approach to data analytics projects.
Even though there will always be something new to learn and it's easier to get sucked into the technological hype and getting overwhelmed in trying to keep up with the latest technologies and tools in the data and analytics space, remember that there are fundamentals of data and analytics that will always be useful. Safety, reliability, speed, and usefulness should be the key considerations for data analytics projects. By adhering to these fundamental guidelines, you will be well-positioned to effortlessly navigate any valuable trend and optimize your data-related endeavours.