Top Trends for Business Analytics for the Rest of 2021
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Data powers business. Organizations rely on it to remain successful and competitive, but they have traditionally performed analysis on huge volumes of historical data to make critical decisions. The impact of the Covid-19 pandemic is now forcing organizations to look at business analytics in a new light. It has shown that models that rely on historical data have become obsolete. The data that informs business decisions needs to be captured, analyzed, and understood in real time to allow organizations to react to what is happening, rather than making retrospective decisions based on things that happened in the past.
As more organizations begin to adopt a new approach to business analytics as they seek actionable real time insights, it will be important to understand the new trends that are emerging in the business analytics space. It’s certainly an interesting moment in time for business analytics as widespread adoption and development means the space is quickly evolving. These are the top trends we can expect to see rising to prominence in the second half of 2021:
1. DataOps
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DataOps (data operations) is an emerging methodology that is helping organizations fast-track their data analytics operations. It answers the increasing demand from data professionals to extract crucial insights from raw data and has been used by the likes of Facebook and Netflix to get the leading edge over their competitors. The DataOps methodology unites data professionals with DevOps teams which are favored for their agile working processes. This combination enhances and automates data orchestration through a collaborative and cross-functional process.
DataOps employs the continuous integration/continuous delivery (CI/CD) method. This method uses automation to accelerate lengthy processes which improves productivity and delivers results faster at every stage of a data analytics project. It is important to remove traditional silos and allow every person in the DataOps team to be able to access all of the relevant business data. Organizations that employ the DataOps methodology typically use cloud-based tools and systems which enables a scalable approach and provides adequate computing power to ensure data can always be processed quickly.
2. Decision Intelligence
While many organizations are relying on automation to help make sense of their data with increased speed and accuracy, many businesses are left asking “so what?” once they have access to the predictions from the data. Decision Intelligence (DI) is an emerging discipline that helps them to understand what they should do about the issues raised from the data. According to Gartner, over a third of analysts in large organizations will be practicing DI by 2023.
DI is thought of as the missing link in many data science projects, using social science and managerial science to enhance those projects, resulting in better business decisions. Data professionals typically look at predictive, prescriptive, diagnostic, descriptive and decisive data to drive DI. They also rely on Artificial Intelligence (AI) and Machine Learning (ML) to rapidly accelerate data analysis that would have previously been performed manually. By enhancing business decision making with DI, organizations can improve user experiences, differentiate from competitors, and increase their revenues.
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3. Processing Data at the Edge
Technologies that enable data analytics have traditionally been hosted within centralized data center and cloud environments, meaning data that is collected by a business has had to travel from where it is generated and across a network until it reaches the physical location where the compute power required to process the data sits. Business analytics requires huge volumes of data to be sent across these networks. Not only can be this be incredibly costly, it can also cause networks to slow down and creates latency for the end users working with the analytics tools.
Edge computing is an emerging trend which sees computing power placed at the edge of data center networks, allowing data to be processed closer to where it is being generated. This reduces the volume of data traveling across the network, resulting in lower costs, fewer latency issues and more real time data processing abilities.
4. Natural Language Processing
Natural language processing (NLP) is removing a traditional barrier in the analytics space. Data professionals and other stakeholders who aren’t proficient at working with data, but need to draw business insights from it, can struggle with different programming languages. NLP can be applied in business analytics tools to give users of all proficiencies the opportunity to ask the right questions about the data in their native language and the technology can answer them. By essentially converging the people, data and analytics tools, NLP allows stakeholders who have zero technical know-how – from the C-suite to the sales, customer service and marketing teams – to gather the insights they need from the data and get the results they require quickly and easily.
Currently, much of the NLP that is applied in the data analytics space relies on text-based queries, but it won’t be long before we see the emergence of NLP-driven voice search, making it even easier and quicker for users to get the insights they need. Not only can these be rolled out into desktop-based tools, but also mobile-based iterations in user friendly apps, allowing users to access the insights they need anywhere with an internet connection.
Looking Ahead
So far 2021 has already been a year of ups and downs, however, the disruption that the pandemic has wrought upon businesses has also been a catalyst for innovation. As we enter the second half of 2021, many organizations will begin to benefit from this innovation, particularly in the business analytics space.
Organizations that are in a position to leverage the powerful business analytics developments that are beginning to emerge will be in the best position to improve products, processes, customer experience, profitability and competitiveness through faster, more accurate data-driven insights. 
About the author: Rakesh Jayaprakash is a product manager at ManageEngine, a subsidiary of Zoho.
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