The COVID-19 pandemic has made a substantial impact on economies and societies, changing consumer behavior and bringing significant changes in business operations with work-from-home and social distancing policies in place. Certain businesses have capitalized on the rise in demand for features like cloud-based connectivity/collaboration, remote health monitoring, and wearable technology. Healthcare, life sciences, and banking and financial services have also remained largely isolated from the disruption.
However, there are some critical challenges:
- Due to the increased demand for digital infrastructure, security and connectivity issues have become more frequent
- Due to new supply chain and production constraints, businesses are struggling to re-optimize their processes for profitability
- Governments’ burden has increased as they are engaged in reviving the economic growth with special industry packages
- Businesses are facing increased volatility and have limited visibility into the future.
Business intelligence and analytics tools hold the key to make informed decisions, predictions, and strategies for the post-COVID-19 world. A ‘MarketsandMarkets’ report predicts that the global BI market will register a CAGR of 7.6% from 2020 to reach USD 33.3 Bn by 2025.
Top BI Tools of 2020
If you are looking for top BI tools of 2020, Gartner’s magic quadrant for analytics and business intelligence platforms can provide a good starting point and direction for your search. As expected, Microsoft (Power BI) and Tableau are placed in the leader’s quadrant in this year’s report again. However, as Gartner cautions, the quadrant should not be used “in isolation as a tool for selecting vendors and products.” There are various other players in the BI market with differentiated offerings. While certain products focus on dashboarding, others offer embedded analytics and integrated performance management. Traditional products focusing on dimensional and hierarchical data models might also meet business requirements for some organizations.
How to Select the Best Business Intelligence Software?
Businesses should consider the following factors while evaluating a business intelligence software:
- Business strategy and goals
- Requirements (weighting, requirement shortlisting)
- Ease of implementation
- Performance (handling of large data volumes, query performance)
- Technical support
Further, in a survey, 46% of respondents cited the lack of flexibility as the main reason for replacing their BI software. Hence, organizations should evaluate business intelligence software in terms of ease of customization and integration with their existing ecosystem.
Top 5 Data Analytics and BI Trends for 2021
Highly Scalable and Faster AI
The use of AI technologies has simplified business analytics for end-users by reducing the work involved in data preparation (scrubbing, parsing, ETL, etc.), making the BI tools more useful for business users and data scientists. Further, businesses today want to rely more on current data as compared to the analysis done on historical big data sets. This means AI technologies capable of analyzing “small data” might gain increased attention. Also, businesses are keen on adaptive machine learning, explainable AI, and efficient algorithms allowing faster analysis.
X-Analytics refers to the analysis of lesser utilized data types, such as video, audio, text, etc. There’s tremendous scope to gather business insights from such data. According to Gartner, AI can help in harnessing this data, driving major developments and innovations in 75% of the Fortune 500 companies in the next few years. X-Analytics has widespread applications; it can help businesses in improving supply chain operations with real-time analysis of weather and traffic. Law enforcement agencies can use the technology in crowd monitoring and control, retailers can enhance in-store customer experience and derive customer insights with social media and sentiment analytics. Further, artists and the entertainment industry can use it for protection against IP infringement.
Today, most businesses operate in a hybrid cloud environment. As a result, their data now exists in multiple locations, including within their network and beyond their firewalls. In such environments, data is highly distributed, dynamic, and can snowball in no time. Data Fabric provides capabilities to deal with the size, complexity and distributed nature of data. It provides both the architecture as well as the set of data services, which enable businesses to deal with data across their hybrid, multi-cloud environments. Organizations seeking higher speed of action and continuous intelligence will drive the demand for data fabric in 2021.
Edge computing is another evolving technology likely to gain traction in 2021. While most vendors have developed cloud-based analytics engines, their technologies do involve certain latency, and businesses have to factor in query time. There’s also significant effort involved in integrating various tools and preparing the data. Edge computing can bring analytics closer to where the business data resides. With edge computing, organizations requiring faster or real-time decisions (e.g., IoT applications, autonomous cars, etc.) can simplify analytics. Edge computing is also highly useful in remote high-latency network areas.
As most enterprises are continuously evolving and developing applications to support their unique data analysis and process management needs, there is an inherent need to simplify and expedite application development. Low-code and robotic process automation platforms, especially when complemented with AI and ML, can fulfill these needs. Such platforms assist business users with minimal or no coding skills to create applications that automate routine, and sometimes even complex workflows. With self-service capabilities, business users can make secure and optimum use of corporate information, without dependence on their technical staff.
Common Challenges in Data Analytics and Visualization
Poor Data Quality
Ensuring data quality is cited as one of the biggest challenges in the data analytics field. The insights drawn from data are highly dependent on completeness, uniqueness, consistency, timeliness, and accuracy of the data. The quality issues often multiply during corporate mergers and acquisitions, when there’s a need to integrate data from various on-premise and cloud-based databases (CRMs, SCMs, etc.).
Disparate Data Sources
Data from a single source can only tell so much; organizations need to correlate data from multiple sources to carry out cause and effect analysis, find hidden patterns, and make informed decisions and forecasts. However, creating a single pane of glass to monitor data from multiple sources is easier said than done. BI tools such as Tableau perform well when connected to a single database or a CSV-like data source, but require significant effort in collecting data from other sources. Further, the time required to prepare, load, and query data from different sources can be a big irritant for end-users.
As discussed earlier, the lack of flexibility is a big challenge for BI users. For instance, most tools have limited templates and support select formulas and metrics for visualization. BI dashboards with custom styling, KPIs, and formulas require significant effort in scripting.
Lack of Adoption
As most business analytics tools are complex, they are used by a limited group of ‘power users’ or those with advanced BI skills. Difficult implementation and lack of flexibility prevent a larger share of potential users from leveraging the benefits of data. Further, limited bandwidth also becomes a challenge. As a result, most BI implementations remain underutilized and provide a lesser than expected return on investment.
It is clear that extracting real-time insights and realizing the true potential of data is a common goal for businesses across all sectors. While business intelligence tools are a big help, their complexity and costs often make them prohibitive for a large number of organizations. Besides, businesses need custom apps to solve their unique data analytics challenges and may not want to invest in a full-feature BI solution. There’s a need for a simpler platform, which allows business users to readily develop apps and make the most of their data. This is where Klera comes into the picture.
Extracting Business Intelligence With Klera
Klera is a rapid no-code intelligent application development platform that helps organizations in collecting, reading, transforming, and analyzing data across disparate tools and databases. It offers smart bi-directional connectors, which can write back to native data sources. Business users can also automate complex workflows and computations and leverage machine learning for predictive analysis. With these capabilities, Klera helps you easily solve most of the challenges frequently encountered with common BI tools in the market; learn more about Klera’s capabilities here.