Editor’s Note: This excerpt was originally published in the Ask Our Experts section of PERFORMANCE Magazine Issue No. 26, 2023 – Data Analytics Edition. Islam Salahuddin, a business research analyst and data expert at The KPI Institute, answers, “How can organizations bridge the knowledge gap between data analysts and non-technical stakeholders and decision-makers?”

Bridging the gap between data teams and non-technical stakeholders should be a multi-way effort. 

From the technical side, data analysts first have to be aware of business needs and then adopt communication best practices. Insights should be delivered in the plain language of stakeholders, eliminating technical jargon and providing business context. Data storytelling techniques should be leveraged to ensure communication is engaging and persuasive.

From the side of stakeholders, having a high level of data literacy is key to avoiding communication gaps and insight misinterpretations that can lead to distorted decision-making.

Moreover, as hierarchical organizational gaps widen in larger businesses, having mediator roles can also be beneficial. These roles can be performed by experienced business analysts or specialized positions like data translators. Making room at the management tables for data-experienced managers and chief officers will bridge the gap more easily.

From a big-picture perspective, developing a wider data culture environment within an organization can eliminate such gaps on different levels. Business data maturity can only be achieved if business objectives are aligned with and then enabled by data uses, which highlights the importance of crafting a data strategy in alignment with the overall business strategy.

Ready to bridge the gap between data teams and non-technical stakeholders? Elevate your data skills through The KPI Institute’s Certified Data Analysis and Certified Data Visualization Professional courses.

Editor’s Note: “Four Business Intelligence Trends in 2022” is an article written by The KPI Institute’s Research Analyst, Brian Kiprop. This piece is originally published in the 22nd Performance Magazine – Printed Edition.

As the year progresses, entrepreneurs and managers are developing growth strategies as they establish data-driven cultures in their companies. Business intelligence assists in making data-driven decisions by utilizing data infrastructure, tools, and best practice benchmarks. Many businesses can identify new opportunities by having a wide view and utilization of data. This could spark change, eradicate inefficiencies, and adjust to market changes.

To determine the trending scientific business intelligence concepts for 2022, we conducted secondary research by collecting recent publications from the Scopus engine, the largest database of highly influential scientific publications. In 2022, there are 45 documents from a range of disciplines; computer science (28), Engineering (22), Decision Sciences (11), Social Sciences (11), Mathematics (6), Business and Management (5), Energy (2), Materials Science (2), and Biochemistry and Molecular Biology (1). 

Since all 45 documents have been published this year in well-recognized journals like Springer and Emerald, we exported the data for analysis to the latest version of Voss viewer software released this year. Voss viewer software is a data visualization tool developed at the Center for Science and Technology Science at Leiden University. 

Our results from a keyword analysis show four keywords (concepts) predominant in up-to-date publications of business intelligence by researchers in 2022. They include business intelligence, information analysis, artificial intelligence, and decision making. 

Business Intelligence

As a standalone concept and the most occurring keyword (32 instances) in our research, business intelligence is an attractive area of interest for businesses and researchers. Business intelligence enables companies to make fast decisions by understanding historical and current data in a business context. These will promote better performance, assess customer behavior patterns, analyze competitors, and identify market opportunities. 

When designing business intelligence tools, IBM suggests that companies adopt an easy-to-operate business intelligence analytical solution, integrating data sources from different platforms and understanding capabilities like artificial intelligence and machine learning before implementation. 

Microsoft uncovers four stages that business intelligence undergoes, from raw data to reports and insights. Step 1 involves data collection, followed by spotting patterns in the data. The third step focuses on visualizing the data, while the last step concerns taking action on findings in real time. 

Information Analysis

The information analysis concept has 16 occurrences across our literature review. Information analysis is known to be part and parcel of intelligence gathering to influence firm decisions. After gathering the business data, assessing data sets to provide insights is crucial for deciding the actions to take from them. Data can then be processed using a different range of tools, and some of the major software are Microsoft Power BI and Tableau. 

The tools for data analysis usually consisted of dashboards, visualizations, data mining, Extract Transfer Load, reporting, and Online Analytical Processing. However, the most popular among users are visualizations and dashboards.

Artificial Intelligence 

Artificial intelligence refers to systems and machines that draw knowledge from previous experiences, adapt fresh inputs, and conduct human-like activities. In our study, we found artificial intelligence to have four keyword occurrences. 

According to Harvard Business Review, three types of Artificial Intelligence relate to solving business needs. The first one, process automation, enables administrative tasks like data transfer through emails to be updated in the recording system. The second is cognitive insight, which utilizes algorithms to determine patterns in large amounts of data and generate insights. Lastly, cognitive engagement comprises intelligent agents and chatbots that respond to customer inquiries. 

Decision Making

In our findings, decision-making had a keyword occurrence of five across all 45 papers. Decision-making is an essential outcome of collecting business intelligence. Many companies want to make better decisions concerning their strengths, weaknesses, opportunities, and threats within their business environment. 

In Minggao Yang’s paper on the influence of business intelligence on the financial performance of innovative companies, the author finds a direct correlation between business intelligence and business performance, thereby confirming that data-driven choices influence business profitability positively.

Managers and founders looking into expanding business intelligence capabilities to identify and act on sustainable and profitable opportunities could benefit from understanding the four emerging concepts in business intelligence. As collecting business intelligence requires basic skills in analyzing data, one can consider taking The KPI Institute’s Certification in Data Analysis to build or upgrade their data analysis skills.

Big data now plays a crucial role in every business’ success. Through the application of analytics, data analysis is allowing businesses to become even more competitive. 

Organizations adopt big data and analytics to analyze massive volumes of data generated by offline and online trading. Based on the Big Data & Business Analytics Market Statistics 2030, the value of the global big data and business analytics market is estimated to increase to $684.12 billion by 2030 from $198.08 billion in 2020. 

The goal of data analysis is to extract meaningful information and use it to make informed business decisions. By analyzing and comparing previous and new data, organizations can understand what happened in between. Then, organizations can come up with metrics and action plans to drive their performance. It involves boosting customer loyalty, increasing sales, improving and retaining successful practices, and enhancing employee productivity.

According to a PwC poll of 1,000 corporate executives, data-driven firms were three times more likely to achieve big gains in their decision-making capabilities. 

A data-first approach is required for a digital-first enterprise strategy. In a multi-cloud context, a well-planned data strategy for a digital company gives “business transformation opportunities, cost reduction, better engagement, and maximum flexibility.”

To get the most out of data, organizations must create an effective data analysis process. This procedure entails objective setting, data collection, cleaning and analyzing data, and data visualization.

Before collecting data, defining the goals of the organization is important. Businesses can choose the type of data to collect and evaluate it to ensure it is aligned with their goals. Then, businesses must integrate data using tools such as Informatica PowerCenter, SQL Server Integration Services (SSIS) by Microsoft, AWS Glue by Amazon Web Services (AWS), and Alteryx Designer.. 

All data must be gathered in one place to be examined. Companies must clean their data by removing irrelevant and unwanted information, fixing structural errors, and filling out the missing data in order to produce accurate findings. To better understand the data, organizations should use data analysis software and other technologies. 

Some data analysis software available are XLSTAT, the leading data analysis and statistical solution for Microsoft Excel; Python, a powerful tool consisting of materials for any aspect of scientific computing; and SAS, a statistical software for business intelligence and data management.

To acquire useful insights, businesses should dive deep into the data. They should analyze and narrow down the results of the gathered data to draw conclusions. Then, organizations must interpret the results to figure out the best next steps to reach the goals established in the beginning of the process.

Organizations should deliver information in a readable and understandable format. Businesses can present data using graphs, maps, and charts. Data visualization can help companies compare data sets and observe their relationships.

Data analysis helps anticipate future trends more precisely, allowing organizations to determine where they are headed and what they need to get there.

To learn more about data analysis, explore The KPI Institute’s reports and free webinars and stay tuned for the next schedule of the Certified Data Analysis Live Online Course.