Business intelligence (BI) is a term used to define the tools and methods to create summaries and actionable steps for an organization‘s business decision making. Linking big data and tools like artificial intelligence, BI collects and processes large volumes of data to provide a snapshot of the current state of the organization’s financial and operational status, and those of its customers and partners.
BI usually falls under the data analytics umbrella, but where analytics gathers and processes large data sets and often uses those for predictive analysis, BI transforms data into actionable recommendations for an organization’s key decision-makers, often in the form of reports, charts, graphs and other summaries to provide users with detailed intelligence about the state of the business.
Business Intelligence Basics
BI analysts process huge amounts of data. They are tasked with the collection and analysis of business information and must distill the findings into actionable steps. This type of analysis is critical for planning, resource management and financial decisions. BI can condense data findings for aiding specific operations or projects like user testing. Unlike its predictive analytic cousin, BI focuses on the here-and-now, giving key decision makers real-time access to critical data.
As often is the case in any digital process, security from data breaches, hacks and cybercriminals is needed for organizations to keep their resources safe. BI is usually a part of an organization’s larger data security initiative, yet a balance is needed to provide analysts with access to the data they need to collaborate with colleagues around the world.
Security in the Finance Industry
One of the first industries to leap into BI is the finance sector. Whether used for business financial professionals or in the personal/business advising consultancy vertical, BI now plays a major part in gathering and presenting data. Yet these same data users are at high risk for cyber theft.
According to Datapine, Database security has become a heated debate, both in the public and in private organizations. This will only pick up speed in 2019. Business owners will increasingly search for the most secure solution that averts the risk of data breach and losses.
For example, accountants’ work is prone to hacking due to the types of data processed, like Social Security numbers, personal contact information and financial records that are desirable for cybercriminals. Cybersecurity professionals like information security analysts, computer forensic analysts and ethical hackers have grown in numbers and stature to counteract the growth in cybercrime in the finance industry.
Future Security Trends in Business Intelligence
New concepts, methods, software and even professions have sprouted in the past few years using BI. Search-based discovery tools, AI and machine learning are also strong future trends. How have needs for security shaped these developing technologies?
Search-based Discovery Tools
According to the Business Application Research Center, Data discovery is not a tool. It is a business user-oriented process for detecting patterns and outliers by visually navigating data or applying guided advanced analytics. Discovery is an iterative process that does not require extensive upfront model creation.
Data discovery is like data analytics and BI that uses big data to find very specific patterns of information for specific users. It allows simplified handling of big data in a user-friendly package for presentations and a high volume and variety of data. As the large amount and increasing variety of data require more efficient and user-oriented methods of presentation, look for data discovery to grow.
Another advantage of data discovery is its limited need for information technology (IT) and large data pulls that can become security threats. By limiting the scope of sensitive information needed, data discovery can create new opportunities for companies to gain value from data more quickly in a safer environment.
Artificial Intelligence
AI has become synonymous with data analytics and BI in recent years. Its advantage is the real-time insights that can help average users make sense of large swaths of data. AI is becoming popular at small-to-mid-sized organizations that can’t afford a fully staffed analytics department but can take dense reports and make them palatable for timely insights and decisions.
It’s ironic that a tool used in a cyber-prone industry like big data can actually help stop data thieves using AI. According to IBM, AI is being used to quicken the processing of threats among millions of data points and detects potential issues with minimal programming or monitoring by overworked IT departments.
Machine Learning
Machine learning is an extension of AI that learns new insights as more data are analyzed. According to SAS, Machine learning is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention.
Banks and other businesses in the financial industry use machine learning technology for two key purposes: to identify important insights in data and prevent fraud. The insights can identify investment opportunities, or help investors know when to trade. Data mining can also identify clients with high-risk profiles or use cyber surveillance to pinpoint warning signs of fraud.
Future Global Business Intelligence Security
The future looks bright for BI and its data analytical brethren. As more industries incorporate its use in their planning and execution strategies, BI will evolve and strengthen in speed and ease-of-use. However, keeping data safe from cybercriminals is a never-ending task for the IT industry and computer security professionals worldwide.
