Business Intelligence enhances the ability of companies to make meaningful use of the data collected during business operations on a day-to-day basis. It can assist businesses to boost their performance by exploring new opportunities, identifying main threats while providing new insights to speed up the decision -making process.

Research shows  Business Intelligence Growth Value: 7 Telling Stats  that companies using analytics are five times more likely to make faster decisions.

Advanced Analytics, on the other hand, is used to answer some of the most challenging business questions and can help in boosting operational efficiency of the company, drive investment decisions as well as improve customer experiences.

While both business intelligence and advanced analytics operate in similar areas, there are some subtle differences between them. Let us take a look at those and also understand the key benefits of each –

What is Business Intelligence?

Business Intelligence is a set of technologies and architecture that can help in transforming raw data into more relevant and meaningful information that may be useful to the organizations. It mainly focuses on storage and retrieval of data from the past by using technologies such as query engines and data cubes. The main purpose of Business Intelligence is to help interpret vast amounts of data and measure past performance which is critical in the challenging business environment. Companies can find out about new opportunities using the past data and frame powerful strategies based on Business Intelligence insights to give them a competitive edge over the others.

What is Advanced Analytics?

Advanced Analytics is related to the automatic exploration and communication of meaningful patterns that may be found both in structured and unstructured data. The focus of Advanced Analytics is more on forecasting using the data to find the trends to determine what is likely to happen in the future. Basic Analytics provides a summary of data whereas; Advanced Analytics goes a step ahead in providing a deeper knowledge about data and helps in granular data analysis. This can be more rewarding for companies in getting better business outcomes using data driven decision making.

Key Benefits of Business Intelligence

Let us take a look at some of the key benefits of Business Intelligence

Consolidation of Data from Different Sources

Business Intelligence solutions provide an automated system of data collection through which, it is possible for companies to consolidate the data from multiple sources. This can help in boosting their efficiency and thereby enhance their business performance.

Deliver Actionable Intelligence

Users can gain access to information in their databases along with the new data which is created by devices, sensors, apps, server logs and geo-location coordinates through BI software. The actionable intelligence derived from this data assists companies in taking decisions and enable business transformation.

Improved Inventory Management

With Business Intelligence software, it’s possible to manage your inventory as the companies can order the correct amount of inventory at any point in time. This helps in reducing wastage and also lower the costs of inventory for the business in the long term.

Cut Down Labor Expenses

Data collection and report generation is automated through Business Intelligence which helps companies to bring down their employee training and development expenses. It can also help in assessing how well your business is performing using a specific number of employees and how many more may be needed to accomplish your business goals.

Advantages of Advanced Analytics

Advanced Analytics can provide better transparency for end-to-end supply chain visibility with its robust data visualization capabilities and helps in managing large volumes of data leading to efficient decision making. Artificial intelligence integrated with Advanced Analytics can help in reducing wastage and boost efficiencies in supply chain management.

Advanced Analytics is also helpful in providing a what-if analysis, where the values are flexible and can take hypothetical circumstances or data into account. Predictive analysis techniques based on data mining, statistical analysis along with machine learning can be used to deliver extremely accurate predictions to depict the future business trends.

Key differences between Business Intelligence and Advanced Analytics

 *   The role of traditional Business Intelligence was to provide information to the users about past performance of their business operations and used mainly for reporting purposes. But today, companies want to get a more holistic view of their customers and their expectations over a period of time. Business Intelligence alone cannot satisfy all the needs or predict future uncertainties in business and find out the root causes of business failure.

Advanced Analytics uses forecasting techniques which help in addressing the complex issues of the business environment. It also uses different kinds of data with more advanced quantitative methods including descriptive and predictive data mining, simulations that can provide better business insights as compared to the traditional approaches used by Business Intelligence.

 *   Business Intelligence relies on methods such a querying, reporting, dashboards and OLAP using a set of metrics with focus on past performance. On the other hand, Advanced Analytics helps in predicting future events and helps in exploring patterns which may be more complex to detect.

 *   The process and the approach used for solving a business problem is different in both these methods. With Business Intelligence, the analysis is designed to be more repetitive based on reporting templates which extract specific information related to the business to assess historical performance. The information that is analyzed and the format of presentations is pre-defined.

Advanced Analytics comes up with a question first and then a set of analysis is performed to do a deep research using statistical and quantitative data along with algorithms to provide insights on the question. For example – Predictive analysis helps in finding the hidden relationships among factors and their outcomes to come up with a forecast for an unknown value.

Top Business Intelligence and Advanced Analytics Trends

Let’s take a quick look at some of the future trends in these areas

Growth of Artificial Intelligence

There will be increasing demand for real time tools of analysis and the arrival of IoT is also likely to bring an enormous amount of data which is likely to push statistical analysis on the priorities list. According to Gartner<Top 11 Business Intelligence and Analytics Trends for 2017, more than half of the organizations around the world will be relying on advanced analytics and algorithms built using Artificial Intelligence which is likely to get more competitive by 2018.

Artificial Intelligence will be the core behind these algorithms for understanding the data and will be used for predicting the upcoming trends. The application of deep learning will enable machines to work independently and assist them in taking decisions in place of humans.

Emergence of Collaborative Business Intelligence

There is a growing challenge in this competitive environment where managers and employees need to communicate in different ways. This has led to the emergence of a new kind of business intelligence called as the collaborative business intelligence which is an integration of social media, collaboration tools and 2.0 technologies along with business intelligence software. This kind of business intelligence tools can be used to generate automated reports that may be shared along with key people at specific times.

Demand for IoT and Real-Time Data Streams

There is a significant demand for analytics for real-time data streams – this is further rising as more number of devices are being connected to the Internet. Many online shopping sites are exploring the possibilities of combining advanced analytics with streaming data to provide personalized recommendations to shoppers.

Of course, there cannot be analytics without intelligence. Businesses need to use the right tool based on the business demands.

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