- July 20, 2020
- Posted by:
- Category: Artificial Intelligence
Living in the digital world is simply living in the world of data. The modern world of businesses revolves around processing the 0-1 bits of data which is mushrooming at a very fast pace.
“Worldwide data storage demand in the present year(2020) is more than 40k exabytes.”
With such an exponential figure upfront, do businesses have any solution or system to correctly and accurately manage the heaps of data?
Every business works towards profit and this can be accomplished by making the right business decisions. Business leaders make countless decisions that influence the ways of working and criteria. In the framework of an increasingly competitive and challenging business environment, companies need to find solutions to generate competitive advantages and pave the path for profits of an organization.
Business Intelligence proves to be an effective technique here that analyzes and transforms data into strategic decisions that allow entrepreneurs to craft and design successful plans to support business management in different areas and departments.
In definitive terms, Business Intelligence is:
“A central place where you can tie in all your data and make better-informed decisions so you can optimize for your lifetime value instead of your short-term income.”
The BI technology helps its users to collect, store, access, and analyze data in a more structural manner. The set of applications covered by Business Intelligence development allows companies to effectively implement the Decision Support System, applying concepts of Online Analytical Processing (OLAP), Statistical Analysis, Forecasting, and Data Mining.
Business Intelligence serves to send information to the right decision-makers at the right time. BI is preferred by many users as it leads them to arrive at facts based on the best end product and leads an organization to convert raw data into useful information; therefore, bringing profit.
Data Processing in Business Intelligence
In general the business intelligence software tools are based on the use of complex information systems that use data extracted from three different sources: production systems, information related to the company or its areas from both inside/outside of the organization and economic data.
Regardless of the origin the data is subjected to three different processes with the aim of profiling and homogenizing them so that they can fulfill their role as generators of useful and effective quality information for decision making.
- Extraction (E)
- Transformation (T)
- Load (L)
Extraction of data occurs from different data sources irrespective of the file formats. The robust systems involved in the processes extracts and maps data from different sources into a unified and structured format before processing.
The following is ensured by the ETL process:
- Eliminating duplicate, fragmented data
- Checking data types
- Removing spam (unwanted data)
- Reconciling records with source data
Algorithms are applied, modifying data as per the business rules. The transformation stage involves a few functions namely, concatenation, filtering, computation and some string operations.
It further involves:
- Data cleaning
- Threshold validation
- Data standardization
As the name says it is migrating structured data into the warehouse where large chunks of data sets are loaded in a stipulated time period which is usually short.
Three types of loading involves:
- Initial load
- Incremental load
- Full refresh
Business Intelligence in Business
BI is merely optimum use of data for an oriented decision making. Which further forms the knowledge base necessary to support the decisions and actions taken in the past, present, and future.
The first step is to detect what type of process map your company needs to implement. Within each process map, you have strategic, and support frameworks to comply with. Once the flow of communication, work routine, and data are correct, it’s time to extract intelligence from data through BI-powered tools.
These tools allow you to acquire real data extracted from your business data in a fast, agile, and reliable way. Then there comes Business Intelligence, that is, from the data obtained, it is possible to draw a better picture of the company’s strong points and the weaknesses it holds.
From there, progress in improving strategies to achieve benefits and improve customer service, taking advantage of the structured business data stored in the company’s data systems.
Now let’s dig deep into the stages of a Business Intelligence Process:
This stage works on collecting data from different sources, including email messages, images, formatted tables, reports, and relevant resources. The main role of data sourcing is to collect data digitally through computer files, digital cameras, scanners, etc.
#Analysis of Data
The next stage is to organize the data collected from data sourcing and estimate the data based on current and future trends. This is known as data mining which predicts information needed in the future.
#Knowledge of the Situation
This stage of the Business Intelligence process helps filter relevant data and use it in relation to the business environment. Users compile the data by closely observing the market and policies to make decisions easier.
Combinations of different algorithms are used to adequately identify the awareness of the situation.
Taking risks is part of any business, but if one can take precautions it’s extremely useful.
The risk assessment stage helps to identify current and future risks, including cost benefits; choosing the best options, and comparison between two decisions to identify which one will be beneficial.
The last stage of the BI process helps to use information intelligently. The goal of this stage is to warn users of various critical events, such as poor staff performance, acquisitions, changing market trends, sales fluctuations, and much more. Help make better business decisions to improve staff morale and customer satisfaction.
Evolution of Augmented Analytics, Empowering BI Systems
As the data volumes are expanding with monumental speed, the traditional BI and methods of data analytics seem to be inefficient to match the ever-increasing demands of data handling and processing. With the shortage of proficient data scientists and experts, the future innovations to be brought up by technological advancements are frowned upon by many.
Nevertheless, Augmented Analytics came out as a boom that combines the most crucial components of the technology that are involved in data processing, namely, natural language generation, text mining, natural language processing, and automated data processing capabilities in Business Intelligence.
Realizing the thin availability of data experts, Augmented Analytics helps relax an organization with the dependency of the manual force involved in data filtering, sorting, and segmenting by automating the insight generation process with the aid of advanced artificial intelligence and machine learning algorithms.
The exceptional phenomenon also eliminates the potential possibilities of errors and inconsistencies generated through manual intervention in delivering useful data insights.
Let’s take a look at the three major stages of Business Intelligence that have been transformed and automated by this ever-new segment of technology:
The modern BI systems of today have been empowered by robust technicalities and advanced analytics that are effective and sound enough to analyze large chunks of data at ease.
Using the enhanced capabilities of machine learning, augmented analytics can now identify metadata, modeling, data profiling, cleaning, and manipulation (which were earlier done at the human level) and helps accelerate the data preparation phase, thus, amplifying the productivity of the data scientists.
#Discovering Patterns in Data
With advanced BI techniques, users can explore and find the data they intend to look for and understand various related data patterns. However, there are some limitations to view hidden data trends and deflection which might pull them back to get the useful insights. This impacts businesses where even executing data exploration manually won’t deliver the expected results
Augmented data discovery, with the help of power-packed algorithms, are used to determine the outliers and correlations in data which automatically applies relationships to data and diminishes the risks of missing out significant insights.
#Operationalizing Insights from Data
Despite offering advanced visualization and interactive dashboards by the latest BI systems, they fail to provide ease with which users can even comprehend the data made available to them.
Augmented analytics platforms bolstered by NLG (Natural Language Generation) keep the users informed about all the useful and obligatory data insights they must be aware of.
[Prefer Reading: “How Natural Language Processing Aids Sentiment Analysis?”]
Business Intelligence plays a significant role in the functioning of organizations and helps them to continue with their progression and business growth. One can study the changing trends and market demands through BI tools and acquire accurate and up-to-date information on customer preferences.
So get your business future plans ready based on well-organized relevant data insights and get expected outcomes out of your efforts.